Starforming Galaxies – Extragalactic

2023-08-172023-08-17

MaNGA in M31 – details part 2 – the inner disk

Instead of trying a systematic investigation I’m just going to go through each IFU and discuss whatever I found interesting, with no particular theme in mind. I still don’t really know what I’m going to find since it’s been a while since I looked at model results. Besides modeling star formation histories for each spectrum I calculate summaries in the form of posterior marginal means, standard deviations, and a few quantiles for a large number of quantities. Some of these are highly model dependent such as 100 Myr averaged star formation rates and specific star formation. Some are only weakly model dependent, such as emission line fluxes¹. One thing I haven’t looked at much is stellar metallicities and especially their evolution in the models. There are always contributions from all metallicity bins at all times in my models, and how to interpret them or whether even to try still puzzles me. I am starting to look more seriously at strong emission line metallicity estimates. The estimator proposed by Dopita et al. (2016) based on [N II], [S II], and Hα seems especially promising since they’re usually detected with reasonable precision in SDSS spectra.

So, the plan is to look at each IFU, working my way outward in the disk in the same order as my second post in this series.

plateifu 9677-12705 (mangaid 52-4)

This is the innermost IFU with a projected distance from the nucleus of 1.9 kpc. According to Walterbos and Kennicutt (1988) the effective radius of the bulge is 2 kpc, so a significant fraction of the light is coming from bulge stars.

What’s most interesting about this IFU is what it lacks, which is any significant star formation. I also saw little spatial variation in model star formation histories, so I’ll simply repeat the IFU wide history compared to the nearest PHAT tile:

Innermost IFU 9677-12705 SFR and mass growth histories compared to models for nearest PHAT region.

This region had the most rapid initial stellar mass growth and conversely the steepest decline in SFR of any of the MaNGA IFU’s, which is completely consistent with the consensus “inside out” growth paradigm.

One other moderately interesting result is that despite the lack of young stars there are detectable emission lines throughout with a mix of “LINER” and composite like line ratios from the [N II]/Hα vs. [O III]/Hβ diagnostic and the classification scheme of Kauffmann with Schawinski’s addition of the LINER/AGN divide. As is well known by now LINER (and presumably “composite” although I haven’t seen literature on the issue) emission can be spatially extended and does not at all necessarily indicate ionization by an AGN. M31 has widespread emission from diffuse ionized gas. About 14% of all binned spectra had line ratios in these categories and “AGN” like, and 90% of the LINER-like spectra are in this IFU. A similar fraction of spectra have star forming emission line ratios, which reflects the patchy nature of star formation in M31.

plateifu 9677-12705 – BPT class per [N II]/Hα vs [O III]/Hβ diagnostic

plateifu 9677-6102 (mangaid 52-3)

There’s little to say about this one. The entire IFU is offset by a small amount from some GALEX UV bright sources and there are no objects in any of the catalogs I’ve loaded within the footprint. The only prominent feature is a very prominent dust lane that covers the southeastern half of the IFU. Oddly, the estimated specific star formation rate tracks the dust rather closely.

plateifu 9677-6102 (M31 inner disk). Specific star formation rate

There’s a clear correlation between SSFR and optical depth of attenuation, and also with the “tilt” of the attenuation relation:

plateifu 9677-6102 (M31 inner disk). Specific star formation rate vs. dust optical depth.

Whether this is meaningful or a modeling artifact I can’t say at this point. I kept my simple single component dust model for these runs even though M31 is known to have both a foreground screen and embedded dust.

plateifu 9677-6103 (mangaid 52-2)

This again is in a nearly featureless area except for a prominent dust lane, with no sources in any catalog I consulted. The entire IFU lacks significant emission and there is no evidence in the models for significant recent star formation. Oddly, there’s a very similar relation between model specific star formation rate and model optical depth:

plateifu 9677-6103 (M31 inner disk). Specific star formation rate vs. dust optical depth.

plateifu 9677-12701 (mangaid 52-8)

This is the closest IFU to the nucleus that lies within a significant spiral structure as seen by GALEX. The thumbnail below shows its position overlaid on the false color GALEX image available within Aladin. The IFU appears to lie in a spur off a spiral arm a little farther out² and a number of discrete UV bright sources are visible within its footprint, which is marked with the irregular set of blue symbols. Also shown are cataloged positions of H II regions (yellow dots), red supergiants (red diamonds), and an OB association (blue square)³.

9677-12701-galexcutout — Thumbnail of plateifu 9677-12701 (M31 inner disk) overlaid on GALEX false color image. Yellow dots: cataloged H II regions. Red dots: cataloged red supergiants. Blue square: OB association.

Let’s look at a couple of maps. The blank area at upper right was masked due to a likely foreground star. The spectra in the chain of blank areas at bottom had Hα partially masked. Units in the Hα luminosity density map are log₁₀ ergs/sec/kpc², uncorrected for attenuation. Units of the SFR density maps are log₁₀ M_☉/yr/kpc².

9677-12701_ha_sfrmaps — plateifu 9677-12701 (M31 inner disk). (L) Uncorrected Hα luminosity density. (R) 100 Myr average SFR density.

To a pretty good approximation regions that are relatively bright in Hα track the UV bright areas and cataloged H II regions. There are two areas that stand out as having much higher than average SFR density. One, at lower left, coincides with a bright H II region. The other one, at center right, has low Hα luminosity but lies right on the cataloged position of a red supergiant. The presence of an evolved star and absence of emission suggests that star formation has recently (in the last ~70 Myr, say) ended in that area. Comparing the model star formation histories the region with little Hα emission does show a sharp drop-off after a peak at 10 Myr lookback time:

9677-12701_sfh_2regions — plateifu 9677-12701 (M31 inner disk) – model star formation histories for 2 star forming regions.

One other thing I’ll just note for now is that regions with the highest star formation rate tend to have neighboring regions with higher than average star formation as well. These seem to occur in clumps or chains a few 10s of parsec in size. I will get, eventually, to some more dramatic examples.

plateifu 9677-9101 (mangaid 52-9)

This and the next IFU are in a spiral segment that some authors call the “6 kpc ring,” but the GALEX false color image shows no very bright UV sources and there are no cataloged young objects within the footprint.

One mildly interesting result is that the modeled 100 Myr SFR density correlates rather strongly with Hα luminosity density, but an order of magnitude higher than predicted from Calzetti’s calibration. All of the emission in this region appears to be from diffuse ionized gas as there are no cataloged discrete sources of emission, and no regions with starforming line ratios. A literal interpretation of this, which might even be true, is that star formation has ceased in the recent past.

9677-9101_ha_sfr — plateifu 9677-9101 (M31 inner disk). Star formation rate density vs. Hα luminosity density.

plateifu 9677-12704 (mangaid 52-5)

This is also in the 6 kpc spiral feature but in an area with no bright UV sources and that appears to be heavily dust obscured in optical images. Since I don’t have anything very interesting to say about this region I’ll just post the modeled star formation history for the region within the IFU footprint with the highest modeled SFR density. This is near the western edge of the IFU and isn’t associated with any cataloged young objects.

plateifu 9677-12704 (M31 inner disk). Star formation rate history for a region within the IFU footprint with the highest modeled recent SFR.

The region with the highest Hα luminosity is near the southwest edge and covers the position of a cataloged planetary nebula. The emission line ratios are inconsistent with a starforming region, falling in Kauffmann’s “AGN” region.

plateifu 9677-12703 (mangaid 52-6)

This and the last IFU are in an inter-line region between the 6 and 10 kpc structures as seen by GALEX, but with lots of diffuse starlight and relatively little dust. Emission lines are weak or undetected throughout, but there is a cataloged H II region near the southern edge. The peak in Hα luminosity density is easily seen in the map below in the bottom left pane. The region with the highest SFR density is displaced by ~10 pc. from the region with highest Hα luminosity. Interestingly, the SFR models show significant differences in recent histories: the region with highest SFR shows a very sharp and short lived peak at ~10 Myr, while the highest Hα luminosity region is still growing in SFR (per the model). Again, I hesitate to take these model histories too literally, especially at the youngest ages, but these are consistent with the fact that ionized gas emission will fade rather rapidly as the most massive stars in a region evolve away from the main sequence.

plateifu 9677-12703 (M31 inner disk). (TL) SFR density (100 Myr average) (BL) Hα luminosity density. (TR) SFR history for the region with highest SFR density. (BR) SFR history for the region with highest SFR Hα luminosity density.

plateifu 9678-12705 (mangaid 52-21)

I don’t have much to say about this one either. It lies in a region that’s almost completely blank in the GALEX imaging, with a rather uniform sprinkle of stars in PHAT and the DSS2 image displayable in Aladin. Ionized gas emission is weak or undetected throughout. For the sake of having a graph to display here is a histogram of the per spectrum mean specific star formation rate (100 Myr average as always) comparing this IFU to the innermost one — plateifu 9677-12705.

9678-12705_ssfr — Distributions of mean specific star formation rate in two MaNGA M31 IFU’s

I hope to finish off M31 in one or at most two more posts. Next up are IFUs that fall in or near the 10 kpc ring, followed by the outer disk.

2023-07-052023-07-14

The MaNGA M31 ancillary program – model details (part 1?)

After a fairly long break I want to get back to M31 and MaNGA for one, or perhaps several posts and take a more detailed look at my model results. I still haven’t decided where I’m going to take this investigation. I may examine every IFU or just the ones that I found most interesting, and I’m not sure which of the many quantities that I estimate I’ll discuss. Besides my models I’ve retrieved a number of catalogs of interesting objects using Aladin. These include in particular H II regions (Azimlu et al. 2011), OB associations (Magnier et al. 1993), and red supergiants (Ren et al. 2021). All of these are products of recent or ongoing star formation. There are of course a huge number of catalogs of just about every type of astronomical object found in galaxies, and I may examine some more depending on what interests me.

For orientation here’s a screencap of the Legacy Survey sky browser’s false color GALEX image of the northern half of M31 with the IFU positions overlaid and labelled with MaNGA’s plateifu identifiers. As a reminder these are all located within the PHAT survey footprint and specifically within the region for which star formation histories were estimated by Williams et al. (2017).

lg_manga — Screen capture of Legacy Survey Galex image of M31 with MaNGA IFU overlay

Before getting to individual IFU’s here is one more set of IFU-wide results. The following three graphs are model mass growth histories in units of present day solar mass per kiloparsec². These are uncorrected for projection effects.

There are a couple interesting points here. There’s a clear stratification of mass density with projected radius, with about a factor 30 decline from the innermost to outermost IFU. This is in fairly good agreement with Williams’ estimate in their Figure 14.

The other thing to note is that all regions had most (> 55%) of their stellar mass in place by 8 Gyr ago and 92-99% in place by 1 Gyr ago. The largest fraction of recent star formation is in the IFU 9678-12703, which is very close to the region with the highest SFR in this half of the galaxy. There is also a trend towards later mass build up with increasing radius, which is completely consistent with the “inside-out” growth paradigm. The outermost IFU, 9678-12701 at about 16kpc radius has formed about 5% of its present day stellar mass in the past Gyr.

As I said in the previous post I don’t see clear evidence for a widespread burst of star formation that’s widely believed to have occurred around 2-4 Gyr ago. A confounding factor in my models is that they invariably show jumps in SFR at times when the interval between SSP model ages change and the two oldest of these occur at 1 and 4 Gyr, so this produces a possibly spurious period of apparently accelerated star formation. I hope to find (or perhaps produce) a set of SSP models with a better age distribution this year.

mghden_innerdisk — Growth of stellar mass density – inner disk M31 MaNGA IFU’s

Growth of stellar mass density – M31 MaNGA IFU’s in 10 kpc ring

Growth of stellar mass density – outer disk M31 MaNGA IFU’s

I think I’m going to hit publish now and resume with inner disk IFU’s next time.

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.

2023-02-28

The MaNGA M31 ancillary program – preliminaries

One of the ancillary programs (with principal investigator Julianne Dalcanton) in the final MaNGA release targeted 18 fields in the disk of the Andromeda galaxy M31. The targets were selected from within the footprint of the “Panchromatic Hubble Andromeda Treasury,” aka PHAT¹, also with PI Dalcanton. The initial PHAT survey description was in Dalcanton et al. (2012) and was followed by a lengthy series of papers. Especially relevant for this discussion are two papers describing estimates of the recent and ancient star formation histories of the disk outside the area dominated by bulge light: Lewis et al. (2015), “The Panchromatic Hubble Andromeda Treasury. XI. The Spatially Resolved Recent Star Formation History of M31” and Williams et al. (2017), “PHAT. XIX. The Ancient Star Formation History of the M31 Disk.” For reference here is a mosaic of HST images in the F475W filter with the IFU locations overlaid:

m31manga_phathst — Mosaic of HST F475W images of PHAT study region with M31 MaNGA IFU positions overlaid

Zooming out to show the whole disk here they are overlaid on a false color FUV+NUV image from GALEX, which gives a pretty good picture of where stars are actually forming:

m31manga_galex — GALEX false color NUV+FUV image of M31 with MaNGA IFU positions overlaid – screencap from Aladin

This data set provides an excellent opportunity to compare my SFH modeling code to a completely different, more direct, method of inferring star formation histories namely counting resolved stars in color magnitude diagrams. I recently completed model runs for all 18 IFU’s with the same Voronoi binning of stacked RSS spectra, the same modeling code and SSP model spectra as I’ve used for a while now.

There’s no redshift listed in the DRP catalog; NED gives a heliocentric redshift of -0.001, but for purposes of calculating intrinsic quantities I need the “Hubble flow” redshift. I adopted a distance of 761 (± 11) kpc or distance modulus of (m-M)₀ = 24.407 from Li et al. (2021), which is the most recent and according to the authors most precise determination to date. With my adopted Hubble constant of H₀ = 70 km/sec/Mpc this makes the Hubble flow recession velocity 53.27 km/sec or z_dist = 0.0001777. The angular scale is 3.69 pc/arc-second. This distance estimate is a few percent smaller than the PHAT team authors and most other recent literature I reviewed, but fortunately most other sources of uncertainty are much larger.

An issue I noticed early on was the modelled values of the optical depth of attenuation were right at 0 for almost all spectra with only a few much larger exceptions. A quick check of the metadata showed that the values adopted for the foreground galactic extinction almost certainly were taken from the SFD dust maps which faithfully capture the intrinsic dust content of M31 albeit at rather low resolution. These hugely overestimate the actual foreground galactic extinction and that has multiple undesirable consequences. So, I assigned a single extinction value of E(B-V) = 0.055 to all IFU’s, consistent with the NED value of A_V = 0.17 mag. The preliminary runs were redone with the newly adopted extinction value.

After binning to a minimum mean SNR of 5 there were 2,624 spectra in the 18 IFUs, of which I ran models for 2,621. Three spectra had apparent foreground stars, although one of those might actually be a red supergiant in M31. The fibers are basically sampling star cluster size and stellar mass regions so a single extremely luminous star could potentially affect a spectrum.

I’m only going to show a few summary results for the entire sample in this post. My goal is to do a more detailed quantitative comparison to (at least) the SFH models of Wilson, for which there are extensive results tabulated. There are of course many catalogs of interesting objects within M31, and I plan to look at some of them.

First, here is a plot of the (100 Myr averaged) star formation rate density against stellar mass density, color coded by BPT diagnostic. The solid line is my estimate of the “spatially resolved star forming main sequence” based on a small sample of non-barred spiral galaxies. The dashed line is the estimate of Bluck et al. (2020), which I commented previously appears to mark approximately the location of the green valley at least with regard to my models. A striking feature of this plot is the apparent stratification into at least three distinct groups that can be interpreted as starforming, quiescently evolving, and passively evolving. I suspect this observed stratification is just the result of hand picking a small number of “interesting” regions. Most or perhaps all of the points in the passively evolving group are in the IFU closest to the bulge, while most of those along and above the SFMS lie near the most vigorously star forming regions in the PHAT footprint. Especially noteworthy are 5 outliers that are well above any others in the plot in terms of SFR density. These are all in the same IFU (plateifu 9678-12703) which is located within the largest star forming region in that quadrant of the “10 kpc ring.”

m31manga_sigmam_sigmasfr — 100 Myr average star formation rate density vs. stellar mass density for 2621 binned spectra in M31 disk. Solid and dashed lines are my and Bluck’s central estimates of the “spatially resolved star forming main sequence.

Next are plots of star formation rate density against Hα luminosity density. The left panel is for all spectra color coded by BPT diagnostic, with Hα adjusted by the modeled amount of stellar attenuation. The right panel shows regions with star forming BPT diagnostics only, with Hα corrected by the observed Balmer decrement. The solid line in both panels is Calzetti’s calibration of the Hα – SFR relationship. The relationships plotted here are consistent with what I’ve seen in other MaNGA samples and with published values, which is encouraging.

m31manga_sigmaha_sigmasfr — Star formation rate density vs. Hα luminosity density for 2621 binned spectra in M31 disk. (L) Emission corrected for modeled stellar attenuation. (R) For regions with star forming emission line ratios only: emission corrected from estimated Balmer decrement.

The obvious point of comparison to my models are the detailed star formation histories in the two PHAT papers mentioned at the top. Unfortunately there is no detailed tabulation of model results in the paper by Lewis et al. The paper by Williams et al. has extensive tables, but there are still a few obstacles to detailed comparisons which I will discuss next time.

A few more items from my handwritten notes that I want to get in pixels. I have never previously tried to correct surface densities for inclination in disk galaxies, but for comparison purposes and because of the large inclination of M31’s disk I need to do so here. I adopted an inclination angle of 77°, so a 1″ radius fiber covers a 3.69 x 16.4 pc (semi major and minor axes) elliptical region, or 190 pc². Densities need to be adjusted downward by a factor 4.45 or -0.648 dex².

In order to achieve 100% coverage of the IFU footprints the exposures were dithered to three different positions with overlapping fiber positions. Comparing the area in fibers to the area in spaxels in the cubes the overfilling factor averages 0.217 dex or 65%. The total area in all cubes is 10,731 arcsec², or a deprojected area of 0.65 kpc². The most distant IFU from the nucleus is at a projected radius of about 16 kpc. A simple extrapolation to the ≈800 kpc² area of the disk within that radius is probably unsafe.

One final map to anticipate the next post(s). Wilson provides tables of model star formation rates for 16 age bins, 826 regions, and 4 different sources of isochrones including the same BASTI isochrones I use. The complete data set is available through Vizier. In the plot below I created a map of the recent star formation rate density interpolated to nominal 10″ resolution from their Table 2 models with BASTI isochrones. This should be compared to their Figure 16 (they use logarithmic scaling).

m31manga_wilsonsfr — Current (300 Myr average) star formation rate density in the PHAT footprint per models of Wilson et al. (2017) with positions of MaNGA IFUs overlaid.

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.

2022-01-282022-04-02

What fraction of Schawinski’s “Blue early type galaxies” are ellipticals?

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)¹I 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.

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.

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.

2021-11-242021-12-01

Another look at the PYPOPSTAR SSP model library

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 10^6.6 years (4 Myr) and then turn around, becoming noticeably fainter and redder by 10⁷ years.

pyspecz02 — 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

youngspec_combo — 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:

ssfr_comp — 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:

sigma_sfr_dist_comp — 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 cutout

Model 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.

2021-04-20

Confronting SFH models with observables – some results for normal disk galaxies

I’ve posted versions of some of these graphs before for both individual galaxies and a few larger samples, but I think they’ve all been unusual ones. I recently managed to complete model runs on 40 of the spirals from the normal barred and non-barred sample I discussed back in this post. The 20 barred and 20 non-barred galaxies in the sample aren’t really enough to address the results in the paper by Fraser-McKelvie that was the starting point for my investigation and more importantly the initial sample was chosen entirely at my whim. Unfortunately I don’t have the computer resources to analyze more than a small fraction of MaNGA galaxies. The sampling part of the modeling process takes about 15 minutes per spectrum on my 16 core PC (which is a huge improvement) and there are typically ~120 binned spectra per galaxy, so it takes ~30 hours per galaxy with one PC running at full capacity. I should probably take up cryptocurrency mining instead.

This sample comprises 5086 model runs with 2967 spectra of non-barred and 2119 of barred spirals. For some of the plots I’ll add results for 3348 spectra of 33 passively evolving Coma cluster galaxies.

Anyway, first: the modeled star formation rate density versus the rate predicted from the Hα luminosity density, which is easily the most widely used star formation rate calibrator at optical wavelengths. The first plot below shows all spectra with estimates for both values. Red dots are (non-barred) spirals, blue are barred. Both sets of quantities have uncertainties calculated, but I’ve left off error bars for clarity. Units on both axes are log₁₀(M_☉/yr/kpc²). I adopted the relation log(SFR) = log(L_Hα) – 41.26 from a review by Calzetti (2012), which is the straight line in these graphs. That calibration is traceable back to Kennicutt (1983), which as far as I know has never been revisited except for small adjustments to account for changing fashions in assumed stellar initial mass functions. In the left panel of the plot below Hα is uncorrected for attenuation. In the right it’s corrected using the modeled stellar attenuation, which as I noted some time ago will systematically underestimate the attenuation in H II regions. Not too surprisingly almost all points lie above the calibration line — the SFH models include a treatment of attenuation that might be too simple but still does make a correction for starlight lost to dust. The more important observation though is there’s a pretty tight relationship between modeled SFR density and estimated Hα luminosity density that holds over a nearly 3 order of magnitude range in both. The scatter around a simple regression line in the graphs below is about 0.2 dex. It’s not really evident on visual inspection but the points do shift slightly to the right in the right hand plot and there’s also a very slight reduction in scatter. These galaxies are actually not especially dusty, with an average model optical depth of around 0.25 (which corresponds to E(B-V) ≈ 0.07).

sfr_ha_40spirals — SFR density vs. prediction from Hα luminosity for 40 normal spirals. (L) Hα luminosity uncorrected for attenuation. (R) Hα corrected using estimated attenuation of stellar component.

To take a more refined look at this I limited the sample to regions with star forming emission line ratios using the standard BPT diagnostic based on [O III]/Hβ vs. [N II]/Hα. I require at least a 3σ detection in each line to make a classification, so besides limiting the analysis to regions that are in fact (I hope) forming stars it allows correcting Hα attenuation for the observed Balmer decrement since Hβ is by construction at least nominally detected. Now we get the results shown in the plot below. Units and symbols are as before. Hα luminosity is corrected using the Balmer decrement assuming an intrinsic ratio of 2.86 and the same attenuation curve shape as returned by the model. The SFR-Hα calibration line is the thick red one. The blue lines with grey ribbons are from “robust” simple regressions using the function lmrob in the R package robustbase¹

Model estimates of star formation rate density vs. SFR predicted from Hα luminosity density.

So the model SFR density straddles the calibration line, but with a distinct tilt — regions with relatively low Hα luminosity have higher than expected star formation. To quantify this here is the output from the function lmrob:

Call:
lmrob(formula = sigma_sfr_m ~ sigma_sfr_ha, data = df.sfr)
 \--> method = "MM"
Residuals:
      Min        1Q    Median        3Q       Max 
-3.862996 -0.142375  0.004122  0.137030  1.305471 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -0.174336   0.019224  -9.069   <2e-16 ***
sigma_sfr_ha  0.785954   0.009948  79.008   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Robust residual standard error: 0.2097 
Multiple R-squared:  0.7402,	Adjusted R-squared:  0.7401 
Convergence in 10 IRWLS iterations

Robustness weights: 
 6 observations c(781,802,933,941,2121,2330) are outliers with |weight| = 0 ( < 3.8e-05); 
 223 weights are ~= 1. The remaining 2424 ones are summarized as
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0107  0.8692  0.9525  0.9020  0.9854  0.9990

I also ran my Bayesian measurement error model on this data set and got the following estimates for the intercept, slope, and residual standard deviation:


         mean      se_mean          sd       2.5%        25%        50%        75%      97.5%    n_eff      Rhat
b0 -0.1942387 1.943297e-04 0.018346806 -0.2312241 -0.2063781 -0.1943811 -0.1819499 -0.1589849 8913.379 0.9997482
b1  0.7767853 9.828814e-05 0.009436693  0.7579702  0.7706115  0.7768086  0.7830051  0.7949343 9218.014 0.9995628
s   0.2044701 3.837428e-05 0.003319280  0.1981119  0.2021872  0.2043949  0.2067169  0.2110549 7481.821 0.9997152

Almost the same! So, how to interpret that slight “tilt”? The obvious comment is that the model results probe a very different time scale — by construction 100 Myr — than Hα (5-10 Myr). As a really toy model consider an isolated, instantaneous burst of star formation. As the population ages its star formation rate will be calculated to be constant from its birth up until 100 Myr when it drops to 0, while its emission line luminosity declines steadily. So its trajectory in the plot above will be horizontally from right to left until it disappears. In fact in spiral galaxies in the local universe star formation is generally localized, usually along the leading edges of arms in grand design spirals. Slightly older populations will be more dispersed.

This can be seen pretty clearly in the SFR maps for two galaxies from this sample below. In both cases regions with high star formation rate track the spiral arms closely, but are more diffuse than regions with high Hα luminosity.

Second topic: the spectral region around the 4000Å “break” has long been known to be sensitive to stellar age. Its use as a quantitative specific star formation rate indicator apparently dates to Brinchmann et al. (2004)². More recently Bluck et al. (2020) used a similar technique at the sub-galactic level on MaNGA galaxies. Both studies use D4000 as a secondary star formation rate indicator, preferring Hα luminosity as the primary SFR calibrator with D4000 reserved for galaxies (or regions) with non-starforming emission line ratios or lacking emission. Oddly, I have been unable to find an actual calibration formula in a slightly better than cursory search of the literature — both of the cited papers present schematic graphs with overlaid curves giving the adopted relationships and approximate uncertainties. The Brinchmann version from the published paper is copied and pasted below.

In the two graphs below I’ve added data from the passively evolving Coma cluster sample comprising 3348 binned spectra in 33 galaxies. There are two versions of the same graphs. Individual points are displayed in the first, as before with error bars suppressed to aid (slightly) clarity. The second displays the density of points at arbitrarily spaced contour intervals. The straight line is the “robust” regression line calculated for the spiral sample only, which for the sake of completeness is

\( \log10(sSFR) = -7.11 (\pm 0.02) – 2.11 (\pm 0.015) D_n(4000)\)

d4000_ssfr_40spirals_asscatter — Model sSFR vs. measured value of D4000. 40 barred and non-barred spirals + 33 passively evolving Coma cluster galaxies.

Model sSFR vs. measured value of D4000. 40 barred and non-barred spirals + 33 passively evolving Coma cluster galaxies. — Model sSFR vs. measured value of D4000 (2D density version). 40 barred and non-barred spirals + 33 passively evolving Coma cluster galaxies.


Call:
lmrob(formula = ssfr_m ~ d4000_n, data = df.ssfr)
 \--> method = "MM"
Residuals:
       Min         1Q     Median         3Q        Max 
-0.9802409 -0.0916555 -0.0005187  0.0962981  7.1748499 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -7.10757    0.02009  -353.8   <2e-16 ***
d4000_n     -2.10894    0.01418  -148.7   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Robust residual standard error: 0.1384 
Multiple R-squared:  0.9043,	Adjusted R-squared:  0.9043 
Convergence in 13 IRWLS iterations

Robustness weights: 
 39 observations c(45,958,1003,1165,1200,1230,1249,1279,1280,1281,1282,1283,1294,1298,1299,1992,2040,2047,2713,2722,2723,2729,2735,2736,2974,3212,3226,3250,3667,3668,3671,3677,3685,3687,3688,3691,4056,4058,4083)
	 are outliers with |weight| <= 1.1e-05 ( < 2.1e-05); 
 418 weights are ~= 1. The remaining 4310 ones are summarized as
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0001994 0.8684000 0.9514000 0.8911000 0.9850000 0.9990000

The relation between D4000 and sSFR as estimated by Brinchmann et al. 2004

All three groups follow the same relation but with some obvious differences in distribution. The non-barred spiral sample extends to higher star formation rates (either density or sSFR) than barred spirals, which in turn extend into the passively evolving range. The Coma cluster sample has a long tail of high D4000 values (or high specific star formation rates at given D4000) — this is likely because D4000 becomes sensitive to metallicity in older populations and this sample contains some of the most massive (and highest metallicity) galaxies in the local universe. Also, as I’ve noted before these models “want” to produce a smoothly varying mass growth history, which means that even the reddest and deadest elliptical will have some contribution from young populations. This seems to put a floor on modeled specific SFR of ∼10^-11.5 yr^-1.

Just to touch briefly on the paper by Fraser-McKelvie et al. barred spirals in this sample do have lower overall star formation than non-barred, with large areas in the green valley or even passively evolving. This sample is too incomplete to say much more. For the sake of having a visualization here is the spatially resolved Σ_SFR vs. Σ_M* relation. The dashed line is Bluck’s estimate of the star forming “main sequence,” which looks displaced downward compared to my estimates.

mstar_sfr_40spirals+33etg — Model SFR density vs. stellar mass density. 40 barred and non-barred spirals + 33 passively evolving Coma cluster galaxies.

Finally, here are a couple of grand design spirals, one barred and one (maybe) not to illustrate how model results track morphological features. In the barred galaxy note that the arms are clearly visible in the SFR maps but they aren’t visible at all in the stellar mass map, which does show the presence of the very prominent bar.

NGC 6001 – thumbnail with MaNGA IFU footprint

NGC 6001 (MaNGA plateifu 9041-12701) (L) Model SFR surface density (M) Hα luminosity density (R) sSFR

*NGC 5888- thumbnail with MaNGA IFU footprint*

NGC 5888 (MaNGA plateifu 9871-12702) (L) Model SFR surface density (M) Hα luminosity density (R) sSFR

9871-12702_stellar_mass_density — NGC 5888 (MaNGA plateifu 9871-12702) – Log model stellar mass density (M_sun/kpc²

I’m not sure how much more I’m going to do with normal spirals. As I’ve said repeatedly the full sample is much too large for my computing resources.

Next time (probably) I’m going to return to a very small sample of post-starburst galaxies, which I may also return to when the final SDSS public data is released.

2020-02-16

First complete model run with modified attenuation curve

Here’s an SDSS finder chart image of one of the two grand design spirals that found its way into my “transitional” galaxy sample:

Central galaxy: Mangaid 1-382712 (plateifu 9491-6101), aka CGCG 088-005

and here’s a zoomed in thumbnail with IFU footprint:

This galaxy is in a compact group and slightly tidally distorted by interaction with its neighbors, but is otherwise a fairly normal star forming system. I picked it because I had a recent set of model runs and because it binned to a manageable but not too small number of spectra (112). The fits to the data in the first set of model runs were good and the (likely) AGN doesn’t have broad lines. Here are a few selected results from the set of model runs using the modified Calzetti attenuation relation on the same binned spectra. First, using a tight prior on the slope parameter δ had the desired effect of returning the prior for δ when τ was near zero, while the marginal posterior for τ was essentially unchanged:

9491-6101_tauv_calzetti_mod — Estimated optical depth for modified Calzetti attenuation vs. unmodified. MaNGA plateifu 9491-6101

At larger optical depths the data do constrain both the shape of the attenuation curve and optical depth. At low optical depth the posterior uncertainty in δ is about the same as the prior, while it decreases more or less monotonically for higher values of τ. A range of (posterior mean) values of δ from slightly shallower than a Calzetti relation to somewhat steeper. The general trend is toward a steeper relation with lower optical depth in the dustier regions (per the models) of the galaxy.

9491-6101_tauv_delta_std — Posterior marginal standard deviation of parameter δ vs. posterior mean optical depth. MaNGA plateifu 9491-6101

There’s an interesting pattern of correlations¹ here, some of which are summarized in the sequence of plots below. The main result is that a steeper (shallower) attenuation curve requires a smaller (larger) optical depth to create a fixed amount of reddening, so there’s a negative correlation between the slope parameter δ and the change in optical depth between the modified and unmodified curves. A lower optical depth means that a smaller amount of unattenuated light, and therefore lower stellar mass, is needed to produce a given observed flux. so there’s a negative correlation between the slope parameter and stellar mass density or a positive correlation between optical depth and stellar mass. The star formation rate density is correlated in the same sense but slightly weaker. In this data set both changed by less than about ± 0.05 dex.

(TL) change in optical depth (modified – unmodified calzetti) vs. slope parameter δ
(TR) change in stellar mass density vs. δ
(BL) change in stellar mass density vs. change in optical depth
(BR) change in SFR density vs change in optical depth Note: all quantities are marginal posterior mean estimagtes.

Here are a few relationships that I’ve shown previously. First between stellar mass and star formation rate density. The points with error bars (which are 95% marginal credible limits) are from the modified Calzetti run, while the red points are from the original. Regions with small stellar mass density and low star formation rate have small attenuation as well in this system, so the estimates hardly differ at all. Only at the high end are differences about as large as the nominal uncertainties.

SFR density vs. stellar mass density. Red points are point estimates for unmodified Calzetti attenuation. MaNGA plateifu 9491-6101

Finally, here is the relation between Hα luminosity density and star formation rate with the former corrected for attenuation using the Balmer decrement. The straight line is, once again, the calibration of Moustakas et al. Allowing the shape of the attenuation curve to vary has a larger effect on the luminosity correction than it does on the SFR estimates, but both sets of estimates straddle the line with roughly equal scatter.

9491-6101_sigma_ha_sfr_mod — SFR density vs. Hα luminosity density. Red points are point estimates for unmodified Calzetti attenuation. MaNGA plateifu 9491-6101

To conclude for now, adding the more flexible attenuation prescription proposed by Salim et al. has some quantitative effects on model posteriors, but so far at least qualitative inferences aren’t significantly affected. I haven’t yet looked in detail at star formation histories or at effects on metallicity or metallicity evolution. I’ve been skeptical that SFH modeling constrains stellar metallicity or (especially) metallicity evolution well, but perhaps it’s time to take another look.