High mass and halo resolution from fast low resolution simulations
Open accessAbstract
Generating mocks for future sky surveys requires large volumes and high resolutions, which is computationally expensive even for fast simulations. In this work we try to develop numerical schemes to calibrate various halo and matter statistics in fast low resolution simulations compared to high resolution N-body and hydrodynamic simulations. For the halos, we improve the initial condition resolution and develop a halo finder "relaxed-FoF", where we allow different linking lengths for different halo mass and velocity dispersions. We show that our relaxed-FoF halo finder improves the common statistics, such as halo bias, halo mass function, halo auto power spectrum, cross correlation coefficient with the reference halo catalog, and halo-matter cross power spectrum. We also calibrate small-scale velocities of small halos to improve the power spectrum in redshift space. For the matter statistics, we incorporate the potential gradient descent (PGD) method into fast simulations to improve the matter distribution at nonlinear scales. By building a lightcone output, we show that the PGD method significantly improves the weak lensing convergence tomographic power spectrum. With these improvements FastPM is comparable to the high resolution full N-body simulation of the same mass resolution, with two orders of magnitude fewer time steps. These techniques can be used to improve the halo and matter statistics of FastPM simulations for mock catalogs of future surveys such as DESI and LSST.
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Dai & Seljak (2020) successfully demonstrated that Lagrangian Deep Learning reproduces a range of maps from IllustrisTNG, including stellar mass distribution and electron momentum and pressure.
“This scheme was successfully demonstrated to reproduce a range of maps from IllustrisTNG, including stellar mass distribution and electron momentum and pressure.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Dai & Seljak (2020)
Cites
A gradient based method for modeling baryons and matter in halos of fast simulations
Dai, B., Feng, Y., & Seljak, U. (2018) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2018/11/009 · Cited by 35 · cited 4× · see citing works
“We show that the fully coupled PGD correction improves the matter power spectrum measured from static snapshot at all redshifts, just as the previously studied static PGD method [10].”
BackgroundMethodInfluentialSelf-citation
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The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2022.55 · Cited by 102 · see citing works
Modi, C., Chen, S.-F., & White, M. J. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa251 · Cited by 78 · see citing works
“However, it does produce halo catalogs which are close to those produced by a more traditional N-body code (Feng et al. 2016; Ding et al. 2018; Modi et al. 2019b; Dai et al. 2019).”
BackgroundAI-assisted superresolution cosmological simulations
Li, Y., Ni, Y., Croft, R. A. C., Matteo, T. D., Bird, S., & Yu, F. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022038118 · Cited by 99 · see citing works
“In other work, (17, 18) sharpened the particle distribution using a potential gradient descent (PGD) method starting from low-resolution simulations.”
BackgroundSelf-citationDai, B., & Seljak, U. (2020) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2020324118 · Cited by 37 · see citing works
“…of such a process applied to time and spatial coarse graining is the dark matter evolution with a few time steps only, which combines ideas such as the approximate N-body solvers, with a force sharpening process called the Potential Gradient Descent (PGD) to capture the coarse graining [31, 32].”
MethodSelf-citationDeRose, J., Kokron, N., Banerjee, A., Chen, S.-F., White, M., Wechsler, R., Storey-Fisher, K., Tinker, J., & Zhai, Z. (2023) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2023/07/054 · Cited by 35 · see citing works
“Different choices of halo definition can lead to very different resolution requirements [41, 42], and different conclusions about the necessity of parameters beyond halo mass in the HOD parameterization [43, 44].”
BackgroundHahn, C., Lemos, P., Parker, L., Blancard, B. R.-S., Eickenberg, M., Ho, S., Hou, J., Massara, E., Modi, C., Dizgah, A. M., et al. (2024) · Nature Astronomy · doi:10.1038/s41550-024-02344-2 · Cited by 32 · see citing works
“130,131) and some can leverage graphics processing units for further speed-up (for example, refs.”
Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation
Kokron, N., Chen, S.-F., White, M. J., DeRose, J., & Maus, M. (2022) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2022/09/059 · Cited by 31 · see citing works
SimBIG: mock challenge for a forward modeling approach to galaxy clustering
Hahn, C., Eickenberg, M., Ho, S., Hou, J., Lemos, P., Massara, E., Modi, C., Dizgah, A. M., Blancard, B. R.-S., & Abidi, M. M. (2023) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2023/04/010 · Cited by 32 · see citing works
“Their accuracy can also be enhanced using methods such as potential gradient descent [143].”
MADLens, a python package for fast and differentiable non-Gaussian lensing simulations
Böhm, V., Feng, Y., Lee, M. E., & Dai, B. (2020) · Astronomy and Computing · doi:10.1016/j.ascom.2021.100490 · Cited by 16 · see citing works
“utputs for inference leads to unbiased parameter posteriors. Another, more safeguard approach is to use machinelearning inspired techniques to boost the accuracy of low resolution N-body simulations (Dai et al., 2020; Dai and Seljak, 2020) and to construct lightcones from those. This is the avenue we have chosen in th…”
MethodSelf-citationThe HalfDome multi-survey cosmological simulations: N-body simulations
Bayer, A., Zhong, Y., Li, Z., DeRose, J., Feng, Y., & Liu, J. (2024) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/05/016 · Cited by 14
“To find halos we use the Relaxed-Friends-of-Friends (RFoF) algorithm [45].”
Self-citationField-level emulation of cosmic structure formation with cosmology and redshift dependence
Jamieson, D., Li, Y., Villaescusa-Navarro, F., Ho, S., & Spergel, D. N. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/03/072 · Cited by 12 · see citing works
Simulation budgeting for hybrid effective field theories
Bartlett, A., DeRose, J., & White, M. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2026/03/078 · Cited by 3
“This method faces several limitations, including but not limited to its reliance on halo-finding algorithms, which impose stringent requirements on the resolution of N-body simulations and increase computational costs [26, 27].”
Suppressing the sample variance of DESI-like galaxy clustering with fast simulations
Ding, Z., Variu, A., Alam, S., Yu, Y., Chuang, C.-H., Paillas, E., García-Quintero, C., Chen, X., Mena-Fernández, J., Aguilar, J., et al. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/01/143
“In addition, several methods have been proposed to further improve the FastPM matter and halo clustering at small scales [62, 63].”
Ridged Lagrangian Perturbation Theory (RLPT)
Kitaura, F., & Sinigaglia, F. (2026) · doi:10.48550/arxiv.2603.13106 · Cited by 1
Primordial non-Gaussianity — fast simulations and persistent summary statistics
Calles, J., Contardo, G., Noreña, J., Yip, J. H. T., & Shiu, G. (2025) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2026/08/051 · Cited by 1
“In our case, fast solvers are typically designed to reproduce two-point statistics and halo abundance at the percent level [62], but tend to diffuse particles within halos, leading halo finders to fragment massive structures and underestimate smaller ones [111].”
Prakash, S., & Kumbamoorthy, S. S. (2024) · Nutrición clínica y dietética hospitalaria · doi:10.12873/442prakash · Cited by 1 · see citing works
Parker, L., Seljak, U., Bayer, A., Feder, R. M., & Valcin, D. (2026) · doi:10.48550/arxiv.2609.14862
“Halos are identified using the Relaxed Friends-of-Friends (RFOF) algorithm [52], which is a variant of the standard Friends-of-Friends method.”
Self-citationSanga, S., & Makorere, R. (2025) · MUST Journal of Research and Development. · doi:10.62277/mjrd2025v6i30007
- Authors
- Dai, B., Feng, Y., Seljak, U., & Singh, S.
- Affiliations
- University of California, Berkeley (US)
- Lawrence Berkeley National Laboratory (US)
- Venue
- Journal of Cosmology and Astroparticle Physics
- Publisher
- IOP Publishing
- Published
- 2020-04-01
- Volume
- 2020
- Issue
- 04
- Pages
- 002-002
- ISSN
- 1475-7508 (ISSN-L)
- 1475-7516
- OpenAlex
- W2968825471
- Cited by
- 22
- References
- 20
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 1 · 2021: 4 · 2020: 4
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Cosmology and Astroparticle Physics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1814370, DEAC02-05CH11231, 1839217)
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, DE-AC02-)
- National Aeronautics and Space Administration (DE-AC02-05CH11231, 80NSSC18K1274)
- National Energy Research Scientific Computing Center (DE–AC02–05CH11231, 05CH11231, AC02-05CH11231)
- Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02, 05CH11231)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
- Keywords
- halo bias, halo mass function, light cone, mock catalogues
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