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.]

Terra Hunting Fall Science Meeting, day 4

Today we delved into even more detail about how the HARPS3 instrument works, looking at engineering drawings and discussing how charge-coupled devices (CCDs) read out. We discussed the time stability of various parts of the instrument and electronics. We are all very excited about assembly, verification, and testing in Cambridge this summer.

Terra Hunting Fall Science Meeting, day 3

Today was a delight! In a working session, Clark Baker (Cambridge) gave a beautiful, conceptual and concrete description of how an echelle spectrograph works and the blaze and the resolution and etc. My favorite moment was the aha! moment I had when he described the Littrow condition. This was followed by Alicia Anderson (Cambridge) explaining how the data reduction proceeds. Then she and Federica Rescigno (Exeter) helped us install the data-reduction software for the ESO instruments (ESPRESSO, HARPS-N, etc) and we started reducing raw echelle data.

Before all this there was a wide-ranging discussion of measuring 3-point functions of radial-velocity time series data. This was inpired by the question: Is a Gaussian process a good model for these data? I hope this turns into a project or set of projects.

linear regression

Valentina Tardugno (NYU) and I are looking at the NASA TESS housekeeping data: What parts of it are relevant to understanding the light curves? The weird thing is: We are asking this by asking: What housekeeping data can be reliably predicted using the light curves? Why this way? Because the light curves are higher in signal-to-noise (in general) than most channels of the housekeeping data. Today we went through all the relevant linear algebra for big linear models (which is where we are starting, of course!).

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.

biases from machine learning

Today I gave a talk (with these slides) at a meeting in Denver for the NSF initiative Harnessing the Data Revolution. I spoke about the necessity and also the dangers of using machine-learning methods in scientific projects. I brought up two very serious possible biases. The first is that if emulators are used to replace simulations, and they can't be easily checked (because the simulation requirements are too expensive), the emulators will lead to a confirmation-bias problem: We will only carefully check the emulations if they lead to results that we don't like! The second bias I raised is that if we perform joint analyses on objects (stars, say) that have been labeled (with ages, say) by a machine-learning regression, there will in general be strong biases in those joint analyses. For example, the average value of 1000 age labels for stars labeled by a standard ML regression will not be anything like an unbiased estimate of the true average age of those stars. These biases are very strong and bad! That said, I also gave many example locations where using machine learning methods is not just okay but actually intellectually correct, in areas of instrument calibration, foregrounds, and other confounders.

The question period was great! We had 25 minutes of questions and answers, which ranged across a very wide set of topics, including statistics, experimental design, and epistemology.

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?

kinematic dipole and dust

I had a long conversation with Kate Storey-Fisher (NYU) and Abby Williams (Caltech) about the dipole in the Quaia catalog caused by the kinematic motion of the Solar System barycenter with respect to the cosmic rest frame. Williams has found that the amplitude of the dipole we get depends very strongly on how we account for dust in our sample. There is currently a controversy about the amplitude of the dipole seen in WISE quasars. We now think that it is possible that the measured amplitude is a strong function of how dust is corrected for in the sample? We designed new tests for next week.

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.

how to extract XP spectra from raw Gaia data?

On the plane home from meetings at Cambridge, Warwick, and Paris, I worked on a long document I am writing for Gaia DPAC CU5, which is the organization responsible for calibrating and extracting the Gaia XP spectra. They are doing a beautiful self-calibration to extract all the spectra on the same system, in the sense of resolution, dispersion, and throughput. But their system has some pathologies, which we discussed last week. I think I know how to solve some of them. My document is reporting those thoughts.

Writing like this reminds me of graduate school: One of my advisors (Blandford) often encouraged me to write up thoughts, ideas, projects, and proposals, even when we had no intention of submitting them anywhere. It's good practice, I think, because you can't understand anything if you don't write about it.

calibrating Gaia

I was honored today by being invited to a meeting of the group at Cambridge (UK) that extracts and calibrates the low-resolution ESA Gaia XP spectra. The model is bilinear: Each source is represented as a linear sum of basis functions, and each observation of each source has an expectation which is that linear sum multiplied by a convolution kernel, which is also represented as a sum of components. These components are smooth functions of position in the device and wavelength. It's a very nice system! I went through it all with them and said what I would have done differently (which is not much, I have to admit).

AB magnitudes from ESA Gaia

I got frustrated by this ESA Gaia documentation today. People: If you put something in a table, name it in the table the same way you name it in the equations in your paper! And if you explain completely two magnitude systems, then make note of which one you used in the catalog. Anyway, I finally figured out how to convert ESA Gaia magnitudes to and from calibrated flux densities (and therefore AB magnitudes) after re-reading the documentation a few times. This is for my quasar homogeneity projects with Abby Williams (NYU) and Kate Storey-Fisher (NYU).

First Science Results from JWST, day one

Today was day one of the First Science Results from JWST meeting at STScI. Today (like all days, I expect) was a barrage of information on different topics, filled with exciting results and systematic errors! I love meetings like this, because it is fun to see people struggling with data they don't quite understand yet. And I can see lots of opportunities for my interests in spectrographs and imagers to be useful in this community. My favorite talks today (unfairly!) were the talks on the instruments and their status. There are some beautiful lens-flare-like artifacts in the NIRISS instrument; that would be a fun problem (for example!). There are insane “snowball” cosmic-ray hits in the NIRSpec data, the likes of which I've never seen before. One nice thing about contemporary NASA: The plan is to make all the calibration pipelines completely open and user-operable, so it is easy to intervene on these data.

the discussion section of a paper

I spent the afternoon writing the discussion section of my nascent paper with Andy Casey (Monash), about spectrum combinations. My philosophy of the discussion section is: Return to each of the most important assumptions you made (and, hopefully, stated explicitly in some early section), and say what you would do, what you would get, and what you would pay, if you wanted to relax that assumption. I spent a lot of time speaking about spectral variability, which can come not just from the source itself, but from the hardware, from backgrounds, or from data processing issues.

JWST and open science

Today I hosted Sarah Kendrew (STScI) at NYU. She gave the Physics Colloquium, about NASA JWST launch, commissioning, and early science. She has been the lead of a JWST instrument mode for something like 14 years; now she has data! She talked about how JWST works and showed some beautiful exoplanet results. One of the great things about her talk is that she explained a point on which they made some mistakes, and how interactions with the user community helped them to fix those mistakes. It was a great endorsement of the open model for science.

patches of imaging

I am discussing with Sean Ku (NYU) and Victor Kuang (NYU) the NASA Cassini imaging of Saturn. We want to make (from the data) a high-quality face-on picture of the rings. This is a problem (from my perspective) in computer vision, so we need a camera model (and a lot of other things). One thing I hypothesized about this problem today is the following (am I right?):

Any sufficiently small patch of a camera image can be modeled with a pinhole-camera-like camera model, provided that we give the camera model the freedom to make the image plane not perpendicular to the line from the pinhole to the patch of the image plane. Is this correct? We are about to find out, the hard way.

simulating a patch of a spectrograph

In preparation for writing something (or proposing something, maybe?) about new methods for extracting spectra from spectrograph data, I wrote a tiny simulation code that makes fake spectroscopy data. The issue is that (except in rare circumstances) the spectral trace is not aligned perfectly with a CCD row (or column) and (except in rare circumstances) the cross-wavelength direction directions of constant wavelength) are not aligned perfectly with a CCD column (or row). How to adjust current methods to address this? I think I know! And I think it doesn't require a full instrument model.

spherical-harmonic transforms of point sets

On the weekend I computed the spherical-harmonic transform of Kate Storey-Fisher's quasar sample made from ESA Gaia data. I also computed the spherical-harmonic transform of the random catalog we use to map the selection function. The two transforms are extremely similar in their complex amplitudes! Since the random catalog is made assuming perfect homogeneity and isotropy, this similarity directly translates into a measurement of the isotropy of the Universe.

is the time just a housekeeping datum?

I had a lunch conversation with Melissa Hobson (MPIA) about finding Earth-like planets in long-term radial-velocity surveys. We discussed instrument calibration, and how one interpolates the calibration data from the arcs or LFCs onto the science exposures. I think we should be doing this not in time (or not only in time) but in other housekeeping quantities like instrument temperature state. That is, the most relevant calibration exposure might not be the closest in time, it might be the closest in instrument temperature. From my perspective, the time is just another piece of housekeeping data, and its value for calibration is to be determined empirically.

do the stars make up a coordinate system?

Long, long ago, when I worked with Sam Roweis (deceased) and Dustin Lang (Perimeter) on locating images on the sky, we used to discuss coordinate systems: You don't actually need a long-lat or theta-phi coordinate system to describe the locations of things on the sky, right? You can just use angular relationships among sources to locate everything precisely and unambiguously! And with that approach, you don't need to make as many choices and standards and lines of code about reference frames. But, alas, this point of view is not in the ascendent.

Not being deterred, I put the bright stars on my maps (from this weekend) of Kate Storey-Fisher's ESA Gaia quasar sample. Can you find the big dipper and Orion? And Sirius?