Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 63 tok/s
Gemini 2.5 Pro 50 tok/s Pro
GPT-5 Medium 19 tok/s Pro
GPT-5 High 29 tok/s Pro
GPT-4o 101 tok/s Pro
Kimi K2 212 tok/s Pro
GPT OSS 120B 438 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Concentration, Ellipsoidal Collapse, and the Densest Dark Matter haloes (1510.03868v2)

Published 13 Oct 2015 in astro-ph.CO

Abstract: The smallest dark matter haloes are the first objects to form in the hierarchical structure formation of cold dark matter (CDM) cosmology and are expected to be the densest and most fundamental building blocks of CDM structures in our universe. Nevertheless, the physical characteristics of these haloes have stayed illusive, as they remain well beyond the current resolution of N-body simulations (at redshift zero). However, they dominate the predictions (and uncertainty) in expected dark matter annihilation signal, amongst other astrophysical observables. Using the conservation of total energy and the ellipsoidal collapse framework, we can analytically find the mean and scatter of concentration $c$ and 1-D velocity dispersion $\sigma_{\rm 1d}$ for haloes of different virial mass $M_{200}$. Both $c$ and $\sigma_{\rm 1d}/M_{200}{1/3}$ are in good agreement with numerical results within the regime probed by simulations -- slowly decreasing functions of mass that approach constant values at large masses. In particular, the predictions for the 1-D velocity dispersion of cluster mass haloes are surprisingly robust as the inverse heat capacity of cosmological haloes crosses zero at $M_{200} \sim 10{14} M_\odot$. However, we find that current extrapolations from simulations to smallest CDM haloes dramatically depend on the assumed profile (e.g. NFW vs. Einasto) and fitting function, which is why theoretical considerations, such as the one presented here, can significantly constrain the range of feasible predictions.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.