sidmkit: A Reproducible Toolkit for SIDM Phenomenology and Galaxy Rotation-Curve Modeling
Nalin Dhiman
TL;DR
sidmkit provides a reproducible Python toolkit that unifies SIDM microphysics with macro halo observables and implements a batch SPARC rotation-curve fitting pipeline. It implements velocity-dependent Yukawa cross sections, velocity-averaged moments, and halo diagnostics, and demonstrates the workflow on 191 rotmod galaxies by fitting both NFW and Burkert halos. The results show Burkert profiles are preferred in the majority of cases by information criteria, while highlighting degeneracies and diagnostics that must be addressed before astrophysical inferences. Overall, sidmkit offers a robust baseline for reproducible SIDM phenomenology and rotation-curve sanity checks, enabling systematic exploration and extension toward rigorous SIDM inference.
Abstract
Self-interacting dark matter (SIDM) is a well-motivated extension of cold dark matter that can modify halo structure on galactic and group scales while remaining consistent with large-scale structure. However, practical SIDM work often requires bridging several layers, including microphysical scattering models, velocity-dependent effective cross sections, phenomenological astrophysical constraints, and (separately) data-driven halo fits, such as rotation curves. In this paper, we describe \texttt{sidmkit}, a transparent and reproducible Python package designed to support SIDM ``micro$\rightarrow$macro'' calculations and to provide a robust batch pipeline for fitting rotation curves in the SPARC data. On the SIDM side, \texttt{sidmkit} implements velocity-dependent momentum-transfer cross sections for a Yukawa interaction using standard analytic approximations (Born, classical, and Hulthén-based) with a numerical partial-wave option for spot checks. It also provides consistent velocity-moment averaging for Maxwellian relative speeds, scattering-rate utilities, and curated literature \emph{summary} constraints for regression tests and exploratory scans. On the rotation-curve side, we implement bounded non-linear least squares fits of NFW and Burkert halo models to SPARC baryonic decompositions, with optional mass-to-light priors and information-criterion summaries (AIC/BIC). For the demonstration dataset, we process 191 \texttt{rotmod} galaxies (LTG+ETG bundles) and fit both NFW and Burkert models (382 total fits). We find that Burkert is preferred by $Δ\mathrm{BIC} > 0$ for $65.4\%$ of galaxies, with ``strong'' preference ($Δ\mathrm{BIC}>6$) in $32.5\%$ of galaxies;