publication . Article . Preprint . Other literature type . 2019

An interpretable machine-learning framework for dark matter halo formation

Lucie-Smith, Luisa; Peiris, Hiranya V; Pontzen, Andrew;
Open Access
  • Published: 16 Sep 2019 Journal: Monthly Notices of the Royal Astronomical Society, volume 490, pages 331-342 (issn: 0035-8711, eissn: 1365-2966, Copyright policy)
  • Publisher: Oxford University Press (OUP)
  • Country: United Kingdom
Abstract
We present a generalization of our recently proposed machine learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the formation of haloes over the mass range $11.4 \leq \log(M/M_{\odot}) \leq 13.4$. The algorithm is trained on an N-body simulation to infer the final mass of the halo to which each dark matter particle will later belong. We then quantify the difference in the predictive accuracy between machine learning models using a metric based on the Kullback-Leibler divergence. We first train the algorithm with information about the density contr...
Subjects
arXiv: Astrophysics::Cosmology and Extragalactic AstrophysicsAstrophysics::Galaxy Astrophysics
free text keywords: Space and Planetary Science, Astronomy and Astrophysics, methods: statistical, galaxies: haloes, dark matter, large-scale structure of Universe, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Funded by
EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
27 references, page 1 of 2

Berger P., Stein G., 2019, MNRAS, 482, 2861

Bond J. R., Myers S. T., 1996, ApJS, 103, 1

Bond J. R., Cole S., Efstathiou G., Kaiser N., 1991, ApJ, 379, 440

Doroshkevich A. G., 1970, Astrofizika, 6, 581

Dunkley J. et al., 2009, ApJS, 180, 306

Fakhouri O., Ma C.-P., Boylan-Kolchin M., 2010, MNRAS, 406, 2267

Freund Y., Schapire R. E., 1997, J. Comput. Syst. Sci., 55, 119

Friedman J. H., 2001, Ann. Statist., 29, 1189

Friedman J. H., 2002, Comput. Stat. Data Anal., 38, 367

Genel S., Genzel R., Bouche´ N., Naab T., Sternberg A., 2009, ApJ, 701, 2002

Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T.- Y., 2017, in Guyon I., Luxburg U. V., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R., eds, Advances in Neural Information Processing Systems 30. Curran Associates, Inc, Red Hook, NY, p. 3146

Kohavi R., 1995, Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2. IJCAI'95. Morgan Kaufmann Publishers Inc., San Francisco, p. 1137

Kullback S., Leibler R. A., 1951, Ann. Math. Statist., 22, 79

Louppe G., Wehenkel L., Sutera A., Geurts P., 2013, in Burges C. J. C., Bottou L., Welling M., Ghahramani Z., Weinberger K. Q., eds, Advances in Neural Information Processing Systems 26. Curran Associates, Inc., Red Hook, NY, p. 431

Lucie-Smith L., Peiris H. V., Pontzen A., Lochner M., 2018, MNRAS, 479, 3405

27 references, page 1 of 2
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue