number of exposures per visit?

I wrote code this weekend to look at the question of how we should visit a star in the upcoming Terra Hunting Experiment. The current (straw-person) plan is that we will observe each visible star once per night for ten years, with one exposure of a sensibly-chosen exposure time at each visit. Is this a good idea? I was interested in this problem for two reasons. The first is that binning is sinning, with the corollary that bigger bins are worse than finer bins, and a single, long exposure is a very big bin. The second reason is that when there are non-trivial noise sources (like the quasi-periodic variations from p-mode oscillations of the surfaces of Sun-like stars), a few negatively- (or interestingly-) correlated noise draws can be combined in ways that are substantially more informative than by taking the average.

Of course, if you split an exposure (with a standard CCD, say) into sub-exposures, you take on real costs: There is a read time, which is time you aren't integrating, and there is a read noise, that affects each new exposure. So the best strategies are a complicated function of read time, read noise, and the signal-to-noise at which the stellar p-mode oscillations are visible in any realistic data. Related: There are amazingly different and interesting strategies with up-the-ramp detectors that are used in the infrared.

One final comment is that the objective, in my strongly held view, is to optimize the amount of information (about, say, the center-of-mass radial-velocity changes of the target star) per unit wall-clock time. We are paying for wall-clock time; let's get as much as we can out of it.

Terra Hunting Fall Science Meeting, day 2

So many good things happened in the meeting today! Highlights were presentations by Niamh O'Sullivan (Oxford) Ben Lakeland (Exeter) who showed amazing results running models of stellar variability on data from the Sun. O'Sullivan can see that the sun goes through many different phases of spots, granulation, and super-granulation. She finds these by fitting Gaussian processes of certain forms. Related: Suzanne Aigrain (Oxford) showed that even in very gappy data, the GP fits are unbiased, whereas naive use of periodograms is biased!

Lakeland showed that super-granulation can in principle be modeled in the Solar time series, and maybe the tiniest hint that when he corrects for super-granulation well, the RV variability might be even lower than at times at which there is no super-granulation in play at all. Does super-granulation suppress other kinds of variability?

I'm very optimistic—between Liang yesterday, Zhou's work at Flatiron, and these presentations—that we will be able to mitigate many difficult sources of stellar variability. I was inspired to outline a conceptual paper on why or how this is all going to work.

Terra Hunting Fall Science Meeting, day 1

Today was the first day of the Terra Hunting annual science meeting. One highlight of the day was a presentation by Yan Liang (Princeton), who is modeling stellar spectral variability (the tiny variability) that affects extremely precise radial-velocity measurements. Her method involves a neural network, which is trained to distinguish RV variations and spectral shape variations through a self-supervised approach (with a data augmentation). Then it separates true stellar RV variations from spectral-variability-induced wrong RV variations by requiring (essentially) that the RV variations be uncorrelated with the (latent) description of the stellar spectral shape. This connects to various themes I am interested in, including wobble by Bedell, a spectral variability project by Zhao, and causal structure in machine learning.

information theory for spectroscopy

I had a meeting this morning with Megan Bedell (Flatiron) about our dormant paper about information theory and extreme-precision radial-velocity measurements. We see the paper a bit differently (is it about methods or is it about concepts?), but we were able to re-state a scope with which we are both happy. We assigned tasks (Bedell writing and me coding, mainly), and promised to make progress before next week. It is very, very, very hard to finish a paper! Especially when all authors are above some seniority, where they spend most of their time with others. I would love to get a lot more personal coding time!

precision spectroscopy

One research highlight from the day was a conversation with Madeleine MacKenzie (ANU) about many things, including measuring magnesium isotopes in high-quality (high resolution and high SNR) stellar spectra. This comes just after a conversation (yesterday) with Matt Daunt (NYU) saying that he wants to do something with the extremely high-quality stellar spectra produced by the jax-wobble pipeline we are building. So I think there is a project to do. If we measure Mg isotopes, even for a few stars, we might be able to fit them into the 2d model of disk abundances that Emily Griffith (Colorado) and I are building. The model is so simple that we would only need a few stars to learn something interesting—including learn that the isotope ratio variations (seen by MacKenzie) represent some new kind of variability. Do they?

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?

convection, granulation, stellar spectra

I had a great and long conversation today with Maria Bergemann (MPIA) about building a model of a full stellar spectrum out of the models they build of small patches of stellar surface, with full 3D convection and full radiative transfer. Their models are sophisticated, and give a full spectrum in every direction from the surface patch. Thus we can integrate a set of patches into a surrogate combined spectrum for one rotating star covered in convecting patches. We discussed how we might do that, technically, and what projects we might then do with the output. What I want to do (yes, you guessed it) is build data-driven models of stellar granulation to improve radial-velocity surveys.

wobble, star spots, quasar dipole

[Time to try to re-start this forum.]

I spent this morning on three different small activities. One was giving feedback to Matt Daunt (NYU) who is trying to re-build the wobble concept for stellar radial-velocity measurement in jax. He has annoying optimization issues, which are very hard to diagnose! Optimization is always nasty, in my experience.

Another activity was working on the abstract for Lily Zhao's (Flatiron) upcoming paper on stellar variability in the spectral domain, generated by rotating, spotty stars. She is concerned that the paper is too conceptual. I love conceptual papers! I think science moves forward through concepts and implentations, and no individual paper has to do it all.

My third activity this morning was working through the mathematics on a project of Abby Williams (NYU, Caltech) to measure the kinematic dipole in the all-sky Quaia quasar catalog. There are so many different ways to measure it. I think I have a justifiable likelihood function approach, and one in which we could marginalize out—or profile out—the uncertainties in the selection function we have estimated. It's a controversial subject, so I would like to do things correctly.

how to maximize the yield of planets?

There were discussions this week at University of Warwick about the Terra Hunting Experiment strategy and likely detection capability. Various take-homes include that we need to mitigate lots of stellar noise, and that we care deeply about the covariance (as a function of separation in time) of adjacent measurements. I advocated that we split our ten-year survey into two or three surveys, of varying length. In the first, we learn about the stars, and in the last, we go to town on the very most promising targets. There was general agreement that this is a good idea. But now we need a very specific plan for what this means. As my loyal reader knows, in my view, the decisions must be based on repeatable operations, so that we have some hope of learning statistical things about populations in the end.

predicting RVs from SDO imaging

I'm at the Terra Hunting annual Science Working Group meeting, held this year at University of Warwick. There were many great talks today, some technical and some science. My mind was blown by Ben Lakeland (Exeter), who showed Solar Dynamics Orbiter data of the Sun, and then showed that, from these images, he can predict the magnetic-activity-generated RV signals in simultaneous EPRV measurements of the Solar RV. That's pretty exciting. He also showed that much of the time, the RV variations are dominated not by magnetic activity per se. If we are going to beat one meter per second, we are going to have to correct for convective shifts. Somehow!?

causal structure in ML

Today I had the honor on serving on the PhD advising committee of Yan Liang (Princeton), who is designing her PhD project. She is adding causal structure to an autoencoder such that it can separate stellar variability-induced radial-velocity signals from exoplanet-induced signals in extreme precision radial-velocity data. Her method design is novel, and tests suggest that it might work. The committee recommended adding even more causal structure and physics knowledge (more is probably always better, provided that it isn’t incorrect)! As my loyal reader knows, I think this is the frontier for machine learning in the natural sciences: adding causal structure.

can you see the orientation of a star?

Stars don't have uniform surfaces, and they rotate. Can you see the orientation of the star in a single spectrum? Of course the answer is no: You don't have a coordinate system! But if you have some previous spectra of the star, can you establish a rotation period and define an angular coordinate system, and then follow that by taking a new spectrum and saying where the star is in its rotational phase? It looks like the answer to this question might be yes, based on experiments that Lily Zhao (Flatiron) is doing. Of course we don't really care about the stellar orientation. What we care about is capturing or correcting the artificial radial-velocity signals introduces to the data from the rotating, non-uniform surface.

parameters and nuisance parameters

Long ago, Adrian Price-Whelan (Flatiron) and I and others built The Joker, which is a Monte Carlo method (but not a MCMC method) for dealing with the Kepler problem. It exploits the fact that some parameters are linear, and some are nonlinear. This week, Lawrence Peirson (Stanford) is visiting Flatiron to generalize this point. Peirson's point is that the trick we use for linear parameters can be used for any parameters that have smooth, unimodal-ish posteriors. We just have to add some linearization and some optimization. So we are working on writing that down. And coding it up.

Along the way, Peirson found another linear parameter in The Joker, so we can now make it way, way faster. That's awesome!

Korg

Today Andy Casey (Monash) joined a regular meeting I have with Megan Bedell (Flatiron) and Lily Zhao (Flatiron) about things related to precision spectroscopy. We discussed projects we can do with surface spectra of the Sun, one from the quiet part, and one from a spot. Casey is involved in the Korg project led by Adam Wheeler (OSU); we discussed fitting both spectra with Korg, and learning about the physical differences between the quiet and active regions in the Sun. We also discussed Zhao's projects to empirically correct for stellar activity in time-domain spectroscopy looking for planets.

SDSS-V Science Festival, day 1

Today was the first day of the SDSS-V Collaboration Meeting in Toronto. We talked about the state of the survey and the survey mission, shared values, and operating principles. This was great; it is the first full in-person meeting since survey start. Much of the day was open working and break-out time.

Late in the day, Adam Wheeler (OSU) made a great plot comparing SDSS-IV velocities (Doppler shifts) to ESA Gaia velocities, as a function of APOGEE fiber. It looks like there are substantial differences, and systematic with fiber. If this is real, fixing it will have a big impact on work I've done on spectroscopic binaries in the sample.

stellar noise as a physical process

Today I was privileged to be part of a great and productive meeting between Jesse Cisewski (Wisconsin), Megan Bedell (Flatiron), and Lily Zhao (Flatiron) about noise sources in extreme-precision radial-velocity measurements. The conversation was inspired by the realization (obvious, really) that any physical effect on the surface of stars (spots, plages, convection pattern, p-modes, flares) that affect the radial-velocity measurement must (unless the Universe is truly adversarial) leave other imprints on the spectrum at the same signal-to-noise or even higher signal-to-noise. This means that any claim that RV measurements are affected by spots (say) should be backed up by an observation in the spectrum that is orthogonal to the RV signal that supports the claim. We discussed relevant research and decided to jointly read this paper before our next meeting!

continuum normalization of spectra

How do you continuum-normalize a spectrum in a low-resolution spectrograph? You can't really, unless you have exceedingly good models for stars (which you could use to fit the normalization); but of course if you had extremely good models for stars you probably wouldn't have to normalize!

I had an interesting conversation about this with Alex Dimoff (MPIA). He is continuum-normalizing spectra in a high-resolution echelle spectrograph. He has a good measurement of the “blaze function” so I suggested that he just add some polynomial or sine-and-cosine adjustments to that in each order. My main advice about continuum normalization is to avoid things that are very sensitive to signal-to-noise: As you degrade the signal-to-noise, your continuum estimate should not systematically deviate low. Most methods in play right now have this problem, and bad. Think: Fit to pixels that are “consisten” with being continuum pixels. That's going to depend very strongly on signal-to-noise.

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.

writing in the discussion section

I spent time in an undisclosed location on the weekend writing in the discussion section of my draft with Megan Bedell (Flatiron) on information theory and extreme precision radial-velocity measurement. I think my language is a bit loose when I write on vacation!

Doppler shifts and radial velocities

I am a big believer that there is a difference between a Doppler shift and a radial velocity. For one, they have different units! For another, the former is measurable (sometimes) and the latter is not (or rarely). But today I agreed with Megan Bedell (Flatiron) that we should write our paper on the subject in terms of the words “radial velocity” and not “Doppler shift”. After all, we are talking to a community with a common language! I spent some time on the train from Vienna to Heidelberg editing the figures for our paper on this subject.