Events at Physics

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Events During the Week of August 8th through August 15th, 2021

Monday, August 9th, 2021

No events scheduled

Tuesday, August 10th, 2021

Network in Neutrinos, Nuclear Astrophysics, and Symmetries (N3AS) Seminar
Probing the Dense-Matter Equation of State with Neutron Star Mergers
Time: 2:00 pm - 3:00 pm
Speaker: Carolyn Raithel, Institute for Advanced Study
Abstract: Binary neutron star mergers provide a unique laboratory for studying the dense-matter equation of state (EOS) across a wide range of parameter space, from the cold EOS during the inspiral to the finite-temperature EOS following the merger. In this talk, I will discuss the impact of the EOS on the post-merger phase of a binary neutron star coalescence, during which time the matter is heated to significant temperatures and can deviate away from its initial equilibrium composition. I will present a new set of neutron star merger simulations, which use a parametrized framework for calculating the EOS at arbitrary temperatures and compositions. I will show how systematically varying the properties of the particle effective mass affects the thermal profile of the post-merger remnant and how this, in turn, influences the post-merger evolution. Finally, I will discuss the impact of varying the slope L of the nuclear symmetry energy on the properties of the post-merger phase. In particular, I will show that the post-merger gravitational wave emission is mostly insensitive to L, but that, in contrast, the dynamical ejecta carry a weak signature of the slope of the symmetry energy.
Host: Baha Balantekin
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Wednesday, August 11th, 2021

Physics ∩ ML Seminar
Interpretable Deep Learning for Physics
Time: 11:00 am - 12:15 pm
Place: Online Seminar: Please sign up for our mailing list at for zoom link
Speaker: Miles Cranmer, Princeton University
Abstract: If we train a neural network on some dynamical system in some region of phase space, and it learns a way to execute the dynamics more efficiently than a handwritten code, how do we distill physical insight from the learned model? In this talk, I will argue that symbolic learning should play a major role in the process of interpreting a machine learning model for physical systems. I will discuss our generic method for converting a neural network that has been trained on a physical system into a symbolic model, via genetic algorithm-based symbolic regression. One of the problems with this process is working with the fact that neural networks have high-dimensional latent spaces, and genetic algorithms scale poorly with the number of features. To work around this issue, I’ll then introduce our “Disentangled Sparsity Network,” which encourages a neural network to learn an easy-to-interpret representation. I will then share several recent applications of our techniques to real physical systems, and the various insights we have discovered and rediscovered.
Host: Gary Shiu
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Thursday, August 12th, 2021

No events scheduled

Friday, August 13th, 2021

No events scheduled