Back in July a paper by Millan-Irigoyen et al. titled “HY-PYPOPSTAR: high wavelength-resolution stellar populations evolutionary synthesis model” was posted to arxiv, and shortly thereafter data in the form of the promised high resolution spectra were made available at https://www.fractal-es.com/PopStar/#ingredients. Unlike MILES and its variations or BC03 this is a purely theoretical library, with the spectra calculated from model atmospheres instead of using empirical spectra of actual stars.
I looked briefly at one other theoretical library some time ago and concluded (IIRC) that the model spectra had much too blue continua at all ages, making it unsuitable for full spectrum fitting. A brief visual inspection of this library (as well as Figure 8 in the paper) indicates that’s not the case here. One thing that may compromise its usefulness is that although there are 106 age bins in the models they are very irregularly spaced and heavily weighted towards younger ages as shown below.
Age range
Number of spectra
5 ≤ log T < 6
4
6 ≤ log T < 7
34
7 ≤ log T < 8
35
8 ≤ log T < 9
9
9 ≤ log T < 10
15
log T ≥ 10
9
Number of SSP model spectra by age range in HR-pypopstar
At least in the wavelength range of SDSS/MaNGA spectra there is little evolution in spectroscopic properties between 105 and 106 years and even though it speeds up afterwards the effective time resolution of SFH models is still lower than the supplied number of time bins for the next two decades.
Sample young population spectra from hrPypopstar
For a preliminary look at the library’s suitability for full spectrum modeling I selected a 42 time bin subset with all 4 available metallicity bins and Kroupa IMF, truncating the wavelength range to 3400-9000 Å, which is just a little larger than the Emiles subset I use. The time bins were chosen by hand — I was trying to get evenly spaced bins in log time but this proved not to be feasible. The authors produced two sets of libraries for each of 4 IMFs: they did an apparently careful job of counting the number of ionizing photons for several species and calculated sets of SSP models with and without emission continuum. For these trial runs I used both sets of libraries, which I’ll compare below. No code modifications were required because they use the same peculiar but computationally convenient flux units for spectra.
I just ran a few models for the central fiber spectrum of KUG 0859+406 (MaNGA plateifu 8440-6104). First, here is the star formation rate history compared to the most recent Emiles run:
Model star formation histories for central fiber of MaNGA plateifu 8440-6104 (T) Emiles (M) hrPypopstar with emission continuum ( B) hrPypopstar stellar light only
Or, looking at the model mass growth histories:
Model mass growth histories for central fiber of MaNGA plateifu 8440-6104
Red: Emiles
Blue: hrpypopstar including emission continuum
Green: hrpypostar stellar light only
The starburst occurs later and is somewhat weaker in the pypopstar models. Interestingly all models have a late time revival of star formation after a period of quiescence. To get all the graphs to line up I truncated the pypopstar model star formation histories at 10 Myr. Here are the full histories:
Model star formation histories for central fiber of MaNGA plateifu 8440-6104
(T) hrPypopstar with emission continuum
(B) hrPypopstar stellar light only
Emission continuum is significant mostly at ages < 10Myr and this is reflected in some difference in late time model star formation histories. This has little effect on other modeled quantities.
At a glance fits to the galaxy flux data look very similar. Both sets of models have problems in some wavelength ranges and both have some issues with the [N II]+Hα emission complex, probably because the lines don’t quite have gaussian profiles. In terms of summed log-likelihood the Emiles fit is actually almost a factor of 2 better than pypopstar.
Comparison of model fits to data
(L) Emiles
(R) Hrpypopstar
The pypopstar models have larger optical depths of attenuation and steeper attenuation curves than the Emiles models, demonstrating once again the interplay among attenuation, attenuation relationship, and stellar ages:
Model distributions of attenuation parameters τV and δ for runs with Emiles library and hrPypopstar on the central fiber of MaNGA plateifu 8440-6104
Some other modeled quantities are very similar, for example the stellar mass density:
Comparison of model stellar mass density
red: Emiles
blue: hrpypopstar with emission continuum
While the modeled specific star formation rate differs by ~0.4 dex thanks to the more recent starburst in the pypopstar models:
Comparison of model specific star rate (sSFR)
red: Emiles
blue: hrpypopstar with emission continuum
I still haven’t decided exactly what to do with these interesting SSP model libraries. I will probably take a more systematic look at extracting a subset of time bins that evolve at a consistent rate by some measure. This will certainly require many fewer than the published 106 bins. What may be more promising is to graft some young age SSPs onto my existing Emiles library. The 4 published metallicity bins are pretty closely matched to the Emiles subset I use, and 4 or 5 SSP’s would fill out the ages up to the youngest (30 Myr) in the BaSTI isochrones. I already use unevolved BC03 models for this purpose. Using the models that include continuum emission would also solve the problem of how to model that in starforming galaxies (but not galaxies with strong AGN emission unfortunately).
I did have my old data and model runs of course, in fact they were spread over several directories on two machines. I’m going to refer to it by this catalog designation, KUG standing for the “Kiso survey of Ultraviolet-excess Galaxies.” It’s also a low power radio source with catalog entries in both FIRST and NVSS, and of course it’s in MaNGA with plateifu ID 8440-6104 (mangaid 01-216976).
In my 2018 model runs, which were interesting enough to write 3 postsabout, I found this galaxy had undergone an extraordinarily large burst of star formation that began ~1 Gyr ago with locally as much as 60% of the present day stellar mass born in the burst and something like 40% of the mass over the footprint of the IFU. In this years model runs the peak burst fraction was a considerably more modest ~25% and globally barely amounted to a slight enhancement of star formation. The starburst was also much more localized than in the earlier runs:
Fractional stellar mass in stars between 0.1 and 1.75 Gyr old in 2018 and 2021 model runs
So what happened? First, here is a comparison of modeled star formation histories for the innermost fiber, which got the largest injection of mass in the starburst.
Model star formation histories for central fiber of MaNGA plateifu 8440-6104, 2018 and 2021 model runs
The obvious remark is the double peaked starburst noted back in 2018 (and discussed at some length) has been replaced with a single narrow peak with a slow ramp up and fast decay. The peak SFR is a little larger than before but the total mass in the burst is lower.
I’ve made several changes in model formulation since 2018, of which the most important in the current context is adopting the more flexible “modified Calzetti” attenuation relation that adds an additional slope parameter to the prescription. In the current year model runs a steeper than Calzetti relation is favored throughout the IFU footprint, particularly in the central region where the starburst was strongest:
Map of modified Cal;zetti slope parameter δ — MaNGA plateifu 8440-6104
A smaller optical depth of attenuation is also favored throughout:
Modeled optical depth of attenuation – 2021 runs vs. 2018
MaNGA plateifu 8440-6104
This has a couple predictable consequences. Steeper attenuation will favor an intrinsically bluer, hence younger population while a lower optical depth requires less light, and hence mass in the stellar population. I can test this directly by returning to a model with Calzetti attenuation, and here is the result for the central fiber (this model run is labeled 2021 (c) in the legend below):
Mass growth histories –
2021 run
2021 run with Calzetti attenuation
2018 run
Central fiber of MaNGA plateifu 8440-6104
So, an eyeball analysis suggests about 3/4 of the difference between the 2018 and 2021 runs is due to the modification to the attenuation relation. The other changes I’ve made to the models are to change the stellar contribution parameters from a non-negative vector to a simplex, and at the same time changing the way I rescale the data. In early runs the SSP model fluxes were scaled to make the maximum stellar contribution ≈ 1, while the current models scale both the galaxy and SSP fluxes to ≈ 1 in the neighborhood of V, making the individual stellar contributions approximately the fraction of light contributed. An additional scale factor parameter in the model is used to adjust the overall fit. Assuming I did this right this should have no effect on a deterministic maximum likelihood solution, but with MCMC who knows?
Although the fit to the data looks about the same between the model with and without the attenuation modification the summed log-likelihood is consistently about 1% higher for the modified Calzetti model with no overlap at all in the distribution of likelihood. This suggests the case for a steeper than Calzetti attenuation is a fairly robust result.
“Posterior predictive” fits to galaxy flux data – modified Calzetti attenuation vs. Calzetti – central fiber of MaNGA plateifu 8440-6104
The galaxy flux data also changed a little bit. The early runs were on the DR14 release (version 2_1_2 of the MaNGA DRP) while the recent ones used the DR15 release (ver 2_4_3). Most of the calibration differences resemble random noise, but there is some curvature that systematically affects both the red and blue ends of the spectrum and could cause some change in the temperature distribution of the models:
Difference in measured flux from DR14 to DR15 – central fiber of MaNGA plateifu 8440-6104
While the detailed star formation histories changed, quantities that aren’t too model dependent didn’t very much. One example is shown below. Also, the kinematic maps agree with the earlier ones in detail.
Hα luminosity density – 2021 runs vs. 2018
MaNGA plateifu 8440-6104Velocity field of MaNGA plateifu 8440-6104 from 2021 model runs. Map interpolated from RSS file spectra.
One input that hasn’t changed are the emiles SSP model spectra, although there have been some procedural changes in how I handle the modeling. Early on I often used a much smaller subset of SSP models with just 27 time bins and 3 metallicities for preliminary modeling, including my first models on the same binning of these data. I also routinely ran 250 warmup iterations with just 250 more per chain. My current standard practice is always to use the largest emiles subset with 216 SSP models in 54 time bins and 4 metallicities, and I generally run 750 post-warmup iterations per chain but still with only 250 warmup iterations. This is generally enough and if adaptation fails it is usually fairly obvious. The small sample size of the earlier runs mostly effects the precision of inferences rather than means.
To conclude for now, my speculation about whether it might be possible to say something about the timing of critical events in a merger from the model star formation history was too optimistic. On a positive note though both sets of model runs retrodict that coalescence occurred at a lookback time around 500Myr ago, which is consistent with the fact that tidal tails and other merger signatures are clearly visible even in SDSS imaging. Both sets of model runs also have that odd uptick in star formation at 30Myr in the central fiber. And while the difference in burst mass contributions is a little disconcerting the current runs are more consistent with the likely gas content of ordinary spiral galaxies.
This example illustrates another well known “degeneracy” among attenuation (and adopted attenuation relation), mass, and stellar age. Whether I’ve broken the degeneracy by adopting the more flexible attenuation prescription described some time ago remains to be validated.
This paper (arxiv ID 2103.16070) is pretty old by now, having been posted on arxiv back in early April. The basic premise of the work is mildly interesting: the author searched MaNGA for galaxies that would meet conventional criteria for post-starburst (aka K+A etc.) spectra if observed at a redshift high enough that the entire galaxy would be covered by a single fiber like the original SDSS spectroscopes. Somewhat surprisingly, he found just 9 that met his selection criteria in the DR15 sample of ~4500 galaxies.
I have to say the paper itself is forgettable, but a manageably sized sample of MaNGA data that’s complete by some criterion is worth a look, and I have a long-standing interest in post-starburst galaxies in particular. So, I ran my current SFH modeling code on all 9 — by the way this was completed some time ago. It’s just taken me a while to get around to generating some graphics and sitting down to write.
The author only measured a few observable quantities: Hδ equivalent width and the 4000Å break index Dn(4000), along with Hα emission equivalent width and (normalized) fluxes. I long ago validated my own absorption line measurements of SDSS single fiber spectra against the MPA-JHU measurement pipeline, which was the gold standard for several years (but last run on DR8). My measurements and uncertainty estimates are in excellent agreement with theirs, so I have a fair amount of confidence in them. Emission line fluxes also agree with published measurements with considerably more scatter. My emission line equivalent widths on the other hand are completely unchecked. So, one of my tasks was to compare my equivalent width measurements with Wu’s. I did not attempt to exactly reproduce his work – I binned spatially using my usual Voronoi partitioning approach whereas Wu binned in elliptical annuli. With that difference in mind the next two plots should be compared to his Figures 4 and 5.
The first two graphs show the radial trends (relative to the effective radii per the NASA/SLOAN catalog) in the Lick HδA and Dn(4000) indexes. These both show very similar trends to Wu’s measurements although with more scatter. This is expected because fewer spectra go into each point in general — from the text it appears Wu binned several separate measurements for each displayed point. Also, I made no attempt to deproject distances. One feature of the Hδ versus radius plot that’s a little different is the trend generally flattens out beyond ∼1 effective radius, while Wu shows a roughly linear trend out to 1.5 Re. This might just be a visible effect of me displaying the trends out to larger radii.
Radial trends of Lick HδA and Dn4000 for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070
The Hα emission line measurements are similarly in broad agreement. Like Wu, I find that there are two distinct trends in emission: either moderately strong centrally with a rapid decline or weak throughout with a relatively flat trend. One galaxy (with MaNGA plateifu 9876-12701) has no detectable emission. I haven’t looked in detail at emission line ratios to compare to Wu’s Figure 7, but there’s general agreement that some residual star formation is present in some of the sample and weak AGN or ionization by hot evolved stars in others.
Radial trends of Lick Hα equivalent width and luminosity density for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070
A fairly common failing of this literature (IMO) is the use of proxies for recent star formation but not attempting actually to model star formation histories. There are plenty of publicly available tools for that available now, so there’s really no reason not to perform such modeling exercises. Wu did do some toy evolutionary modeling and posted a graph of trajectories through the Hα emission – Hδ absorption plane, which can scarcely unambiguously constrain star formation histories. Of course much of my hobby time is spent generating fine grained model star formation histories, so let’s take a look at a few selected results.
First, here are maps of the modeled fraction of the current stellar mass in stars of ages between 0.1 and 1 Gyr, very roughly the age range that produces a post-starbursty spectrum. Six of the galaxies have more or less strongly centrally concentrated intermediate age populations, which is generally what’s expected especially in the major merger pathway to a post-starburst interval. I’ll discuss this a little further below.
Maps of fractional stellar mass in intermediate age populations for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070
In more detail here are summed mass growth histories for the sample, that is all modeled star formation histories for a given observation are summed to produce a single global estimate. I’ve shown total masses here. Because of the pointing strategy MaNGA uses the fiber positions overlap to produce a 100% filling factor, so simply summing overestimates masses by about 0.2dex according to a calculation I performed some time ago. The present day masses in the plot below actually agree pretty well with the values listed in Table 1 of the paper, with an average difference of ~0.1 dex (this is probably because at least some of the light falls outside the IFU footprint in most of these galaxies, offsetting some of the overcounted mass).
Integrated mass growth histories for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070
Somewhat surprisingly several of these galaxies show little evidence of an actual burst of star formation in the recent past, at least at the global level. Some of these could simply have had star formation truncated recently, which can produce a poststarburst spectral signature for a time. Overall intermediate age stars contribute ~ 6-20% of the present day stellar mass, with the two largest contributions in the low mass galaxies in the bottom row of the plot.
There are some other oddities in this small sample. At least 3 galaxies are dwarf ellipticals or perhaps dwarf irregulars (in the case of plateifu 9876-12701), and two others have stellar masses under ~5 x 109 M⊙. Two of the low mass galaxies are in or near the Coma cluster, which suggests environmental effects as the probable cause of quenching. Another possible issue with the low mass galaxies is the infamous “age-metallicity degeneracy,” which refers to the fact that old, low metallicity populations “look like” younger, more metal rich ones by many measures. The Balmer lines in particular fade more slowly with age in lower metallicity populations, and the 4000Å break also becomes metallicity sensitive (smaller at low metallicities) at older ages.
There is only one clear merger remnant in the sample (with plateifu 8440-6104, which I will get to in a moment). One other galaxy (plateifu 8458-6102) is located in a compact group that appears (in Legacy survey imaging) to be embedded in a cloud of extragalactic light. Finally, two galaxies in this sample have been cataloged as K+A based on SDSS spectra — 8080-3702 and 9494-3701, while two others in the catalog of Melnick and dePropris (2013) are not.
SDSS thumbnails of the sample
The one clear merger remnant in the sample is an old friend of mine, and in fact I wrote three lengthy posts about this one back in 2018. In perusing those posts I noticed that the current set of model runs have a slightly weaker and more recent burst than the earlier runs. Also a double peak in the earlier runs has gone away in these, which means my early speculation that it might be possible to time crucial events in a merger from the detailed SFH model was too optimistic. On the other hand the model burst strength in the earlier runs was uncomfortably large, indicating an exceptionally gas rich merger and efficient processing of gas into stars. The current runs have a more reasonable ~10% of mass in the burst. So, I will look into those earlier runs and try to figure out what changed. Fortunately I’m a data hoarder and R is self-archiving to some extent.
KUG 0839+406, one of 9 “post-starburst” galaxies in Wu (2021)
The idea of looking at the integrated properties of IFU data to pick a post-starburst sample seems reasonable, but this sample appears to me to be both incomplete and possibly with some false positives. When DR17 is finally released I plan to try to develop my own criteria. As I’ve already shown using SDSS spectra alone to select a sample is doomed to produce lots of false positives.
I should finally mention one other paper pursuing a similar idea by Greene et al. (2021) showed up on arxiv recently. The authors lost me when they used the phrase “carefully curated” in their introduction, which was otherwise pretty well written up to that point. Maybe I’ll take another look anyway.
It took a few months but I did manage to analyze 28 of the 29 galaxies in the sample I introduced last time. One member — mangaid 1-604907 — hosts a broad line AGN and has broad emission lines throughout. That’s not favorable for my modeling methods, so I left it out. It took a while to develop a more or less standardized analysis protocol, so there may be some variation in S/N cuts in binning the spectra and in details of model runs in Stan. Most runs used 250 warmup and 750 total iterations for each of 4 chains run in parallel, with some adaptation parameters changed from their default values1I set target acceptance probability adapt_delta to 0.925 or 0.95 and the maximum treedepth for the No U-Turn Sampler max_treedepth to 11-12. A total post-warmup sample size of 2000 is enough for the inferences I want to make. One of the major advantages of the NUTS sampler is that once it converges it tends to produce draws from the posterior with very low autocorrelation, so effective sample sizes tend to be close to the number of samples.
I’m just going to look at a few measured properties of the sample in this post. In future ones I may look in more detail at some individual galaxies or the sample as a whole. Without a control sample it’s hard to say if this one is significantly different from a randomly chosen sample of galaxies, and I’m not going to try. In the plots shown below each point represents measurements on a single binned spectrum. The number of binned spectra per galaxy ranged from 15 to 153 with a median of 51.5, so a relatively small number of galaxies contribute disproportionately to these plots.
One of the more important empirical results in extragalactic astrophysics is the existence of a fairly well defined and approximately linear relationship between stellar mass and star formation rate for star forming galaxies, which has come to be known as the “star forming main sequence.” Thanks to CALIFA and MaNGA it’s been established in recent years that the SFMS extends to subgalactic scales as well, at least down to the ∼kpc resolution of these surveys. This first plot is of the star formation rate surface density vs. stellar mass surface density, where recall my estimate of SFR is for a time scale of 100 Myr. Units are \(\mathrm{M_\odot /yr/kpc^2} \) and \(\mathrm{M_\odot /kpc^2} \), logarithmically scaled. These estimates are uncorrected for inclination and are color coded by BPT class using Kauffmann’s classification scheme for [N II] 6584, with two additional classes for spectra with weak or no emission lines.
If we take spectra with star forming line ratios as comprising the SFMS there is a fairly tight relation: the cloud of lines are estimates from a Bayesian simple linear regression with measurement error model fit to the points with star forming BPT classification only (N = 428). The modeled relationship is \(\Sigma_{sfr} = -11.2 (\pm 0.5) + 1.18 (\pm 0.06)~ \Sigma_{M^*}\) (95% marginal confidence limits), with a scatter around the mean relation of ≈ 0.27 dex. The slope here is rather steeper than most estimates2For example in a large compilation by Speagle et al. (2014) none of the estimates exceeded a slope of 1., but perhaps coincidentally is very close to an estimate for a small sample of MaNGA starforming galaxies in Lin et al. (2019). I don’t assign any particular significance to this result. The slope of the SFMS is highly sensitive to the fitting method used, the SFR and stellar mass calibrators, and selection effects. Also, the slope and intercept estimates are highly correlated for both Bayesian and frequentist fitting methods.
One notable feature of this plot is the rather clear stratification by BPT class, with regions having AGN/LINER line ratios and weak emission line regions offset downwards by ~1 dex. Interestingly, regions with “composite” line ratios straddle both sides of the main sequence, with some of the largest outliers on the high side. This is mostly due to the presence of Markarian 848 in the sample, which we saw in recent posts has composite line ratios in most of the area of the IFU footprint and high star formation rates near the northern nucleus (with even more hidden by dust).
Σsfr vs. ΣM*. Cloud of straight lines is an estimate of the star-forming main sequence relation based on spectra with star-forming line ratios. Sample is all analyzed spectra from the set of “transitional” candidates of the previous post.
Another notable relationship that I’ve shown previously for a few individual galaxies is between the star formation rate estimated from the SFH models and Hα luminosity, which is the main SFR calibrator in optical spectra. In the left hand plot below Hα is corrected for the estimated attenuation for the stellar component in the SFH models. The straight line is the SFR-Hα calibration of Moustakas et al. (2006), which can be traced back to early ’90s work by Kennicutt.
Most of the sample does follow a linear relationship between SFR density and Hα luminosity density with an offset from the Kennicutt-Moustakas calibration, but there appears to be a departure from linearity at the low SFR end in the sense that the 100 Myr averaged SFR exceeds the amount predicted by Hα (which recall traces star formation on 5-10 Myr scales). This might be interpreted as indicating that the sample includes a significant number of regions that have been very recently quenched (that is within the past 10-100 Myr). There are other possible interpretation though, including biased estimates of Hα luminosity when emission lines are weak.
In the right hand panel below I plot the same relationship but with Hα corrected for attenuation using the Balmer decrement for spectra with firm detections in the four lines that go into the [N II]/Hα vs. [O III]/Hβ BPT classification, and therefore have firm detections in Hβ. The sample now nicely straddles the calibration line over the ∼ 4 orders of magnitude of SFR density estimates. So, the attenuation in the regions where emission lines arise is systematically higher than the estimated attenuation of stellar light. This is a well known result. What’s encouraging is it implies my model attenuation estimates actually contain useful information.
(L) Estimated Σsfr vs. Σlog L(Hα) corrected for attenuation using stellar attenuation estimate.
(R) same but Hα luminosity corrected using Balmer decrement. Spectra with detected Hβ emission only.
One final relation: some measure of the 4000Å break strength has been used as a calibrator of specific star formation rate since at least Brinchmann et al. (2004). Below is my version using the “narrow” definition of D4000. I haven’t attempted a quantitative comparison with any other work, but clearly there’s a well defined relationship. Maybe worth noting is that “red and dead” ETGs typically have \(\mathrm{D_n(4000)} \gtrsim 1.75\) (see my previous post for example). Very few of the spectra in this sample fall in that region, and most are low S/N spectra in the outskirts of a few of the galaxies.
Specific star formation rate vs. Dn4000
Two obvious false positives in this sample were a pair of grand design spirals (mangaids 1-23746 and 1.382712) with H II regions sprinkled along the full length of their arms. To see why they were selected and verify that they’re in fact false positives here are BPT maps:
Map of BPT classification — mangaid 1-23746 (plateifu 8611-12702)Map of BPT classification — mangaid 1-382712 (plateifu 9491-6101)
These are perfect illustrations of the perils of using single fiber spectra for sample selection when global galaxy properties are of interest. The central regions of both galaxies have “composite” spectra, which might actually indicate that the emission is from a combination of AGN and star forming regions, but outside the nuclear regions star forming line ratios prevail throughout.
These two galaxies contribute about 45% of the binned spectra with star forming line ratios, so the SFMS would be much more sparsely populated without their contribution. Only one other galaxy (mangaid 1-523050) is similarly dominated by SF regions and it has significantly disturbed morphology.
I may return to this sample or individual members in the future. Probably my next posts will be about Bayesian modelling though.
I’ve mentioned a few times that I took a shot several years ago at selecting a sample of post-starburst, or to use a term that’s gained some currency recently, “transitioning” galaxies1transitioning in this context implies that star formation has recently ceased or is in the process of shutting down. To cite one example Alatalo et al. (2017) use the word 18 times with this meaning. One thing that astronomers have come to realize is that traditional K+A spectroscopic selection criteria (basically requiring a combination of strong Balmer absorption lines and weak emission) potentially miss important populations, for example galaxies hosting an AGN. Selecting for weak emission also implies that star formation has already shut down, which precludes finding galaxies that are in the process of shutting down but still forming stars. On the other hand simply dropping constraints on emission won’t work because the resulting sample would be contaminated with a large fraction of normal star forming galaxies. Here is a popular spectroscopic diagnostic diagram, plotting the (pseudo) Lick HδA index against the 4000Å break strength index Dn(4000). This version used the MPA-JHU measurements for approximately 1/2 of SDSS single fiber galaxy spectra from DR8.
The lick Hδ – 4000Å break strength plane for a large sample of SDSS galaxies. Measurements from the MPA-JHU pipeline.
As is true of many observed properties of galaxies the distribution in this diagram is strongly bimodal, with one of the modes centered right around HδA ≈ 5Å. For the most part this region of the Hδ-D4000 plane is occupied by starforming galaxies — somewhere around 8-10% of all SDSS spectra with MPA measurements have HδA ≥ 5Å, while true K+A galaxies are no more than a fraction of a percent of the local population. I took the simplest possible approach to try to minimize contamination from starforming galaxies: besides a traditional K+A selection with slightly relaxed emission line constraints I made a selection of spectra with emission line ratios in the “other than starforming” region of the [N II]/Hα vs [O III]/Hβ BPT diagnostic. I decided to use Kauffmann’s empirical starforming boundary as the selection criterion, and therefore included the so-called “composite” region of the BPT diagnostic. For the sake of completeness and reproducibility here is part of the CASJobs query I used. I saved a number of measurements from the MPA spectroscopic pipeline as well as the SDSS photo pipeline that aren’t included in the listing below. I did include a few lines just to point out that it’s important to use the emission line subtracted version of the Lick Hδ index because it can be significantly contaminated. Most of the where clause of the query is selecting spectra with firm detections and good error estimates of the relevant emission and absorption lines. There is also a redshift range constraint — the upper limit is set to avoid contamination of Hδ by the 5577Å terrestrial night sky line. Finally there are some modest quality constraints.
select into mydb.kaufpostagn
gi.lick_hd_a_sub as lick_hd_a,
gi.lick_hd_a_sub_err as lick_hd_a_err,
gi.d4000_n,
gi.d4000_n_err
from specObj s
left outer join PhotoObj as p on s.bestObjid = p.Objid
left outer join galSpecline as g on s.specObjid = g.specObjid
left outer join galSpecIndx as gi on s.specObjid = gi.specObjid
left outer join galSpecExtra as ge on s.specObjid = ge.specObjid
where
(g.h_alpha_flux/g.h_alpha_flux_err > 3 and g.h_alpha_flux_err > 0) and
(g.nii_6584_flux/g.nii_6584_flux_err > 3 and g.nii_6584_flux_err > 0) and
(g.h_beta_flux/g.h_beta_flux_err > 3 and g.h_beta_flux_err > 0) and
(g.oiii_flux/g.oiii_flux_err > 3 and g.oiii_flux_err > 0) and
((0.61/(log10(g.nii_6584_flux/g.h_alpha_flux)-0.05)+1.3 <
log10(g.oiii_flux/g.h_beta_flux)) or (log10(g.nii_6584_flux/g.h_alpha_flux)>=0.05)) and
(gi.lick_hd_a_sub > 5 and gi.lick_hd_a_sub_err > 0) and
(s.z > .02 and s.z < 0.35) and
(s.snMedian > 10) and
(s.zWarning = 0 or s.zWarning = 16)
order by
s.plate, s.mjd, s.fiberid
I used a second query to make a more traditional K+A selection. This all could have been done with a single query but the conditions get a bit complicated. These queries run in the DR10 “context”2the data release doesn’t actually matter as long as it’s later than DR7 since the last release the MPA pipeline was run on was DR8. should return 4,235 and 874 hits respectively, with some overlap.
select into mydb.myka
s.ra,
s.dec,
s.plate,
s.mjd,
s.fiberid,
s.specObjid,
from specObj s
left outer join PhotoObj as p on s.bestObjid = p.Objid
left outer join galSpecline as g on s.specObjid = g.specObjid
left outer join galSpecIndx as gi on s.specObjid = gi.specObjid
left outer join galSpecExtra as ge on s.specObjid = ge.specObjid
where
(g.oii_3729_eqw > -5 and g.oii_3729_eqw_err > 0) and
(g.h_alpha_eqw > -5 and g.h_alpha_eqw_err > 0) and
(gi.lick_hd_a_sub > 5 and gi.lick_hd_a_sub_err > 0) and
(s.z > .02 and s.z < 0.35) and
(s.snMedian > 10) and
(s.zWarning = 0 or s.zWarning = 16)
order by
s.plate, s.mjd, s.fiberid
I thought at the time based solely on examining SDSS thumbnails that there were rather too many false positives in the form of normal starforming galaxies for the intended purpose of the sample. The choice to include galaxies in Kauffmann’s composite region played some role in this — although even the people responsible for this classification scheme admit now that it’s too simple some galaxies really do have spectra that are composites of starforming regions and AGN. But the more important reason I think is the well known problem that single fiber spectra only cover a portion of most nearby galaxies and galaxy nuclei (which were the intended targets) just aren’t representative of the global properties of galaxies. The SPOGS3an acronym formed somehow from the term “shocked post-starburst galaxy survey.” sample of Alatalo et al. 2016, which has similar aims to my selection but more elaborate selection criteria, has (in my opinion) a similar issue of false positives for the same reason.
MaNGA provides a unique opportunity to examine the causes of false positives in my transitional sample, and I hope also confirm some real positives. A simple position crossmatch of my sample, which has about 5500 members, with MaNGA DR15 object coordinates produced 29 matches with a position tolerance of about 3″. SDSS thumbnails of the 29 are shown below. By my count at least 13, or 45%, of the sample are visibly disturbed in some way with 7 of those being clear merger remnants with evident shells and tidal tails.
I’ve been calculating star formation history models for these at a leisurely pace and plan to write more about the sample in future posts. Two of these I’ve already written about — besides Mrk 848 number 15 in the postage stamps was the subject of a series of posts starting here. I may write more case studies like those, or perhaps take a more holistic look at the sample and compare to a control group.
As something of an aside, with a looser position matching tolerance a 30th object turns up:
CGCG 390-0666 (SDSS finder chart image)
CGCG 390-066 (Legacy survey cutout)
This made my sample based on the spectrum of the eastern nucleus. This object turns out to be doubly strange. Besides having two apparent nuclei, when I did some preliminary spectral fitting I noticed a peculiar pattern of residuals that turned out to be due to this:
Extract from a single spectrum of plateifu 8083-9101 (mangaid 1-38017)
Many of the spectra have emission lines at exactly their rest frame wavelengths. This particular spectrum, which came from somewhere in the SE quadrant of the IFU footprint, clearly shows Hα plus the [N II] and [S II] doublets offset from the same lines at the redshift of the galaxy. It turns out this galaxy lies near the edge of the Orion-Eridanus superbubble, a large region of diffuse emission in our galaxy. Should I choose to study this galaxy in more detail these lines could be masked easily enough, but the metadata for this data cube says it shouldn’t be used.
I’m going to close out my analysis of Mrk 848 for now with three topics. First, dust. Like most SED fitting codes mine produces an estimate of the internal attenuation, which I parameterize with τV, the optical depth at V assuming a conventional Calzetti attenuation curve. Before getting into a discussion for context here is a map of the posterior mean estimate for the higher S/N target binning of the data. For reference isophotes of the synthesized r band surface brightness taken from the MaNGA data cube are superimposed:
Map of posterior mean of τV from single component dust model fits with Calzetti attenuation
This compares reasonably well with my visual impression of the dust distribution. Both nuclei have very large dust optical depths with a gradual decline outside, while the northern tidal tail has relatively little attenuation.
The paper by Yuan et al. that I looked at last time devoted most of its space to different ways of modeling dust attenuation, ultimately concluding that a two component dust model of the sort advocated by Charlot and Fall (2000) was needed to bring results of full spectral fitting using ppxf on the same MaNGA data as I’ve examined into reasonable agreement with broad band UV-IR SED fits.
There’s certainly some evidence in support of this. Here is a plot I’ve shown for some other systems of the estimated optical depth of the Balmer emission line emitting regions based on the observed vs. theoretical Balmer decrement (I’ve assumed an intrinsic Hα/Hβ ratio of 2.86 and a Calzetti attenuation relation) plotted against the optical depth estimated from the SFH models, which roughly estimates the amount of reddening needed to fit the SSP model spectra to the observed continuum. In some respects this is a more favorable system than some I’ve looked at because Hβ emission is at measurable levels throughout. On the other hand there is clear evidence that multiple ionization mechanisms are at work, so the assumption of a single canonical value of Hα/Hβ is likely too simple. This might be a partial cause of the scatter in the modeled relationship, but it’s encouraging that there is a strong positive correlation (for those who care, the correlation coefficient between the mean values is 0.8).
The solid line in the graph below is 1:1. The semi-transparent cloud of lines are the sampled relationships from a Bayesian errors in variables regression model. The mean (and marginal 1σ uncertainty) is \(\tau_{V, bd} = (0.94\pm 0.11) + (1.21 \pm 0.12) \tau_V\). So the estimated relationship is just a little steeper than 1:1 but with an offset of about 1, which is a little different from the Charlot & Fall model and from what Yuan et al. found, where the youngest stellar component has about 2-3 times the dust attenuation as the older stellar population. I’ve seen a similar not so steep relationship in every system I’ve looked at and don’t know why it differs from what is typically assumed. I may look into it some day.
τV estimated from Balmer decrement vs. τV from model fits. Straight line is 1:1 relation. Cloud of lines are from Bayesian errors in variables regression model.
I did have time to run some 2 dust component SFH models. This is a very simple extension of the single component models: a single optical depth is applied to all SSP spectra. A second component with the optical depth fixed at 1 larger than the bulk value is applied only to the youngest model spectra, which recall were taken from unevolved SSPs from the updated BC03 library. I’m just going to show the most basic results from the models for now in the form of maps of the SFR density and specific star formation rate. Compared to the same maps displayed at the end of the last post there is very little difference in spatial variation of these quantities. The main effect of adding more reddened young populations to the model is to replace some of the older light — this is the well known dust-age degeneracy. The average effect was to increase the stellar mass density (by ≈ 0.05 dex overall) while slightly decreasing the 100Myr average SFR (by ≈ 0.04 dex), leading to an average decrease in specific star formation rate of ≈ 0.09 dex. While there are some spatial variations in all of these quantities no qualitative conclusion changes very much.
Results from fits with 2 component dust models.
(L) SFR density.
(R) Specific SFR
Contrary to Yuan+ I don’t find a clear need for a 2 component dust model. Without trying to replicate their results I can’t say why exactly we disagree, but I think they erred in aggregating the MaNGA data to the lowest spatial resolution of the broad band photometric data they used, which was 5″ radius. There are clear variations in physical conditions on much smaller scales than this.
Second topic: the most widely accepted SFR indicator in visual wavelengths is Hα luminosity. Here is another plot I’ve displayed previously: a simple scatterplot of Hα luminosity density against the 100Myr averaged star formation rate density from the SFH models. Luminosity density is corrected for attenuation estimated from the Balmer decrement and for comparison the light gray points are the uncorrected values. Points are color coded by BPT class determined in the usual manner. The straight line is the Hα – SFR calibration of Moustakas et al. (2006), which in turn is taken from earlier work by Kennicutt.
Model SFR density vs. Hα luminosity density corrected for extinction estimated from Balmer decrement. Light colored points are uncorrected for extinction. Straight line is Hα-SFR calibration from Moustakas et al. (2006)
Keeping in mind that Hα emission tracks star formation on timescales of ∼10 Myr1to the extent that ionization is caused by hot young stars. There are evidently multiple ionizing sources in this system, but disentangling their effects seems hard. Note there’s no clear stratification by BPT class in this plot. this graph strongly supports the scenario I outlined in the last post. At the highest Hα luminosities the SFR-Hα trend nicely straddles the Kennicutt-Moustakas calibration, consistent with the finding that the central regions of the two galaxies have had ∼constant or slowly rising star formation rates in the recent past. At lower Hα luminosities the 100Myr average trends consistently above the calibration line, implying a recent fading of star formation.
The maps below add some detail, and here the perceptual uniformity of the viridis color palette really helps. If star formation exactly tracked Hα luminosity these two maps would look the same. Instead the northern tidal tail in particular and the small piece of the southern one within the IFU footprint are underluminous in Hα, again implying a recent fading of star formation in the peripheries.
(L) Hα luminosity density, corrected for extinction estimated by Balmer decrement.
(R) SFR density (100 Myr average).
Final topic: the fit to the data, and in particular the emission lines. As I’ve mentioned previously I fit the stellar contribution and emission lines simultaneously, generally assuming separate single component gaussian velocity dispersions and a common system velocity offset. This works well for most galaxies, but for active galaxies or systems like this one with complex velocity profiles maybe not so much. In particular the northern nuclear region is known to have high velocity outflows in both ionized and neutral gas due presumably to supernova driven winds. I’m just going to look at the central fiber spectrum for now. I haven’t examined the fits in detail, but in general they get better outside the immediate region of the center. First, here is the fit to the data using my standard model. In the top panel the gray line, which mostly can’t be seen, is the observed spectrum. Blue are quantiles of the posterior mean fit — this is actually a ribbon, although its width is too thin to be discernable. The bottom panel are the residuals in standard deviations. Yes, they run as large as ±50σ, with conspicuous problems around all emission lines. There are also a number of usually weak emission lines that I don’t track that are present in this spectrum.
Fit to central fiber spectrum; model with single gaussian velocity distributions.
I have a solution for cases like this which I call partially parametric. I assume the usual Gauss-Hermite form for the emission lines (as in, for example, ppxf) while the stellar velocity distribution is modeled with a convolution kernel2I think I’ve discussed this previously but I’m too lazy to check right now. If I haven’t I’ll post about it someday. Unfortunately the Stan implementation of this model takes at least an order of magnitude longer to execute than my standard one, which makes its wholesale use prohibitively expensive. It does materially improve the fit to this spectrum although there are still problems with the stronger emission lines. Let’s zoom in on a few crucial regions of the spectrum:
Zoomed in fit to central fiber spectrum using “partially parametric velocity distribution” model. Grey: observed flux. Blue: model.
The two things that are evident here are the clear sign of outflow in the forbidden emission lines, particularly [O III] and [N II], while the Balmer lines are relatively more symmetrical as are the [S II] doublet at 6717, 6730Å. The existence of rather significant residuals is likely because emission is coming from at least two physically distinct regions while the fit to the data is mostly driven by Hα, which as usual is the strongest emission line. The fit captures the emission line cores in the high order Balmer lines rather well and also the absorption lines on the blue side of the 4000Å break except for the region around the [Ne III] line at 3869Å.
I’m mostly interested in star formation histories, and it’s important to see what differences are present. Here is a comparison of three models: my standard one, the two dust component model, and the partially parametric velocity dispersion model:
Detailed star formation history models for the northern nucleus using 3 different models.
In fact the differences are small and not clearly outside the range of normal MCMC variability. The two dust model slightly increases the contribution of the youngest stellar component at the expense of slightly older contributors. All three have the presumably artifactual uptick in SFR at 4Gyr and very similar estimated histories for ages >1 Gyr.
I still envision a number of future model refinements. The current version of the official data analysis pipeline tracks several more emission lines than I do at present and has updated wavelengths that may be more accurate than the ones from the SDSS spectro pipeline. It might be useful to allow at least two distinct emission line velocity distributions, with for example one for recombination lines and one for forbidden. Unfortunately the computational expense of this sort of generalization at present is prohibitive.
I’m not impressed with the two dust model that I tried, but there may still be useful refinements to the attenuation model to be made. A more flexible form of the Calzetti relation might be useful for example3there is recent relevant literature on this topic that I’m again too lazy to look up.
My initial impression of this system was that it was a clear false positive that was selected mostly because of a spurious BPT classification. On further reflection with MaNGA data available it’s not so clear. A slight surprise is the strong Balmer absorption virtually throughout the system with evidence for a recent shut down of star formation in the tidal tails. A popular scenario for the formation of K+A galaxies through major mergers is that they experience a centrally concentrated starburst after coalescence which, once the dust clears and assuming that some feedback mechanism shuts off star formation leads to a period of up to a Gyr or so with a classic K+A signature4I previously cited Bekki et al. 2005, who examine this scenario in considerable detail.Capturing a merger in the instant before final coalescence provides important clues about this process.
To the best of my knowledge there have been no attempts at dynamical modeling of this particular system. There is now reasonably good kinematic information for the region covered by the MaNGA IFU, and there is good photometric data from both HST and several imaging surveys. Together these make detailed dynamical modeling technically feasible. It would be interesting if star formation histories could further constrain such models. Being aware of the multiple “degeneracies” between stellar age and other physical properties I’m not highly confident, but it seems provocative that we can apparently identify distinct stages in the evolutionary history of this system.
This galaxy1also known as VV705, IZw 107, IRAS F15163+4255, among others has been my feature image since I started this blog. Why is that besides that it’s kind of cool looking? As I’ve mentioned before I took a shot a few years ago at selecting a sample of “transitional” galaxies from the SDSS spectroscopic sample, that is ones that may be in the process of rapidly shutting down star formation2See for example Alatalo et al. (2017) for recent usage of this terminology.. I based the selection on a combination of strong Hδ absorption and either weak emission lines or line ratios other than pure starforming, using measurements from the MPA-JHU pipeline. This galaxy pair made the sample based on the spectrum centered on the northern nucleus (there is also a spectrum of the southern nucleus from the BOSS spectrograph, but no MPA pipeline measurements). Well now, these galaxies are certainly transitioning to something, but they’re probably not shutting down star formation just yet. Simulations of gas rich mergers generally predict a starburst that peaks around the time of final coalescence. There is also significant current star formation, as high as 100-200 \(M_\odot/yr\) per various literature estimates, although it is mostly hidden. So on the face of it at least this appears to be a false positive. This was also an early MaNGA target, and one of a small number where both nuclei of an ongoing merger are covered by a single IFU:
Markarian 848. SDSS image thumbnail with MaNGA IFU footprint.
Today I’m going to look at a few results of my analysis of the IFU data that aren’t too model dependent to get some insight into why this system was selected. As usual I’m looking at the stacked RSS data rather than the data cubes, and for this exercise I Voronoi binned the spectra to a very low target SNR of 6.25. This leaves most of the fibers unbinned in the vicinity of the two nuclei. First, here is a map of BPT classification based on the [O III]/Hβ vs. [N II]/Hα diagnostic as well as scatterplots of several BPT diagnostics. Unfortunately the software I use for visualizing binned maps extends the bins out to the edge of the bounding rectangle rather than the hexagonally shaped edge of the data, which is outlined in red. Also note that different color coding is used for the scatter plots than the map. The contour lines in the map are arbitrarily spaced isophotes of the synthesized R band image supplied with the data cube.
Map of BPT class determined from [O III]/Hβ vs. [N II}/Hα diagnostic diagram, and BPT diagnostics for [N II], [S II] and [O I]. Curves are boundary lines form Kewley et al. (2006).
What’s immediately obvious is that most of the area covered by the IFU including both nuclei fall in the so-called “composite” region of the [N II]/Hα BPT diagram. This gets me back to something I’ve complained about previously. There was never a clear physical justification for the composite designation (which recall was first proposed in Kauffmann et al. 2003), and the upper demarcation line between “pure” AGN and composite systems as shown in the graph at upper right was especially questionable. It’s now known if it wasn’t at the turn of the century (which I doubt is the case) that a number of ionization mechanisms can produce line ratios that fall generally in the composite/LINER regions of BPT diagnostics. Shocks in particular are important in ongoing mergers. High velocity outflow of both ionized and neutral gas have been observed in the northern nucleus by Rupke and Veillux (2013), which they attributed to supernova driven winds.
The evidence for AGN in either nucleus is somewhat ambiguous. Fu et al. (2018) called this system a binary AGN, but that was based on the “composite” BPT line ratios from the same MaNGA data as we are examining (their map, by the way, is nearly identical to mine; see also Yuan et al. 2018). By contrast Vega et al. 2008 were able to fit the entire SED from NIR to radio frequencies with a pure starburst model and no AGN contribution at all, while more recently Dietrich et al. 2018 estimated the AGN fraction to be around 0.25 from NIR to FIR SED fitting. A similar conclusion that both nuclei contain both a starburst and AGN component was reached by Vardoulaki et al. 2015 based on radio and NIR data. One thing I haven’t seen commented on in the MaNGA data that possibly supports the idea that the southern nucleus harbors an AGN is that the regions with unambiguous AGN-like optical emission line ratios are fairly symmetrically located on either side of the southern nucleus and separated from it by ∼1-2 kpc. This could perhaps indicate the AGN is obscured from our view but visible from other angles.
There are also several areas with starforming emission line ratios just to the north and east of the northern nucleus and scattered along the northern tidal tail (the southern tail is largely outside the IFU footprint). In the cutout below taken from a false color composite of the F435W+F814W ACS images several bright star clusters can be seen just outside the more heavily obscured nuclear region, and these are likely sources of ionizing photons.
Markarian 848 nuclei. Cutout from HST ACS F814W+F435W images.
Finally turning to the other component of the selection criteria, here is a map of the (pseudo) Lick HδA index and a plot of the widely used HδA vs Dn(4000) diagnostic. It’s a little hard to see a clear pattern in the map because this is a rather noisy index, but strong Balmer absorption is seen pretty much throughout, with the highest values outside the two nuclei and especially along the northern tidal tail.
Location in the Hδ – D4000 plane doesn’t uniquely constrain the star formation history, but the contour plot taken from a largish sample of SDSS spectra from the NGP is clearly bimodal, with mostly starforming galaxies at upper left and passively evolving ones at lower right, with a long “green valley” in between. Simple models of post-starburst galaxies will loop upwards and to the right in this plane as the starburst ages before fading towards the green valley. This is exactly where most of points in this diagram lie, which certainly suggests an interesting recent star formation history.
(L) Map of HδA index.
(R) HδA vs Dn(4000) index. Contours are for a sample of SDSS spectra from the north galactic pole.
I’m going to end with a bit of speculation. In simulations of gas rich major mergers the progenitors generally complete 2 or 3 orbits before final coalescence, with some enhancement of star formation during the perigalactic passages and perhaps some ebbing in between. This process plays out over hundreds of Myr to some Gyr. What I think we are seeing now is the 2nd or third encounter of this pair, with the previous encounters having left an imprint in the star formation history.
I’ve done SFH modeling for this binning of the data, and also for data binned to higher SNR and modeled with higher time resolution models. Next post I’ll look at these in more detail.
A paper showed up on arxiv in early August titled “An early-type galaxy with an inner star-forming disk” by Li et al. (article id 1808.01730) that describes a system observed by MaNGA that’s rather similar to the one I have been writing about in recent posts, namely an apparent post-merger ETG with ongoing star formation. The paper is a little light on details, but it’s refreshingly short and not technically demanding. Unfortunately their discovery claim is incorrect: this galaxy was identified as a star forming elliptical in Helmboldt (2007), who also measured its HI mass to be 5.4±0.5 × \(10^8 \mathsf{M}_{\odot}\), about 5% of its baryonic mass.
Of course I did my own analysis which I’ll summarize in this post, also filling in some details missing from the paper. The first step in my analysis is to compute a velocity field (actually what I compute are redshift offsets, but these are easily converted to line of sight velocities). One of the first things I noticed is that while this is qualitatively similar to the one published by Li et al. (and shown twice!), the rotating inner component has nearly 3 times the maximum rotation velocity as their stellar velocity map.
Velocity field estimated from RSS data
So, naturally concerned that there was an error in my processing of the RSS data I downloaded the data cube and ran my code on that, getting the velocity field shown below on the left, which is evidently nearly identical.
Recall that my redshift offset measurement routine does template matching very similar to the SDSS spectro pipeline, using for templates a set of eigenspectra from a principal components analysis of a largish set of SDSS single fiber spectra from a high galactic latitude sample. These naturally encode information about both emission and absorption lines, with the code returning a single blended estimate. This works fine when ionized gas and stars are kinematically tightly coupled, but not so well when they’re not. Suspecting that emission lines were dominating the velocity estimates in the inner regions, as something of a one-off experiment I masked the area around emission lines in the galaxy spectra and reran the routine, getting the velocity field on the right.
Now this agrees in some detail with the stellar velocity field published by Li et al. (visual differences are mostly due to different color palettes). So an interesting first result is that not only is there a rotating, kinematically decoupled core but the stars and ionized gas within the core are decoupled from each other. This would seem to point to a recent injection of fresh cold gas.
Unfortunately they only published the stellar velocity field even though the official data analysis pipeline calculates separate ones for stars and gas, so I won’t be able to verify this result until value added kinematic data is made available.
(L) Velocity field estimated for data cube
(R) Stellar velocity field
For reference here is the spectrum from the central fiber. Wavelengths are vacuum rest frame and fluxes are corrected for foreground galactic extinction.
Central fiber spectrum, plateifu 8335-6101
As with the previous galaxy I did two sets of star formation history models. The first set a low S/N threshold for binning spectra and used an 81 SSP model subset of EMILES for fitting. For the second run I bin to a considerably higher target S/N and fit with the larger 216 SSP model set with the full BaSTI time resolution. The second data set has 25 coadded spectra with mean S/N per pixel ranging from 18-60.
For a quick comparison of the two sets of runs here are model mass growth histories summed over all spectra. Both indicate that a very substantial burst of star formation occurred beginning ~1.25Gyr ago (curiously, this is the same time as the initial burst in the galaxy KUG 0859+406 that I previously wrote about — part 1, part 2, part3). The present day stellar mass is just under \(1 times 10^{10} mathsf{M}_{odot}\), in agreement with the value cited by Li et al.
Summed model mass growth histories, emiles subset and “full” emiles
I find that ongoing star formation is largely confined to the KDC, in agreement with Li et al., so after running the models for the 25 bins I partitioned them into a core set and an outer set, with the core set comprising the bins within about 4×2″ (1.5 x 0.75 kpc semi-major and semi-minor axes) of the nucleus as shown below.
Stellar mass density for binned data, showing outlines of bins and core region
The summed star formation history models are shown below. As with my previous subject there was a galaxy wide burst of star formation ~1Gyr ago, but unlike it star formation declined monotonically after that, with no more recent secondary burst. The ongoing star formation in the central region is seen to be a relatively recent (last ~100Myr) uptick. This leads me to a slightly different interpretation of the data — the authors suggest that we are seeing the late stages of a gas rich major merger. I would say rather that the merger was completed ~1Gyr ago and that gas has recently been recaptured. This is consistent with simulations that show in the absence of AGN feedback star formation can proceed for some time after a merger. It’s also consistent with the observed reservoir of neutral hydrogen, which is sufficient to fuel star formation at the current level for another ~1Gyr.
(L) Model Star formation history, inner core
(R) Star formation history, outside core
Here are some additional results of the analysis of the 25 binned spectra. All of these agree with Li et al. where comparable results are displayed.
First, the Hδ absorption line index vs. 4000Å break strength index Dn(4000):
Lick HδA vs. Dn(4000)
Contour lines are my measurements of a sample of ~20K single fiber SDSS spectra. As with many quantities there is a distinct bimodality in this distribution, with star forming galaxies at upper left and passively evolving, mostly early type galaxies at bottom right. If star formation were to stop today there would be no K+A phase in this galaxy. A 1Gyr population has already passed its peak Balmer line strength, so if there was a K+A phase it was in the past.
Turning to emission lines, here are the 3 BPT diagnostic diagrams that are most commonly used with SDSS spectra. The curved lines are the starforming/something else demarcation lines of Kewley et al. 2006. In spatially resolved spectroscopy these lines seem not so useful. I find, in agreement with Li et al., that many of the spectra have “composite” line ratios, but except for the central fiber these are mostly near the edge or outside the KDC. The bottom right panel below shows the trend with radius of the [S II]/Hα line ratio (the other two show similar but weaker trends). This trend is the opposite of what we’d expect if an AGN were the ionizing source. Shocks or hot evolved stars are more likely.
Emission line diagnostic (BPT) plots
TL: [N II]/Hα vs [O III]/Hβ
TR: [O I]6300/Hα
BL: [S II]/Hα
BR: trend of [S II]/Hα with radius
I estimate a 100Myr average star formation rate from the SFH models. The log of the star formation rate density (in \(\mathsf{M}_{\odot}/\mathsf{yr/kpc}^2\)) is plotted against log stellar mass density (in \(\mathsf{M}_{\odot}/\mathsf{kpc}^2\)) below (the line is the estimate of the SF main sequence of Renzini and Peng 2015). Again in agreement with Li et al. the areas of highest SFR lie near the SF main sequence, while the outskirts of the IFU footprint fall below it. log SFR density vs. log stellar mass density
The radial trend of star formation rate is shown below on the left. On the right is my estimate of SFR density plotted against Hα luminosity density along with the calibration of Moustakis et al. Hα is corrected for attenuation using the Balmer decrement.
(L) log SFR density vs. radius
(R) log SFR density vs. attenuation corrected log Hα luminosity density
By the way my models directly estimate dust attenuation of the stellar light, typically assuming a Calzetti attenuation curve. A comparison with attenuation estimated with the Balmer decrement is shown below. To anyone familiar with SDSS spectra it’s not too surprising that estimates from the Balmer decrement have a huge amount of scatter due mostly to the fact that Hβ emission line strength is often poorly constrained. Despite that there is a clear positive correlation between these two estimates. I will probably examine this relationship in more detail in a future post, perhaps based on more favorable data.
τV estimated from Balmer decrement vs SFH model estimates
Finally, trend with radius of the metallicity 12+log(O/H) estimated with the strong line O3N2 method. As with the post merger galaxy in the previous posts there is a hint of a weak negative gradient. Overall the gas phase metallicity is around solar or just a little below. This is somewhat lower than my other post merger example, which is not unexpected given an approximately factor of 4 difference in stellar masses.
Metallicity 12+log(O/H) vs radius, estimated by O3N2 index
I’m going to end this series for now with a more detailed look at the spatially resolved star formation history of this galaxy and make a highly selective comparison to some recent theoretical and empirical work on mergers. This continues from part 1 and part 2.
My main theoretical sources for the following discussion include a paper titled “The fate of the Antennae galaxies” by Lahén et al. (2018) and a similar paper also simulating an Antennae analog by Renaud, Bournaud and Duc (2015). I didn’t pick these because I necessarily think this galaxy is an evolved analog of the Antennae; in fact I think there are important differences in the likely precursors. The main reason is these studies are among the first to run very high resolution simulations through and beyond coalescence. Some other significant papers include high resolution simulations with detailed treatment of feedback by Hopkins et al. (2013), Di Matteo et al. (2008), who studied a large sample of merger and flyby simulations, Bekki et al. (2005) who specifically looked at the formation of K+A galaxies through mergers, and Ji et al. (2014) who studied the lifetime of merger features using a suite of simulations. This doesn’t begin to scratch the surface of this literature. Merger simulations are very popular!
Some recent observational papers that examine individual post-merger systems in more or less detail include Weaver et al. (2018) and Li et al. (2018). I will return to the latter paper, which was based on MaNGA data, in a later post. Other observational work that’s more statistical in nature includes Ellison et al. (2015), Ellison et al. (2013), and earlier papers in the same series.
As I’ve mentioned several times already the model spectra I use mostly come from the 2017 EMILES extension of the MILES SSP library with BaSTI isochrones and Kroupa IMF, supplemented with unevolved model spectra from the 2013 update of BC03 models. The results reported in the previous two posts used a small subset of the library — just 3 metallicity bins and 27 time bins, with every other time bin in the full library starting with the second excluded. For my “full” MILES subset I use 4 metallicity bins with \([Z/Z_{\odot}] \in \{-0.66, -0.25, 0.06, 0.40\}\) and all 54 time bins (the lowest metallicity bin is dropped in the reduced subset). Execution time of the Stan SFH models is roughly proportional to the number of parameters, so everything else equal it takes about 2.5 times longer to run a single model with the full set.
For this exercise I binned the MaNGA spectra with a considerably higher target SNR than usual, ending up with 53 coadded spectra out of the original 183 fiber/pointing combinations. One of the mods I made to Cappellari’s Voronoi binning code was to drop a “roundness” check. The bins in this case still end up with reasonably compact shapes since the surface brightness within the IFU footprint is fairly symmetrical. All but one of the spectra in the inner 2 kpc. ended up unbinned, so the spatial resolution in that critical region is unchanged.
S/N in binned spectra
To directly compare the results from the current round of models with the previous lower time resolution runs here are the modeled mass growth histories summed over the entire IFU footprint (blue is the EMILES subset):
Total mass growth histories – emiles subset and full emiles library
Note: MaNGA’s dithering strategy results in considerable overlap in fiber positions in order to obtain a 100% filling fraction. That means the area in fibers exceeds the area of the IFU footprint by about 60%. So, sums of quantities like masses or star formation rates need to be adjusted downwards by about 0.2 dex.
The only real difference we see in the higher time resolution runs is that the initial, strongest starburst is a few percent weaker, and there are some subtle differences in late time behavior. The first large starburst begins at 1.25 Gyr in both sets of runs, and unfortunately this bin is 250 Myr in width in the full resolution set, vs 350 Myr in the subset, so we don’t really get significantly finer grained estimates of the SFH in the initial burst.
We can get a rough idea of the nature of the precursors from this graph. The present day stellar mass is \(4 \times 10^{10} M_{\odot}\), of which just about a third was formed in or after the initial starburst. The present day combined stellar mass of the precursors was just over \(2.6 \times 10^{10} M_{\odot}\) just before the burst — the then stellar mass would have been somewhat larger since this mass growth estimate includes mass loss to stellar evolution. Also, there’s considerable light and therefore stellar mass outside the IFU footprint — the drpcat value for the effective radius is 5.5″ while the Petrosian radius per the SDSS photo pipeline is 12.5″. The IFU footprint is about 10″ radius, or about 1.8 effective radii. Taking a guess that we’re sampling about 80% of the stellar mass the values above need to be adjusted upwards by 25%. Assuming the precursors were equal mass and rounding up their pre-merger stellar masses would be around \(1.65 \times 10^{10} M_{\odot}\). Guessing the current total stellar mass to be \(5 \times 10^{10} M_{\odot}\) then \(1.7 \times 10^{10} M_{\odot}\) was added by the merger. The amount of gas turned into stars was actually higher — about \(2.4 \times 10^{10} M_{\odot}\), but some, maybe most, of the gas lost to stellar evolution might have been recycled. But, I’ll be conservative and adopt the higher value. If that was divided equally between the progenitors they would have had gas masses around \(1.2 \times 10^{10} M_{\odot}\) just before the merger, or just over 40% of the baryonic mass. While high that’s not extraordinarily so for a late type spiral in that stellar mass range (see for example Ellison et al. 2015, figures 6-7).
These values are rather different from the putative Antennae analogs in the two studies linked above, which are in turn rather different from each other. Lahén et al. assign equal stellar masses of \(4.7 \times 10^{10} M_{\odot}\) to each progenitor, equal gas masses of \(0.8 \times 10^{10} M_{\odot}\) (14.5% of the total baryonic mass), and a baryonic to total mass ratio of 0.1. The simulations of Reynaud et al. have stellar masses of \(6.5 \times 10^{10} M_{\odot}\) apiece, gas masses of just \(0.65 ~\mathrm{and}~ 0.5 \times 10^{10} M_{\odot}\), and dark matter halos of \(23.8 \times 10^{10} M_{\odot}\) (for a baryonic to total mass ratio of about 23%). The merger timescales are rather different too: 180 Myr from first pericenter passage to coalescence for the Reynaud et al. simulations vs. ~600 Myr for Lahén et al. (the latter seems closer to the observational evidence; for example Whitmore et al. 1999 found clusters with 3 distinct age ranges up to ~500Myr, with the oldest argued to have formed in the first pericenter passage).
The qualitative features of the merger progress are similar however, and some are fairly generically observed in simulations. There are two pericenter passages, with a second period of separation followed shortly by coalescence at the third pericenter. Star formation is widespread but clumpy in the first flyby, with a large but short starburst and slow decline. A stronger, shorter, and more centrally concentrated starburst occurs around the time of the second pericenter passage through coalescence. The Lahén simulations follow the merger remnant for another Gyr — they show an exponentially declining SFR from an elevated level immediately after coalescence. Neither of these sets of simulations attempt to model AGN feedback, which would presumably cause more rapid quenching. Notably, the Lahén merger remnant develops a kinematically decoupled core although overall the remnant is a fast rotator. Unless the rotation axis is pointing right at us this galaxy is a slow rotator except for the core.
Getting back to data for this galaxy I show some star formation history plots below, first for the inner 9 fibers (all within <1.25 kpc of the nucleus). Below that are a pair of plots of aggregate SFH for fibers within 2.5 kpc (left) of the nucleus and outside that radius (right). The time axis on these plots is logarithmic since I want to focus on the late time behavior (and also this is closer to the real time resolution). Note that in the inner region there are 3 broad peaks in star formation rate — the previously discussed one at 1-1.25Gyr, one centered around 500Myr, and a recent (\(\lesssim\)100Myr revival that peaks at the youngest BaSTI age of 30Myr. The relative and absolute sizes of the peaks are highly variable, indicating that the stars aren’t fully relaxed yet. The outer region covered by the IFU by contrast has just a single peak at 1-1.25Gyr, with a more or less monotonic decline thereafter.
If we believe the models the straightforward interpretation is the first pericenter passage happened about 1-1.25Gyr ago, with coalescence around 500Myr ago and continued infall or recycling driving the ongoing star formation. This is consistent with simulations, which can have merger timescales ranging anywhere from a few hundred Myr to several Gyr. The main problem with this interpretation is that in most simulations the highest peak SFR occurs around coalescence, with the integrated SF roughly equally divided between the first passage and during/after coalescence. In these models about 75% of the post-burst star formation occurs in the 1-1.25Gyr bin, with about 23% in the later burst. An alternative scenario then would be that the entire merger sequence occurred in the 250Myr window of the 1-1.25Gyr bin, and everything since is due to post-merger infall. Of course a third possibility, which I don’t dismiss, is that the details of the modeled episodes of star formation are just artifacts having no particular relationship to the real recent star formation history. The main argument I have against that is that all the spectra seem to tell a consistent story of multiple recent episodes of star formation of varying strength in space and time, and with clear radial trends.
Regardless of the details this galaxy nicely confirms several of the predictions of Bekki et al. (linked above). As we saw in the last post there is a positive color gradient and negative Balmer absorption gradient as they predict for dissipative major mergers, with a break in the color gradient trend that corresponds approximately with the boundary between multi-peaked and single peaked late time star formation histories.
The obvious morphological indicators of disturbance are provocative but don’t seem to be highly constraining. My recollection is that folklore guesstimates that tidal features have a visible lifetime of ~1 Gyr. Ji et al. (linked above) on the other hand found based on a suite of simulations that merger features remain visible at the 25 mag./arcsec\(^2\) somewhat longer, on average about 1.4Gyr. It may be significant that the disturbance is easily seen even in the SDSS Navigate imaging, especially the northern loop. The somewhat similar star forming elliptical SDSS J1420555.01+400715.7 discussed by Li et al. (which I will turn to later) required deeper imaging and some enhancement to show signs of disturbance. One thing I’m working on learning is galaxy profile fitting and photometric decomposition. I’d like to see if the single component Sersic fit that fit the mass density profile also works for the surface brightness (I suspect there is a steeper core). I’d also like to quantify the surface brightness in the tidal features. These are exercises for later.
Star formation histories estimated from innermost 9 spectra (d < 1.25 kpc)
Binned star formation histories (full SSP library) L – d ≤ 2.5 kpc R – d > 2.5 kpc
I’m going to conclude with a few plots of quantities that I thought might benefit from the higher S/N in the binned spectra. First is the gas phase metallicity from the O3N2 index plotted against radius. Unfortunately emission line strengths are still too weak beyond about 2.5 kpc radius to say much about the outskirts, but we still see a fairly flat trend with radius up to ~1.5 kpc with a turnover to lower values that continues at least out to ~2.5kpc. This is broadly similar with the simulations of Lahén et al., who obtained a shallower metallicity gradient for their merger remnant than an undisturbed spiral galaxy.
gas phase metallicity 12 + log(O/H) vs radius, estimate from O3N2 index. Binned spectra
Here I again plot the estimated SFR density against radius and the estimated SFR density against Hα luminosity density. The high luminosity end is essentially unchanged from the plot in the last post since these spectra are unbinned. Again, the points at the high luminosity end lie above the Hα-SFR calibration of Moustakas et al. by considerably more than the nominal length of the error bars, which points to a likely recent (< ~10Myr) fading of star formation. This is consistent with the detailed SFH models. At the other end of the luminosity scale there seems to be an excess of points to the right of the calibration line. This could indicate that the main ionization source in the outer regions of the IFU footprint is something other than massive young stars (presumably shocks or hot evolved stars).
L – SFR density vs. radius R – SFR density vs. Hα luminosity density Binned spectra with enlarged EMILES SSP library
Despite the “composite” emission line ratios in the central region there’s little evidence for an AGN. The galaxy is a compact FIRST radio source, but the size of the starforming region is right around the FIRST resolution. The raw WISE colors are W1-W2 = +0.07, W2-W3 = +2.956, which is well within the locus of spiral colors.
In part two of this series that I began here I’m going to look at various quantities derived from the modeled star formation histories and emission line fits. Later I’ll dive into some selected recent literature on major gas rich mergers and do some comparisons. I’m especially interested in whether the model star formation histories have anything to tell us about the chronology of the merger and the nature of the progenitors.
First, here is a representative sample of spectra. These are reduced to the local rest frame and fluxes are corrected for foreground galactic extinction but not any internal attenuation. The middle spectrum in the second row is from the central fiber/pointing combination (remember these are from the stacked RSS data). The peripheral spectra are roughly equally spaced along a ring about 7″ or 4kpc from the center.
The central spectrum does show moderately strong emission lines along with fairly strong Balmer absorption, which points to some ongoing star formation along with a significant intermediate age population. Emission lines are evidently lacking or very weak in the peripheral spectra (most of the spikes are terrestrial night sky lines or simply noise), indicating the galaxy is currently passively evolving at 4kpc radius. In more detail…
The most basic but also finest grain quantities I look at are posterior estimates of star formation histories and mass growth histories. These contain similar, but not quite the same information content. I show star formation histories as the “instantaneous” star formation rate vs. look back time defined as the total stellar mass born in an age bin divided by its width in years.
Sample star formation histories – plateifu 8440-6104, mangaid 1-216976
Mass growth histories estimate the cumulative fraction of the present day stellar mass. These incorporate a recipe for mass loss to stellar evolution and as such aren’t quite integrals of the star formation histories. By convention remnant masses are included in stellar mass, and there are recipes for this as well (in this case taken from the BaSTI web page). I display posterior marginal means and 95% confidence intervals for both of these against time.
I think I first encountered empirical estimates of mass growth histories in McDermid et al. (2015), although earlier examples exist in the literature (eg Panter et al. 2007). I may be the first to display them for an individual galaxy at the full time resolution of the input SSP library. Most researchers are reluctant to make detailed claims about star formation histories of individual galaxies based on SED modeling. I’m not quite so inhibited (not that I necessarily believe the models).
Sample mass growth histories — plateid 8440-6104
The most obvious feature in these is a burst of star formation that began ≈1.25 Gyr ago that was centrally concentrated but with enhanced star formation galaxy wide (or at least IFU footprint wide). In the central fiber the burst strength was as much as 60%, with the amplitude fading away with distance. Is this percentage plausible? First, just summing over all fibers the mass formed during and after the initial burst is about 1/3 of the present day stellar mass. This probably overestimates the burst contribution since the galaxy extent is considerably larger than the IFU and the outer regions of the progenitors likely experienced less enhanced star formation, but even 1/3 is consistent with the typical neutral hydrogen content of present day late type spirals. I will look at the merger simulation literature in more detail later, but simulations do predict that much of the gas is funneled into the central region of the merger remnant where it’s efficiently converted into stars, so this is broadly consistent with theory. But second, I have some evidence from simulated star formation histories that the presence of a late time burst destroys evidence of the pre-burst history, and this leads to models underestimating the early time mass growth. This could have an ≈0.1 dex effect on the total stellar mass estimate.
Most of the SFH models show more or less monotonically declining star formation rates after the initial burst, but the central fiber shows more complex late time behavior with several secondary peaks in star formation rate. It’s tempting to interpret these literally in hopes of constraining the timeline of the merger. In the next post I’ll look at this in more detail. One limitation of this set of models is the coarse time resolution. I only used half the available SSP ages from the EMILES/BaSTI library — the critical 1.25Gyr model in particular is 350Myr wide, which is a significant fraction of the expected timescale of the merger. Next time I’ll look at the results of some higher time resolution model runs.
I look at a large and still growing number of quantities derived from the models. For visualization it’s sometimes useful to create maps like the ones I showed in the first post on this galaxy. Many interesting properties of this particular galaxy are fairly radially symmetric with strong radial trends, so simple scatter plots are most effective.
I posted several graphs in a post on the current GZ Talk. Since those posts I’ve learned how to do Voronoi binning by translating Cappellari’s Python code to R. I also noticed that some of the EMILES spectra are truncated in the red within the range I was using for SED fitting, so I trimmed it to a wavelength range with strictly positive model fluxes (currently rest frame wavelengths 3465-8864 Å) and reran the models on the binned data. Binning has little effect on the results since out of 183 fiber/pointing combinations I ended up with 179 binned spectra. There are small changes due to the reduced wavelength range.
One of the more interesting plots I posted there was the estimated stellar mass density (in log solar masses/kpc^2) against radius (measured in effective radii; the effective radius from the drpall catalog is 5.48″ or about 3.2 kpc). The updated plot is below. The blue line is a single component Sersic model with Sersic index n=3.35 from a nonlinear least squares fit using the R function nls() (no Bayes this time). This is close enough to a deVaucouleurs profile (n=4) and definitely not disky (n=1). I will someday get around to comparing this to a photometric decomposition — they should be similar, although this fit is undoubtedly affected by the low spatial resolution of the IFU data.
Stellar mass density vs. radius in effective radii with single component Sersic fit plateid 8440-6104, mangaid 1-216976
The other plots I posted there included the star formation rate density (in \(M_\odot/yr/kpc^2\), averaged over an arbitrarily chosen 100 Myr) against radius and Hα luminosity density (uncorrected for internal attenuation) against radius:
Star formation rate density and Hα luminosity density against radius
The trends look rather similar, which leads to the obvious (SFR density vs. luminosity density):
Star formation rate vs. Hα luminosity. L uncorrected for attenuation; R corrected via Balmer decrement
This is one of the more remarkable and encouraging results of these modeling exercises — the star formation rate estimates come entirely from the stellar component fits and are for an order of magnitude longer time scale than is probed by Hα emission. Optical SED fitting is rarely considered suitable for estimating the recent star formation rate, yet we see a tight linear relationship between the model estimates and Hα luminosity, one of the primary SFR calibrators in the optical, that only begins to break down at the lowest luminosities. In the right pane Hα is corrected for attenuation using the Balmer decrement with an assumed intrinsic Hα/Hβ ratio of 2.86. The straight line in both graphs is the Hα-SFR calibration of Moustakas, Kennicutt, and Tremonti (2006) with a 0.2 dex intercept shift to account for different assumed IMFs. At the well constrained high luminosity end most of the points appear to lie above the relation, which could indicate that recent star formation has declined relative to the 100Myr average (or of course it could be a fluke).
Two other measures of star formation rate I track are specific star formation rate, that is SFR divided by stellar mass (which has units of inverse time), and relative star formation rate, SFR divided by the average SFR over cosmic time. Trends with radius (note that these and just about all other quantities I track are scaled logarithmically):
SSFR and relative star formation rate
On longer time scales I track the stellar mass fraction in broad age bins (this has been the customary sort of quantity reported for some years). Here is the intermediate age mass fraction (0.1 < T ≤ 2.3 Gyr), which basically measures the burst strength for this galaxy:
Burst mass fraction – trend and map
Oddly, the peak burst strength is somewhat displaced from the photometric center.
For completeness, here are trends of the modeled optical depth of stellar attenuation and intrinsic g-i color (a long wavelength baseline color serves as a rough proxy for specific star formation rate):
Optical depth of stellar attenuation
This is very similar to the color trend calculated from the spectral cubes I showed in the first post.
Model intrinsic g-I color trend
Finally, in recent months I have begun to track several “strong line” gas phase metallicity indicators. Here is an oxygen abundance estimate from the O3N2 index as calibrated by Pettini and Pagel (2004). Astronomers present “heavy” element abundances in the peculiar form 12 + log(O/H) where O/H is the number ratio of Oxygen (or other element) to Hydrogen.
Oxygen abundance 12 + log(O/H) by O3N2 index
Unlike just about everything else there’s no clear abundance trend here, although the precision drops dramatically outside the central few kpc. For reference the solar Oxygen abundance (which is surprisingly unsettled) is around 8.7, so the gas phase metallicity is around or perhaps a little above solar.
So, to summarize, there was a powerful burst of star formation that began ~1Gyr ago that was clearly triggered by a gas rich major merger. The starburst was centrally concentrated but enhanced star formation occurred galaxy wide (perhaps not uniformly distributed). Star formation is ongoing, at least within the central few kiloparsecs, although there is some evidence that it is fading now and is certainly much lower than the peak.
The stellar mass distribution is elliptical-like although perhaps not quite fully relaxed to a new equilibrium. As we saw in the previous post there is no evidence of regular rotation, although there is a rotating kinematically decoupled core that coincides in position with the region where the starburst was strongest.
Next post I’ll look at some of the recent merger literature, and also show some results from higher time resolution modeling.
Velocity field estimated from RSS data
(L) Velocity field estimated for data cube
Central fiber spectrum, plateifu 8335-6101
Summed model mass growth histories, emiles subset and “full” emiles
Stellar mass density for binned data, showing outlines of bins and core region
(L) Model Star formation history, inner core
Lick HδA vs. Dn(4000)
Emission line diagnostic (BPT) plots
log SFR density vs. log stellar mass density
(L) log SFR density vs. radius
τV estimated from Balmer decrement vs SFH model estimates
Metallicity 12+log(O/H) vs radius, estimated by O3N2 index
