Novel Challenges in Tracking Self-Interacting Dark Matter Subhalos
Demao Kong, Hai-Bo Yu, Ethan O. Nadler, Philip Mansfield, Andrew Benson

TL;DR
This paper evaluates the performance of particle-tracking algorithms for identifying self-interacting dark matter subhalos in cosmological simulations, revealing limitations and proposing a hybrid approach for improved accuracy.
Contribution
It demonstrates that existing particle-tracking methods like Symfind have limitations in SIDM scenarios and suggests a hybrid strategy to enhance subhalo detection accuracy.
Findings
Particle-tracking algorithms struggle with SIDM subhalos undergoing core expansion or collapse.
Symfind outperforms traditional methods for subhalos with large pericentric distances.
Performance depends on SIDM cross section and model parameters.
Abstract
Cosmological N-body simulations are among the primary tools for studying structure formation in the Universe. Analyses of these simulations critically depend on accurately identifying and tracking dark matter subhalos over time. In recent years, several new algorithms have been developed to improve the accuracy and consistency of subhalo tracking in cold dark matter simulations. These algorithms should be revisited in the context of new physics beyond gravity, which can modify the evolution and final properties of subhalo populations. In this work, we apply the particle-tracking-based subhalo finder Symfind to velocity-dependent self-interacting dark matter simulations with large cross section amplitudes to assess the performance of particle-tracking methods beyond the CDM paradigm. We find that the core-particle-tracking technique, which is key to the success of these algorithms in…
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Taxonomy
TopicsDark Matter and Cosmic Phenomena · Atomic and Subatomic Physics Research · CCD and CMOS Imaging Sensors
