The circum-galactic medium is under intense scrutiny recently because of the role it is suspected to play in sustaining star-formation and the metal (anything heavier than Helium) content of galaxies.
There are a bunch of different approaches to the question of the balance within a galaxy: one can map various species in the galaxy using spectroscopy, one can look for absorption features in ultraviolet spectra (see Tumlinson+ 2017) and one can look for gas directly. The absorption route is popular in the US, the direct observation of cold gas is more of a European/South African thing. It depends on which telescope you have most likely access to.
This paper uses a simulation — TNG50 — to estimate how to stack the 21cm HI atomic hydrogen line signal around nearby galaxies. There is an extensive argument on how to account for any motion before stacking. Here is the map of where one can expect HI (in TNG50 again):
The 21cm HI atomic hydrogen map of two TNG galaxies.
And this is the motion of that gas in the same two galaxies:
The velocity map of the two TNG galaxies. Both are rotating and given the side of the galaxy it is on, that rotation is a reasonable prior for any stacking of the small stuff on the outskirts which is probably not directly detected in observations.
So the models show how one could stack using the motion of the galaxy itself as a prior: there is a receding and approaching side and one can extrapolate how much redshift correction one should apply before stacking the spectra into a single spectrum.
A TNG galaxy with identified areas for stacking. A stacked spectrum. One can classify stacked spectra as either visually visible in the cube or not even visible as a signal in the cube, only in the stacked spectrum.
So equipped with this approach, the authors now stack where practical in the 18 MHONGOOSE galaxies already observed for this survey. This is not the full sample but it is about half, a fair number.
This is the stacked spectrum detections and their properties. Both visible and non-visible stacks work out fine. The issue is that there are not as many easily stacked images. There is not that much signal outside these disks!
The line properties of stacked areas either visually identified in the cube or stacked without a hint in the cube.
This is bad news for deep HI surveys. The hope was that the cold gas refueling nearby galaxies could be mapped with 21cm and further examined. As it stands, deeper observations will not reveal that much more 21cm line signal in ever more diffuse disks; at those column densities, the gas is ionized.
This remains to be seen but I think it makes a good case that just hammering away blindly to get deeper 21cm obervations on single galaxies is maybe not the best approach. Spreading the observing time around a sample is a much better use of the time. It really validates the MHONGOOSE choice of depth and trade-off between depth and sample size.
More to come of course. How much gas galaxies are receiving from their IGM remains a big unknown.
If there is one galaxy story from JWST that has caught the news, it’s all the big galaxies at high redshift, forcing a rethink on how galaxies form in the very early Universe. If there a second story from JWST, it is how many galaxies have lots and lots of dust in them apparently in the epochs right after. This was not completely a surprise, the Madau plot already had the option that the lead up to cosmic noon (z=1–2), a lot of the star-formation is hidden by dust. But with JWST, we are really getting a handle on how much of it is hidden.
For example, dust is apparent in early galaxies. This is the point on D. Burgarella’s recent paper [astroph]. Or this recent paper by Tarasse [astroph]. And this week a paper came out on the hidden star-formation in galaxies at 2.5<z<3.5. Cheng et al [astroph]:
Unveiling the Dark Side of UV/Optical Bright Galaxies: Optically Thick Dust Absorption
This uses the CEERS observations of galaxies at this redshift to explore how many are dust-dominated and hiding star-formation.
Quick primer: what does a certain amount of reddening look like in the JWST filters of CEERS at z=3? Note that there is an optically thick regime.
One can select objects that are candidate/not-a-candidate based on their SEDs and compare against the color-color diagram (the original way to select different galaxy populations) and the stellar-mass vs star-formation plot (with the *shudder* “main sequence”)
The candidate and not-a-candidate optically dark galaxies with rest-fram optical colors and the stellar mass and star-formation plot with the MS, the “star-forming galaxy main sequence”, a poor choice or term but we’re stuck with it now.
So how do the authors select candidates, by identifying galaxies with an excess in the longerst wavelength filter (F444W) compared to the SED. Below is an example SED with the MIRI contribution highlighted.
The interesting point here is the F444W just short of that. If there is more flux than expected, that is an indication there is radiation being reprocessed by dust inside this galaxy.
An example SED of a z=2.5 galaxy in CEERSOne of the sanity checks is to see if we are not looking at a disk edge-on. Easy to select for opaque disks in that case.
Now the results show that there is a sizeable fraction of star-formation that is obscured, not just dimmed, by star-formation at z>2. This is something we see in the local Universe, some 40% of disks is not transparent.
You can see that as well in the slope of the blue colors, beta.
And just to remind myself which way beta goes again, here is the slopes for aging (therefore redder) populations from Qin+ 2022 [astroph]: negative means bluer. positive means redder. The obscured structures redden on average. This is the age/reddening degeneracy that plagues SED fitting.
Two examples from Qin+ 2022. Either a burst of star-formation ages ago or constant star-formation over the last 500 million years. As populations age, their blue/ultraviolet slope \beta becomes redder (positive).
Long story short: we see more and more obscured star-formation in galaxies; either in dust-obscured dwarfs galaxies, or part of galaxies (this paper), or even completely optically thick “dark galaxies” not detected with Hubble but now observed with JWST. The early Universe is a lot more dusty place than previously thought. Should be interesting to look at in the coming years.
Okay I have been tentatively dipping my curiosity into machine learning. I’m coming from a bit behind the curve because well… it’s got a learning curve and I have a day job. But I’ve been steady picking things up. But mostly, I have been noting where there are opportunities to check Machine Learning’s work. Maybe later a deeper survey came along and we can check ML predictions using traditional astronomy techniques we understand much better.
And that is where I have been putting some of my recent research. A little while ago I checked the XSAGA catalog, a prediction for objects that are below z<0.03 against the deeper GAMA spectroscopic sample. The overlap is much smaller than the XSAGA sample but it gives us a direct measure as to how well this ML technique did (well done John Wu, it was spot on where you predicted the effectiveness was). More about it in this paper in MNRAS (or astroph here).
So that was fun. But I wanted to talk about a second paper where I did a check of a ML prediction against more traditional astronomy. In this case a Galaxy Zoo comparison.
The Galaxy Zoo Catalogs for the Galaxy And Mass Assembly (GAMA) Survey
This started as a much more modest idea: we have two Galaxy Zoo catalogs on the equatorial GAMA fields, let’s compare the voting and make these catalogs public so they can be used by students. My main motivation was to generate something that could be used as a reference for students using the GAMA catalogs and make them easy to use by adding CATAID column to them.
The three GAMA fields that seemed to be included in DESI, for which there is a GZ catalog from Walmsley+ (2023).
All well and good and I started comparing voting fractions across both the catalog originally made for the GAMA collaboration, using KiDS imaging and voting from the Galaxy Zoo citizen scientists.
But on closer inspection, the Walmsley+ (2023, astroph) catalog only includes voting fractions! And those are from the ZooBot machine learning algorithm that is trained on early voting and then helps with predictions for the rest of the survey. This is a necessary step as voting on the full survey would take too long. So instead of an A/B test of surveys (KiDS imaging vs DESI), it also became a comparison of Galaxy Zoo voting from people and voting according to people+ZooBot!
Fortunately, the questions had remained the same between the KiDS Galaxy Zoo effort and the DESI Galaxy Zoo (+ZooBot) effort. Well mostly. The question tree looked like this:
The Galaxy Zoo question tree. All volunteers start at the top with Question T00.
The main difference is in the very first question. The DESI voting (and the ZooBot trained on those) seem to vote more for smooth galaxies, than one with features. Why would that be?
Galaxy Zoo voting for KiDS (x-axis) and the fraction in favor of ``smooth galaxy’’ in the DESI survey, including ZooBot predictions.
The difference is depth: the DESI survey is much shallower than KiDS, by design, to get more of the sky. But that means dim features surrounding a bulge, e.g. a disk with spiral arms, will not be as readily visible.
But once DESI Galaxy Zoo (+ ZooBot) can detect a galaxy with features, the voting agrees with each other! How tightly wound are the spiral arms?
Or how many are there?
Sure there is some variance. That was to be expected. But as long as the volunteers (and ZooBot!) identify features, the follow-up questions agree well enough.
That brought me to the next question, can you use the voting predictions from ZooBot for the shallower DESI data to get a similar result. This was of interest to me since I have had a few undergraduate students work on the KiDS Galaxy Zoo catalog with some intresting results:
The Loneliest Galaxies in the Universe: A GAMA and GalaxyZoo Study on Void Galaxy Morphology
And that last one seemed like a good cross-check. Can we get Ren’s result but using the DESI Galaxy Zoo voting fractions?
Doing the same but the number of spiral arms from the DESI ZooBot catalog from Holwerda+ 2024. The distributions of stellar mass for a given number of spiral arms in the KiDS Galaxy Zoo voting from Porter-Temple+ (2022).
And yes. The results are qualitatively the same. The statistics are a little worse because the DESI does not have as much voting on number of spiral arms as the deeper KiDS (because of the first question difference). But there is a pretty clear rise in stellar mass with the number of spiral arms. And the main conclusion, that the specific star-formation goes down is also recovered: the specific star-formation drops slightly with the number of arms.
The specific star-formation of galaxies with 1,2,3, and 4 spiral arms.
For the GAMA fields, this may not be relevant since the statistics in Porter-Temple (2022) were much better. BUT you can redo the experiment perhaps with DESI at scale.
Checking ML work remains critical in my opinion. You can’t just shrug and accept black box results. If there are opportunities to cross-check with a different data-set, I think that is perhaps unexciting but critical science.
