young stars in SDSS-V

Over the last two weeks, I built a new robust dimensionality-reduction method called Robust-HMF. This method is a hammer, looking for a nail. This week, Hans-Walter Rix (MPIA) suggested that I use the method on young stellar objects observed in SDSS-V BOSS spectra. I did that, and I found hundreds of young stars with narrow H-alpha emission lines. It turns out that the Robust-HMF method does really well on these data; it can fit all the absorption lines in the stars, plus all the wacky continuum shapes generated by a combination of instrumental effects and dust attenuation. That is, I can run the method on more-or-less raw data, provided that it has been shifted to rest frame (and the raw SDSS-V pipelines do that pretty well on stars like these).

Anyway, I don't know what to say about my results quantitatively yet, but I find hundreds of emission-line stars, and my equivalent-width sensitivity is excellent. Who wants this sample?

The Cannon and El Cañon

At the end of the day I got a bit of quality time in with Danny Horta (Flatiron) and Adrian Price-Whelan (Flatiron), who have just (actually just before I met with them) created a new implementation of The Cannon (the data-driven model of stellar photospheres originally created by Melissa Ness and me back in 2014/2015). Why!? Not because the world needs another implementation. We are building a new implementation because we plan to extend out to El Cañon, which will extend the probabilistic model into the label domain: It will properly generate or treat noisy and missing labels. That will permit us to learn latent labels, and de-noise noisy labels.

APOGEE spectra as a training set

I spent a lot of the day building a training set for a machine-learning problem set. I am building the training set out of the SDSS-V APOGEE spectra, which are like one-dimensional images for training CNNs and other kinds of deep learning tasks. I wanted relatively raw data, so I spent a lot of time going deep in the SDSS-V data model and data directories, which are beautiful. I learned a lot, and I created a public data set. I chose stars in a temperature and log-gravity range in which I think the APOGEE pipelines work well and the learning problem should work. I didn't clean the data, because I am hoping that contemporary deep learning methods should be able to find and deal with outliers and data issues. If you want to look at my training set (or do my problem set), start here.

getting the absolutely rawest APOGEE data

I spent time today (at the bar!) understanding the data model and directory structure for the raw, uncalibrated APOGEE data. The idea is that I want to do a real-data example for my paper with Casey (Monash) on combining spectra, and I want to get back to the raw inputs. I also might use these spectra for a problem set in my machine-learning class. The code I wrote is all urllib and request and re, because I think it is necessary to read directories to understand the data dependencies in the survey. Is that bad?

Putting aside my concerns: The coolest thing about this project is that the SDSS family of projects (currently SDSS-V) puts absolutely every bit of its data on the web, in raw and reduced form, for re-analysis at any level or stage. That's truly, really, open science. If you don't believe me, check out this this code that spelunks the raw data. It's all just URL requests with no authentication!

auto-encoder for calibration data

Connor Hainje (NYU) is looking at whether we could build a hierarchical or generative model of SDSS-V BOSS spectrograph calibration data, such that we could reduce the survey's per-visit calibration overheads. He started by building an auto-encoder, which is a simple, self-supervised generative model. It works really well! We discussed how to judge performance (held-out data) and how performance should depend on the size of the latent space (I predict that it won't want a large latent space). We also decided that we should announce an SDSS-V project and send out a call for collaboration.

[Note added later: Contardo (SISSA) points out that an autoencoder is not a generative model. That's right, but there are multiple definitions of generative model; only one of which is that you can sample from it. Another is that it is a parameterized model that can predict the data. Another is that it is a likelihood function for the parameters. But she's right: We are going to punk parts of the auto-encoder into a generative model in the sense of a likelihood function.]

abundance gradients wrt positions or actions

It is traditional to plot things like the mean iron abundances of stars (or ratios of magnesium to iron, or other ratios) as a function of position in the Galaxy. However, stars change their positions over time, so the gradients (the features in any abundance–position plots) will be smeared out over cosmic time by their motions.

At the same time, stars have approximately invariant actions or integrals of motion, which don't change (much) as they orbit. These invariants are only approximate, both because the Galaxy isn't exactly integrable, and also because we don't know or measure everything we need to compute them precisely for any observed star.

Putting these two ideas together, the abundance–action features, or really abundance–invariant features should be much clearer and more informative than the abundance–position features. Awesome, let's go! The only problem is: Selection effects are often simple in the position space, but are almost never simple in the dynamical-invariant-space. So any plots are harder to interpret generally.

These are issues that I have discussed over many years with Hans-Walter Rix (MPIA). Today I discussed them with Danny Horta (Flatiron) and Adrian Price-Whelan (Flatiron), in preparation for an exploratory study by Horta.

ARC BOG meeting

I stayed on at Cloudcroft after the SDSS-V Advisory Council meeting for the ARC Board of Governors meeting, which is the meeting of the organization that runs the Apache Point Observatory. I spent a lot of the meeting learning about the 3.5m and the site, which was interesting, and which made me think about how we apportion our resources in astronomy. These are huge facilities, run very lean (money wise), and they produce a lot of science. The SDSS family of projects has had simply immense scientific impact.

One success of the meeting: I have successfully coined and propagated the term SDSS Classic to mean SDSS-I and SDSS-II. Multiple people at the meeting now use this terminology!

SDSS-V AC meeting

Today I chaired the annual Advisory Council (AC) meeting for the SDSS-V project. The AC protects the interests of the partners, who gave money and other resources to the project. We had many presentations from different parts of the project and OMG this project is amazing. I learned a ton and feel very happy that our money is well spent. This activity counts as research because project management is a key part of science.

The AC meeting was followed by touring the observing hardware (I love it; the SDSS Telescope is incredibly important to everything I have done since the late 1990s), followed by actually looking through the 3.5m telescope at Apache Point Observatory.

toning down my language

I spent travel time (at airports and on airplanes) working on the title, abstract, and introduction of the forthcoming paper with Andy Casey (Monash) about combining visit spectra into mean spectra. This was mainly about me changing the tone from “You are all doing it wrong!” to a tone more like “Here's a way to think about it, and the consequences thereof.” After all, no method is the best for all situations and cases. Our method is best for situations where the individual visit spectra are barely sampled or under-sampled.

first-ever Blanton–Hogg group meeting?

Today was not the first-ever Blanton–Hogg group meeting. But it was the first ever for me, since I missed the first two for health and travel reasons. It was great! Tardugno (NYU) showed simulations of gas disks with embedded planets. The planets affect the disk, and the disk causes the planets to interact. Daunt (NYU) showed that his method for inferring (simultaneously) the spectrum, tellurics, and radial velocities in stellar spectra all works. I am stoked! Novara (NYU) showed that he has a bug in his code! But we had a good discussion inspired by that bug about surfaces of section in a real dynamics problem. Gandhi (NYU) showed us a paper with questionable claims about the CMB light passing through galaxy halos?

mapping the image plane of a spectrograph

I had a phone conversation about wavelength-calibrating the multi-object APOGEE instrument with Karlo de Leon (NYU) today. He has arc images from each night, and line lists for the arc lamps. But before even using the arc lamps, I recommended that he try to find a model for the 2D images that is an outer product of 1D functions: One is the intensity as a function of wavelength from the arc lamp, and the other is the intensity as a function of slit position from the fibers on the slithead.

The thing we realized in the call is that the coordinate system is right when the image is well described as the outer product of these two functions, warped according to that coordinate system! Okay that's nice, now what will the residuals look like? One issue is that there are cosmic rays, hot pixels, and so on. Another issue is that there will be some vignetting that violates the strict outer-product model. We'll address these issues once we get close.

co-writing

My only real work time today came at the end of the day, working with Emily Griffith (Colorado) on our paper about element abundance ratios in the SDSS-V APOGEE data. We find (like others) that the abundances can be explained pretty precisely with only two processes. My position (contrary to others) is that this is not because there are two dominant kinds of supernovae! It is because the disk mixes gas quickly in the azimuthal direction, such that a star's properties are mainly set by the (cosmic) time at which it was born, and the radius at which it was born. If I'm right, then you can't really use disk stars to understand process yields, since they are always an ugly mixture of processes. If I am wrong, there's lots more to do!

Are there young, alpha-rich stars?

I asked this question in Data Group meeting: With Emily Jo Griffith (Colorado) and I have a data-driven nucleosynthetic story for essentially every red-giant-branch star in the SDSS-IV APOGEE survey. Since the parameters of this model relate to the build-up of elements over time, they might be used to indicate age. We matched to the NASA Kepler asteroseismic sample and indeed, our nucleosynthetic parameters do a very good job of predicting ages.

On the RGB, age is mass, and the asteroseismology gives you masses, not ages. There are some funny outliers: Stars with large masses, which means young ages, but with abundances that strongly indicate old ages. Are they young or old? I am betting that they are old, but they’ve undergone mass transfer, accretion, or mergers. If I’m right, what should we look for? The Data Group (plus visitors) suggest looking for binarity, for vertical action (indicating age), for ultraviolet excess (indicating white dwarf companion), for abundance anomalies, and Gaia RUWE. Will do! My money is that all these stars are actually old.

two-dimensional disk?

I had a great set of conversations today with Griffiths and Eisner about the relationship between the idea that the abundance patterns in stars in the disk look very two-dimensional and the idea that the formation of stars in the disk might depend only on birth radius and birth time. If these two things are related, it creates all sorts of new projects for measuring stellar ages and for reconstructing the formation history of the disk.

scope of a paper

Emily J Griffith (Colorado) and I have been working on a two-process (or really few-process) model for the creation of the elements, fit to the abundances measured in the APOGEE survey. Our big conversation this week has been about the scope for our first paper: We have so many results and ideas we don’t know how to cut them into papers. Today we made a tentative scope for paper one: we’ll explain the model, deliver a huge catalog of abundance information, and demonstrate the usefulness for practitioners of Galactic archaeology. Then, later, we can actually do that archaeology!

a blackboard talk on orbital torus imaging

I gave the brown bag talk (chalk only) today at the NYU Center for Cosmology and Particle Physics. I spoke about torus imaging—using moments of the abundance distribution to measure or delineate the orbits in the Galaxy. I focused on the theory of dynamics and what it looks like if you can insert new invariants. The questions were great, including hard ones about non-equilibrium structures, radial migration, and chaos. All these things matter! Talking at a board in front of a skeptical, expert audience is absolutely great for learning about one's own projects, communication, and thinking.

publication and collaboration policies

I spent some time in travel working on ideas for the Terra Hunting Experiment's publication, collaboration, and data-release policies. Megan Bedell (Flatiron) and I are not doing this in any official capacity; we are just brainstorming things that might be a good idea. One theme of our comments is that we want to make sure that the rules very strongly incentivize participation in the project by postdocs and students, who often don't have long enough time horizons to be at one institution for the full scientific arc of a project in this space. Another theme of our ideas is transparency: The Sloan Digital Sky Survey rules do a lot with transparency, and it works well there. When things are transparent to all, you often need fewer rules, because transparency leads to constructive, inclusive discussions.

degeneracies and optimization

With Emily Griffith (Colorado) I have been working on a purely data-driven nucleosynthetic model, trained on the abundances measured in stars by the APOGEE surveys. This model looks a lot like a non-negative matrix factorization, so it is a kind of model I've worked with many times in my life. We've figured out an optimization scheme and made it (exceedingly) fast with jax. Nonetheless, we have been having troubles with the optimization, getting stuck in bad local minima or even pathological locations in parameter space.

Today I discussed this model with Soledad Villar (JHU) who warned me that the model has potential pathologies, and strong degeneracies. I thought I was breaking these degeneracies with regularizations, but in fact the degeneracies are bigger than I thought. Villar's advice (which aligns with the machine learning zeitgeist) was to leave the degeneracies free and then rotate or transform the model to where I want it to be at the end. She also had useful advice about optimizing non-convex functions.

non-parametric model of the density of the Milky Way disk?

Danny Horta-Darrington (Flatiron) has been working with Adrian Price-Whelan (Flatiron) to measure things about abundances and dynamics of stars in the Milky Way disk. Horta is finding that there are way better abundance gradients, in way more directions in phase space, than previously have been (usefully) visualized. But along the way, he stumbled upon a plot that clearly shows the variation of the Milky Way thin disk density with radius. We discussed today how to make the simplest possible measurement of this, with a variation of Orbital Torus Imaging, or really a simplification of it. We realized today that there is enough data to just make this measurement in patches all over the (nearby) disk. The scale length looks short!

bi-linear-ish models

Before my day got ruined by a deadly SDSS-V Advisory Council meeting, I worked with Emily Griffith (Colorado) on a data-driven 2-process model for nucleosynthesis. This model is amplitudes (2 per star) times process yield vectors (2 per element). In this sense it is like a matrix factorization. But it involves a log-sum-exp (rather than just a matrix multiply), so it is mildly nonlinear. It can still be optimized the same way and it is well behaved. In some sense, I realized, it is very like The Cannon in form. But different! We failed to fully implement before I turned into a (very unhappy) technocrat.