| Autor: |
Haley Bowden, Peter Behroozi, Andrew Hearin |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
The Open Journal of Astrophysics, Vol 6 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2565-6120 |
| DOI: |
10.21105/astro.2307.07549 |
| Popis: |
The stellar mass - halo mass relation provides a strong basis for connecting galaxies to their host dark matter halos in both simulations and observations. Other observable information, such as the density of the local environment, can place further constraints on a given halo's properties. In this paper, we test how the peak masses of dark matter halos and subhalos correlate with observationally-accessible environment measures, using a neural network to extract as much information from the environment as possible. For high mass halos (peak mass $>10^{12.5} M_{\odot}$), the information on halo mass contained in stellar mass - selected galaxy samples is confined to the $\sim$ 1 Mpc region surrounding the halo center. Below this mass threshold, nearly the entirety of the information on halo mass is contained in the galaxy's own stellar mass instead of the neighboring galaxy distribution. The overall root-mean-squared error of the best-performing network was 0.20 dex. When applied to only the central halos within the test data, the same network had an error of 0.17 dex. Our findings suggest that, for the purposes of halo mass inference, both distances to the $k$th nearest neighbor and counts in cells of neighbors in a fixed aperture are similarly effective measurements of the local environment. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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