
CAMELS project
@camels_project
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project.
ID: 1440834707302731777
https://www.camel-simulations.org 23-09-2021 00:25:53
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Paper day: arxiv.org/abs/2302.14101. We train GNNs to perform field-level likelihood-free inference using galaxy catalogs from CAMELS project. Our models have no scale cutoff, achieved a precision of ~12% when inferring Ωm, and it is robust for 5 != subgrid models! Check it out!

In our new PNAS paper, we use machine learning to discover novel equations for deriving masses of clusters of galaxies from observational quantities! (1/10) pnas.org/doi/10.1073/pn… with Miles Cranmer @paco_astro@jcolinhill David Spergel Shirley Ho,Leander,Nick,Daniel,Lars.


The CAMELS project keeps growing. We have made publicly available 2,124 hydrodynamic simulations of CAMELS-ASTRID; a new suite run with the MP-Gadget code using the ASTRID subgrid physics model. All the data is accessible through globus and binder at Flatiron Institute


CAMELS project NASA Webb Telescope CDSportal Daniel Pomarède Hubble Chandra Observatory NSF-DOE Rubin Observatory Nancy Grace Roman Space Telescope ALMA Observatory📡 Event Horizon 'Scope Alyssa A. Goodman Dark Energy Survey Noam Libeskind LIGO NANOGrav PFC @iaa_coin Galaxy Map (@[email protected]) CosmoStat Laboratory Atacama Cosmology Telescope NOIRLab Anton Petrov Ai2 Dr Becky Smethurst Fraser Cain Scott Manley P(David|Kipping) ∝ P(Kipping|David) P(David) RASC Calgary Centre ESA Gaia JWST CEERS Collaboration youtu.be/JFAB1MhYGyc

Here's something exciting about what the CAMELS project can do for low-redshift filament science in the near future.


Paper day: arxiv.org/abs/2310.15234. We are measuring the impact of systematics training GNNs to perform field-level simulation-based inference with galaxy catalogs from CAMELS project. We do not have and cut on scale and the models are robust. First steps before using real data!