(abs, pdf) Acquaviva, Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges
(abs, pdf) Dosopoulou et al., Galaxy Core Formation by Supermassive Black Hole Binaries: the Importance of Realistic Initial Conditions and Galaxy Morphology
(abs, pdf) Schauer et al., Globular Clusters and Streaming Velocities: Testing the new formation channel in high-resolution cosmological simulations
(abs, pdf) Valentini et al., Host galaxies of high-redshift quasars: supermassive black hole growth and feedback
(abs, pdf) Scannapieco et al., The n-capture and alpha-elements abundance ratios scatter in old stellar populations. Cosmological simulations of the stellar halo
(abs, pdf) Gallegos-Garcia et al., Binary Black Hole Formation with Detailed Modeling: Stable Mass Transfer Leads to Lower Merger Rates
(abs, pdf) Ebisu et al., Constraining Self-Interacting Dark Matter with Dwarf Spheroidal Galaxies and High-resolution Cosmological $N$-body Simulations
(abs, pdf) Xu et al., Predicting halo occupation and galaxy assembly bias with machine learning
(abs, pdf) Vikaeus et al., Conditions for detecting lensed Population III galaxies in blind surveys with the James Webb Space Telescope, the Roman Space Telescope and Euclid
(abs, pdf) Nagele et al., Collapse of $\sim 10^4$ $M_\odot$ population III supermassive stars with neutrino transfer
(abs, pdf) Ricarte et al., Unveiling the Population of Wandering Black Holes via Electromagnetic Signatures