sidmkit: A Reproducible Toolkit for SIDM Phenomenology and Galaxy Rotation-Curve Modeling

TL;DR

sidmkit is a Python toolkit that streamlines the modeling of self-interacting dark matter effects and galaxy rotation curves, enabling reproducible analysis and comparison with astrophysical constraints.

Contribution

This paper introduces sidmkit, a comprehensive, open-source Python package for SIDM microphysics calculations and galaxy rotation-curve fitting, integrating multiple models and datasets.

Findings

Burkert profile preferred in 65.4% of galaxies

Strong model preference (ΔBIC>6) in 32.5% of cases

Demonstrates sidmkit's capability on 191 galaxy dataset

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 Hulth\'en-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 $\Delta \mathrm{BIC} > 0$ for $65.4\%$ of galaxies, with ``strong'' preference ($\Delta \mathrm{BIC}>6$) in $32.5\%$ of galaxies;