James O. Baldwin

We perform field-level likelihood-free inference of the matter density parameter Ω _m from simulated galaxy catalogs using machine learning models with differing inductive biases. Using hydrodynamic simulations from CAMELS, we examine how observable choice and architecture govern cosmological information extraction. We consider galaxy positions and line-of-sight peculiar velocities, separately and jointly, and compare permutation-invariant deep sets, implemented with either multilayer perceptrons (MLPs) or Kolmogorov–Arnold networks (KANs), to graph neural networks (GNNs), which explicitly encode spatial relations. We test in-distribution and out-of-distribution (OOD) performance across simulations with different subgrid galaxy-formation prescriptions. Deep sets infer Ω _m from velocities alone with mean relative errors of approximately 18% in-distribution and ∼25% OOD, with KANs and MLPs achieving comparable performance. In contrast, the same set-based approach does not yield useful σ ₈ predictions in either in-distribution or cross-suite tests. Adding positions does not improve deep sets, while GNNs infer Ω _m with mean relative errors of about 10% in-distribution and 10%–17% OOD. These results indicate that peculiar velocities provide the dominant source of Ω _m information for set-based models in this setting, while spatial information is most effectively used by architectures that explicitly encode galaxy–galaxy relations. Because the velocity inputs are exact simulated peculiar velocities, applications to survey data will require validation under realistic velocity-measurement noise, selection effects, and survey geometry.

GN-z11 is the highest redshift galaxy spectroscopically confirmed with the Hubble Space Telescope (HST). Previous measurements of the effective radius of GN-z11 utilized galfit, which is not optimized to measure structural parameters for such a faint, distant object. Using new software called forcepho, we derive a size from images in the F160W band obtained both from the complete CANDELS survey and additional midcycle observations in order to contribute to the knowledge base on the size evolution, size–luminosity, and size–mass relation of early galaxies. We find a half-light radius mean of 0.″036 ± 0.″006 corresponding to a physical size of 0.15 ± 0.025 kpc. This size, smaller than the point-spread function, is dramatically smaller than previous estimates with shallower HST data using galfit but consistent with recent measurements using forcepho on new JWST data. Such a small size suggests that GN-z11's high luminosity is dominated by an active galactic nucleus.

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfvén waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, α = 2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: preflare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that α = 1.63 ± 0.03. This is below the critical threshold, suggesting that Alfvén waves are an important driver of coronal heating.