Both Flatiron group meetings were great today. In the first, Nathan Leigh (AMNH) Spoke about collisions of star systems (meaning 2+1 interactions, 2+2, 2+3, and 3+3), using collisionless dynamics and the sticky star approximation (to assess collisions). He finds a simple scaling of collision probabilities in terms of combinatorics; that is, the randomness or chaos is efficient, or more efficient than you might think. The crowd had many questions about scattering in stellar systems and equipartition.
This led to a wider discussion of dynamical scattering. We asked the question: Can we learn about dynamical heating in stellar systems by looking at residual exoplanet populations (for example, if the heating is by close encounters by stars, systems should be truncated)? We concluded that wide separation binaries are probably better tracers from the perspective that they are easier to see. Then we asked: Can the Sun's own Oort cloud be used to measure of star-star interactions? And: Are there interstellar comets? David Spergel (Flatiron) pointed out the (surprising, to me) fact that there are no comets on obviously hyperbolic orbits.
Raja Guhakathurta (UCSC) is in town; he showed an amazing video zooming in to a tiny patch of Andromeda’s disk. He discussed Julianne Dalcanton’s dust results in M31 (on which I am a co-author). He then showed us detailed velocity measurements he has made for 13,000 (!) stars in the M31 disk. He finds the velocity dispersion of the disk grows with age, and grows faster and to larger values than in the Milky-Way disk. That led to more lunch-time speculation.
In the cosmology meeting, Shirley Ho (CMU) spoke about large-scale structure and machine learning. She asked the question: Can we use machine learning to compare simulations to data? In order to address this, she is doing a toy project: Compare simulations to simulations. Finds that a good conv-net does as well as the traditional power-spectrum analysis. This led to some productive discussion of where machine learning is most valuable in cosmology. Ben Wandelt (Paris) hypothesized that a machine-learning emulator can’t beat an n-body simulation. I disagreed (though on weak grounds)! We proposed that we set up a challenge of some kind, very well specified.
Ben Wandelt then spoke about linear inverse problems, on which he is doing very creative and promising work. He classified foreground approaches (for LSS and CMB) into Avoid or Adapt or Attack. On Avoid: He is using a low-rank covariance constraint to find foregrounds (This capitalizes on smooth wavelength (frequency) dependences, but reduces detailed assumptions). He showed that this separates signal and foreground—by the signal being high-rank and CDM-like (isotropic, homogeneous, etc), while the foreground is low rank (smooth in wavelength space). He then switched gears and showed us an amazingly high signal-to-noise void–galaxy cross-correlation function. We discussed how the selection affects the result. The cross-correlation is strongly negative at small separations and shows an obvious Alcock–Paczynski effect. David Spergel asked: Since this is an observation of “empty space”, does it somehow falsify modified GR or radical particle things?
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