BASILISK: Bayesian Hierarchical Inference of the Galaxy-Halo Connection using Satellite Kinematics--I. Method and Validation
Frank C. van den Bosch, Johannes U. Lange, Andrew R. Zentner

TL;DR
Basilisk is a Bayesian hierarchical method that infers the galaxy-halo connection from satellite kinematics without stacking or summary statistics, handling flux-limited data and simultaneously constraining halo mass and orbital anisotropy.
Contribution
It introduces a novel Bayesian hierarchical framework that directly models satellite kinematics in raw form, improving accuracy and applicability over traditional methods.
Findings
Accurately recovers the full PDF of halo mass and luminosity relation.
Provides unbiased constraints comparable to galaxy-galaxy lensing.
Simultaneously constrains satellite orbital anisotropy.
Abstract
We present a Bayesian hierarchical inference formalism (Basilisk) to constrain the galaxy-halo connection using satellite kinematics. Unlike traditional methods, Basilisk does not resort to stacking the kinematics of satellite galaxies in bins of central luminosity, and does not make use of summary statistics, such as satellite velocity dispersion. Rather, Basilisk leaves the data in its raw form and computes the corresponding likelihood. In addition, Basilisk can be applied to flux-limited, rather than volume-limited samples, greatly enhancing the quantity and dynamic range of the data. And finally, Basilisk is the only available method that simultaneously solves for halo mass and orbital anisotropy of the satellite galaxies, while properly accounting for scatter in the galaxy-halo connection. Basilisk uses the conditional luminosity function to model halo occupation statistics, and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
