36 citations · 71 across the 12 of their papers we have counts for
13 papers
BIND (Baryonic INpainting with Deep learning): A Field-level Emulator for Galaxy Groups and Clusters
Max E. Lee, Shy Genel, Zoltan Haiman +3
Baryonic feedback is a dominant source of systematic uncertainty for upcoming weak-lensing surveys, but current tools for modeling its effect rely on spherical approximations and d…
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
Shy Genel, Yongseok Jo, Boon Kiat Oh +10
We present a new set of 1,192 cosmological simulations as part of the CAMELS project, in which a space of 35 cosmological, astrophysical, and numerical parameters is explored aroun…
Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations
Laura Sommovigo, Deaglan J. Bartlett, Rachel K. Cochrane +3
Dust attenuation is a major source of systematic uncertainty in both SED fitting and forward modeling of galaxy populations, yet the functional form used to parameterize attenuatio…
CAMELS Environments: The Impact of Local Neighbours on Galaxy Evolution across the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE Simulations
Xavier Sims, Daniel Anglés-Alcázar, Boon-Kiat Oh +6
Internal feedback from massive stars and active galactic nuclei (AGN) play a key role in galaxy evolution, but external environmental effects can also strongly influence galaxies.…
Cosmological back-reaction of baryons on dark matter in the CAMELS simulations
Matthew Gebhardt, Daniel Anglés-Alcázar, Shy Genel +13
Baryonic processes such as radiative cooling and feedback from massive stars and active galactic nuclei (AGN) directly redistribute baryons in the Universe but also indirectly redi…
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Andreas Filipp, Tri Nguyen, Laurence Perreault-Levasseur +5
Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally infer…
How does feedback affect the star formation histories of galaxies?
Kartheik G. Iyer, Tjitske K. Starkenburg, Greg L. Bryan +20
Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the CAMELS suite…
Probing the co-evolution of SMBHs and their hosts from scaling relations pairwise residuals: dominance of stellar velocity dispersion and host halo mass
Francesco Shankar, Mariangela Bernardi, Daniel Roberts +17
The correlations between Supermassive Black Holes (SMBHs) and their host galaxies still defy our understanding from both the observational and theoretical perspectives. Here we per…
The Spectral Energy Distributions of Galaxies
Kartheik G. Iyer, Camilla Pacifici, Gabriela Calistro-Rivera +1
The spectral energy distribution (SED) of a galaxy represents the distribution of electromagnetic radiation emitted across all wavelengths, from radio waves to gamma rays. The gala…
Learning the Universe: physically-motivated priors for dust attenuation curves
Laura Sommovigo, Rachel K. Cochrane, Rachel S. Somerville +9
Understanding the impact of dust on the spectral energy distributions (SEDs) of galaxies is crucial for inferring their physical properties and for studying the nature of interstel…
Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
Christopher C. Lovell, Tjitske Starkenburg, Matthew Ho +9
We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference ap…
On the Significance of Rare Objects at High Redshift: The Impact of Cosmic Variance
Christian Kragh Jespersen, Charles L. Steinhardt, Rachel S. Somerville +1
The discovery of extremely luminous galaxies at ultra-high redshifts () has challenged galaxy formation models. Most analyses of this tension have not accounted for the…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…