44 papers
Learning Cosmology from Nearest Neighbour Statistics
Atrideb Chatterjee, Arka Banerjee, Francisco Villaescusa-Navarro +1
Extracting cosmological parameters from galaxy/halo catalogues with sub-percent level accuracy is an important aspect of modern cosmology, especially in view of ongoing and upcomin…
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
Raul Jimenez, Boris Bolliet, Francisco Villaescusa-Navarro +7
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code…
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…
Reconciling the Fundamental Plane of Early-Type Galaxies with hydrodynamical simulations: The case of IllustrisTNG100-1
Pedro de Araujo Ferreira, Nicola R. Napolitano, Crescenzo Tortora +2
The Fundamental Plane (FP) of Early-Type Galaxies (ETGs) encapsulates a tight correlation among their structural and dynamical properties and provides an important benchmark for ga…
Cosmology with one galaxy: An analytic formula relating with galaxy properties
Kito Liao, Francisco Villaescusa-Navarro, Romain Teyssier +1
Standard cosmological analyses typically treat galaxy formation and cosmological parameter inference as decoupled problems, relying on population-level statistics such as clusterin…
Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters
Christopher C. Lovell, Max E. Lee, William J. Roper +4
We present a new method for emulating the halo mass function (HMF) and other distribution functions in large effective volumes, down to low halo masses, whilst simultaneously modif…