Stan – Extragalactic

2024-01-282024-01-31

Priors on stellar contributions

One thing I’ve wondered about for a while is the extent that priors on SSP model contributions affect modeled star formation histories. Previous experience suggests not much at all unless the prior is highly constraining. To be a little more specific in my current workflow I normalize the SSP model spectra to have average flux values of 1 in (approximately) the V band, and also adjust the galaxy fluxes in the same way. In the Stan model the stellar contribution parameters are declared as a simplex, that is a vector with non-zero elements summing to 1. That makes the parameter values the fractional contributions to the (unreddened) galaxy flux at V. My current working code doesn’t provide an explicit prior for the stellar parameters, but they have an implicit proper prior of a uniform distribution on the appropriate dimensional simplex. More technically the prior is a Dirichlet distribution with all concentration parameters equal to 1. Note the marginal distributions aren’t uniform, but they are all the same. One implication of that is that a typical draw from the prior¹ will have jumps in the star formation rate at exactly the times where the width of the age bins jump, a problem that I’ve noted several times before.

A possible solution to this problem is simply to alter the prior to encourage smoothly varying star formation rate rather than smoothly varying light contributions. It turns out I’ve been feeding my Stan code the data I need to do that: the initial mass in a given model SSP is inversely proportional to the normalization factor applied to the spectrum, and the star formation rate is just the mass divided by the time interval assigned to the SSP. Both of those quantities are passed as data to the Stan model even though they weren’t used in any way previously. I added just 3 lines to the code to change the prior on the stellar contributions. In the “transfored data” section

norm_sfr = 1.0 ./ (dT .* norm_st);
norm_sfr = norm_sfr / mean(norm_sfr);

defines the reweighting of the prior, which is written in the model section as

    target += dirichlet_lupdf((b_st_s .* norm_sfr)/sum(b_st_s .* norm_sfr)|rep_vector(1., nt*nz));

I~~f I did this right the prior is completely agnostic to any star formation history, whereas the previous implicit prior was completely agnostic to any run of light contributions.~~

The modified code compiled without complaint and there’s no discernible difference in either execution time or convergence diagnostics.

I’ve only run this on one set of galaxy data, from MaNGA plateifu 8565-3703 (mangaid 1-92735). This is one of Schawinski’s “blue early type galaxies,” chosen mostly because the data were binned to just 34 spectra so a complete set of model runs only took a few hours. And, as seen below the other things that don’t change are the modeled star formation histories.

I need to do some more validation exercises, but it appears my long ago conclusion that the choice of prior on the star formation history has little effect was correct. The data dominates the model outcome through the likelihood.

Update

Since I hit publish a few days ago I realized two things. First, the weights I applied to the stellar contributions to “encourage” a smoothly varying star formation rate were inverted. What I should have used was:

transformed data {
  vector[nt*nz] norm_sfr = (dT .* norm_st) / mean(dT .* norm_st);
}

The second thing I realized is this makes no difference at all given the form of the prior. The transformation simply maps a point on the simplex to another point on the simplex that has exactly the same probability density since by construction the prior is uniform on the simplex.

So, it should have been expected, and it’s a good thing that it in fact happened that the model runs produced the same results. What happens if I use the following prior, with the weights supplying the parameters for the Dirichlet?

target += dirichlet_lupdf(b_st_s | norm_sfr);

This failed to sample almost always. I’m not entirely sure why, but I suspect this turns a problem with relatively simple geometry at least with regards to the prior to one with a complex and troublesome geometry.

This little experiment actually told me nothing about the effect of priors on model star formation histories. Two of the priors are actually the same, and the third fails for reasons that aren’t completely clear. I may experiment with different forms of prior. I’m still, of course, looking for a new SSP model library.

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.

2023-02-28

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.

2022-06-21

A little more on Schawinski’s blue early type galaxies

As I mentioned two posts ago there are 24 of these galaxies in the final MaNGA data release, a remarkable 11% of the full sample. I ran my SFH model code on all of these along with the prerequisite redshift offset routine¹. SDSS thumbnails of the sample are shown below. As expected none of these have significant spiral structure visible at SDSS resolution, but at least a few are noticeably disturbed.

thumbnails_blueetg — SDSS thumbnail images of Schawinski et al.’s blue early type galaxies in MaNGA final data release (SDSS DR17)

I’m just going to discuss a few topics in this post. I’ll save a more detailed discussion for when I’ve completed analysis of the ancillary post-starburst sample, which is underway now. First, here are velocity fields calculated for the stacked RSS data, with a signal to noise cutoff of 3, the same as I used for my analysis of rotation curves of disk galaxies. Note in the graphic below the ordering is different from the image thumbnails.

vfs_blueetg — Line of sight velocity fields of Schawinski et al.’s blue early type galaxies in the final MaNGA data release

By my count (based entirely on visual inspection) all but 2 of these exhibit large scale rotation, with perhaps 15 or 16 classifiable as regular rotators with the remainder containing multiple velocity components including a couple with (perhaps) kinematically distinct cores. The preponderance of rotating systems surprised me at first, but according to a review by Cappellari (2016) large scale rotation is predominant at least at lower stellar masses (Schawinski et al. characterized their sample as being “low to intermediate mass” among early type galaxies). The velocity fields indicate that many of these contain stellar disks, perhaps embedded in large bulges. That’s still consistent with classification as “early type galaxies.” Apparently the original Galaxy Zoo classification page used the term “elliptical” as the early type galaxy choice, but in the data release paper by Lintott et al. (2011) there’s a statement that the “elliptical” class should comprise ellipticals, S0’s, and perhaps Sa’s from Hubble’s classification scheme.

Depending on how my effort to do non-parametric line of sight velocity modeling goes I may return to examine the kinematics of this sample in more detail, in particular to look for evidence of gas and stellar kinematic decoupling.

Turning to the recent star formation history this sample runs the gamut from large scale starbursts to passively evolving as seen in the plot of (100 Myr averaged) star formation rate versus stellar mass density for all analyzed binned spectra (of which there were 1525 in the full sample). For reference the straight line is my estimate of the center of the local “spatially resolved star formation main sequence.” This is just a weighted least squares fit to the sample of 20 non-barred spirals with star forming BPT diagnostics that I discussed some time ago. My SFMS relation has the same slope as estimated by Bluck but is offset higher by about 0.7 dex, which probably just reflects the very different methods used to estimate star formation rates. The contour lines are the densest part of the relationship from the passively evolving Coma cluster sample that I also discussed in that post. The majority of the blue etg sample falls in the green valley, consistent with Schawinski et al.’s observation that only about 1/2 of the sample showed evidence for ongoing star formation.

sfr_mstar_blueetg — “Spatially resolved” star formation rate density versus stellar mass density for 24 blue early type galaxies in final MaNGA data release. Contour lines are corresponding values for 33 passively evolving Coma cluster galaxies.

Most of the points offset the most on the high side of the SFMS come from just two galaxies: MRK 888, which I’ve discussed in the last few posts, and SDSS J014143.18+134032.8 (this is apparently not in any “classical” catalog). The legacy survey cutout below clearly shows an extended tidal tail that’s a certain sign of a relatively recent merger.

SDSS J014143.18+134032.8, a disturbed, star-bursting blue early type galaxy

I just want to take a quick look at this one: below are maps of the star formation rate density and SSFR as well as scatterplots of the same against distance from the IFU center. As with MRK 888 ongoing star formation is widespread with a peak near the center, a classic case of a merger fueled starburst. In this galaxy star formation peaks in a ring somewhat outside the nucleus. The ring can be seen clearly in the SDSS cutout and must consist of HII regions.

8095-1902_sfr_ssfr — SDSS J014143.18+134032.8 (mangaid 1-41541; plateifu 8095-1902) Star formation rate density and specific star formation rate – maps and scatterplots against radius in kpc.

Schawinski et al. briefly discuss the possibility that their blue ETG’s could be progenitors of E+A (aka K+A) galaxies. This galaxy and MRK 888 are plausible candidates — if star formation shut off rapidly they would certainly exhibit strong Balmer absorption for a time after emission lines disappeared since they already do. Other members of this sample are already fading towards the red sequence, and if they ever qualified as “post-starburst” it must have been in the past.

I plan to look at star formation histories in more detail after I’ve completed model runs on the MaNGA post-starburst sample.

2022-04-26

Adding emission lines to non-parametric kinematic models

This is still experimental and I’m not sure how much farther I’ll pursue it, but I tried a straightforward way to add emission lines to non-parametric line of sight velocity distribution (LOSVD) models. The idea is simple enough: model the line profiles directly using Stan’s simplex data type with each modeled line represented by a vector of mostly zeroes and with the simplex centered on the line’s rest wavelength. Although not essential I’m assuming I will have estimates of redshift offsets for each fiber or spaxel in a MaNGA data file (RSS or cube), so any additional offsets should be small. I’ve chosen to ignore the fact that the discretized line profiles will differ between lines because their central wavelengths will fall at different points within their assigned wavelength bins. Also, different lines could arise from kinematically distinct regions, which is not uncommon in galaxies with broad line AGNs. The obvious solution to this is to allow multiple line profiles. For these initial exercises I’m using a single line profile for all modeled lines (I fit 18 at present). As I’ve done since I started these modeling exercises I am fitting emission lines and stellar contributions simultaneously, with the stellar part represented by a small set of eigenspectra derived from my usual EMILES based library.

Below the “fold” I’ve included the Stan code in it’s current (but certainly not final) form. About half of the code for modeling the stellar LOSVD, is adopted from the original version that I wrote about last year. The emission line model portion takes advantage of an odd feature of Stan, namely the ability to store a matrix in sparse form and perform one specific operation — matrix multiplication with a vector. I still haven’t figured out the particular matrix representation used, so I just create a dummy matrix for the emission lines in the transformed data section and extract the two vectors describing the positions of the non-zero elements of the matrix. In the model section the simplex vector representing the line profiles is repeated as many times as there are lines to fill in the non-zeros.

Another thing to note is that Stan doesn’t know how to work with missing data. In general there will be gaps in spectra that were masked for some reason, while the input templates must be complete over the covered spectrum (plus a few extra at each end for the convolution). This requires a bit of housekeeping that’s mostly done in the R code that sets up the input data.

In the models I ran last year I ignored dust reddening since I didn’t expect it to be significant in the passively evolving Coma cluster galaxies I tested them on. In general reddening isn’t ignorable though and there’s the potential for template mismatch without some allowance for it. For now I inserted the function I use for “modified Calzetti” attenuation but it’s not actually used. In the one test I tried including attenuation in the model significantly increased execution time. I will probably look at using a multiplicative polynomial for the same purpose. A final thing to note is there are no explicit priors for the two simplex vectors that represent the stellar velocity convolution kernel and emission line profile, which means that the priors default to a maximally diffuse dirichlet distribution. This turns out to be an important issue that I will discuss further below.

I’ve run sets of models for a small sample of galaxies so far. so lets look at some results. For these runs I used 500 warmup and 500 post-warmup iterations per chain with 4 parallel chains for a total post-warmup sample size of 2000. The stellar templates are represented by 6 eigenspectra created from singular value decompositions of my standard EMILES based SSP library. I currently fit 18 emission lines — Balmer lines from Hα through Hζ and a selection of the stronger forbidden lines. Model runs typically take 2-3 minutes for sampling on my old 4th generation Intel core I7 based PC. This can undoubtedly be reduced by factors of at least several with multithreading.

In this post I’m going to look in some detail at results for Markarian 888, which was the main topic of my last post. Recall this is one of Schawinski’s “blue early type galaxies” that turns out to have obvious spiral-like structure in its inner region and clear evidence for a relatively recent merger.

First, the graph below shows the central fiber spectrum, and below that the results of a model run for the stellar velocity convolution kernel. From those I can calculate velocity offsets¹

\( \delta v = 69 \times \sum\limits_{k = -\lfloor K/2 \rfloor}^{\lfloor K/2 \rfloor} k p_i \)

and velocity dispersions

\(
v_{disp} = 69 \times \sqrt{\sum\limits_{k = -\lfloor K/2 \rfloor}^{\lfloor K/2 \rfloor} k^2 p_i – \delta v^2}
\)

for each draw from the posteriors, and these are shown as histograms in the middle and right graphs. This was a high signal to noise spectrum with prominent emission and absorption lines, a favorable situation for this modeling exercise and in fact the results look very promising with an especially well determined distribution for the emission lines. In summary, the posterior mean stellar velocity offset was 25 km/sec. with a 95% credible interval of (19.8, 31.3), while the corresponding values for emission lines were 24.3 and (22.8, 25.9), so the credible interval for emission lines lies entirely within that for stars.

The corresponding values for velocity dispersions on the other hand differ quite a lot: 141 km/sec. for stars with a 95% credible interval of (133.6, 150.3) versus 109 km/sec and (106, 111) for gas. I’ll say some more about this below, but I think this is already a sign that the second moments (at least) of these velocity distributions need to be treated with caution.

mrk888_centralspec_losv — Markarian 888 – mangaid 9894-3703 Central fiber spectrum, modeled stellar and ionized gas losvd

After running the code for every binned spectrum in the IFU I get stellar and gas velocity maps as shown below. These look similar to each other and to the map in the last post based on my hybrid red shift offset fitting routine, although a closer look will show a systematic decoupling of gas and stellar velocities.

mrk888_st_em_losvd — Stellar and ionized gas velocity maps for Mrk 888 (MaNGA plateifu 9894-3703)

I often take a look at the official MaNGA data analysis pipeline results, usually through the “Marvin” interface. Generally my results look similar for any given quantity, but it’s hard to tell how closely since we’re looking at different things (binned RSS spectra vs. cubes, for one) and I’m really just doing a visual comparison. As more or less a one off exercise I decided to compare velocity fields from the cubes. First, here are stellar and Hα maps from the DAP. These are from the “HYB10-MILESHC-MASTARSSP” sequence, which refers to the binning strategy and template inputs.

mrk888_vel_maps_per_dap — Mrk 888 – stellar and gas velocity fields derived from non-parametric LOSVD models

Next are my stellar and ionized gas velocity maps. Evidently these are considerably “noisier” in appearance, but similar overall. The noisiness may be due to different binning strategies: I modeled velocity distributions for every spaxel that met a minimal signal to noise requirement.

mrk888_vel_maps_per_moi — Mrk 888 – stellar and gas velocity fields derived from non-parametric LOSVD models

For a more quantitative comparison here are scatterplots of the mean stellar and gas velocity offsets from my runs against the MaNGA DAP. Both of these show slight tilts, that is the slopes are less than one, and small offsets. I’m tempted to suspect systematic errors somewhere, but I haven’t found any yet. And I have no hypothesis about the decidedly non-random behavior of the gas velocity plots. So there are still questions to be answered, but the magnitude of differences is no more than about ±1 wavelength bin (69 km/sec). For my main purposes this is good enough.

mrk888_vel_comp — Mrk 888 – scatter plot of mean stellar and gas velocities – my models vs. DAP

One more pair of graphs for this galaxy. In the initial maximum likelihood fit to a spectrum I estimate stellar and gas velocity dispersions with a single component gaussian. The graphs below plot the velocity dispersions from the Bayesian models calculated by the formula above against the ML fit values. The stellar velocity dispersions behave pretty much the same as I noted before: there’s a weak positive correlation between the non-parametric models and the maximum likelihood estimates with the former generally offset to higher values. The gas velocity dispersion estimates show an even larger discrepancy along with an apparently nonlinear trend.

mrk_888_vdisp — Mrk 888 – velocity dispersions from Bayesian nonparametric losvd models vs. estimates from maximum likelihood fitting

More briefly now, since this got longer than I wanted. I also ran models for the “Zoogems” target NGC 810, which I noted two posts ago has interesting kinematics, in particular a rapidly rotating disk that isn’t evident in the stellar velocity map from the DAP. From the maps below we see that the rapidly rotating component is the ionized gas, which has a mean rotation velocity as much as 150 km/sec larger than the stellar component.

ngc_810_st_em — NGC 810 – mean stellar and gas velocity maps

ngc_810_em-st — NGC 810 – difference between mean gas and stellar velocities

Finally, here are two more graphs of the kinematic models for the spectra with the largest positive and negative differences between stellar and gas velocity (these are the reddest and bluest patches in the map above. These show the spectra in the top row, followed by the posterior distribution of the stellar LOSVD and distributions of the first two moments, then the same for the emission line profiles.

ngc810_175_spec_los — NGC 810 – sample MaNGA spectrum, non-parametric stellar LOSVD and emission line profile

ngc810_90_spec_los — NGC 810 – sample MaNGA spectrum, non-parametric stellar LOSVD and emission line profile

The second set of graphs illustrates a significant problem: when there’s poor data — in this case no detectable emission lines — the models tend to return the prior, which for now is a maximally diffuse dirichlet. This will bias second moment estimates to the high side and can lead to spurious correlations, for example the increase of velocity dispersion with radius that I noted in the last post on this topic.

At the same time this spectrum is in the region of overlap between the main galaxy and its companion, and two peaks in the stellar velocity distribution are clearly seen at about the right velocity offset (~340 km/sec).

A short to do list:

Investigate informative priors.
Allow for dust reddening. I tried using a modified Calzetti attenuation relation in the code (note its presence in the code below, although it’s not used in the model), but it adds too much to execution time. A low order multiplicative polynomial might suffice.
Investigate systematics between my estimates and those in the DAP.

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.

2022-01-152022-01-17

NGC 810 – interesting kinematics in a Zoogems and MaNGA target

The final release of SDSS MaNGA went public back in early December as promised, and I’ve spent the last month or so of my hobby time looking for manageable sized samples of interesting galaxies. One sample I looked at out of curiosity was the Zoogems target list, which is an HST gap filler imaging program with about 300 galaxies selected (mostly) by Galaxy Zoo volunteers. It turns out there are 11 targets with MaNGA data, of which 5 have been observed by HST so far.

As can be seen from the thumbnails above this is a pretty diverse lot, with several in progress mergers and merger remnants, some normal looking spirals at least two of which were from Masters’ red spirals sample, and 3 of Schawinski’s blue early type galaxies. Only one of those 3 has HST imaging so far (number 8 in the thumbnails above), although there are a surprising 24 blue ellipticals in the final MaNGA release out of 215 in Schawinski’s original sample.

Of the 5 Zoogems galaxies that have been observed so far the one that caught my eye as deserving an early look was number 3 in the top row, NGC 810, an apparent elliptical with an unusual dust lane that’s almost perfectly aligned with the minor axis. There are also hints of shells indicating a likely merger sometime in the past.

NGC 810 HST ACS, proposal id 15445, PI Keel

The MaNGA data, which is new in DR17, only covers the central part of the galaxy with the companion just photobombing the edge. A larger IFU would have been nice for this observation, but the data quality is better than average in terms of nominal signal to noise. I was able to use all 183 fiber/position combinations in the RSS file without binning.

NGC 810 – plateifu 9514-6101 – MaNGA IFU footprint

The first step in the analysis process after loading the data is to estimate redshift offsets from the system redshift for each spectrum, and from that it’s straightforward to calculate a velocity field, which in this galaxy looks like¹:

NGC 810 (plateifu 9514-6101) – losvd estimated from cube

It turns out the redshift assigned to this system was that of the companion galaxy, which was the only SDSS spectroscopic target in the immediate vicinity and is evidently blueshifted by ~350 km/sec from the target. What’s more interesting though² is the apparent rapidly rotating disk that’s aligned with and somewhat thicker than the dust lane. There may also be overall prolate rotation outside the disk although the presence of the companion makes it hard to tell based solely on visual inspection. In hopes of separating out multiple velocity components I returned to the non-parametric line of sight velocity distribution models that I wrote some posts about last year. Unlike the ones I practiced on previously this galaxy has non-negligible amounts of emission, at least in the central region, so I just temporarily masked the regions around the emission lines that I fit. That results in a pure stellar velocity distribution. The results were a bit surprising:

NGC 810 (plateifu 9514-6101) (L) velocity field estimated from RSS file (M) stellar velocity offsets (R) net stellar velocity

In the left pane above is the velocity field from the RSS data, with the system redshift adjusted to the IFU center. For the LOSVD models I set the adopted redshift of each spectrum to the system redshift plus the offset calculated previously. Now I had hoped to be able to cleanly separate the contribution of the companion from that of the main galaxy, which so far I haven’t been able to do. But what I did find that was unexpected is that the average stellar velocities in the disk partially offset the original measurements (middle pane), so the net stellar velocity field shows a much more slowly rotating stellar disk.

As I’ve written before I use a set of 15 eigenspectra from a principal components analysis of some tens of thousands of SDSS spectra that I performed some years ago for redshift offset estimation. Those galaxies were of all types and include systems both with and without significant emission. The redshift estimation routine just does straightforward template matching and returns a single value for the best fitting offset. Since the templates encode information about both emission and absorption lines that estimate could be most applicable to the ionized gas, stars, or some combination. In this case it’s possible emission lines were driving the original fits, implying the gas and stars in the disk are kinematically decoupled. I have not verified that though.

Another issue I noticed is that the stellar velocity field from the official MaNGA Data Analysis Pipeline looks rather different from mine, with barely a hint of a kinematically distinct disk. This wasn’t really evident in the Marvin webpage, which makes some really unfortunate choices for color palettes. So here is the same data rendered with a more nearly perceptually uniform rainbow palette³:

NGC 810 (plateifu 9514-6101) Stellar velocity field per MaNGA DAP

I decided to re-run my LOSVD modeling code on the RSS data, this time setting the redshift offset to 0 for each fiber, so this is now measuring velocities relative to the overall system velocity. I also used a larger convolution kernel (25 vs. 21 in the first set of runs). The map of the average velocity offsets is:

NGC 810 (plateifu 9514-6101) Stellar losvd from nonparametric model

Although not a perfect match this is somewhat closer to the DAP map. I suspect what’s happening here is that there really are at least two, and more likely 3 distinct kinematic components. I haven’t read the DAP release paper in a while and don’t know exactly how they estimate stellar velocities, but in any case their model just returns a single value for visualization purposes. To see the (possible) complexity of the actual data here are the results for a single fiber with the largest positive velocity offset in the map above. Again, I don’t know how much of the structure in the posterior distribution of the convolution kernel is real, but it’s evident there’s more complexity than is captured in the first two moments shown in the middle and right panes.

Besides the kinematic modeling I did run star formation history models on the full RSS data set using the same tools as in recent posts. I’m not going to discuss them in detail, but some summary maps are worth showing. In the graphic below these are, from top left, stellar mass density, Hα luminosity density uncorrected for attenuation, SFR density (as usual 100Myr average), and stellar dust attenuation.

There’s no sign of a disk in the stellar mass map, which faithfully follows the distribution of light. A disk is visible in Hα and the small amount of recent starformation is also confined to a disk and nuclear region. In the fourth pane I show the modeled stellar dust attenuation, mostly just to demonstrate that this component of the model does capture something of reality.

ngc810_model_summaries — NGC 810 – a sampling of quantities derived from star formation history models

Getting back briefly to the first paragraph, there are 8 post-starburst galaxies from the catalog of Melnick and de Propris and 24 from an ancillary program to observe post-starburst galaxies from various sources that was added for DR17. There’s just one galaxy from the former catalog in the latter set, so that makes 31 total, an easily manageable number. There are also 24 of Schawinski’s blue ellipticals. Of course there are many disk galaxies, far too many for me to look at.

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.