citing things

I spent a big part of today working on finishing up a paper with Megan Bedell (Flatiron). My job was to fill in missing references. I'm still not efficient at this, more than 30 years in to my astronomy career.

Gothamfest

Once a year (and differently every year), we get together as much of the astronomical community in New York City as we can and have them give fast talks. Today was great! I learned a huge amount, and no highlight reel would do. But here are some examples: Amanda Quirk (Columbia) has great data on M33 stars that maybe we could use to build images of the orbital toruses using technology that Price-Whelan and I developed over the last few years? Marc Huertas-Company (Paris) said (confidently?) that many of the star-forming galaxies found by JWST at very high redshift are likely prolate. Michael Higgins (CUNY) and Keaton Bell (CUNY) have a beautiful system to separate sources of variability out in NASA TESS data using structure in frequency space. Kate Storey-Fisher (NYU) showed results from Giulio Fabbian cross-correlating her ESA Gaia quasar sample with the ESA Planck lensing map, with better error bars than any previous survey! Ben Cassese (Columbia) showed a moving-object pipeline with NASA TESS imaging that detects outer Solar System objects, much like old work by Dustin Lang and myself.

doing cosmology differently

Today Chirag Modi (Flatiron) gave a really great lunchtime talk about new technologies in cosmology and inference or measurement of cosmological parameters. He beautifully summarized how cosmology is done now (or traditionally): Make summary statistics of the observables, make a theory of the summary statistics, make up a surrogate likelihood function for use in inference, measure covariance matrices to use in the latter, and go. He's trying to obviate all of these things by using the simulations directly to make the measurements. He has nice results in forward modeling of the galaxy field, and in simulation-based inferences. Many interesting things came up in his talk, including the idea that I have discussed over the years with Kate Storey-Fisher (NYU) of enumerating all possible cosmological statistics! So much interesting stuff in the future of large-scale structure.

defining passive and active symmetries

What is a passive symmetry, and what is an active symmetry? I think I know: A passive symmetry is a symmetry that emerges because there are choices (like coordinate system, units system, gauge choice) in the representation of the data. An active symmetry is a symmetry that is observed to be there (like energy conservation). The passive symmetries are true by definition or by construction. THe active symmetries are subject to empirical test. Today Soledad Villar and I spent time talking about a truly formal definition in terms of commutative diagrams.

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.

is it possible to write a conceptual ML paper?

With Schölkopf (MPI-IS) and Villar (JHU) and others I am trying to write a conceptual paper about the structure of machine-learning methods. Physicists love conceptual papers! But the ML literature is all about performance of implemented methods. That makes it hard to write a conceptual paper. Referees expect to see performance that beats SOTA on some problem (at least a toy problem). I'm struggling.

dimensionless and coordinate-free?

A lot of talk about Buckingham pi in my world. This is a theorem that says that any dimensional equation in physics with k dimensional inputs can be re-written as a dimensionless equation with fewer than k dimensionless inputs. But this is useless when we think about geometric equations—and many equations in physics are geometric.

Consider, for example, the coordinate-free equation F=ma. This equation has two dimensional vector terms. If we apply Buckingham pi, we get three coupled equations with non-scalar, non-coordinate-free dimensionless ratios. That's terrible, and useless! Can we replace Buckingham pi with something that makes equations that are both dimensionless and coordinate-free?

non-convolutional neural networks

Here's a quotation from an email I sent to Schölkopf (MPI-IS) and Villar (JHU) today:

First, I believe (am I wrong?) that a CNN works by repeating the precisely identical weights for every pixel. So if, in a CNN layer, there are k channels of 3x3 filters, there are only 9k weights that set all the k responses of every pixel in the layer to the 3x3 pixels centered on that pixel in the layer below. The sparsity comes not just from the fact that each pixel in one layer connects to only the 9 pixels below it, but also from the fact that the 9k weights are the same for every pixel (except maybe at edges). That enforces a kind of translation symmetry.

Okay, now, we could make a non-convolutional neural net (NCNN) layer as follows: Each pixel is connected, like in the CNN, to just the 3x3 pixels in the layer below, centered on that pixel. And again, there will be k channels and only 9k weights for the whole layer. The only difference is that at each pixel, a rotation (of 0, 90, 180, or 270 degrees) gets applied and a flip (by the identity or across the x direction) gets applied to the weight maps. That is, every pixel has the same k filters applied but at each pixel, there has been one of the 8 rotation-reflection transformations assigned to the 9k-element 3x3 weight map. This NCNN layer would, like the CNN layer, have 9k weights in the layer, and it would be just as local and sparse as the matching CNN layer.

My conjecture is that the NCNN will perform far worse on image-recognition tasks than the CNN. It is also (fairly) easy (I believe) to build a NCNN from a light modification of a CNN code. Comparison is clean and straightforward. I am ready to bet substantial cash on this one.

First Science Results from JWST, day two

Today was day two of the First Science Results from JWST meeting at STScI. Once again, it was a blast of results from all different fields. Some things I'll think about more going forward include: Something like 3 percent of white dwarf stars show an infrared excess that is consistent with them having a Saturn-like ring system? How did I not know this previously? It makes me want to find a WD with a transiting exoplanet to map the rings and maybe even ring gaps! There is a huge class of red luminous outbursts that appear to be the result of mergers of binary stars (maybe often when one of the binary pair starts to go off the main sequence and engulf its partner). Some of these, for energetic and other reasons, look like they are created not by binary-star systems but instead by star–planet systems. I wonder if the populations can be connected to the population of stars with weird lithium and refractory abundances?

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.

towards flexible dynamical models

There are many conversations going on right now in the Flatiron dynamics community about making flexible models for galaxy dynamics. For example, there is work on replacing parametric models with non-parametric basis expansions in various bases. For another, Price-Whelan and I have been trying to think about how one might just image the orbital tori directly with the stellar element-abundance maps. We brought two of these conversations together today, in which Ben Cassese (Columbia) and Danny Horta-Darrington (Flatiron) showed that they are using near-identical forms for data-driven orbit forms in the vertical dynamics of the disk. We also spent a lot of time talking about what constitutes a sensible likelihood function for torus imaging and distribution-function-fitting projects.

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.

phase and frequency variations

If a star has a (relatively) coherent oscillation mode, and you can monitor it over a long period of time, then orbital motion of the star can be seen as either phase or frequency variations of that mode. I've been working on this in different collaborations, with Dan Hey, with Simon J Murphy, with Abby Shaum (CUNY), and recently with Nora Eisner (Flatiron). Right now, Shaum, Eisner, and I are looking at signal-processing approaches that look like demodulators. What I'm interested in—at least in terms of me learning about signal processing—is how can we make a demodulator that demodulates both phase and frequency simultaneously. There must be hybrid and combined approaches. I'm also interested in what we can measure from incoherent oscillators.

new LIGO events

Today Mathias Zaldarriaga (IAS) gave the NYU Astro Seminar. He told us about work he has been doing to increase the sensitivity of the LIGO data to inspiral events, and how that is impacting beliefs about populations of black holes.

the stability of the vacuum

The research highlight of my day today was our weekly lunchtime blackboard talk, as it often is on Mondays. TOday it was Isabel Garcia Garcia (NYU), talking about the stability of the vacuum. She was specifically talking about the stability of a false vacuum, and specifically when there are large extra dimensions. The weird thing is, in all string-like models for the fundamental particle physics model there are both large extra dimensions and an exceedingly low probability that we live in the true vacuum state. That means a decay to a different state is possible (inevitable?). Why has this vacuum lived so long?