40 pages, 22 figures (11 made of subfigures)
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 must be trained carefully on large and representative data sets. We developed and generated 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 ($\Omega_m$ and $\sigma_8$) 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 function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.
27 pages, 21 Figures, submitted to MNRAS, comments welcome
22 pages, 14 figures, submitted to MNRAS, comments welcome
34 pages, 27 figures; Submitted to ApJ; Comments welcome
A&A accepted 04/04/2022, 31 pages, 12 figures and 3 appendixes
20 pages + Appendix, 14 Figures. Submitted to Astronomy & Astrophysics. Abstract is abridged
8 pages, 4 figures, Accepted for publication in The Astrophysical Journal Letters
24 pages, 14 figures, submitted to ApJ
Accepted to Astronomy and Astrophysics (A&A) Journal. 27 pages, 21 Figures
8 Pages. arXiv admin note: text overlap with arXiv:2108.11093
20 pages, 18 figures, submitted to MNRAS
10 pages, 4 figures. Accepted to ApJ Letters
28 pages, 7 figures, 16 tables; accepted for publication in MNRAS
14 pages, 5 figures; Accepted for publication on Astronomy & Astrophysics
25 pages, 16 figures and 2 tables. Accepted for publication in MNRAS
13 pages, 8 figures, VSOLJ Variable Star Bulletin No. 98
14 pages, 10 figures, submitted to A&A
31 pages, 26 figures
7 pages, 7 figures. MNRAS in press
Submitted to MNRAS
10 pages, 6 figures, 4 tables, MNRAS in press
10 pages, 3 figures, Invited Talk at the Winter Simulation Conference WSC2021, Phoenix, AZ, USA
12 pages, 10 figures, Accepted by MNRAS
17 pages, 20 figures, submitted to A&A and under revision. Considered acceptable after minor corrections
17 pages, 12 figures
8 pages, 8 figures
55 pages, 10 figures, Accepted for publication in ApJS
9 pages, 4 figures, to appear in the Proceedings of the 40th Meeting of the Polish Astronomical Society
16 pages, 4 figures. Comments and suggestions are welcome
Accepted for publication in Revista Mexicana de Astronomia y Astrofisica
8 pages, 7 figures
Accepted for publication in Astronomy and Astrophysics
Accepted for publication in ApJS. (26.March.2022)
32 pages, 9 figures, 2 tables. Comments are welcome!
6 latex pages, 1 figures, final version for publication
25 pages, 4 figures
26 pages + appendix; 12 figures
NPB Accepted
20 pages, 11 figures
12 pages, 11 figures, submitted to APS
7 pages, 6 figures