I just have a quick comment about my last two subjects. I mentioned both of them have exceptionally strong Balmer absorption as measured by the Lick index HδA. They also have similar 4000Å break strengths:
IC 0976: Dn4000 = 1.308±0.005, HδA = 8.05±0.31
MCG +07-33-040: Dn4000 = 1.153±0.009, HδA = 8.06±0.41
For context here’s a variation of the same plot I’ve shown several times of the MPA-JHU measurements for a large sample of SDSS galaxy spectra with their locations overlaid:
Dn4000 – Hδ of SDSS spectra of post-starburst galaxies IC 976 and MCG +07-33-040 overlaid on measurements for a large sample of SDSS spectra
Both galaxies have HδA indexes near the upper limits of any measurements in SDSS, and both are clearly in the post-starburst area of the HδA-Dn4000 plane. Depending on your interpretation of the 4000Å break strength index IC 976 could be slightly older or have a slightly lower specific star formation rate, but the difference is small. Using the toy evolutionary models that people often use these two galaxies could easily be at slightly different stages of the same evolutionary trajectory.
In fact though the detailed star formation history models show rather different trends over the last ~Gyr, with recall MCG+07-33-040 having a more extended and more recently terminated period of enhanced star formation than IC 976, while the latter had considerably more stellar mass added by the starburst.
This nicely illustrates a point I raised 3 posts ago, which is that this particular pair of indexes can’t break the “burst age – burst mass” degeneracy. Full spectrum fitting with non-parametric star formation histories potentially can. I’m still not prepared to take these models too literally.
I’m going to try to keep this one short. IC 976 is another post-starburst galaxy that was selected and recently observed by HST for the Zoogems project (proposal ID 15445, PI Keel). I took a shot at creating a color image by combining the ACS observation taken with the F475W filter (approximately equivalent to SDSS g band) with r and z band images from the Legacy Survey. Well that wasn’t too rewarding since this galaxy appears quite featureless.
IC 976 – RGB image created for Legacy Survey r and z band images + HST ACS F475W image from proposal ID 15445, PI W. Keel
Like the galaxy in the previous post the SDSS spectro pipeline misclassified this galaxy’s spectrum as a star with a recession velocity of ≈ 1200 km/sec. Unlike the galaxy in the previous post IC 976 is well known to have a post-starburst nuclear spectrum, and its correct heliocentric redshift of 0.00509 is listed in NED and confirmed with my own redshift estimation code. If that’s its Hubble flow redshift (doubtful) its distance would be about 21.8 Mpc (distance modulus m-M=31.7) and the 3″ SDSS fiber would cover 315 pc.
SDSS spectrum of IC 976 nucleus with best fit template overlay
Once again I ran my SFH modeling code on the SDSS spectrum, using only my metal rich PYPOPSTAR+EMILES ssp library, with results below:
Modeled star formation and mass growth histories of central region of IC 976 from SDSS spectrum 340044889930622976.
Despite the superficially similar spectra1this has a nearly identical HδA index of 8.1 ± 0.3 Å. this model favors an older (peak at 800 Myr lookback time), stronger, and shorter burst than the previous example. The model’s burst strength of ≈ 40 % of the present day stellar mass seems high, but the estimated total stellar mass within the fiber footprint is only ≈108.5 M☉, which is likely a small fraction of the galaxy’s total stellar mass. For a rough estimate of the total mass the SDSS g band Petrosian magnitude is listed as 13.6, making the absolute magnitude -18.1. With a solar g band absolute magnitude of 5.11 the galaxy’s luminosity is ≈ 109.3 L☉, and assuming a stellar mass to luminosity ratio around 1 the mass would therefore be ≈ 2×109 M☉. If the merger added a little over 108 M☉ to the system as implied by this model the mass ratio of the progenitors would be on the order of 20:1.
IC 976 was one of 7 post-starburst galaxies in an IFU based spectroscopic study by Pracy et al. (2012). This galaxy2designated “E+A 6” in the paper. had a very strong negative radial gradient in the Balmer absorption index, as did 5 of the 6 others in the study. They concluded that centrally concentrated starbursts fueled by minor mergers was the most likely cause of their present evolutionary state. The lack of any apparent tidal features in the available imaging of this galaxy likely reflects the age of the merger and mass ratio of the progenitors.
The Hubble Space Telescope “gap filler” program “Gems of the Galaxy Zoos” (proposal ID 15445, PI William Keel) had several prospective targets that I played a small role in selecting, and this recent HST observation was one of them. The actual target was the small disturbed galaxy at top left, which I will refer to as MCG +07-33-040. I don’t know if it was fortuitous that the larger and brighter UGC 10200 was also imaged in the same ACS field, but these are clearly interacting or at least have in the recent past, as is the small system in the upper right, which is identified as a blue compact galaxy with redshift z=0.00556 in Pustilnik et al. (1999). I’m going to focus on the top left galaxy in this post.
Galaxies UGC 10200 (lower right) and MCG +07-33-040 (upper left). HST/ACS, F475W filter. Proposal ID 15445, PI Keel.
What interested me wasn’t the galaxy image so much as its SDSS spectrum, which has three interesting characteristics:
SDSS spectrum of central part of MCG +07-33-040
First, this is a classic post starburst galaxy spectrum with extremely strong Balmer absorption lines1My code measures the Lick index HδA as an exceptionally strong 8.06 ± 0.41 Å. and no obvious evidence of emission. In fact, although this designation isn’t used much anymore, it’s actually a classic “A+K” spectrum which reverses the usual “K+A” terminology to indicate the light is dominated by early type (i.e. young) stars. Second and third, the spectrum was misclassified as coming from a white dwarf star, and the redshift was erroneously estimated as around 0.004 which was the maximum allowed for stars in the SDSS data reduction pipeline. Using a variation of the code that I use to measure redshift offsets I get a robust value of z = 0.006682 ± 9E-06
Template fit to SDSS spectrum of MCG +07-33-040
This is almost exactly the same redshift as its nearby companion UGC 10200 (also in the HST image above), which has a securely determined z = 0.00664
SDSS spectrum of central region of UGC 10200
For the rest of this post I’m going to assume the Hubble flow redshift is the measured one, which with my adopted cosmological parameters2which for the record are H0 = 70 km/sec/Mpc, Ωm = 0.27, Ωλ = 0.73. make the luminosity distance 28.8 Mpc, the distance modulus m-M = 32.3 mag, and the angular scale 138 pc/” or about 7 pc per ACS pixel. The projected distance between the centers of the two bright galaxies in the HST image is about 96″ or 13.2 kpc.
I spent some time last weekend downloading and starting to learn the software Aperture Photometry Tool (APT), which is interactive software for manually performing aperture photometry. Zooming in on the center of the presumed post starburst galaxy I located the reported position of the SDSS fiber as closely as I could. In the screenshot below the aperture radius was set to 30 pixels, the same size as the SDSS spectroscopic fibers. I measured the F475W AB magnitude to be 17.90 ± 0.013 without sky subtraction, which is close enough to the SDSS g band fiberMag estimate of 18.05. The SDSS g band Petrosian magnitude estimate is 15.16, so the fiber contains about 7% of the total galaxy light.
Central region of MCG +07-33-040 with position and size of SDSS fiber overlaid. Screenshot from APT
A striking feature of the HST image is there are many point-like symmetrical objects embedded within the otherwise nearly featureless diffuse light of the galaxy. Within the SDSS fiber footprint I count about 8-10 of these (the range being due to some uncertainty about what to call point-like and symmetrical). In order to get a handle on their contribution to the spectrum I did aperture photometry on them using a 3 pixel radius aperture with median sky subtraction from a 5 to 8 pixel radius annulus. The apparent magnitudes of the 5 brightest objects range from about 22.6 to 23.1. The summed luminosity of those 5 amounts to only 3.5% of the total light in the fiber, so the spectrum is mostly telling us something about the diffuse starlight. Even if one or more of those objects are foreground stars they can’t be a significant source of contamination. Clicking around the blank regions of the HST field I found fewer than one star per SDSS fiber size region, so it’s likely there are few if any foreground stars within the visible extent of the galaxy.
There is plenty of observational and theoretical evidence that massive star clusters are formed in mergers and close encounters of galaxies. As a coincidental example the merger remnant NGC 3921 — which was one of the 4 galaxies in my last post — has over 100 young globular clusters located both in the main body and southern tidal tail (Schweizer et al. 1996; Knierman et al. 2003). The brightest source in this galaxy (near the southern edge of the visible fuzz) has an apparent magnitude of m ≈ +21.7, which for the adopted distance modulus is M ≈ -10.6. With a solar g band absolute magnitude of 5.11 this corresponds to L ≈ 1.9×106 L☉ . The 5 brightest objects within the fiber have absolute magnitudes between about -9.7 and -9.2. These would be quite luminous for galactic globular clusters, but they’re likely to be fairly young and would fade by at least a few magnitudes as they age.
I haven’t tried a more sophisticated analysis of these objects’ sizes, but using the APT radial profile tool the presumed clusters look little different from nearby foreground stars and all that I’ve examined have FWHM diameters around 2-2.5 pixels. A strict upper limit to their sizes is therefore around 14 pc.
Someday I may undertake a complete census and luminosity function of the cluster system in this galaxy, and perhaps also look at the neighboring starburst galaxy UGC 10200. These systems by the way are cataloged as an interacting dwarf galaxy pair by Paudel et al. (2018) with a total stellar mass of log(M*) = 9.5 and a 3:1 mass ratio, which makes the estimated stellar mass of this galaxy just under 109 M☉. The system is very gas rich, with a neutral hydrogen mass estimated (by the same source) of 109.69 M☉. There are actually at least two published HI maps of this system. The one below, from Thomas et al. (2004) shows that atomic hydrogen extends over essentially the entire extent of the Hubble image above, including the target galaxy.
VLA map of HI gas in UGC 10200 system
Next I turn to star formation history models for the post starburst spectrum at the top of the post. This uses the same Stan model code as my MaNGA investigations with some minor pre- and post-processing adjustments. I ran two separate models. One used a metal poor subset of the EMILES SSP libraries with Z ∈ {[-2.27], [-1.26], [-0.25]} with, as usual, Kroupa IMF and BaSTI isochrones. I did not attempt to append younger models, so the youngest age is 30Myr. For completeness I also ran a model with my usual EMILES subset + PYPOPSTAR models and Z ∈ {[-0.66], [-0.25], [+0.06], [+0.40]}. First, here is the modeled star formation history with the metal poor subset. I’ve again used a logarithmic time scale and linear star formation rate scale.
Model star formation history of central region of MCG +07-33-040 using metal poor subset of EMILES SSP library
Next is the metal rich subset:
Model star formation history of central region of MCG +07-33-040 using metal rich subset of EMILES+pypopstar SSP library
Both model runs show a fairly steep ramp up in star formation beginning at about 600Myr lookback time and a steep decline around 50Myr ago. The lingering star formation in the metal rich model might be a manifestation of the infamous “age metallicity degeneracy” since Balmer Hα emission is too low to support this level of star formation. Comparing the mass growth histories exposes a more subtle effect: the metal poor models have a consistently higher mass fraction at any given epoch. Also, the period of accelerated star formation involved a slightly smaller fraction of the present day stellar mass.
Mass growth histories of MCG +07-33-040 using metal poor and metal rich subsets of EMILES SSP library
Both models fit the data well. In terms of mean log-likelihood the metal poor model outperformed the metal rich, but only by about 0.4%. The proper Bayesian way to compare models is through the “evidence,” which is hard to estimate accurately. I suspect the metal poor model would be at least slightly flavored because it has fewer parameters than the metal rich one.
Posterior predictive fit to SDSS spectrum of MCG +07-33-040
The duration of accelerated star formation (about which both models agree) is a little surprising in light of simulations that usually show a fairly short SF burst in the first passage in mergers. But, simulations have only explored a small range of the potential parameter range. Studies of low mass galaxies with extended, massive HI haloes might be of interest.
One more sanity check. Suppose the closest approach between our target and UGC 10200 was 60Myr ago, allowing another 10Myr before (presumably) supernova feedback quenched star formation. Assuming the relative motion is transverse to our line of sight traveling 13.2 kpc in 60Myr implies an average separation speed of ≈215 km/sec. This is a perfectly reasonable value for a galaxy pair or loose group.
Finally for this spectrum, here is a quick look at emission line fluxes. Even though visually not at all obvious several lines were detected at marginal (>2σ) to high (>5σ) confidence. A couple of surprises are the [O I] 6300Å line, which is often only marginally detected even in star forming systems, is a firm (3σ) detection and stronger than the usually more prominent [O III] doublet. Also, the [S II] 6717-6730 doublet is a firm detection while the [N II] doublet is not. What this means is unclear to me. Most of the “strong emission line” metallicity indicators that I have formulae for include [N II] (or [O II] which are out of the wavelength range of these spectra), so it isn’t really possible to make a gas metallicity estimate. It’s a safe guess it’s subsolar though.
line
[Ne III] 3869
Hζ
[Ne III] 3970
Hε
Hδ
Hγ
Hβ
[O III] 4959
[O III] 5007
[O I] 6300
[O I] 6363
[N II] 6548
Hα
[N II] 6584
[S II] 6717
[SII] 6730
mean
17.1
2.3
1.5
1.6
1.9
2.1
7.9
2.4
4.9
8.2
2.8
2.9
39.1
2.5
14.4
14.2
s.d.
6.3
2.0
1.4
1.4
1.6
1.8
3.1
2.0
2.9
2.8
1.9
2.0
2.6
1.8
2.8
2.8
ratio
2.7
1.1
1.1
1.1
1.2
1.2
2.6
1.2
1.7
3.0
1.5
1.5
15.2
1.4
5.2
5.2
Flux measurements for tracked emission lines in spectrum of MCG +07-33-040. Units are 10-17 erg/sec/cm2
There are at least two questions about this galaxy that it would be nice to have answers for. First, since the SDSS fiber only includes about 7% of the luminosity and a similar fraction of the stellar mass we really don’t know if it is recently quenched globally or just where SDSS happened to target. My guess from this HST image is that it is globally quenched because its companion UGC 10200 shows clear evidence of dust lanes and extended star forming regions even in this monochromatic image, while the diffuse light in this galaxy looks relatively featureless. A definitive answer would require IFU spectroscopy though.
A second question is whether the star cluster system is truly young or primordial (or both). This would require color measurements from a return visit by HST using at least one more filter in the red. Estimating a luminosity function is feasible with the existing data, although it would have rather shallow coverage. From my casual clicking around the image it appears to be possible to reach magnitudes a little larger than +24 with reasonable precision.
When this topic first came up on the old Galaxy Zoo talk I thought these might comprise a new and overlooked category of galaxies. In fact though all of the examples I investigated belonged to cataloged galaxies and most of the spectra were of small regions in much larger nearby galaxies. A few galaxies that were in the original Virgo Cluster Catalog and excluded from the EVCC because of lack of redshift confirmation should be added back. There were probably only a few like this one with large errors in redshift estimates and high signal to noise spectra. I haven’t spent enough time with the literature to know if rapidly quenched dwarf galaxies are especially interesting. Maybe they are.
While browsing through the ADS listing of papers that cite Schawinski’s paper that I’ve been discussing for a while I came across this one by Haines et al. with the full title “Testing the modern merger hypothesis via the assembly of massive blue elliptical galaxies in the local Universe”. Besides being on the same theme of searching for post-starburst or “transitional” galaxies in the local universe that I’ve been pursuing for some time the paper was interesting because it made use of IFU based spectroscopic data that predates MaNGA. As it happens 4 of the 12 galaxies have observations in the final MaNGA release, providing an excellent opportunity to compare results from completely independent data sets.
The “modern merger hypothesis” that the authors tested relates to a topic I’ve discussed before, which is that N-body simulations show that strong, centrally concentrated starbursts are a possible outcome of major gas rich galaxy mergers around the time of coalescence. If some feedback process (an AGN or supernovae) rapidly quenches star formation there will ensue a period of time when the galaxy will be recognizable as post-starburst.
In a series of long and rather difficult (and influential judging by the number of citations) Hopkins and collaborators (2006, 2008a, 2008b) have made a case that major gas rich mergers with accompanying starbursts are in fact the major pathway to the formation of modern elliptical galaxies. They claim that their merger hypothesis accounts for a variety of phenomena, including the growth and evolution of supermassive black holes and quasars.
The specific aspect of the merger hypothesis this study tried to address was the prevalence of strong centrally concentrated starbursts in a sample of ellipticals in the process of forming as evidenced by visible disturbances consistent with recent mergers. The main tool they used was a suite of simple star formation history models with exponentially decaying star formation rate with single (also exponentially decaying) bursts on top of varying ages and decay time scales. They used these to predict just two quantities: Balmer absorption line strength measured by the average of the Lick HδA and HγA indexes, and the 4000Å break strength index Dn4000. For reference here is a screen grab of their model trajectories:
Predected trajectories in the Hδ – Dn4000 plane per Haines et al. (2015). Clipped from the electronic journal paper.
This is a pretty standard calculation variations of which have been performed for decades, and this graph looks much like others I have seen in the literature. A fairly basic problem with it though is that position in the Balmer – D4000 plane doesn’t uniquely constrain even the recent stellar evolution. In astronomers’ parlance there is a “degeneracy”1the term refers to a situation in which multiple combinations of some parameters of interest produce effectively equivalent values of some observable(s), or of course the converse. The best known example is the “age-metallicity degeneracy,” which refers to the fact that an old metal poor population looks like a younger metal rich one in several respects such as broad band colors. between burst strength (if any) and burst age. This is a well known problem with the Balmer line strength index that was already recognized by Worthey and Ottaviani (1997), who developed these indexes. Adding a second index in the form of the 4000Å break strength doesn’t break the degeneracy: there are regions of the plane where bursting and non-bursting populations overlap, as can be seen clearly in the graphic above. This is actually a problem for any attempt to identify post-starburst galaxies. After correcting for emission most ordinary starforming galaxies have strong Balmer absorption lines, so using that index alone will certainly produce many false positives. On the other hand selection criteria like those used by Goto and many others before and after — selecting for both strong Balmer absorption and weak emission — will capture only a small interval in post-starburst galaxies’ life cycles.
Hδ line strength vs. 4000Å break index for a large (~380K) sample of SDSS galaxy spectra. Measurements from the MPA-JHU analysis pipeline downloaded from SDSS Skyserver
Let’s get to results. Some basic details of the sample are in the table below. Morphological classifications are from McIntosh et al. (2014) as given in this paper. The abbreviations are SPM: spherical post merger; pE: peculiar Elliptical. The two marked pE/SPM didn’t have a strong consensus among several professional classifiers. I list them in order of my own visual impression of degree of disturbance. I also list redshifts taken from the MaNGA catalog and Petrosian colors.
NED name
NYU ID
mangaid
plateifu
Morph
z
u-r
g-i
NGC 3921
541044
1-617445
10510-6103
SPM
0.019
1.97
0.86
MRK 385
719486
1-604970
8940-6102
pE/SPM
0.028
1.43
0.63
MRK 366
100917
1-603309
7993-1902
pE/SPM
0.027
1.59
0.79
NGC 1149
22318
1-37155
8154-6103
pE
0.029
2.29
1.11
Columns: (1) Common catalog designation (NED name). (2) NYU VAC ID. (3) MaNGA mangaid. (4) MaNGA plateifu. (5) Morphology (see text). (6) redshift from MaNGA DRP catalog. (7-8) Petrosian u-r and g-i colors from NYU VAC via the MaNGA DRP catalog.
The main prediction of the merger with accompanying centrally concentrated starburst hypothesis the paper tests is that the Balmer absorption index should be large and have a negative gradient with radius while the 4000Å break strength should be low with a positive gradient. The authors concluded that only one member of their sample — nyu541044 — clearly falls in the post-starburst region (marked as region 4 in the graph above) of the <Hδ, Hγ> – Dn4000 plane. The two pE/PM galaxies, both of which are in my sample, lie in the starforming region 1. They inferred from this that these galaxies are undergoing at most a weak burst. I’m going to mildly disagree with that conclusion.
Measured values for the specified indexes from Haines et al. (2015). Clipped from the electronic journal paper.
I have calculated the pseudo Lick index HδA and Dn4000 as part of my analysis “pipeline” since I started this hobby. I actually make these measurements in the initial maximum likelihood fitting step since they don’t depend on modeling except for small (usually) emission corrections. I don’t calculate an Hγ index, but its theoretical behavior is similar to Hδ. I’m trying here just to verify the approximate magnitude and radial trends of the chosen indexes. The two IFUs used in the Haines study had larger spatial coverage than these MaNGA observations (but much smaller wavelength coverage, which will become important). Instead of their strategy of binning in annuli I used my usual Voronoi binning strategy with a minimum target S/N. There were some oddities in the NYU estimates of effective radii so I chose to use distances from the IFU center in kpc for these plots. The distances assigned to the multiply binned spectra are the same as Cappelari’s published code produces; for single fiber spectra it’s just the position of the fiber center.
My measurements agree reasonably well with those of Haines et al. All three of the most disturbed galaxies have central Hδ indexes > 5Å with NGC 3921 (plateifu 10510-6103, nyu541044) having a larger central value and steeper gradient in the inner few kpc than the two pE/SPM galaxies. The fourth galaxy shows no obvious trend in either index with radius2The next several plots show trend lines for each galaxy computed by fitting simple loess curves to the data using the default parameters in ggplot2. These, and especially the confidence bands included in the plots, should not be taken seriously!. The central values where the S/N is highest are in good agreement.
Lets turn to the results of star formation history models, which I ran on all 4 data sets. First, here are 100Myr averaged star formation rate density and specific star formation rate versus distance:
Star formation rate density vs. distance from IFU center (kpc) for 4 disturbed early type galaxies.Specific star formation rate density vs. distance from IFU center (kpc) for 4 disturbed early type galaxies.
Three of these galaxies are clearly experiencing centrally concentrated episodes of star formation, and two are at or near starburst levels in specific star formation rate near their centers. As seen below two of these straddle my estimate of the “spatially resolved star forming main sequence” while the one presumed post-starburst galaxy reaches it in the central region.
Star formation rate density versus stellar mass density for 4 disturbed early type galaxies
As I’ve shown several times before there’s a reasonably tight linear relationship between modeled star formation rate and Hα luminosity density. The plot shows Hα luminosity density corrected for modeled stellar redenning, which certainly underestimates attenuation in emission regions. The modeled star formation rates are consistently above the Kennicut relation shown as the straight line as I’ve seen in every sample I’ve looked at.
Star formation rate density vs. Hα luminosity density for 4 disturbed early type galaxies
Finally, lets take a look at detailed star formation histories. Instead of my usual practice of plotting them all in a grid here I just display 2 binned star formation histories. One comprises the innermost 7 bins, which since the fibers are arranged in a hexagonal grid should form a regular hexagon around the IFU center. These range in “radius” from about 0.75 to 1.1 kpc in these four galaxies. The second is for an “annulus” in approximately the outer kpc of each IFU. The extent of the IFU footprints ranges from 3.1 to 5.9 kpc. I calculate these by summing the contributions in each SFH model contributing to the bins, not by running new models for binned spectra. Since the dithered fiber positions overlaps this overestimates the total mass in each bin, but I care about the shape and timing of events rather than the absolute values of star formation rate estimates.
The next 4 plots display the results. Lookback time is logarithmically scaled with the same range and ticks for each SFH. Vertical scales are linear and differ for each graph. The graphs are in the same order as the basic information table above. As I’ve written before these models “want” to have smoothly varying mass per time bin which has the unfortunate effect of producing jumps in the apparent SFR when the bin widths change. In the BaSTI isochrone based SSP models these occur at 100 Myr, 1 Gyr, and 4 Gyr and can sometimes be quite prominent.
With caveats out of the way the one clear post-starburst in the sample had (per the model) a powerful and short starburst at ≈300 Myr lookback time, with a small amount continuing to the present (this can’t be seen at the scale of the graph, but ongoing star formation is ~1 M☉/yr). The total mass contribution from the burst and subsequent star formation is around 15%.
The two apparent ongoing starbursts have later bursts of star formation that are slightly weaker in terms of total mass contribution and peak star formation rate, but still quite significant. All three of the starburst/post-starburst galaxies appear to have had two major waves of late time (last ~2 Gyr or less) star formation. As I’ve written before in merger simulations the progenitors usually complete a few orbits before coalescence, with some enhanced star formation around each perigalactic passage. I hesitate to take these models that literally.
Turning finally to the last and least disturbed galaxy, NGC 1149, despite the bursty appearance of the SFH there’s no evidence for a major starburst in the cosmologically recent past. Whether an older starburst can be detected in this kind of modeling approach needs investigating.
One last set of graphs that may be useful. These show cumulative star formation histories — basically the cumulative sum of mass contributions starting from the oldest time bin. This is similar to a mass growth history which is a popular visualization. In my calculation of the latter the contributions are to the present day stellar mass, so an allowance for mass loss and remnant mass is made3these come from the source of the SSP models and are themselves models. Probably they are somewhat better than guesses. These things are basically black boxes to users.. The graphs are for the central regions only. Note the major virtue of these is that the contributions of major episodes of star formation can be estimated at a glance.
Cumulative star formation histories for central regions of 4 disturbed early type galaxies
To wrap up this part of the post 3 of these galaxies are compatible with the “modern merger hypothesis,” that is they have experienced centrally concentrated but spatially wide spread starbursts. The reason two of them don’t have post-starburst characteristics in the Hδ – D4000 plane is their starbursts are still underway. The current burst of star formation contributes about 5-10% of the mass in the central regions of these two. How much more is available is unknown (at least to me until I get around to finding out if there are HI mass estimates available).
Future plans: I’ve completed model runs on the 24 “post-starburst” galaxies in the MaNGA ancillary program dedicated to them. I may have something to say about them. I also may have something to say about one of the Zoogems targets that I had a small part in selecting.
As I mentioned two posts ago there are 24 of these galaxies in the final MaNGA data release, a remarkable 11% of the full sample. I ran my SFH model code on all of these along with the prerequisite redshift offset routine1I actually completed these some time ago. I just haven’t had time to do much analysis or write about them. SDSS thumbnails of the sample are shown below. As expected none of these have significant spiral structure visible at SDSS resolution, but at least a few are noticeably disturbed.
SDSS thumbnail images of Schawinski et al.’s blue early type galaxies in MaNGA final data release (SDSS DR17)
I’m just going to discuss a few topics in this post. I’ll save a more detailed discussion for when I’ve completed analysis of the ancillary post-starburst sample, which is underway now. First, here are velocity fields calculated for the stacked RSS data, with a signal to noise cutoff of 3, the same as I used for my analysis of rotation curves of disk galaxies. Note in the graphic below the ordering is different from the image thumbnails.
Line of sight velocity fields of Schawinski et al.’s blue early type galaxies in the final MaNGA data release
By my count (based entirely on visual inspection) all but 2 of these exhibit large scale rotation, with perhaps 15 or 16 classifiable as regular rotators with the remainder containing multiple velocity components including a couple with (perhaps) kinematically distinct cores. The preponderance of rotating systems surprised me at first, but according to a review by Cappellari (2016) large scale rotation is predominant at least at lower stellar masses (Schawinski et al. characterized their sample as being “low to intermediate mass” among early type galaxies). The velocity fields indicate that many of these contain stellar disks, perhaps embedded in large bulges. That’s still consistent with classification as “early type galaxies.” Apparently the original Galaxy Zoo classification page used the term “elliptical” as the early type galaxy choice, but in the data release paper by Lintott et al. (2011) there’s a statement that the “elliptical” class should comprise ellipticals, S0’s, and perhaps Sa’s from Hubble’s classification scheme.
Depending on how my effort to do non-parametric line of sight velocity modeling goes I may return to examine the kinematics of this sample in more detail, in particular to look for evidence of gas and stellar kinematic decoupling.
Turning to the recent star formation history this sample runs the gamut from large scale starbursts to passively evolving as seen in the plot of (100 Myr averaged) star formation rate versus stellar mass density for all analyzed binned spectra (of which there were 1525 in the full sample). For reference the straight line is my estimate of the center of the local “spatially resolved star formation main sequence.” This is just a weighted least squares fit to the sample of 20 non-barred spirals with star forming BPT diagnostics that I discussed some time ago. My SFMS relation has the same slope as estimated by Bluck but is offset higher by about 0.7 dex, which probably just reflects the very different methods used to estimate star formation rates. The contour lines are the densest part of the relationship from the passively evolving Coma cluster sample that I also discussed in that post. The majority of the blue etg sample falls in the green valley, consistent with Schawinski et al.’s observation that only about 1/2 of the sample showed evidence for ongoing star formation.
“Spatially resolved” star formation rate density versus stellar mass density for 24 blue early type galaxies in final MaNGA data release. Contour lines are corresponding values for 33 passively evolving Coma cluster galaxies.
Most of the points offset the most on the high side of the SFMS come from just two galaxies: MRK 888, which I’ve discussed in the last few posts, and SDSS J014143.18+134032.8 (this is apparently not in any “classical” catalog). The legacy survey cutout below clearly shows an extended tidal tail that’s a certain sign of a relatively recent merger.
SDSS J014143.18+134032.8, a disturbed, star-bursting blue early type galaxy
I just want to take a quick look at this one: below are maps of the star formation rate density and SSFR as well as scatterplots of the same against distance from the IFU center. As with MRK 888 ongoing star formation is widespread with a peak near the center, a classic case of a merger fueled starburst. In this galaxy star formation peaks in a ring somewhat outside the nucleus. The ring can be seen clearly in the SDSS cutout and must consist of HII regions.
SDSS J014143.18+134032.8 (mangaid 1-41541; plateifu 8095-1902)
Star formation rate density and specific star formation rate – maps and scatterplots against radius in kpc.
Schawinski et al. briefly discuss the possibility that their blue ETG’s could be progenitors of E+A (aka K+A) galaxies. This galaxy and MRK 888 are plausible candidates — if star formation shut off rapidly they would certainly exhibit strong Balmer absorption for a time after emission lines disappeared since they already do. Other members of this sample are already fading towards the red sequence, and if they ever qualified as “post-starburst” it must have been in the past.
I plan to look at star formation histories in more detail after I’ve completed model runs on the MaNGA post-starburst sample.
This is still experimental and I’m not sure how much farther I’ll pursue it, but I tried a straightforward way to add emission lines to non-parametric line of sight velocity distribution (LOSVD) models. The idea is simple enough: model the line profiles directly using Stan’s simplex data type with each modeled line represented by a vector of mostly zeroes and with the simplex centered on the line’s rest wavelength. Although not essential I’m assuming I will have estimates of redshift offsets for each fiber or spaxel in a MaNGA data file (RSS or cube), so any additional offsets should be small. I’ve chosen to ignore the fact that the discretized line profiles will differ between lines because their central wavelengths will fall at different points within their assigned wavelength bins. Also, different lines could arise from kinematically distinct regions, which is not uncommon in galaxies with broad line AGNs. The obvious solution to this is to allow multiple line profiles. For these initial exercises I’m using a single line profile for all modeled lines (I fit 18 at present). As I’ve done since I started these modeling exercises I am fitting emission lines and stellar contributions simultaneously, with the stellar part represented by a small set of eigenspectra derived from my usual EMILES based library.
Below the “fold” I’ve included the Stan code in it’s current (but certainly not final) form. About half of the code for modeling the stellar LOSVD, is adopted from the original version that I wrote about last year. The emission line model portion takes advantage of an odd feature of Stan, namely the ability to store a matrix in sparse form and perform one specific operation — matrix multiplication with a vector. I still haven’t figured out the particular matrix representation used, so I just create a dummy matrix for the emission lines in the transformed data section and extract the two vectors describing the positions of the non-zero elements of the matrix. In the model section the simplex vector representing the line profiles is repeated as many times as there are lines to fill in the non-zeros.
Another thing to note is that Stan doesn’t know how to work with missing data. In general there will be gaps in spectra that were masked for some reason, while the input templates must be complete over the covered spectrum (plus a few extra at each end for the convolution). This requires a bit of housekeeping that’s mostly done in the R code that sets up the input data.
In the models I ran last year I ignored dust reddening since I didn’t expect it to be significant in the passively evolving Coma cluster galaxies I tested them on. In general reddening isn’t ignorable though and there’s the potential for template mismatch without some allowance for it. For now I inserted the function I use for “modified Calzetti” attenuation but it’s not actually used. In the one test I tried including attenuation in the model significantly increased execution time. I will probably look at using a multiplicative polynomial for the same purpose. A final thing to note is there are no explicit priors for the two simplex vectors that represent the stellar velocity convolution kernel and emission line profile, which means that the priors default to a maximally diffuse dirichlet distribution. This turns out to be an important issue that I will discuss further below.
I’ve run sets of models for a small sample of galaxies so far. so lets look at some results. For these runs I used 500 warmup and 500 post-warmup iterations per chain with 4 parallel chains for a total post-warmup sample size of 2000. The stellar templates are represented by 6 eigenspectra created from singular value decompositions of my standard EMILES based SSP library. I currently fit 18 emission lines — Balmer lines from Hα through Hζ and a selection of the stronger forbidden lines. Model runs typically take 2-3 minutes for sampling on my old 4th generation Intel core I7 based PC. This can undoubtedly be reduced by factors of at least several with multithreading.
In this post I’m going to look in some detail at results for Markarian 888, which was the main topic of my last post. Recall this is one of Schawinski’s “blue early type galaxies” that turns out to have obvious spiral-like structure in its inner region and clear evidence for a relatively recent merger.
First, the graph below shows the central fiber spectrum, and below that the results of a model run for the stellar velocity convolution kernel. From those I can calculate velocity offsets1these formulas are approximate but close enough for the present purpose.
\(
\delta v = 69 \times \sum\limits_{k = -\lfloor K/2 \rfloor}^{\lfloor K/2 \rfloor} k p_i
\)
for each draw from the posteriors, and these are shown as histograms in the middle and right graphs. This was a high signal to noise spectrum with prominent emission and absorption lines, a favorable situation for this modeling exercise and in fact the results look very promising with an especially well determined distribution for the emission lines. In summary, the posterior mean stellar velocity offset was 25 km/sec. with a 95% credible interval of (19.8, 31.3), while the corresponding values for emission lines were 24.3 and (22.8, 25.9), so the credible interval for emission lines lies entirely within that for stars.
The corresponding values for velocity dispersions on the other hand differ quite a lot: 141 km/sec. for stars with a 95% credible interval of (133.6, 150.3) versus 109 km/sec and (106, 111) for gas. I’ll say some more about this below, but I think this is already a sign that the second moments (at least) of these velocity distributions need to be treated with caution.
Markarian 888 – mangaid 9894-3703
Central fiber spectrum, modeled stellar and ionized gas losvd
After running the code for every binned spectrum in the IFU I get stellar and gas velocity maps as shown below. These look similar to each other and to the map in the last post based on my hybrid red shift offset fitting routine, although a closer look will show a systematic decoupling of gas and stellar velocities.
Stellar and ionized gas velocity maps for Mrk 888 (MaNGA plateifu 9894-3703)
I often take a look at the official MaNGA data analysis pipeline results, usually through the “Marvin” interface. Generally my results look similar for any given quantity, but it’s hard to tell how closely since we’re looking at different things (binned RSS spectra vs. cubes, for one) and I’m really just doing a visual comparison. As more or less a one off exercise I decided to compare velocity fields from the cubes. First, here are stellar and Hα maps from the DAP. These are from the “HYB10-MILESHC-MASTARSSP” sequence, which refers to the binning strategy and template inputs.
Mrk 888 – stellar and gas velocity fields derived from non-parametric LOSVD models
Next are my stellar and ionized gas velocity maps. Evidently these are considerably “noisier” in appearance, but similar overall. The noisiness may be due to different binning strategies: I modeled velocity distributions for every spaxel that met a minimal signal to noise requirement.
Mrk 888 – stellar and gas velocity fields derived from non-parametric LOSVD models
For a more quantitative comparison here are scatterplots of the mean stellar and gas velocity offsets from my runs against the MaNGA DAP. Both of these show slight tilts, that is the slopes are less than one, and small offsets. I’m tempted to suspect systematic errors somewhere, but I haven’t found any yet. And I have no hypothesis about the decidedly non-random behavior of the gas velocity plots. So there are still questions to be answered, but the magnitude of differences is no more than about ±1 wavelength bin (69 km/sec). For my main purposes this is good enough.
Mrk 888 – scatter plot of mean stellar and gas velocities – my models vs. DAP
One more pair of graphs for this galaxy. In the initial maximum likelihood fit to a spectrum I estimate stellar and gas velocity dispersions with a single component gaussian. The graphs below plot the velocity dispersions from the Bayesian models calculated by the formula above against the ML fit values. The stellar velocity dispersions behave pretty much the same as I noted before: there’s a weak positive correlation between the non-parametric models and the maximum likelihood estimates with the former generally offset to higher values. The gas velocity dispersion estimates show an even larger discrepancy along with an apparently nonlinear trend.
Mrk 888 – velocity dispersions from Bayesian nonparametric losvd models vs. estimates from maximum likelihood fitting
More briefly now, since this got longer than I wanted. I also ran models for the “Zoogems” target NGC 810, which I noted two posts ago has interesting kinematics, in particular a rapidly rotating disk that isn’t evident in the stellar velocity map from the DAP. From the maps below we see that the rapidly rotating component is the ionized gas, which has a mean rotation velocity as much as 150 km/sec larger than the stellar component.
NGC 810 – mean stellar and gas velocity mapsNGC 810 – difference between mean gas and stellar velocities
Finally, here are two more graphs of the kinematic models for the spectra with the largest positive and negative differences between stellar and gas velocity (these are the reddest and bluest patches in the map above. These show the spectra in the top row, followed by the posterior distribution of the stellar LOSVD and distributions of the first two moments, then the same for the emission line profiles.
NGC 810 – sample MaNGA spectrum, non-parametric stellar LOSVD and emission line profileNGC 810 – sample MaNGA spectrum, non-parametric stellar LOSVD and emission line profile
The second set of graphs illustrates a significant problem: when there’s poor data — in this case no detectable emission lines — the models tend to return the prior, which for now is a maximally diffuse dirichlet. This will bias second moment estimates to the high side and can lead to spurious correlations, for example the increase of velocity dispersion with radius that I noted in the last post on this topic.
At the same time this spectrum is in the region of overlap between the main galaxy and its companion, and two peaks in the stellar velocity distribution are clearly seen at about the right velocity offset (~340 km/sec).
A short to do list:
Investigate informative priors.
Allow for dust reddening. I tried using a modified Calzetti attenuation relation in the code (note its presence in the code below, although it’s not used in the model), but it adds too much to execution time. A low order multiplicative polynomial might suffice.
Investigate systematics between my estimates and those in the DAP.
The first iteration of Galaxy Zoo led to several collections of distinct objects, including a sample of 215 “blue early type galaxies” published in Schawinski et al. (2009)1which inexplicably and consistently says there were 204 objects while the catalog published in Vizier contains 215.I found this an interesting group of galaxies, partly because of a possible link to post-starburst (K+A) galaxies that was discussed in the original paper. The authors discuss at some length the likelihood that these are results of mergers in the cosmologically recent past, with at least one of the progenitors being gas rich. Many (at least 25% and possibly more than half) were found to be currently starforming and the rest likely to have only recently ceased forming stars as inferred from their blue colors.
The ongoing Zoogems program has 12 of Schawinski’s blue ETGs on its target list, of which 6 have been observed so far as of mid-January 2022. Somewhat surprisingly there are 24 in the final MaNGA release, over 11% of the sample!
Taking a look at the 6 with HST observations I would say none of these are typical ellipticals. Five show some degree of spiral structure although in 4 it’s embedded in a more diffuse body. One appears to me to be an S0 with both inner and outer rings — this is in agreement with the one published morphological classification I’ve found. All of the others appear more disky than ellipsoidal to me, although this is just my possibly flawed qualitative judgment. At least two are visibly disturbed. One (CGCG 315-014) is connected to a nearby galaxy with a long tidal tail as seen in the Legacy Survey thumbnail below. Markarian 888, which will be the subject of the rest of this post, has shells that extend well past the main body of the galaxy and prominent, centrally concentrated dust lanes.
CGCG 315-014 Legacy Survey Thumbnail
So far it’s the only Zoogems blue etg target with a MaNGA observation (two others on the target list are in MaNGA but of course there’s no guarantee they will ever be observed). As is often the case the IFU could have been larger — this was observed with a 37 fiber bundle giving 111 dithered spectra in the RSS file.
MRK 888 SDSS thumbnail with MaNGA IFU footprint
As always the first step in analyzing these data is to estimate redshift offsets for each spectrum, and from there we get a velocity field, which in this case shows a rapid rotator with a fairly symmetrical radial velocity pattern.
Mrk 888 (MaNGA plateifu 9894-3703) velocity field
Visual inspection suggests the line of sight velocity distribution is consistent with a rotating thin disk, so I fed the data to my Gaussian process based rotation modeling code, with results summarized below. In fact the model does an excellent job of accounting for the data, with residuals (not shown) from the model fit (top right) in a range of ±15 km/sec. One unusual feature of the velocity field is the rotation velocity turns over at somewhat less than one effective radius. Whether the rotation curve declines smoothly outside the IFU footprint or is kinematically disconnected from the outer parts of the galaxy is of course unknowable at this time.
Gaussian process rotation model results
I also ran my usual star formation history modeling code on the data binned to 97 spectra. First, here are some summary results. The stellar mass density declines roughly exponentially, which is consistent with a disky morphology:
Model estimate of stellar mass density vs. radius
Next are maps of the estimated Hα luminosity density and, on the right, the BPT classification from the [O III]/Hβ vs. [N II]/Hα diagnostic. The contours are elliptical with major axes closely aligned to the rotation axis (the posterior mean for the angle is the dashed line in the velocity field plot above). Again, the emission appears to arise in a disk.
The proper interpretation of the “composite” BPT classification is something I think I’ve written about in the past. It was originally suggested to indicate a mix of AGN and stellar ionization, but here it arises in a thin ring of weak but detectable emission just outside the star forming region. If it’s truly composite it’s likely to arise from a mix of weak star formation and ionization by hot evolved stars. In any case there’s no evidence for an AGN in the optical data.
(L) Hα luminosity density (R) BPT classification from [O III]/Hβ vs {N II]/Hα diagnostic
Next are maps of the modeled (100 Myr average) star formation rate density and specific star formation rate, and in the second row scatter plots of the same estimates against radius in kpc. The trends with radius are somewhat unusual, especially for SSFR which in a normal disk galaxy typically increases with radius even if the highest total star formation rates are centrally concentrated. Highly centrally concentrated star formation in the aftermath of mergers is predicted by some simulations.
(TL) star formation rate density; (TR) specific star formation rate; (bottom row) scatter plots vs. radius
A couple more graphs will round out my discussion of summary model estimates. As I’ve shown several times before there’s a pretty tight linear relationship between modeled SFR density and estimated Hα luminosity density. In this plot Hα is corrected for modeled stellar attenuation, which is expected always to underestimate the attenuation in emission line emitting regions. That, and the fact that Hα emission and the model star formation rate estimates probe order of magnitude different time scales probably account for the systematic offset from the standard calibration given by the straight line.
Model star formation rate density vs. Hα luminosity density corrected for stellar attenuation. Straight line is calibration from Calzetti (2012).
And, once again I show a map of the modeled optical depth of stellar attenuation. The region of highest optical depth nicely tracks the visible dust (the HST image at the top is rotated about 90º from the SDSS image). Outside the dusty region there appears to be a shallow gradient, which might indicate that the nearer side is to the northeast.
Map of modeled optical depth of stellar attenuation
Finally here are plots of the model star formation history for all spectra ordered by distance from the IFU center. In the inner 1.5 kpc or so there’s some recent burstiness with possibly a very recent acceleration of star formation. For reasons I’ve discussed recently I don’t take either the timing or magnitude of bursts of star formation too seriously, but the behavior of the models is consistent with a recent revival of star formation due presumably to a merger, for which there are multiple lines of evidence.
model star fomation histories for all spectra
With 24 of these galaxies and another 31 from the compilation of Melnick and dePropris and the post-starburst ancillary program in the final release of MaNGA these samples satisfy my criteria of being manageably sized for my computing resources while large enough to say something about the groups. So, when time permits I plan to take a look. I already have the data in hand.
The final release of SDSS MaNGA went public back in early December as promised, and I’ve spent the last month or so of my hobby time looking for manageable sized samples of interesting galaxies. One sample I looked at out of curiosity was the Zoogems target list, which is an HST gap filler imaging program with about 300 galaxies selected (mostly) by Galaxy Zoo volunteers. It turns out there are 11 targets with MaNGA data, of which 5 have been observed by HST so far.
SDSS thumbnail images of Zoogems targets with MaNGA data
As can be seen from the thumbnails above this is a pretty diverse lot, with several in progress mergers and merger remnants, some normal looking spirals at least two of which were from Masters’ red spirals sample, and 3 of Schawinski’s blue early type galaxies. Only one of those 3 has HST imaging so far (number 8 in the thumbnails above), although there are a surprising 24 blue ellipticals in the final MaNGA release out of 215 in Schawinski’s original sample.
Of the 5 Zoogems galaxies that have been observed so far the one that caught my eye as deserving an early look was number 3 in the top row, NGC 810, an apparent elliptical with an unusual dust lane that’s almost perfectly aligned with the minor axis. There are also hints of shells indicating a likely merger sometime in the past.
NGC 810
HST ACS, proposal id 15445, PI Keel
The MaNGA data, which is new in DR17, only covers the central part of the galaxy with the companion just photobombing the edge. A larger IFU would have been nice for this observation, but the data quality is better than average in terms of nominal signal to noise. I was able to use all 183 fiber/position combinations in the RSS file without binning.
NGC 810 – plateifu 9514-6101 – MaNGA IFU footprint
The first step in the analysis process after loading the data is to estimate redshift offsets from the system redshift for each spectrum, and from that it’s straightforward to calculate a velocity field, which in this galaxy looks like1this is actually from the data cube:
NGC 810 (plateifu 9514-6101) – losvd estimated from cube
It turns out the redshift assigned to this system was that of the companion galaxy, which was the only SDSS spectroscopic target in the immediate vicinity and is evidently blueshifted by ~350 km/sec from the target. What’s more interesting though2interesting enough that I made a couple posts on the Galaxy Zoo talk forum, which I rarely do anymore. is the apparent rapidly rotating disk that’s aligned with and somewhat thicker than the dust lane. There may also be overall prolate rotation outside the disk although the presence of the companion makes it hard to tell based solely on visual inspection. In hopes of separating out multiple velocity components I returned to the non-parametric line of sight velocity distribution models that I wrote some posts about last year. Unlike the ones I practiced on previously this galaxy has non-negligible amounts of emission, at least in the central region, so I just temporarily masked the regions around the emission lines that I fit. That results in a pure stellar velocity distribution. The results were a bit surprising:
NGC 810 (plateifu 9514-6101)
(L) velocity field estimated from RSS file
(M) stellar velocity offsets
(R) net stellar velocity
In the left pane above is the velocity field from the RSS data, with the system redshift adjusted to the IFU center. For the LOSVD models I set the adopted redshift of each spectrum to the system redshift plus the offset calculated previously. Now I had hoped to be able to cleanly separate the contribution of the companion from that of the main galaxy, which so far I haven’t been able to do. But what I did find that was unexpected is that the average stellar velocities in the disk partially offset the original measurements (middle pane), so the net stellar velocity field shows a much more slowly rotating stellar disk.
As I’ve written before I use a set of 15 eigenspectra from a principal components analysis of some tens of thousands of SDSS spectra that I performed some years ago for redshift offset estimation. Those galaxies were of all types and include systems both with and without significant emission. The redshift estimation routine just does straightforward template matching and returns a single value for the best fitting offset. Since the templates encode information about both emission and absorption lines that estimate could be most applicable to the ionized gas, stars, or some combination. In this case it’s possible emission lines were driving the original fits, implying the gas and stars in the disk are kinematically decoupled. I have not verified that though.
Another issue I noticed is that the stellar velocity field from the official MaNGA Data Analysis Pipeline looks rather different from mine, with barely a hint of a kinematically distinct disk. This wasn’t really evident in the Marvin webpage, which makes some really unfortunate choices for color palettes. So here is the same data rendered with a more nearly perceptually uniform rainbow palette3I know data visualization experts frown on rainbows, but I think they’re OK for things like velocities or redshifts:
NGC 810 (plateifu 9514-6101)
Stellar velocity field per MaNGA DAP
I decided to re-run my LOSVD modeling code on the RSS data, this time setting the redshift offset to 0 for each fiber, so this is now measuring velocities relative to the overall system velocity. I also used a larger convolution kernel (25 vs. 21 in the first set of runs). The map of the average velocity offsets is:
NGC 810 (plateifu 9514-6101)
Stellar losvd from nonparametric model
Although not a perfect match this is somewhat closer to the DAP map. I suspect what’s happening here is that there really are at least two, and more likely 3 distinct kinematic components. I haven’t read the DAP release paper in a while and don’t know exactly how they estimate stellar velocities, but in any case their model just returns a single value for visualization purposes. To see the (possible) complexity of the actual data here are the results for a single fiber with the largest positive velocity offset in the map above. Again, I don’t know how much of the structure in the posterior distribution of the convolution kernel is real, but it’s evident there’s more complexity than is captured in the first two moments shown in the middle and right panes.
NGC 810 – sample nonparametric losvd
Besides the kinematic modeling I did run star formation history models on the full RSS data set using the same tools as in recent posts. I’m not going to discuss them in detail, but some summary maps are worth showing. In the graphic below these are, from top left, stellar mass density, Hα luminosity density uncorrected for attenuation, SFR density (as usual 100Myr average), and stellar dust attenuation.
There’s no sign of a disk in the stellar mass map, which faithfully follows the distribution of light. A disk is visible in Hα and the small amount of recent starformation is also confined to a disk and nuclear region. In the fourth pane I show the modeled stellar dust attenuation, mostly just to demonstrate that this component of the model does capture something of reality.
NGC 810 – a sampling of quantities derived from star formation history models
Getting back briefly to the first paragraph, there are 8 post-starburst galaxies from the catalog of Melnick and de Propris and 24 from an ancillary program to observe post-starburst galaxies from various sources that was added for DR17. There’s just one galaxy from the former catalog in the latter set, so that makes 31 total, an easily manageable number. There are also 24 of Schawinski’s blue ellipticals. Of course there are many disk galaxies, far too many for me to look at.
After a month off I returned to have another look at Millan-Irigoyen et al.’s high resolution “pypopstar” SSP model spectral libraries. First, I couldn’t find a more suitable subset of the full library than I used last time, so I decided just to try augmenting the existing Emiles based library with some younger spectra from pypopstar. Of course I had already done this with models from the 2013 update of BC03, so the plan was to replace those with a slightly finer grained selection at the young end. That raises the question of which ages to select. The youngest age model in the BaSTI isochrone based library is 30Myr (log T = 7.48), and we’re spoiled for choice of models at younger ages than that: there are 53 between log T = 5 and log T = 7.45, far more than necessary. Looking at the graph below, which just plots model spectra for the solar metallicity bin at decadal time invervals there’s very little spectral evolution between 105 and 106 years with the latter being slightly brighter at all relevant wavelengths. This is no surprise since even the most massive stars have main sequence lifetimes ∼106 years. The model spectra continue to get brighter up to around 106.6 years (4 Myr) and then turn around, becoming noticeably fainter and redder by 107 years.
pypopstar solar metallicity model spectra in decadal age increments
I decided to take the log T = 6 models as youngest, discarding the sub Myr ones altogether. This is mostly due to the inability to distinguish them and also just for purposes of visualization. I usually use logarithmically scaled lookback time axes in SFH history plots, and selecting a minimum value of 5 results in too much real estate given to very recent times where usually nothing much is happening.
Without giving this a lot of thought I selected just 3 ages to add: log T = 6, 6.51, and 7. The youngest Emiles model is log T = 7.48, so this gives nearly constant increments around 0.5 dex. This choice gives a reasonably smooth transition from the theoretical spectra to empirical ones, except for maybe the lowest metallicity bin. I also chose the “total” spectra including both stellar and emission continuum light in hopes of better modeling the continuum in star forming galaxies. To merge the high resolution pypopstar models into the library I just used a spline fit to interpolate the model spectra onto the same wavelength grid as Miles. This should (I hope) preserve total flux nearly enough. A more refined treatment would also consider that these still have higher resolution than Miles spectra, which are around 2.5 Å. I didn’t take the time. The merged library therefore has 56 time bins times 4 metallicity bins for a total of 224 model spectra. I retained the same rest frame wavelength range (3464.9 – 8864 Å) as the Emiles subset I’ve been using for several years
The youngest SSP model spectra for the EMILES library augmented with young pypopstar spectra
The obvious next step is to use this library in some models and see how they compare to Emiles. Paging through my samples of spirals with MaNGA observations I picked, for no really good reason, this one:
MaNGA plateifu 8452-12703 (mangaid 1-148068)
Clearly it has star forming regions in its arms as well as a prominent bar and rather red, possibly passively evolving nucleus. After binning to my usual threshold S/N of 5 there were 122 spectra, which were analyzed in the usual way using both Emiles and Emiles + popstar. And here’s the main result of interest, the model star formation histories for all 122 spectra, ordered by distance from the nucleus.
Model star formation histories for MaNGA plateifu 8452-12703 compared.
Red: Emiles + pypopstar
Blue: Emiles + BC03
There’s little or no difference in the model star formation histories for the common components of the libraries. The pypopstar components indicate that the star formation rate continues at relatively constant rates up to recent times. The modest differences at the young end don’t necessarily mean anything. I more or less arbitrarily assigned an age of 10Myr to the BC03 model spectra, which were actually taken from 1Myr models. There’s no real way to tell what the actual effective age of those contributors is — if it’s typically younger than 10Myr the SFR in the youngest bin would be correspondingly higher and a little lower in the next age bin.
Given the similarities in the detailed star formation histories it shouldn’t be much of a surprise that summary quantities are quite similar too. To illustrate a few, here are mean values of the stellar mass surface density:
Model mean values of stellar mass density for MaNGA plateifu 8452-12703 compared — Emiles + pypopstar vs Emiles + BC03
the star formation rate surface density (100 Myr average):
Model mean values of SFR density for MaNGA plateifu 8452-12703 compared — Emiles + pypopstar vs Emiles + BC03
The specific SFR:
Model mean values of SSFR for MaNGA plateifu 8452-12703 compared — Emiles + pypopstar vs Emiles + BC03
The lines with confidence intervals in these plots are from OLS fits taking no account of nominal uncertainties in either sets of variables, and shouldn’t be used to infer any trends. In any case all differences are very small. Finally, here are histograms of all sample values of SFR density for all spectra. Again, these are nearly identical:
Sample distributions of SFR density over all spectra compared — Emiles + pypopstar vs Emiles + BC03
After running multiple sets of models it became apparent that this wasn’t a very stringent test of the usefulness of the proposed library additions because this galaxy has very anemic star formation. In fact it’s one of Masters et al.‘s “passive” red spirals, which I should have recognized. It was also one of the first several dozen galaxies with AGN found in MaNGA, which doesn’t necessarily (but might, along with perhaps the prominent bar) account for the weak star formation. My model runs show “LINER” like emission line ratios in the center, which does point to the presence of a weak AGN.
Briefly now, I picked two more disk galaxies with obvious regions of vigorous star formation and repeated this exercise. To make this short I’m just going to post the star formation histories for all binned spectra.
MaNGA plateifu 8449-3703 (RA 169.299, DEC 23.586)
MaNGA 1-488712, plateifu 8449-3703 — SDSS cutoutModel star formation histories for MaNGA plateifu 8449-3703 compared.
Blue: Emiles + pypopstar
Red: Emiles + BC03
MaNGA plateifu 8318-9101 (RA 196.086 DEC 45.057)
MaNGA 1-259618, plateifu 8318-9101 — SDSS cutout Model star formation histories for MaNGA plateifu 8318-9101 compared.
Blue: Emiles + pypopstar
Red: Emiles + BC03
Spectra in nearby age and metallicity bins are highly corrrelated, which among other things means that adding or subtracting some from the set of “predictors” potentially changes the values inferred for others as well. In these two sets of model runs we do see some differences in the common Emiles portion of the libraries, but they’re quite small and change no qualitative inferences. So my conclusion for now is that adding these theoretical spectra is a reasonable strategy, but one that doesn’t have much apparent impact on model results.
Well that’s probably all for a while. The final MaNGA data release is now promised for December 2021, which should approximately double the number of galaxies and I hope offer some data reduction improvements. There will also be a very large release of stellar spectra that should form the basis for new SSP libraries in the (hopefully) near future.
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).
