substellar objects (brown dwarfs)

I spent the day at the NSBP / NSHP meeting in San José. My favorite session of the day was the morning astro session, which was entirely about brown dwarfs. I learned a lot in a very short time. Caprice Phillips (UCSC) introduced the session with an introduction to the scientific and technical questions in play. She put a lot of emphasis on using binaries and clusters to put detailed abundance ratios onto substellar objects. This was what I expected: I thought (walking in to this session) that all known abundance ratios for brown dwarfs were from such kinds of studies. I learned different (keep reading).

Gabriel Munoz Zarazua (SFSU) followed by showing spectra from M-dwarfs, brown dwarfs, and Jupiter. It definitely looks like a sequence. He does spectral fitting (what they call, in this business, retrievals). It looks like he is getting very good, somewhat precise, abundance ratios for the photospheres of substellar objects! I asked more about this in the question period, and apparently I am way behind the times (Emily Rauscher, Michigan, helpfully pointed this out to me): Now brown-dwarf photosphere models are so good, they can be used to measure abundances, and pretty well.

I also learned in this session (maybe from Jorge Sanchez, ASU, or maybe from Efrain Alvarado, SFSU) that there is a very strong mass–abundance relation in the Solar System. That is, we don't expect, if brown dwarfs form the way planets do, that the detailed abundances of the brown dwarfs will match exactly the detailed abundances of the primary stars. But now we are really in a position to test that. Sanchez showed that we can get, from even photometry, abundances for substellar objects in the Milky Way halo. Again, totally new to me! And he finds metallicities at or below −3. Alvarado showed data on an amazing system J1416, which is an L–T binary with no stellar companion. Apparently it is the only known completely substellar binary.

is it surprising that there are high-redshift supermassive black holes?

A nice talk at MPIA by Hanna Ũbler (MPE) about very high-redshift (redshifts 8 to 14 even) galaxies and their black-hole contents started some nice discussions in the audience and afterwards about the formation of black holes. Because of the Eddington limit (which is a limit on luminosity), black-hole growth is probably limited. The limit is on luminosity, not mass accretion rate, and the relationship between these is the radiative efficiency. As the efficiency goes down, the mass accretion rate of an Eddington accretor goes up. So the question: When can you first form a super-massive (10 million solar masses, say) black hole is a question simultaneously about seed black holes and about radiative efficiency. Anyway, this is all unfortunate, because if the efficiency couldn't get very low, then the black holes we find with NASA JWST would already be putting very strong pressure on fundamental physics in the early Universe.

IAIFI Symposium, day one

Today was the first day of a two-day symposium on the impact of Generative AI in physics. It is hosted by IAIFI and A3D3, two interdisciplinary and inter-institutional entities working on things related to machine learning. I really enjoyed the content today. One example was Anna Scaife (Manchester) telling us that all the different methods they have used for uncertainty quantification in astronomy-meets-ML contexts give different and inconsistent answers. It is very hard to know your uncertainty when you are doing ML. Another example was Simon Batzner (DeepMind) explaining that equivariant methods were absolutely required for the materials-design projects at DeepMind, and that introducing the equivariance absolutely did not bork optimization (as many believe it will). Those materials-design projects have been ridiculously successful. He said the amusing thing “Machine learning is IID, science is OOD”. I couldn't agree more. In a panel at the end of the day I learned that learned ML controllers now beat hand-built controllers in some robotics applications. That's interesting and surprising.

Happy birthday, Rix

Today was an all-day event at MPIA to celebrate the 60th birthday (and 25th year as Director) of Hans-Walter Rix (MPIA). There were many remarkable presentations and stories; he has left a trail of goodwill wherever he has gone! I decided to use the opportunity to talk about measurement, which is something that Rix and I have discussed for the last 18 years. My slides are here.

I've been very lucky with the opportunities I've had to work with wonderful people.

why study astrophysics?

I spent the day with Neige Frankel (CITA), working on various projects. One of the things we discussed was her slides for an upcoming talk. I made the following blanket statement; is it true? There are only two ways to ultimately justify a subject of study in astrophysics. Either it will tell us something important about fundamental physics (think: dark matter, initial conditions of the Universe, or nucleosynthesis, say), or else it will tell us something about our origins (formation of our Galaxy, occurrence of rocky, habitable planets, origin of life, say). I am not entirely sure this is right, but I can't currently think of much in the way of counter-examples. I guess one other justification might be that we are developing technologies that will help people in other areas (CCDs, spacecraft attitude management, or machine learning, say).

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.

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.

conjectures about pre-training

On Monday of this week, Shirley Ho (Flatiron) gave a talk at NYU in which she mentioned the unreasonable effectiveness of pre-training a neural network: If, before you train your network on your real (expensive, small) training data, you train it on a lot of (cheap, approximate) pre-training data, you get better overall performance. Why? Ho discussed this in the context of PDE emulation: She pre-trains with cheap PDEs and then trains on expensive PDEs and she gets way better performance than she does if she just trains on the expsensive stuff.

Why does this work? One interesting observation is that even pre-training on cat videos helps with the final training! Ho's belief is that the pre-training gets the network understanding time continuity and other smoothness kinds of things. My conjecture is that the pre-training teaches the network about (approximate) diffeomorphism invariance (coordinate freedom). The cool thing is that these conjectures could be tested with interventions!

data augmentation

A highlight of my day was a colloquium by Renée Hložek (Toronto) about cosmology and event detection with the LSST/Rubin. Importantly (from my perspective), she has run a set of challenges for classifying transients, based on simulations of the output of the very very loud LSST event-detection systems. The results are a bit depressing, I think (sorry Renée!), because (as she emphasized), all the successful methods (and none were exceedingly successful) made heavy use of data augmentation: They noisified things, artificially redshifted things, dropped data points from things, and so on. That's a good idea, but it shows that machine-learning methods at the present day can't easily (or ever?) be told what to expect as an event redshifts or gets fainter or happens on a different night. I'd love to fix those problems. You can almost think of all of these things as group operations. They are groups acting in a latent space though, not in the data space. Hard problems! But worthwhile.

symmetry day: crossing, permutation

Today's brown-bag talk, by Grant Remmen (NYU), was about (in part) crossing symmetry. This is the symmetry that any Feynman diagram can be rotated through 90 degrees (converting time into space and vice versa) and the interaction will have the same scattering amplitude. This symmetry relates electron–positron annihilation to electron–electron scattering. The symmetry has an important role in string theory, because it is a constraint on any possible fundamental theory. This symmetry has always seemed incredible to me, but it is rarely discussed outside very theoretical circles.

After the talk, and in the Blanton–Hogg group meeting, I brought up things about invariant functions that I learned from Soledad Villar (JHU) that are really confusing me: It is possible (in principle, maybe not in practice) to write any permutation-invariant function of N objects as a function of a sum of universal functions of the N objects (that's proven). How does that relate to k-point functions? Most physicists believe that any k-point function estimate will require a sum over all N-choose-k k-tuples. That's a huge sum, way bigger than a sum over N. What gives? I puzzled some of the mathematical physicists with this and I remain confused.

Florida, day one

I spent today with Sarah Ballard's group, plus others, at the University of Florida. I gave a talk, to a large, lively, and delightful audience. At the end of this talk I was very impressed by the following thing: Ballard had everyone in the room discuss with their neighbors (turn and talk) for about 3 minutes, after the seminar but before the question period began! This is a technique I use in class sometimes; it increases participation. After those 3 minutes, audience members had myriad questions, as one might imagine.

I spoke with many people in the Department about their projects. One highlight was Jason Dittman, who showed me gorgeous evidence that a particular warm exoplanet on an eccentric orbit has an atmosphere that undergoes some kind of phase change at some critical insolation, as it moves away from its host star on its orbit. Crazy!

Late in the day I discussed n-point functions and other cosmological statistics with Zach Slepian and Jiamin Hou. We discussed the plausibility of getting tractable likelihoods for any n-point functions. We also discussed the oddity that n-point functions involve sums over n-star configurations among N stars (N choose n), but there are mathematical results that show that any permutation-invariant function of any point cloud can be expressed with only a sum over stars (N). That sounds like a research problem!

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.

CZS Summer School, day 5: diffusion

Diffusion models are all the rage in machine learning these days. Today Laurence Levasseur (Montréal) gave a beautiful talk at the CZS Summer School about how diffusion works. She started with a long physics introduction, which was great, and also insightful, about how diffusion works in small physical systems. Then she showed how it can be turned into a method for sampling very difficult probability distributions.

I have a history of working on MCMC methods. These permit you to sample a posterior pdf when you only know a function f that is related to your posterior pdf by some unknown normalization constant. Similarly, diffusion lets you sample from a pdf when you only know the gradient of f. Again, you don't need the normalization. That makes me wonder: Should we be using diffusion in places where we currently use MCMC? I bet the answer is yes, for at least some problems.

CZS Summer School, day 4: GNNs

Today Andreea Deac (Montréal) gave a talk at the CZS Summer School about graph neural networks, and enforcing exact symmetries. It was a great talk, because it was useful to the students and filled with insights even for the experienced machine-learners. She did a great job of connecting GNNs to other methods in use in ML, including convolutional neural networks, and Deep Sets.

Dr Kate Storey-Fisher

Kate Storey-Fisher (NYU) defended her PhD here at NYU today. She killed it! She talked about emulating cosmological simulations (at the level of statistics, not maps), making invariant scalars that encode the shapes and dynamics of dark-matter halos, and her awesome 1.2 million all-sky quasar catalog from ESA Gaia and NASA WISE. It was all things my loyal reader knows lots about but I loved it. It has been an honor and a privilege to work with KSF these years, and I will miss her very very much.

Dr Irina Espejo

Today it was my honor to serve on the PhD defense committee of Irina Espejo (NYU), who is one of the first (ever in the world, actually!) PhDs in Data Science. Her PhD research involved making real, practical, scalable, reproducible tools for the (late-in-pipeline) analysis of high-energy physics data from the Large Hadron Collider. She built tools to speed up likelihood-free inferences, and she built a tool to find exclusion regions (upper limits) in complex parameter spaces. She used the latter to put constraints on a (real, not toy) proposed modification to the standard model.

On the first project, the tools that she built (and built on) make the LHC more sensitive to new physics, because they find better test statistics for distinguishing models. They make some searches far better, which makes me wonder whether particle physics is using our money efficiently??

a well-posed problem in gastrophysics

Magda Siwek (Harvard) gave an execellent NYU Astrophysics Seminar today, about evolution of binary systems when the binary is accreting from a circumbinary disk. She sets a few (just a few) disk parameters, and then sets the mass ratio and eccentricity of the binary, and seeks steady-state (low disk-mass or low accretion-rate) solutions. By ignoring electromagnetic fields and various bits of microphysics, she can create a setup that is completely scale-free, so it applies (approximately) from all scales from exoplanets to super-massive black holes. That's brilliant. She finds that the eccentricities are in general driven to non-zero steady-state values, which depend (strongly) on mass ratio and (maybe weakly) on disk parameters. That's a nice problem, and observationally relevant to projects we are doing right now.

water forces on bacteria

Today we had a really great colloquium by Ned Wingreen (Princeton), about water forces on bacteria and how communities of bacteria can be seen as an active material. He showed theory and data for simple experiments in which they can change the osmotic pressure on a wet surface where bacteria are moving. They can tune the water-driven forces on the bacteria and change their behaviors.

After that, at wine and cheese, David Grier (NYU) showed me (and lots of students) a home-built device that levitates (or really traps) tiny objects using acoustic waves. It was awesome.

L2G2

Today I went to the L2G2 (Local Local Group Group) meeting at Columbia. This meeting started with a dozen of us around a table and is now 50 people packed into the Columbia Astronomy Library! A stand-out presentation was by Grace Telford (Rutgers), who showed beautiful spectroscopy of low-metallicity O stars. From their spectral features and (in one case) surrounding H2 region, she can calculate their production of ionizing photons. This is all very relevant to the high redshift universe and reionization. Afterwards, Scott Tremaine (IAS) argued that The Snail could be created by random perturbations, not just one big interaction.

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.