Poststarburst Galaxies – Extragalactic

2023-04-222023-04-22

IC 3025

I’ll resume my M31 posts soon (I hope), but I wanted to do a short post on the recent Zoogems HST observation of IC 3025 which is a dwarf elliptical in the Virgo cluster that was selected as part of the “post-starburst” galaxy sample. Thanks mostly to its membership in Virgo this galaxy is fairly well studied and even has multiple HST observations. Just for fun I tried to make a false color RGB image from three observations, with two in the IR through F160W and F110W filters, and the blue channel from the Zoogems observation in F475W.

This used a program named SWarp (author Bertin) to rescale and align the images and STIFF (also Bertin) to combine them, with some Photoshop work in a mostly futile attempt to get a more pleasing color balance and clean up some of the hot pixels. I don’t know exactly how STIFF maps counts to gray scale levels, but despite the odd color cast this picture may actually give a reasonably accurate rendering of the relative fluxes in each filter. The galaxy as a whole has a g-J color of about 1.3 mag (based on my measurements with APT and NED) and J-H ≈ 0.2 mag. per Jensen et al. (2015), so an orange or even green color in the body of the galaxy is not so unreasonable.

The blue(er) central region is notable and apparently real also. This is one of a distinct class of dwarf early type galaxies with blue centers, given the designation dE(bc) by Lisker et al. (2006). The blue centers are almost certainly due to recent star formation, as I’ll verify below.

There are 3 bright, unresolved clusters near the center with a number of others scattered around the body of the galaxy. By my measurements with the manual Aperture Photometry Tool the brightest of these has a g band (F475W) magnitude of 20.71 and J (110W) of 20.084, or g-J ≈ 0.62. The other two near the galaxy center are slightly fainter and considerably redder: g = 21.5 and 22.6 for the western and eastern flanking clusters, with g-J ≈ 1.2 for both. Jensen et al. (cited above) measure the distance modulus to be m-M = 31.42, which makes the F475W absolute magnitude of the central cluster equal to -10.71. Like the Zoogems target I discussed several months ago this would be quite luminous for a galactic globular cluster but is typical for a dwarf galaxy’s nuclear star cluster (Neumayer, Seth, and Boker 2020). This distance modulus, which corresponds to a luminosity distance of 19.2 Mpc, is considerably larger than the canonical distance to the Virgo cluster of m-M = 31.09 (per Jensen again). This is one of several lines of evidence that the galaxy is currently falling into the cluster.

Like the other galaxies in the Zoogems “post-starburst” sample the SDSS spectrum was incorrectly classified by the SDSS spectro pipeline as coming from a star, but this one has a correct redshift and has been used in science studies (for example in Lisker et al. cited above). From the reported position the fiber center was just west of the brightest central cluster and includes both that one and the cluster just to the west. The spectrum is very much typical of a post-starburst, with deep Balmer absorption and a shallow 4000Å break. I measure Hδ_A = 7.24 ± 0.60Å and D_n4000 = 1.26 ± 0.014¹, very similar values to the other two that I posted about last year. Finally, although it’s far from evident on visual inspection, there are firm (4-5 σ) detections of Hα and S[II] 6717, 6730 in emission. No other emission lines were detected.

I used my usual star formation history modeling code with the metal poor subset of the EMILES SSP library as described here, which produced the estimated star formation and mass growth histories:

ic3025_sfhmgh — IC 3025 – Star formation history and mass growth history modeled from SDSS spectrum

with a very good fit to the data except for a small region around 7500Å (which is often the case with the EMILES library):

ic3025_ppfit — IC 3025 – posterior predictive fit to spectrum from SFH model

My results can be compared fairly directly to an analysis by Lisker et al. (cited above), who performed some simple stellar population modeling on SDSS spectra with what appears to be their own unreleased code. They limited their populations to 3 discrete ages with the oldest fixed at 5 Gyr and the mass fractions and ages for the other 2 chosen from a finite set of possible values.

Perhaps surprisingly my results agree rather well with theirs. For VCC 21 (the Virgo Cluster Catalog designation for IC 3025) their best fit had about 9% of the total mass in young and intermediate age populations, with the young population chosen at 9 Myr age and 0.3% of mass and the intermediate population age of 509 Myr.

My models also show three broad periods of star formation with some lulls in between that can conveniently be divided into young, intermediate, and old populations. The youngest SSP models in my metal poor subset are 30 Myr, so of course there can’t be any truly young populations in the model. The peak in recent star formation was at ~70 Myr with a steep decline at the youngest lookback times. Around 1% of the present day stellar mass in the fiber footprint is in stars younger than 100 Myr, with just under 10% under 1 Gyr.

Based on the colors we can infer that the acceleration of star formation that began ~1 Gyr ago was limited to the central region and the presumed nuclear star cluster. The remainder of the galaxy and its cluster system must already have been quiescent by then.

Edit

I mentioned above my SFH models indicated there were firm detections of Hα and the [S II] doublet in emission. Although [N II] wasn’t detected at better than the 1σ level it’s still possible to make a strong line metallicity estimate from the posteriors. I also plot the marginal posterior for Hα luminosity below:

ic3025_ha_oh — IC 3025 (L) Hα luminosity from SDSS spectrum (R) log(O/H) estimated from [N II]/Hα and [S II]/Hα

Using Calzetti’s calibration of the Hα – SFR relation this implies a current day star formation rate ~10^-4.5 M_☉/yr. This should be considered an upper limit since we don’t know the ionizing source. Using Dopita’s calibration of the [N II]/Hα plus [S II]/Hα strong line metallicity estimator the upper limit to 12+log(O/H) is around 8, which is subsolar by almost an order of magnitude.

2022-09-28

A little more on the “burst age – burst mass degeneracy”

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 Hδ_A. They also have similar 4000Å break strengths:

IC 0976: D_n4000 = 1.308±0.005, Hδ_A = 8.05±0.31
MCG +07-33-040: D_n4000 = 1.153±0.009, Hδ_A = 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:

hd_d4000_2psb — D_n4000 – 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 Hδ_A indexes near the upper limits of any measurements in SDSS, and both are clearly in the post-starburst area of the Hδ_A-D_n4000 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.

2022-09-17

IC 976

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.

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.

IC 976 redshift measurement — 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 spectra¹ 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 ≈10^8.5 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 ≈ 10^9.3 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 galaxy² 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.

2022-07-272022-08-01

UGC 10200 and MCG +07-33-040

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 lines¹ 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 parameters² 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 10^9.69 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.

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.

2022-07-162022-08-18

Journal notes: Haines et al. (2015), “Testing the modern merger hypothesis…”

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 Hδ_A and Hγ_A indexes, and the 4000Å break strength index D_n4000. For reference here is a screen grab of their model trajectories:

Predected trajectories in the Hδ – D_n4000 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”¹ 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.

hd_d4000_bigsample — 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> – D_n4000 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.

Screenshot from 2022-07-07 15-23-36 — Measured values for the specified indexes from Haines et al. (2015). Clipped from the electronic journal paper.

I have calculated the pseudo Lick index Hδ_A and D_n4000 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 radius². 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.

mstar_sfr_4spm — 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 made³. 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.

2021-08-23

One final look at KUG0859+406 and a new SSP model library

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.

pypop_young_spec — 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:

sfh_emiles_popstar — 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:

mgh_emiles_popstar — 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:

sfh_popstar_popstarst — 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.

ppfits_compared — 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:

tauv_delta_emiles_pypostar — Model distributions of attenuation parameters τ_V 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:

sigma_mstar_comp — 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:

ssfr_comp — 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).

2021-08-102021-08-10

KUG 0859+406 – unravelling the differences between 2018 and 2021 model runs

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 posts about, 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:

burst_strengths_compared — 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.

central_sfh_compared — 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_delta — Map of modified Cal;zetti slope parameter δ — MaNGA plateifu 8440-6104

A smaller optical depth of attenuation is also favored throughout:

tauv_compared — 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):

mgh_compared — 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.

ppfits — “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:

measured_flux_compared — 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.

halpha_luminosity_compared — Hα luminosity density – 2021 runs vs. 2018 MaNGA plateifu 8440-6104

velocity_field_8440_6104 — Velocity 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.

2021-07-31

Arxiv notes: Wu (2021), “Searching for local counterparts of high-redshift post-starburst galaxies…”

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 D_n(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 Hδ_A and D_n(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 R_e. This might just be a visible effect of me displaying the trends out to larger radii.

d4000_hdelta_re — Radial trends of Lick Hδ_A and D_n4000 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.

burst_fraction_maps — 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).

total_mgh — 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.

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.

kug0839+406 — 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.

2020-01-042020-01-08

A few properties of my transitional galaxy candidate sample

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 values¹. 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 estimates², 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).

sigma_sfrXsigma_mstar — Σ_sfr vs. Σ_M*. 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.

sigma_sfrXsigma_logl_ha — (L) Estimated Σ_sfr vs. Σ_{log 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.

ssfrXd4000 — Specific star formation rate vs. D_n4000

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:

8611-12702_bptmap — Map of BPT classification — mangaid 1-23746 (plateifu 8611-12702)

9491-6101_bptmap — 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.

2019-12-062019-12-06

Selecting “transitioning” galaxies

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” galaxies¹ 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 Hδ_A index against the 4000Å break strength index D_n(4000). This version used the MPA-JHU measurements for approximately 1/2 of SDSS single fiber galaxy spectra from DR8.

hd_d4000_380K_mpa_jhu — 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 Hδ_A ≈ 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 Hδ_A ≥ 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”² 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 SPOGS³ 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:

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:

8083-9101_spec_extract — 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.