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, Ly α 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 .
more »
« less
The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence
Abstract We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ∼100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N -body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io .
more »
« less
- Award ID(s):
- 2108944
- NSF-PAR ID:
- 10331321
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- The Astrophysical Journal Supplement Series
- Volume:
- 259
- Issue:
- 2
- ISSN:
- 0067-0049
- Page Range / eLocation ID:
- 61
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract We present
GIGANTES , the most extensive and realistic void catalog suite ever released—containing over 1 billion cosmic voids covering a volume larger than the observable universe, more than 20 TB of data, and created by running the void finderVIDE onQUIJOTE ’s halo simulations. TheGIGANTES suite, spanning thousands of cosmological models, opens up the study of voids, answering compelling questions: Do voids carry unique cosmological information? How is this information correlated with galaxy information? Leveraging the large number of voids in theGIGANTES suite, our Fisher constraints demonstrate voids contain additional information, critically tightening constraints on cosmological parameters. We use traditional void summary statistics (void size function, void density profile) and the void autocorrelation function, which independently yields an error of 0.13 eV on ∑m ν for a 1h −3Gpc3simulation, without cosmic microwave background priors. Combining halos and voids we forecast an error of 0.09 eV from the same volume, representing a gain of 60% compared to halos alone. Extrapolating to next generation multi-Gpc3surveys such as the Dark Energy Spectroscopic Instrument, Euclid, the Spectro-Photometer for the History of the Universe and Ices Explorer, and the Roman Space Telescope, we expect voids should yield an independent determination of neutrino mass. Crucially,GIGANTES is the first void catalog suite expressly built for intensive machine-learning exploration. We illustrate this by training a neural network to perform likelihood-free inference on the void size function, giving a ∼20% constraint on Ωm. Cosmology problems provide an impetus to develop novel deep-learning techniques. WithGIGANTES , machine learning gains an impressive data set, offering unique problems that will stimulate new techniques. -
more » « less
ABSTRACT Feedback from active galactic nuclei (AGNs) and supernovae can affect measurements of integrated Sunyaev–Zeldovich (SZ) flux of haloes (YSZ) from cosmic microwave background (CMB) surveys, and cause its relation with the halo mass (YSZ–M) to deviate from the self-similar power-law prediction of the virial theorem. We perform a comprehensive study of such deviations using CAMELS, a suite of hydrodynamic simulations with extensive variations in feedback prescriptions. We use a combination of two machine learning tools (random forest and symbolic regression) to search for analogues of the Y–M relation which are more robust to feedback processes for low masses ($M\lesssim 10^{14}\, \mathrm{ h}^{-1} \, \mathrm{ M}_\odot$); we find that simply replacing Y → Y(1 + M*/Mgas) in the relation makes it remarkably self-similar. This could serve as a robust multiwavelength mass proxy for low-mass clusters and galaxy groups. Our methodology can also be generally useful to improve the domain of validity of other astrophysical scaling relations. We also forecast that measurements of the Y–M relation could provide per cent level constraints on certain combinations of feedback parameters and/or rule out a major part of the parameter space of supernova and AGN feedback models used in current state-of-the-art hydrodynamic simulations. Our results can be useful for using upcoming SZ surveys (e.g. SO, CMB-S4) and galaxy surveys (e.g. DESI and Rubin) to constrain the nature of baryonic feedback. Finally, we find that the alternative relation, Y–M*, provides complementary information on feedback than Y–M.
-
null (Ed.)more » « less
Abstract Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to accelerate the process. However, the dependence on data induces a unique challenge: An imbalanced dataset containing more of certain shapes or physical properties than others can be detrimental to the efficacy of the approaches and any models built on those sets. In answer, we posit that a smaller yet diverse set of unit cells leads to scalable search and unbiased learning. To select such subsets, we propose METASET, a methodology that 1) uses similarity metrics and positive semi-definite kernels to jointly measure the closeness of unit cells in both shape and property space, and 2) incorporates Determinantal Point Processes for efficient subset selection. Moreover, METASET allows the trade-off between shape and property diversity so that subsets can be tuned for various applications. Through the design of 2D metamaterials with target displacement profiles, we demonstrate that smaller, diverse subsets can indeed improve the search process as well as structural performance. We also apply METASET to eliminate inherent overlaps in a dataset of 3D unit cells created with symmetry rules, distilling it down to the most unique families. Our diverse subsets are provided publicly for use by any designer.
-
Abstract A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract most information. We present HIF low : a fast generative model of the neutral hydrogen (H i ) maps that is conditioned only on cosmology (Ω m and σ 8 ) and designed using a class of normalizing flow models, the masked autoregressive flow. HIF low is trained on the state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. HIF low has the ability to generate realistic diverse maps without explicitly incorporating the expected two-dimensional maps structure into the flow as an inductive bias. We find that HIF low is able to reproduce the CAMELS average and standard deviation H i power spectrum within a factor of ≲2, scoring a very high R 2 > 90%. By inverting the flow, HIF low provides a tractable high-dimensional likelihood for efficient parameter inference. We show that the conditional HIF low on cosmology is successfully able to marginalize over astrophysics at the field level, regardless of the stellar and AGN feedback strengths. This new tool represents a first step toward a more powerful parameter inference, maximizing the scientific return of future H i surveys, and opening a new avenue to minimize the loss of complex information due to data compression down to summary statistics.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3847/1538-4365/ac5ab0
