Betz limit for sailboats?

In the study of sustainable energy, there is a nice result on windmills, called the Betz limit: There is a finite limit to the fraction of the kinetic energy of the wind that a windmill can absorb or exploit. The reason is often stated as: If the windmill took all of the power in the wind, the wind would stop, and then there would be no flow of energy over the windmill. I'm not sure I exactly agree with that explanation, but let's leave that here.

On my travel home today I worked on the possibility that there is an equivalent to the Betz limit for sailboats. Is there an energetic way of looking at sailing that is useful?

One paradox is that a sailboat is sailing steadily when the net force on the boat is zero (just like when a windmill is turning at constant angular velocity). In the Betz limit, the windmill is thought of as having two different torques on it, one from the wind, and one from the turbine. Sailing has no turbine. So this problem has a conceptual component to it.

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.

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.

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.

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.

is machine learning good or bad for science?

I took the train to Vienna today, for a PhD defense and to give a talk about machine learning. My talk is interdisciplinary so I looked at how to generalize my arguments about astrophysics to all of the natural sciences. It turns out that this isn't as easy as I'd like, since it is hard to be specific outside of astrophysics! I'm going to learn a lot getting this talk ready.

MIT visit

I spent the day at MIT today. I learned a huge amount! One highlight was that Rob Simcoe (MIT) showed me the hardware in his lab, and we discussed trade-offs between software and hardware in instrument design. Another was talking to a great group of graduate students over lunch. My talk was about the ontology and epistemology of machine learning. My slides are here. At dinner, Deepto Chakrabarty (MIT) encouraged me to complete my pedagogical note on bolometric magnitudes and so on.

optimal sizes of sails?

I spent some time in-between vacationing to look at the relative sizes of sails and keels on sailboats (yes, sailboats). I find that a boat sailing cross-wind sails fastest when the ram-pressure force prefactor (effective area times density) of the sail and the keel are comparable. That is, you want the effective area of the sail to be something like 800 times larger than the effective area of the keel! Strange, but maybe not false for the fastest competition sailboats?

writing

I hid in an undisclosed location today and worked on various pieces of writing, but especially the nearly-complete paper by Adam Wheeler (Columbia) on finding abundance outliers with an unsupervised method.

#AAS235, day 4 and #hackaas

Today was Hack Together Day #hackaas at #AAS235. We computed that this is the eighth winter AAS meeting to have a hack day, making it (AAS Hack Together Day) one of my scientific accomplishments of the decade. At the hack day, the main thing I did was hack on hack day, working with Jim Davenport (UW) to brainstorm things we can do to keep the event fresh, and keep us experimenting with it. I also had a great conversation with Brigitta Sipocz, Geert Barentsen, and others about ways we can use our hacking and design thinking to support a reduction in CO₂ emissions by astronomers and academics in general. Related to my conversations of yesterday.

But many great things happened in the Hack Together Day. Too many to list here. Look at the wrap-up slides to get a sense of the range and depth of the projects. So many people learned a lot and did a lot. I'm proud, which is a sin, apparently.

remote meetings

A highlight of today was a long meeting with Chris Lintott (Oxford) covering many subjects. But he told me about dot-dot-astronomy, which is a fully-remote reboot they are working on for the niche but extremely influential dot-astronomy meetings. The idea is to go fully remote—all participants remote—but then change the meeting expectations and structure to respect that. The idea is: Maybe not try to do remote meetings so they are just as good as face-to-face meetings, but to try to do remote meetings so they are something very different from face-to-face meetings. That seems like a great idea. Let's re-frame our goals. We have to do something about what we are doing to this planet.

not much

I had a travel day today. Great for getting work done! But all I did was write a few paragraphs in my paper with Bedell on radial-velocity bounds.

selection, EPRV, spirals

[I have been on travel of various kinds, mostly non-work, for almost two weeks, hence no posts!]

While on my travels, I wrote in my project about target selection for spectroscopic surveys (with Rix) and my project about information theory and extreme-precision radial-velocity measurement (with Bedell). I also discovered this nice paper on Cepheid stars in the disk, which is a highly relevant position-space complement to what Eilers and I have been doing in velocity space.

nothing

I took the train from Bordeaux to Heidelberg today. What an opportunity to get some work done! And yet I did... Nada. Or nada research-wise.

words on a plane

I spent time today on an airplane, writing in the papers I am working on with Jessica Birky (UCSD) and Megan Bedell (Flatiron). And I read documents in preparation for the review of the SDSS-V Project that I am leading over the next two days in a Denver airport hotel.

Oregon, falsifiability, and the LIGO project

Today was my first day of a two-day visit to Ben Farr (Oregon) and the University of Oregon. I got lots of work done during the travel phases of the day, because I have a NASA proposal due while I'm here in Oregon! Nothing like a deadline.

I had a great day. Highlights included a discussion with James Schombert (Oregon) about various philosophical matters related to falsification. He explicitly brought up my paper about plausibility and science, which I had nearly forgotten! It's nice to know that people are finding it useful still. I really wrote it to get some things off my chest, things that had been troubling me since graduate school in the 1990s. In that paper I argue that we prefer theories that are both observationally reasonable and also theoretically reasonable; there isn't really such a thing as purely empirical falsification. At least not in the observational sciences.

But of course the main theme of my visit was LIGO. The lure of discussing this project with Farr is what brought me here. We postponed our ideas for new projects until tomorrow and, maybe surprisingly, spent our time talking about university-based project management! Because although LIGO is well funded to build hardware and deliver strain measurements, what is done with those to detect and characterize systems and populations is left to the science community, which is a looser collaboration, and which must raise most of its money externally. And, like with the SDSS family of projects, relies on essentially volunteer efforts from many ornery faculty. That's an interesting set of problems in organizational management, psychology, and political science!

Simpson's Paradox, Li, self-calibration

On the plane home from the UK, I worked on three things. The first was a very nice paper by Ivan Minchev (AIP) and Gal Matijevic (AIP) about Simpson's Paradox in Milky Way stellar statistics, like chemodynamics. Simpson's paradox is the point that a trend can have a different sign in a subset of a population than it does in the whole population. The classic example is of two baseball players over two season: In the first season, player A has a higher batting average than player B. And in the second season, player A again has a higher batting average than player B. And yet, overall, player B has a higher average! How is that possible? It works if player A bats far more in one season, and player B bats far more in the other, and they both bat higher in that other season. Anyway, the situation is generic in statistics about stars!

The second thing I worked on was a new paper by Andy Casey (Monash) and company about how red-giant stars get Lithium abundance anomalies. He shows that Li anomalies happen all over the RGB, and even on stars descending the branch (as per asteroseismology) and thus he can show that the Li anomalies are not caused by any particular stellar evolutionary phase. That argues for either planet engulfment or binary-induced convection changes. The former is also disfavored because of the stars descending the RGB. The real triumph is the huge sample of Li-enhanced stars he has found, working with The Cannon and Anna Y. Q. Ho (Caltech). It's a really beautiful use of The Cannon as a spectral synthesis tool.

The third thing I worked on was a plan to self-calibrate HARPS (and equivalent spectrograph) pixel offsets (that is, calibration errors at the pixel level) using the science data from the instrument. That is, you don't need arcs or Fabry–Perot to find these offsets; since they matter to data interpretation, they can be seen in the data directly! I have a plan, and I think it is easy to implement.

simple, explainable algorithms

I spent the day in Princeton, visiting both the Department of Astrophysical Sciences and the Center for Statistics and Machine Learning. I had many great conversations, and I gave a talk that is about data-driven methods and their relationships to what we think of as being machine learning.

One highlight was a conversation with Ryan Adams (Princeton), who has brought some serious probabilistic methods to astronomy but is himself a computer scientist and statistician. He and I discussed the issues of algorithmic real-time, adaptive target selection for astronomical projects (especially EPRV-like). There is the full Bayesian decision thing, which I know how to do but which is expensive. But there is the idea that the decisions should be simple, and explainable. He pointed out that this is a huge area of research right now, and it connects to many things, especially in ethical situations: We want simple, explainable decisions! That's an interesting idea to bring into astrophysics.

There were many other great conversations, ranging across polarimetry of exoplanets, star shades and coronographs, neuroscience, stellar surface mapping, photometric redshifts, and astronomical catalog making.

Waterloo

I spent the day today at University of Waterloo, where I gave the Astro seminar. It was a great day! I prepared my talk on the bus from Toronto, which wasn't good from a nausea perspective! But I really find I give a better talk if I remake it from scratch before I give it. That is, old talks get stale, at least for me. So I have a brand-new talk about machine learning and data-driven models and criticisms thereof.

Before my talk, in the astro-ph discussion, and after my talk, with James Taylor (Waterloo) and with Mike Hudson (Waterloo), there were good ideas flowing about how to use galaxy morphologies and in particular galaxy granularity to determine galaxy distances and maybe also gravitational-lensing shear. This relates to photometric redshifts and also my ideas about making adversarial galaxies that don't reveal their shear via their ellipticities (or not strongly). Many other great conversations; too many to mention!

My visit ended with quality time with Dustin Lang (Perimeter), who always makes my day.

#DSESummit2018, day 1

Today was the start of the annual Moore-Sloan Data Science Environments summit. I led an ice-breaker in which we split into small groups and discussed figures and data visualizations. It's a great community, so it was fun to get started. But as for research: I read and commented on text for Bedell (Flatiron) on the plane, and I worked with Richard Galvez (NYU) on designing a small project that brings machine learning to the Gaia data.