Deep learning insights into cosmological structure formation
[1];
[2];
[4];
- Max-Planck-Institut für Astrophysik, Garching (Germany); University College London (United Kingdom)
- University College London (United Kingdom); Stockholm University (Sweden)
- University College London (United Kingdom)
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); University of Chicago, IL (United States); University of Chicago, IL (United States). Kavli Institute for Cosmological Physics (KICP)
- Science and Technology Facilities Council (STFC), Oxford (United Kingdom). Rutherford Appleton Laboratory (RAL)
The evolution of linear initial conditions present in the early Universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF); European Union’s Horizon 2020; Science and Technology Facilities Council; Swedish Research Council (VR); Knut and Alice Wallenberg Foundation; Engineering and Physical Sciences Research Council (EPSRC)
- Grant/Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1765967
- Alternate ID(s):
- OSTI ID: 2323936
- Report Number(s):
- FERMILAB-PUB--20-643-SCD; arXiv:2011.10577; oai:inspirehep.net:1832538
- Journal Information:
- Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 6 Vol. 109; ISSN 2470-0010
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Insights into the origin of halo mass profiles from machine learning
Testing the Nature of Dark Matter with Extremely Large Telescopes