The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
The Quijote Simulations
Open accessAbstract
The Q uijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 each simulation follows the evolution of 256 3, 512 3, or 1024 3 particles in a box of 1 h −1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Q uijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions.
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Cites
Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
Riess, A. G., Filippenko, A. V., Challis, P., Clocchiatti, A., Diercks, A., Garnavich, P. M., Gilliland, R. L., Hogan, C. J., Jha, S., Kirshner, R. P., et al. (1998) · The Astronomical Journal · doi:10.1086/300499 · Cited by 19,704
Measurements of Ω and Λ from 42 High‐Redshift Supernovae
Perlmutter, S., Aldering, G., Goldhaber, G., Knop, R. A., Nugent, P., Castro, P. G., Deustua, S., Fabbro, S., Goobar, A., Groom, D. E., et al. (1999) · The Astrophysical Journal · doi:10.1086/307221 · Cited by 18,062
The cosmological simulation code gadget-2
Springel, V. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09655.x · Cited by 6,368
Lewis, A., Challinor, A., & Lasenby, A. (2000) · The Astrophysical Journal · doi:10.1086/309179 · Cited by 4,920
The evolution of large-scale structure in a universe dominated by cold dark matter
Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. (1985) · The Astrophysical Journal · doi:10.1086/163168 · Cited by 3,391
Invariant Scattering Convolution Networks
Bruna, J., & Mallat, S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.230 · Cited by 1,633
Self-citationMallat, S. (2012) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.21413 · Cited by 1,038
Self-citationKarhunen‐Loeve Eigenvalue Problems in Cosmology: How Should We Tackle Large Data Sets?
Tegmark, M., Taylor, A. N., & Heavens, A. F. (1997) · The Astrophysical Journal · doi:10.1086/303939 · Cited by 979
Knollmann, S. R., & Knebe, A. (2009) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/182/2/608 · Cited by 889
Transients from initial conditions in cosmological simulations
Crocce, M., Pueblas, S., & Scoccimarro, R. (2006) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2006.11040.x · Cited by 657
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Cited by
Di Valentino, E., Said, J. L., Riess, A., Pollo, A., Poulin, V., Gómez-Valent, A., Weltman, A., Palmese, A., Huang, C. D., Bruck, C. V. D., et al. (2025) · Physics of the Dark Universe · doi:10.1016/j.dark.2025.101965 · Cited by 319
Self-citationThe CAMELS Project: Cosmology and Astrophysics with Machine-learning Simulations
Villaescusa-Navarro, F., Anglés-Alcázar, D., Genel, S., Spergel, D. N., S. Somerville, R., Dave, R., Pillepich, A., Hernquist, L., Nelson, D., Torrey, P., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abf7ba · Cited by 304
Self-citationFull-shape galaxy power spectra and the curvature tension
Glanville, A., Howlett, C., & Davis, T. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2891 · Cited by 69
Abdalla, E., Abellán, G. F., Aboubrahim, A., Agnello, A., Akarsu, Ö., Akrami, Y., Alestas, G., Aloni, D., Amendola, L., Anchordoqui, L. A., et al. (2022) · Journal of High Energy Astrophysics · doi:10.1016/j.jheap.2022.04.002 · Cited by 37
Self-citationHInet: Generating Neutral Hydrogen from Dark Matter with Neural Networks
Wadekar, D., Villaescusa-Navarro, F., Ho, S., & Perreault-Levasseur, L. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac033a · Cited by 32
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Metadata
- Authors
- Villaescusa-Navarro, F., Hahn, C., Massara, E., Banerjee, A., Delgado, A. M., Ramanah, D. K., Charnock, T., Giusarma, E., Li, Y., Allys, E., Brochard, A., Uhlemann, C., Chiang, C.-T., He, S., Pisani, A., Obuljen, A., Feng, Y., Castorina, E., Contardo, G., Kreisch, C. D., Nicola, A., Alsing, J., Scoccimarro, R., Verde, L., Viel, M., Ho, S., Mallat, S., Wandelt, B., & Spergel, D. N.
- Venue
- The Astrophysical Journal Supplement Series
- Publisher
- American Astronomical Society
- Published
- 2020-08-20
- Volume
- 250
- Issue
- 1
- Pages
- 2
- ISSN
- 0067-0049 (ISSN-L)
- 1538-4365
- OpenAlex
- W2972913944
- Cited by
- 336
- References
- 51
- FWCI
- 17.33
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Villaescusa-Navarro, F., Hahn, C., Massara, E., Banerjee, A., Delgado, A. M., Ramanah, D. K., Charnock, T., Giusarma, E., Li, Y., Allys, E., Brochard, A., Uhlemann, C., Chiang, C.-T., He, S., Pisani, A., Obuljen, A., Feng, Y., Castorina, E., Contardo, G., . . . Spergel, D. N. (2020). The Quijote Simulations. The Astrophysical Journal Supplement Series. https://doi.org/10.3847/1538-4365/ab9d82
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