Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T16:49:26.409Z Has data issue: false hasContentIssue false
  • English
  • Français

On Robustness in Cosmological Simulations

Published online by Cambridge University Press:  01 January 2022

Marie Gueguen
Get access Rights & Permissions [Opens in a new window]

Abstract

The Cold Dark Matter model faces many controversies at small scales, as simulations fail to reproduce the observed properties of dark matter (DM) halos. Since rival DM models differ on their predictions about the structure of DM halos, understanding their mass distribution is crucial for determining the nature of DM. However, only numerical approaches to determining what a hypothesis implies for the mass distribution are possible. Hence, simulations are a crucial part of evaluating DM rival models, as is assessing their reliability. I argue that robustness analysis is not a sufficient criterion for the trustworthiness of cosmological simulations.

Type
Physical Sciences
Information
Philosophy of Science , Volume 87 , Issue 5: PSA 2018 - Proceedings of the 2018 biennial meeting of the Philosophy of Science Association. Part II Symposia Papers , December 2020 , pp. 1197 - 1208
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

I am very grateful to Chris Smeenk for his support and advice. I would also like to thank Frank van den Bosch, Eric Winsberg, Craig Fox, the participants of the Rotman Summer Institute of Cosmology, and an anonymous referee for very helpful feedback and discussion. This article is based on work done while I was funded as a graduate student researcher under John Templeton Foundation grant 61048, New Directions in Philosophy of Cosmology.

References

Baushev, A., Valle, L. del, Campusano, L., Escala, A., Muñoz, R., and Palma, G.. 2017. “Cusps in the Center of Galaxies: A Real Conflict with Observations or a Numerical Artefact of Cosmological Simulations?” Journal of Cosmology and Astroparticle Physics. https://iopscience.iop.org/article/10.1088/1475-7516/2017/05/042.CrossRefGoogle Scholar
Carlberg, R. 1994. “Velocity Bias in Clusters.” Astrophysical Journal 433:468–78.CrossRefGoogle Scholar
Levins, R. 1966. “The Strategy of Model Building in Population Biology.” American Scientist 54 (4): 421–31.Google Scholar
Moore, B., Katz, N., and Lake, G.. 1996. “On the Destruction and Over-Merging of Dark Halos in Dissipationless N-Body Simulations.” Astrophysical Journal 457:455–59.CrossRefGoogle Scholar
Navarro, J. F., Frenk, C. S., and White, S. D.. 1997. “A Universal Density Profile from Hierarchical Clustering.” Astrophysical Journal 490 (2): 493–508.CrossRefGoogle Scholar
Oberkampf, W. L. 2014. “Verification and Validation in Computational Simulation.” Unpublished manuscript, Plasma Science and Fusion Center, MIT.Google Scholar
Orzack, S. H., and Sober, E.. 1993. “A Critical Assessment of Levins’s The Strategy of Model Building in Population Biology (1966).” Quarterly Review of Biology 68 (4): 533–46.CrossRefGoogle Scholar
Parker, W. S. 2011. “When Climate Models Agree: The Significance of Robust Model Predictions.” Philosophy of Science 78 (4): 579–600.CrossRefGoogle Scholar
Power, C., Navarro, J., Jenkins, A., Frenk, C., White, S. D., Springel, V., Stadel, J., and Quinn, T.. 2003. “The Inner Structure of ΛCDM Haloes.” Pt. 1, “A Numerical Convergence Study.” Monthly Notices of the Royal Astronomical Society 338 (1): 14–34.CrossRefGoogle Scholar
Springel, V., Wang, J., Vogelsberger, M., Ludlow, A., Jenkins, A., Helmi, A., Navarro, J. F., Frenk, C. S., and White, S. D.. 2008. “The Aquarius Project: The Subhaloes of Galactic Haloes.” Monthly Notices of the Royal Astronomical Society 391 (4): 1685–711.CrossRefGoogle Scholar
van den Bosch, F. C., and Ogiya, G.. 2018. “Dark Matter Substructure in Numerical Simulations: A Tale of Discreteness Noise, Runaway Instabilities, and Artificial Disruption.” Monthly Notices of the Royal Astronomical Society 475 (3): 4066–87.CrossRefGoogle Scholar
van den Bosch, F. C., Ogiya, G., Hahn, O., and Burkert, A.. 2017. “Disruption of Dark Matter Substructure: Fact or Fiction?” Monthly Notices of the Royal Astronomical Society 474 (3): 3043–66.Google Scholar
Weinberg, D. H., Bullock, J. S., Governato, F., Kuzio de Naray, R., and Peter, A. H. G.. 2015. “Cold Dark Matter: Controversies on Small Scales.” Proceedings of the National Academy of Sciences 112 (40): 12249–55.CrossRefGoogle ScholarPubMed
Weisberg, M. 2012. Simulation and Similarity: Using Models to Understand the World. Oxford: Oxford University Press.Google Scholar
Wimsatt, W. C. 2012. “Robustness, Reliability, and Overdetermination (1981).” In Characterizing the Robustness of Science: After the Practice Turn in Philosophy of Science, ed. Soler, Léna et al., 61–87. Dordrecht: Springer.CrossRefGoogle Scholar