MaNGA – Page 3 – Extragalactic

2021-08-23

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

Back in July a paper by Millan-Irigoyen et al. titled “HY-PYPOPSTAR: high wavelength-resolution stellar populations evolutionary synthesis model” was posted to arxiv, and shortly thereafter data in the form of the promised high resolution spectra were made available at https://www.fractal-es.com/PopStar/#ingredients. Unlike MILES and its variations or BC03 this is a purely theoretical library, with the spectra calculated from model atmospheres instead of using empirical spectra of actual stars.

I looked briefly at one other theoretical library some time ago and concluded (IIRC) that the model spectra had much too blue continua at all ages, making it unsuitable for full spectrum fitting. A brief visual inspection of this library (as well as Figure 8 in the paper) indicates that’s not the case here. One thing that may compromise its usefulness is that although there are 106 age bins in the models they are very irregularly spaced and heavily weighted towards younger ages as shown below.

Age range	Number of spectra
5 ≤ log T < 6	4
6 ≤ log T < 7	34
7 ≤ log T < 8	35
8 ≤ log T < 9	9
9 ≤ log T < 10	15
log T ≥ 10	9

Number of SSP model spectra by age range in HR-pypopstar

At least in the wavelength range of SDSS/MaNGA spectra there is little evolution in spectroscopic properties between 10⁵ and 10⁶ years and even though it speeds up afterwards the effective time resolution of SFH models is still lower than the supplied number of time bins for the next two decades.

pypop_young_spec — Sample young population spectra from hrPypopstar

For a preliminary look at the library’s suitability for full spectrum modeling I selected a 42 time bin subset with all 4 available metallicity bins and Kroupa IMF, truncating the wavelength range to 3400-9000 Å, which is just a little larger than the Emiles subset I use. The time bins were chosen by hand — I was trying to get evenly spaced bins in log time but this proved not to be feasible. The authors produced two sets of libraries for each of 4 IMFs: they did an apparently careful job of counting the number of ionizing photons for several species and calculated sets of SSP models with and without emission continuum. For these trial runs I used both sets of libraries, which I’ll compare below. No code modifications were required because they use the same peculiar but computationally convenient flux units for spectra.

I just ran a few models for the central fiber spectrum of KUG 0859+406 (MaNGA plateifu 8440-6104). First, here is the star formation rate history compared to the most recent Emiles run:

sfh_emiles_popstar — Model star formation histories for central fiber of MaNGA plateifu 8440-6104
(T) Emiles
(M) hrPypopstar with emission continuum (
B) hrPypopstar stellar light only

Or, looking at the model mass growth histories:

mgh_emiles_popstar — Model mass growth histories for central fiber of MaNGA plateifu 8440-6104 Red: Emiles Blue: hrpypopstar including emission continuum Green: hrpypostar stellar light only

The starburst occurs later and is somewhat weaker in the pypopstar models. Interestingly all models have a late time revival of star formation after a period of quiescence. To get all the graphs to line up I truncated the pypopstar model star formation histories at 10 Myr. Here are the full histories:

sfh_popstar_popstarst — Model star formation histories for central fiber of MaNGA plateifu 8440-6104 (T) hrPypopstar with emission continuum (B) hrPypopstar stellar light only

Emission continuum is significant mostly at ages < 10Myr and this is reflected in some difference in late time model star formation histories. This has little effect on other modeled quantities.

At a glance fits to the galaxy flux data look very similar. Both sets of models have problems in some wavelength ranges and both have some issues with the [N II]+Hα emission complex, probably because the lines don’t quite have gaussian profiles. In terms of summed log-likelihood the Emiles fit is actually almost a factor of 2 better than pypopstar.

ppfits_compared — Comparison of model fits to data (L) Emiles (R) Hrpypopstar

The pypopstar models have larger optical depths of attenuation and steeper attenuation curves than the Emiles models, demonstrating once again the interplay among attenuation, attenuation relationship, and stellar ages:

tauv_delta_emiles_pypostar — Model distributions of attenuation parameters τ_V and δ for runs with Emiles library and hrPypopstar on the central fiber of MaNGA plateifu 8440-6104

Some other modeled quantities are very similar, for example the stellar mass density:

sigma_mstar_comp — Comparison of model stellar mass density red: Emiles blue: hrpypopstar with emission continuum

While the modeled specific star formation rate differs by ~0.4 dex thanks to the more recent starburst in the pypopstar models:

ssfr_comp — Comparison of model specific star rate (sSFR) red: Emiles blue: hrpypopstar with emission continuum

I still haven’t decided exactly what to do with these interesting SSP model libraries. I will probably take a more systematic look at extracting a subset of time bins that evolve at a consistent rate by some measure. This will certainly require many fewer than the published 106 bins. What may be more promising is to graft some young age SSPs onto my existing Emiles library. The 4 published metallicity bins are pretty closely matched to the Emiles subset I use, and 4 or 5 SSP’s would fill out the ages up to the youngest (30 Myr) in the BaSTI isochrones. I already use unevolved BC03 models for this purpose. Using the models that include continuum emission would also solve the problem of how to model that in starforming galaxies (but not galaxies with strong AGN emission unfortunately).

2021-08-102021-08-10

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

I did have my old data and model runs of course, in fact they were spread over several directories on two machines. I’m going to refer to it by this catalog designation, KUG standing for the “Kiso survey of Ultraviolet-excess Galaxies.” It’s also a low power radio source with catalog entries in both FIRST and NVSS, and of course it’s in MaNGA with plateifu ID 8440-6104 (mangaid 01-216976).

In my 2018 model runs, which were interesting enough to write 3 posts about, I found this galaxy had undergone an extraordinarily large burst of star formation that began ~1 Gyr ago with locally as much as 60% of the present day stellar mass born in the burst and something like 40% of the mass over the footprint of the IFU. In this years model runs the peak burst fraction was a considerably more modest ~25% and globally barely amounted to a slight enhancement of star formation. The starburst was also much more localized than in the earlier runs:

burst_strengths_compared — Fractional stellar mass in stars between 0.1 and 1.75 Gyr old in 2018 and 2021 model runs

So what happened? First, here is a comparison of modeled star formation histories for the innermost fiber, which got the largest injection of mass in the starburst.

central_sfh_compared — Model star formation histories for central fiber of MaNGA plateifu 8440-6104, 2018 and 2021 model runs

The obvious remark is the double peaked starburst noted back in 2018 (and discussed at some length) has been replaced with a single narrow peak with a slow ramp up and fast decay. The peak SFR is a little larger than before but the total mass in the burst is lower.

I’ve made several changes in model formulation since 2018, of which the most important in the current context is adopting the more flexible “modified Calzetti” attenuation relation that adds an additional slope parameter to the prescription. In the current year model runs a steeper than Calzetti relation is favored throughout the IFU footprint, particularly in the central region where the starburst was strongest:

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

A smaller optical depth of attenuation is also favored throughout:

tauv_compared — Modeled optical depth of attenuation – 2021 runs vs. 2018 MaNGA plateifu 8440-6104

This has a couple predictable consequences. Steeper attenuation will favor an intrinsically bluer, hence younger population while a lower optical depth requires less light, and hence mass in the stellar population. I can test this directly by returning to a model with Calzetti attenuation, and here is the result for the central fiber (this model run is labeled 2021 (c) in the legend below):

mgh_compared — Mass growth histories – 2021 run 2021 run with Calzetti attenuation 2018 run Central fiber of MaNGA plateifu 8440-6104

So, an eyeball analysis suggests about 3/4 of the difference between the 2018 and 2021 runs is due to the modification to the attenuation relation. The other changes I’ve made to the models are to change the stellar contribution parameters from a non-negative vector to a simplex, and at the same time changing the way I rescale the data. In early runs the SSP model fluxes were scaled to make the maximum stellar contribution ≈ 1, while the current models scale both the galaxy and SSP fluxes to ≈ 1 in the neighborhood of V, making the individual stellar contributions approximately the fraction of light contributed. An additional scale factor parameter in the model is used to adjust the overall fit. Assuming I did this right this should have no effect on a deterministic maximum likelihood solution, but with MCMC who knows?

Although the fit to the data looks about the same between the model with and without the attenuation modification the summed log-likelihood is consistently about 1% higher for the modified Calzetti model with no overlap at all in the distribution of likelihood. This suggests the case for a steeper than Calzetti attenuation is a fairly robust result.

ppfits — “Posterior predictive” fits to galaxy flux data – modified Calzetti attenuation vs. Calzetti – central fiber of MaNGA plateifu 8440-6104

The galaxy flux data also changed a little bit. The early runs were on the DR14 release (version 2_1_2 of the MaNGA DRP) while the recent ones used the DR15 release (ver 2_4_3). Most of the calibration differences resemble random noise, but there is some curvature that systematically affects both the red and blue ends of the spectrum and could cause some change in the temperature distribution of the models:

measured_flux_compared — Difference in measured flux from DR14 to DR15 – central fiber of MaNGA plateifu 8440-6104

While the detailed star formation histories changed, quantities that aren’t too model dependent didn’t very much. One example is shown below. Also, the kinematic maps agree with the earlier ones in detail.

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

velocity_field_8440_6104 — Velocity field of MaNGA plateifu 8440-6104 from 2021 model runs. Map interpolated from RSS file spectra.

One input that hasn’t changed are the emiles SSP model spectra, although there have been some procedural changes in how I handle the modeling. Early on I often used a much smaller subset of SSP models with just 27 time bins and 3 metallicities for preliminary modeling, including my first models on the same binning of these data. I also routinely ran 250 warmup iterations with just 250 more per chain. My current standard practice is always to use the largest emiles subset with 216 SSP models in 54 time bins and 4 metallicities, and I generally run 750 post-warmup iterations per chain but still with only 250 warmup iterations. This is generally enough and if adaptation fails it is usually fairly obvious. The small sample size of the earlier runs mostly effects the precision of inferences rather than means.

To conclude for now, my speculation about whether it might be possible to say something about the timing of critical events in a merger from the model star formation history was too optimistic. On a positive note though both sets of model runs retrodict that coalescence occurred at a lookback time around 500Myr ago, which is consistent with the fact that tidal tails and other merger signatures are clearly visible even in SDSS imaging. Both sets of model runs also have that odd uptick in star formation at 30Myr in the central fiber. And while the difference in burst mass contributions is a little disconcerting the current runs are more consistent with the likely gas content of ordinary spiral galaxies.

This example illustrates another well known “degeneracy” among attenuation (and adopted attenuation relation), mass, and stellar age. Whether I’ve broken the degeneracy by adopting the more flexible attenuation prescription described some time ago remains to be validated.

2021-07-31

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

This paper (arxiv ID 2103.16070) is pretty old by now, having been posted on arxiv back in early April. The basic premise of the work is mildly interesting: the author searched MaNGA for galaxies that would meet conventional criteria for post-starburst (aka K+A etc.) spectra if observed at a redshift high enough that the entire galaxy would be covered by a single fiber like the original SDSS spectroscopes. Somewhat surprisingly, he found just 9 that met his selection criteria in the DR15 sample of ~4500 galaxies.

I have to say the paper itself is forgettable, but a manageably sized sample of MaNGA data that’s complete by some criterion is worth a look, and I have a long-standing interest in post-starburst galaxies in particular. So, I ran my current SFH modeling code on all 9 — by the way this was completed some time ago. It’s just taken me a while to get around to generating some graphics and sitting down to write.

The author only measured a few observable quantities: Hδ equivalent width and the 4000Å break index D_n(4000), along with Hα emission equivalent width and (normalized) fluxes. I long ago validated my own absorption line measurements of SDSS single fiber spectra against the MPA-JHU measurement pipeline, which was the gold standard for several years (but last run on DR8). My measurements and uncertainty estimates are in excellent agreement with theirs, so I have a fair amount of confidence in them. Emission line fluxes also agree with published measurements with considerably more scatter. My emission line equivalent widths on the other hand are completely unchecked. So, one of my tasks was to compare my equivalent width measurements with Wu’s. I did not attempt to exactly reproduce his work – I binned spatially using my usual Voronoi partitioning approach whereas Wu binned in elliptical annuli. With that difference in mind the next two plots should be compared to his Figures 4 and 5.

The first two graphs show the radial trends (relative to the effective radii per the NASA/SLOAN catalog) in the Lick Hδ_A and D_n(4000) indexes. These both show very similar trends to Wu’s measurements although with more scatter. This is expected because fewer spectra go into each point in general — from the text it appears Wu binned several separate measurements for each displayed point. Also, I made no attempt to deproject distances. One feature of the Hδ versus radius plot that’s a little different is the trend generally flattens out beyond ∼1 effective radius, while Wu shows a roughly linear trend out to 1.5 R_e. This might just be a visible effect of me displaying the trends out to larger radii.

d4000_hdelta_re — Radial trends of Lick Hδ_A and D_n4000 for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070

The Hα emission line measurements are similarly in broad agreement. Like Wu, I find that there are two distinct trends in emission: either moderately strong centrally with a rapid decline or weak throughout with a relatively flat trend. One galaxy (with MaNGA plateifu 9876-12701) has no detectable emission. I haven’t looked in detail at emission line ratios to compare to Wu’s Figure 7, but there’s general agreement that some residual star formation is present in some of the sample and weak AGN or ionization by hot evolved stars in others.

Radial trends of Lick Hα equivalent width and luminosity density for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070

A fairly common failing of this literature (IMO) is the use of proxies for recent star formation but not attempting actually to model star formation histories. There are plenty of publicly available tools for that available now, so there’s really no reason not to perform such modeling exercises. Wu did do some toy evolutionary modeling and posted a graph of trajectories through the Hα emission – Hδ absorption plane, which can scarcely unambiguously constrain star formation histories. Of course much of my hobby time is spent generating fine grained model star formation histories, so let’s take a look at a few selected results.

First, here are maps of the modeled fraction of the current stellar mass in stars of ages between 0.1 and 1 Gyr, very roughly the age range that produces a post-starbursty spectrum. Six of the galaxies have more or less strongly centrally concentrated intermediate age populations, which is generally what’s expected especially in the major merger pathway to a post-starburst interval. I’ll discuss this a little further below.

burst_fraction_maps — Maps of fractional stellar mass in intermediate age populations for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070

In more detail here are summed mass growth histories for the sample, that is all modeled star formation histories for a given observation are summed to produce a single global estimate. I’ve shown total masses here. Because of the pointing strategy MaNGA uses the fiber positions overlap to produce a 100% filling factor, so simply summing overestimates masses by about 0.2dex according to a calculation I performed some time ago. The present day masses in the plot below actually agree pretty well with the values listed in Table 1 of the paper, with an average difference of ~0.1 dex (this is probably because at least some of the light falls outside the IFU footprint in most of these galaxies, offsetting some of the overcounted mass).

total_mgh — Integrated mass growth histories for 9 MaNGA “post-starburst” galaxies from Wu (2021) – arxiv 2103.16070

Somewhat surprisingly several of these galaxies show little evidence of an actual burst of star formation in the recent past, at least at the global level. Some of these could simply have had star formation truncated recently, which can produce a poststarburst spectral signature for a time. Overall intermediate age stars contribute ~ 6-20% of the present day stellar mass, with the two largest contributions in the low mass galaxies in the bottom row of the plot.

There are some other oddities in this small sample. At least 3 galaxies are dwarf ellipticals or perhaps dwarf irregulars (in the case of plateifu 9876-12701), and two others have stellar masses under ~5 x 10⁹ M_⊙. Two of the low mass galaxies are in or near the Coma cluster, which suggests environmental effects as the probable cause of quenching. Another possible issue with the low mass galaxies is the infamous “age-metallicity degeneracy,” which refers to the fact that old, low metallicity populations “look like” younger, more metal rich ones by many measures. The Balmer lines in particular fade more slowly with age in lower metallicity populations, and the 4000Å break also becomes metallicity sensitive (smaller at low metallicities) at older ages.

There is only one clear merger remnant in the sample (with plateifu 8440-6104, which I will get to in a moment). One other galaxy (plateifu 8458-6102) is located in a compact group that appears (in Legacy survey imaging) to be embedded in a cloud of extragalactic light. Finally, two galaxies in this sample have been cataloged as K+A based on SDSS spectra — 8080-3702 and 9494-3701, while two others in the catalog of Melnick and dePropris (2013) are not.

The one clear merger remnant in the sample is an old friend of mine, and in fact I wrote three lengthy posts about this one back in 2018. In perusing those posts I noticed that the current set of model runs have a slightly weaker and more recent burst than the earlier runs. Also a double peak in the earlier runs has gone away in these, which means my early speculation that it might be possible to time crucial events in a merger from the detailed SFH model was too optimistic. On the other hand the model burst strength in the earlier runs was uncomfortably large, indicating an exceptionally gas rich merger and efficient processing of gas into stars. The current runs have a more reasonable ~10% of mass in the burst. So, I will look into those earlier runs and try to figure out what changed. Fortunately I’m a data hoarder and R is self-archiving to some extent.

kug0839+406 — KUG 0839+406, one of 9 “post-starburst” galaxies in Wu (2021)

The idea of looking at the integrated properties of IFU data to pick a post-starburst sample seems reasonable, but this sample appears to me to be both incomplete and possibly with some false positives. When DR17 is finally released I plan to try to develop my own criteria. As I’ve already shown using SDSS spectra alone to select a sample is doomed to produce lots of false positives.

I should finally mention one other paper pursuing a similar idea by Greene et al. (2021) showed up on arxiv recently. The authors lost me when they used the phrase “carefully curated” in their introduction, which was otherwise pretty well written up to that point. Maybe I’ll take another look anyway.

2020-12-272020-12-28

Using Galaxy Zoo classifications to select MaNGA samples

A while back I came across a paper by Fraser-McKelvie et al. (2020, arxiv id 2009.07859) that used Galaxy Zoo classifications to select a sample of barred spiral galaxies with MaNGA observations. This was a followup to a paper by Peterken et al. (2020, arxiv id 2005.03012) that also used Galaxy Zoo classifications to select a parent sample of spiral galaxies (barred and otherwise). There’s nothing new about using GZ classifications for sample selection of course, although these papers are somewhat notable for going farther down the decision tree than usual. What was new to me though when I decided to get my own samples is the SDSS CasJobs database now has a table named mangaGalaxyZoo containing GZ classifications for (I guess) all MaNGA galaxies. The classifications come from the Galaxy Zoo 2 database supplemented with some followup campaigns to fill in the gaps in GZ2. Besides greater completeness than the zoo2* database tables that can also be queried in CasJobs this table contains the newer vote fraction debiasing procedure described in Hart et al. (2016). It’s also much faster to query because it’s indexed on mangaid. When I put together the sample of MaNGA disk galaxies that I’ve posted about several times I took a somewhat indirect approach of looking for SDSS spectroscopic objects close to IFU centers and joining those with classifications in the zoo2MainSpecz table. The query I wrote took about 3 1/2 hours to execute, whereas the ones shown below required no more than a second.

Pasted below are the complete SQL queries, and below the fold are lists of the positions and plateifu IDs of the samples suitable for copying and pasting into the SDSS image list tool. These queries run in the DR16 “context” produced 287 and 272 hits respectively, with 285 unique galaxies in the barred sample and 263 uniques in the non-barred. These numbers are a little different than in the two papers referenced at the top. Fraser-McKelvie ended up with 245 galaxies in their barred sample — most of the difference appears to be due to me selecting from both the primary and secondary MaNGA samples, while they only used the “Primary+” sample (which presumably include the primary and “color enhanced” subsamples). I also did not make any exclusions based on the drp3qual value although I did record it. The total sample size of 548 galaxies is considerably smaller than the parent sample from Peterken, which was either 795 or 798 depending on which paper you consult. The main reason for that is probably that Peterken’s parent sample includes all bar classifications while I excluded galaxies with debiased f_bar levels > 0.2 in my bar-less sample. My barred fraction of around 52% is closer to guesstimates in the literature.

Both samples contain at least a few false positives, as is usual, but there are only one or two gross misclassifications. One that was especially obvious in the barred sample was this early type galaxy, which clearly has neither a bar or spiral structure and at least qualitatively has a brightness profile more characteristic of an elliptical. Oddly, the zoo2MainSpecZ entry for this object has a completely different set of classifications — the debiased vote fraction for “smooth” was 84%, so most volunteers agreed with me. This suggests maybe a misidentification in the mangaGalaxyZoo data.

Besides this really obvious case I found a few with apparent inner rings or lenses, and a few galaxies in both samples appear to me to be lenticulars with no clear spiral structure. The first of the two below again has a completely different set of classifications in zoo2MainSpecZ than in the MaNGA table.

Although I didn’t venture to count them a fair number of galaxies in the non-barred sample do appear to have short and varyingly obvious bars. Of course the query didn’t exclude objects with some bar votes — presumably higher purity could be achieved by lowering the threshold for exclusion. And again, there are a few lenticulars in the spiral sample. As my sadly departed friend Jean Tate often commented the galaxy zoo decision tree doesn’t lend itself very well to identifying lenticulars.

Unfortunately I have nothing useful to say about Fraser-Mckelvie’s main research topic, which was to decide if, and perhaps why, barred spirals have lower star formation rates than otherwise similar non-barred ones. 500+ galaxies are far more than I can analyze with my computing resources. Perhaps a really high purity sample would be manageable. I may post an individual example or two anyway. The MaNGA view of grand design spirals in particular can be quite striking.

select into gzbars
  m.mangaid,
  m.plateifu,
  m.plate,
  m.objra,
  m.objdec,
  m.ifura,
  m.ifudec,
  m.mngtarg1,
  m.drp3qual,
  m.nsa_z,
  m.nsa_zdist,
  m.nsa_elpetro_mass,
  m.nsa_elpetro_phi,
  m.nsa_elpetro_ba,
  m.nsa_elpetro_th50_r,
  m.nsa_sersic_n,
  gz.survey,
  gz.t01_smooth_or_features_count as count_features,
  gz.t01_smooth_or_features_a02_features_or_disk_debiased as f_disk,
  gz.t03_bar_count as count_bar,
  gz.t03_bar_a06_bar_debiased as f_bar,
  gz.t04_spiral_count as count_spiral,
  gz.t04_spiral_a08_spiral_debiased as f_spiral,
  gz.t06_odd_count as count_odd,
  gz.t06_odd_a15_no_debiased as f_notodd
from mangaDrpAll m
join mangaGalaxyZoo gz on gz.mangaid = m.mangaid
where
  m.mngtarg2=0 and
  gz.t04_spiral_count >= 20 and
  gz.t03_bar_count >= 20 and
  gz.t01_smooth_or_features_a02_features_or_disk_debiased > 0.43 and
  gz.t03_bar_a06_bar_debiased >= 0.5 and
  gz.t04_spiral_a08_spiral_debiased > 0.8 and
  gz.t06_odd_a15_no_debiased > 0.5 and
  m.nsa_elpetro_ba >= 0.5 and
  m.mngtarg1 >= 1024 and
  m.mngtarg1 < 8192
order by m.plateifu

select into gzspirals
  m.mangaid,
  m.plateifu,
  m.plate,
  m.objra,
  m.objdec,
  m.ifura,
  m.ifudec,
  m.mngtarg1,
  m.drp3qual,
  m.nsa_z,
  m.nsa_zdist,
  m.nsa_elpetro_mass,
  m.nsa_elpetro_phi,
  m.nsa_elpetro_ba,
  m.nsa_elpetro_th50_r,
  m.nsa_sersic_n,
  gz.survey,
  gz.t01_smooth_or_features_count as count_features,
  gz.t01_smooth_or_features_a02_features_or_disk_debiased as f_disk,
  gz.t03_bar_count as count_bar,
  gz.t03_bar_a06_bar_debiased as f_bar,
  gz.t04_spiral_count as count_spiral,
  gz.t04_spiral_a08_spiral_debiased as f_spiral,
  gz.t06_odd_count as count_odd,
  gz.t06_odd_a15_no_debiased as f_notodd
from mangaDrpAll m
join mangaGalaxyZoo gz on gz.mangaid = m.mangaid
where
  m.mngtarg2=0 and
  gz.t04_spiral_count >= 20 and
  gz.t03_bar_count >= 20 and
  gz.t01_smooth_or_features_a02_features_or_disk_debiased > 0.43 and
  gz.t03_bar_a06_bar_debiased <= 0.2 and
  gz.t04_spiral_a08_spiral_debiased > 0.8 and
  gz.t06_odd_a15_no_debiased > 0.5 and
  m.nsa_elpetro_ba >= 0.5 and
  m.mngtarg1 >= 1024 and
  m.mngtarg1 < 8192
order by m.plateifu