| Autor: |
Lucia A. Perez, Shy Genel, Francisco Villaescusa-Navarro, Rachel S. Somerville, Austen Gabrielpillai, Daniel Anglés-Alcázar, Benjamin D. Wandelt, L. Y. Aaron Yung |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
The Astrophysical Journal, Vol 954, Iss 1, p 11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1538-4357 |
| DOI: |
10.3847/1538-4357/accd52 |
| Popis: |
As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine-learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but they must be trained carefully on large and representative data sets. We present a new “hump” of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter-only simulations of (100 h ^−1 cMpc) ^3 with different cosmological parameters (Ω _m and σ _8 ) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof of concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation, count-in-cells, and void probability functions, and we probe nonlinear and linear scales across 0.68 < R |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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