wrote like the wind; frequentist vs Bayes on sparsity

My goal this year in Heidelberg is to move forward all writing projects. I didn't really want to start new projects, but of course I can't help myself, hence the previous post. But today I crushed the writing: I wrote four pages in the book that Rix (MPIA) wants me to write, and I got more than halfway done with a Templeton Foundation pre-proposal that I'm thinking about, and I partially wrote up the method of the robust dimensionality reduction that I was working on over the weekend. So it was a good day.

That said, I don't think that the iteratively reweighted least squares implementation that I am using in my dimensionality reduction has a good probabilistic interpretation. That is, it can't be described in terms of a likelihood function. This is related to the fact that frequentist methods that enforce sparsity (like L1 regularization) don't look anything like Bayesian methods that encourage sparsity (like massed priors). I don't know how to present these issues in any paper I try to write.

partial differential equations

I am trying to write a proposal to fund the research I do on machine-learning theory. The proposal is to work on ocean dynamics. It's a great application for the things we have done! But it's hard to write a credible proposal in an area that's new to you. Interdisciplinarity and agility is not rewarded in the funding system at present! At least I am learning a ton as I write this.

grant proposals

There is a non-wrong view of academic science that it is all about applying for funding, and evaluating the proposals of others for funding. That's all I did today (evaluated proposals for a foreign funding program; I submitted my own proposal to the NSF yesterday).

writing proposal

Mike Blanton (NYU) and I are writing an NSF proposal. That took up most of my research time today!

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.

other kinds of machine learning

Astronomy is very focused on machine learning in the sense of regression and classification, but machine learning can do many other things. In addition, machine learning is a sub-field of machine intelligence, which is broader. I started today working on a proposal for the NSF (to be written with Mike Blanton, NYU) in which we propose using other kinds of machine learning and machine intelligence, and apply them earlier in the scientific process (like at operations and calibration) instead of at the end (like at source classification and labeling).

four kinds of emulators

I wrote in a draft grant proposal related to machine-learning emulators today. I wrote about five different kinds of emulators. Yes I think there are five qualitatively distinct kinds. Here they are:

Full replacement

The most extreme—and most standard—kind of emulator is one that simply replaces the full input–output relationship of the entire simulation. Thus if the simulation starts with initial conditions and boundary conditions, and ends with a final state (after an integration), the full-replacement emulator would be trained to learn the full relationship between the initial and boundary conditions and the final state. A full-replacement emulator is a complete, plug-in replacement for the simulator.

Integrator

Simulation run times generally scale linearly with the number of time steps required to execute the integration. A set of emulators can be trained on a set of snapshots of the simulation internal state at a set of times that is much smaller than the full set of integration time steps. Each emulator is trained to learn the relationship between the internal state of the simulation at one time tA and the internal state of the simulation at a later time tB, such that the emulator can be used to replace the integrator during the time interval from tA to tB. A set of such emulators can be used to replace part or all of the integration performed by the simulator.

Resolution translator

Simulation run times generally scale with the number of grid points or basis functions in the representations of the state. Thus the simulator gets faster as resolution is reduced. An emulator can be trained to learn the relationship between a low-resolution simulation and a matched high-resolution simulation. Then a high-resolution simulation can be emulated by running a fast low-resolution simulation and applying the learned translation.

Physics in-painter

In most physical systems, there are coupled physics domains with different levels of computational complexity. For example, in cosmology, the pure gravitational part of the simulation is relatively low in computational cost, but the baryonic part—the atoms, photons, ram pressures, magnetic fields—is very high in computational cost. The simulator gets faster as physics domains, or equations, or interaction terms, are dropped. An emulator can be trained to learn the relationship between a simulation with some physics dropped and a matched full simulation. Then a full-physics simulation can be emulated by running a partial-physics simulation and applying the learned in-painting of the missing physics.

Statistics generator

In many contexts, the goal of the simulation is not to produce the full state of the physical system, but only certain critical statistics, such as the two-point correlation function (in the case of some cosmology problems). In this case, there is no need to emulate the entire simulation state. Instead, it makes sense to train the emulator to learn only the relationship between the initial and boundary conditions of the simulation and the final statistics of particular interest.

generalized flat-relative extraction

I asked, in the Astronomical Data Group meeting at Flatiron, about the method of spectral 2D-to-1D extraction known as flat-relative optimal extraction. It's genius, and simple, but it makes strong assumptions about the spectrograph. I asked how we might improve it. And I think I maybe have a plan. The idea (which was thrown out by Megan Bedell) is to make the spectral representation something continuous, and evaluate it individually at every pixel, not just once per column of the detector. This should improve extraction. And it is relevant to the NASA proposal I am writing with Matt Daunt.

information loss

I wrote words today about how information is being lost in radial-velocity-spectrograph data-analysis pipelines at the stage of going from 2D spectra to 1D spectra. I am proposing to NASA (with Matt Daunt, NYU) to fix these problems! This is important, in my opinion, but I have to admit that it is not currently considered the tall pole in EPRV.

exoplanet roadmaps, plans, and surveys

Inspired by research by Matt Daunt (NYU), I looked at the various reports, presentations, and papers that have been written by NASA panels, committees, and projects about the tall poles and engineering gaps in the exoplanet research ecosystem. Why? Writing a proposal, of course! Daunt and I are proposing to work very close to the metal in radial-velocity work, so we are looking at the critical infrastructure that's close to the metal.

GPRV, day 4

Today was day 4 and the last day of GPRV in Oxford. The day ended with a discussion led by Heather Cegla (Warwick) and Jennifer Burt (JPL) about EPRV and national priorities. Exoplanet science is obviously extremely important to the 2021 Decadal Survey, but in detail, the first seven chapters of that Survey (the chapters to which NASA and NSF must respond) do not actually mention radial velocity! The conversation in the room today was extremely wide-ranging; it covered hardware, software, science goals, and community-building goals. But it also covered months, years, and decade-long time-scales.

The highest level recommendation of the Decadal Survey was that we need to do preparatory work to design and assess feasibility of a large IR–visible–UV telescope that will discover habitable worlds. There is no doubt (I think it's uncontroversial) that this preparatory work will require lots and lots of EPRV science and observations. Of course the fact that this is obvious is separated somewhat from the question of whether there will be abundant funding!

It will come as no surprise to my loyal reader that I was a proponent, in this discussion, of building open-science communities around open data, open-source software, and open science collaborations. I think we have so much evidence now that open-science communities science way better. What I loved is that there were absolutely no objections in the room to this idea. The only controversies were about exactly how open data should be managed and released in that utopian future. I'm optimistic about this business!

And I thank Suzanne Aigrain (Oxford) and her OC for a great meeting!

writing like mad about Astrometry.net

It's crunch time this weekend on Dustin Lang (Perimeter) and my proposal for the NASA Open-Source Tools, Frameworks, and Libraries call. I spent a lot of quality time this weekend cranking out words. After doing some literature review, we find that Astrometry.net is used in a huge number of projects, from NASA missions to cosmic-ray detectors to (of course) amateur astrophotography workflows. That's exciting, and relevant to our proposal. One of the great things about the NASA call is that it requires us to think about project management, community building, and collaboration policies. That is good; it will help our project immensely.

writing proposals is hard!

Today I took a serious shot at getting words down in my upcoming NASA proposal for open-source tools, frameworks, and libraries. This is a new call to support development and maintainance of open-source projects that are aligned with NASA science missions (yay open science and NASA!). Dustin Lang (Perimeter) and I are proposing to support Astrometry.net, which is used in multiple NASA missions, including SOFIA and SPHEREx. It is hard to put together a full proposal; writing a proposal is comparable in intellectual scope to writing a scientific paper! And it must be done on deadline, or not at all.

NSF center proposal

I spent some time today discussing a possible NSF Center on real-time data analysis with Ashley Villar (Columbia) and Tyler Pritchard (NYU), based on the wide-ranging grass-roots interest we found in time-domain astrophysics we have discovered in NYC this pandemic. NSF Centers are big projects!

funding astrometry.net as an open-source project

Dustin Lang (Perimeter) called me today and alerted me to this NASA funding call related to open-source projects. He argued that we need to take Astrometry.net/ to the next level. I agree! So we kicked around project and development ideas and vowed to take a stab at a letter of intent.

time-domain astronomy in Gotham

Today, Tyler Pritchard (NYU) and I assembled a group of time-domain-interested astrophysicists from around NYC (and a few who are part of the NYC community but more far-flung). In a two-hour meeting, all we did was introduce ourselves and our interests in time domain, multi-messenger, and Vera Rubin Observatory LSST, and then discuss what we might do, collectively, as a group. Time-domain expertise spanned an amazing range of scales, from asteroid search to exoplanet characterization to stellar rotation to classical novae to white-dwarf mergers with neutron stars, supernovae, light echoes, AGN variability, tidal-disruption events, and black-hole mergers. As we had predicted in advance, the group recognized a clear opportunity to create some kind of externally funded “Gotham” (the terminology we often use for NYC-area efforts these days) center for time-domain astrophysics.

Also, as we predicted, there was more confusion about whether we should be thinking about a real-time event broker for LSST. But we identified some themes in the group that might make for a good project: We have very good theorists working, who could help on physics-driven multi-messenger triggers. We have very good machine-learners working, who could help on data-driven triggers. And we have lots of non-supernovae (and weird-supernova) science cases among us. Could we make something that serves our collective science interests but is also extremely useful to global astrophysics? I think we could.

EURion

It was a fun morning with Zach Martin (NYU) and Teresa Huang (NYU), talking adversarial attacks against astronomical machine-learning methods. In this context I mentioned the EURion. I said that money can't be photocopied. They didn't believe me. Am I a conspiracy theorist? Yes! But I'm right on this one. We went to the photocopier room and demonstrated the awesome that is secret agreements between electrostatic hardware companies and Western governments. But then Martin said “They went through all that trouble to stop what? Who photocopies money? What kind of a stupid scam is that?”

NASA funding white paper

[I have been off the grid doing important things. Apologies to my loyal reader.]

I spoke to Megan Bedell (Flatiron) about representing Flatiron next week at the Terra Hunting Experiment collaboration meeting in Cambridge UK next week. I think we see eye-to-eye on all things. In general, we at Flatiron are for transparency in operations, openness, data and code releases, and building legacy value.

I spent most of my research time responding (at the very last minute) to this NASA Request For Information. I wrote in response a white paper about supporting methods and software for development of blue-sky methods that might enable qualitatively new missions and capabilities. My (very hastily written; my apologies!) white paper is here. Comments greatly appreciated (even though it is too late for the RFI itself).

not a thing

All NSF proposals, all day! That doesn't count as research (see Rules). Maybe I should change the rules?

nada

Today was almost all teaching, letters, and NSF proposals.