Tootfinder

Wide binaries in an ultra-faint dwarf galaxy: discovery, population modeling, and a nail in the coffin of primordial black hole dark matter
Cheyanne Shariat, Kareem El-Badry, Mario Gennaro, Keyi Ding, Joshua D. Simon, Roberto J. Avila, Annalisa Calamida, Santi Cassisi, Matteo Correnti, Daniel R. Weisz, Marla Geha, Evan N. Kirby, Thomas M. Brown, Massimo Ricotti, Kristen B. W. McQuinn, Nitya Kallivayalil, Karoline Gilbert, Camilla Pacifici, Puragra Guhathakurta, Denija Crnojevi\'c, …

Wide binaries in an ultra-faint dwarf galaxy: discovery, population modeling, and a nail in the coffin of primordial black hole dark matter
We report the discovery and characterization of a wide binary population in the ultrafaint dwarf galaxy Boötes I using deep JWST/NIRCam imaging. Our sample consists of 52 candidate binaries with projected separations of 7,000 - 16,000 au and stellar masses from near the hydrogen-burning limit to the main-sequence turnoff ($\sim0.1$ - $0.8~{\rm M_\odot}$). By forward-modeling selection biases and chance alignments, we find that $1.25\pm0.25\%$ of Boötes I stars are members of wide binaries wit…

Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
Dominic Schuh, Janik Kreit, Evan Berkowitz, Lena Funcke, Thomas Luu, Kim A. Nicoli, Marcel Rodekamp
https://arxiv.org/abs/2506.17015

Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
We present the first proof of principle that normalizing flows can accurately learn the Boltzmann distribution of the fermionic Hubbard model - a key framework for describing the electronic structure of graphene and related materials. State-of-the-art methods like Hybrid Monte Carlo often suffer from ergodicity issues near the time-continuum limit, leading to biased estimates. Leveraging symmetry-aware architectures as well as independent and identically distributed sampling, our approach resol…