Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lyα spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at https://camels.readthedocs.io.

Abstract:

We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a second-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological N-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an eight-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to non-linear scales (k ∼ 0.6hMpc⁻¹). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the universe.

Abstract:

Current and future imaging surveys require photometric redshifts (photo- z s) to be estimated for millions of galaxies. Improving the photo- z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo- z estimates by simultaneously predicting the broadband photo- z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo- z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo- z s that are 13% more precise down to magnitude i AB < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo- z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z > 1. Applying this technique to deeper samples is crucial for future surveys such as Euclid or LSST. For simulated data, training on a sample with i AB < 23, the method reduces the photo- z scatter by 16% for all galaxies with i AB < 25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo- z s, which reduces the photo- z scatter by 20% in the COSMOS field.