Clean Kinematic Samples in Dwarf Spheroidals: An Algorithm for Evaluating Membership and Estimating Distribution Parameters When Contamination is Present

TL;DR

This paper introduces an EM algorithm tailored for astronomical data to accurately identify dwarf spheroidal galaxy members amidst contamination, improving parameter estimation over traditional methods.

Contribution

We develop and apply a specialized EM algorithm for membership determination and parameter estimation in contaminated stellar samples, demonstrating its effectiveness on real and simulated data.

Findings

Successfully identified over 5000 probable dSph members.

Outperformed sigma clipping in distinguishing members from contaminants.

Provided reliable estimates of distribution parameters across various sample sizes.

Abstract

(abridged) We develop an algorithm for estimating parameters of a distribution sampled with contamination, employing a statistical technique known as ``expectation maximization'' (EM). Given models for both member and contaminant populations, the EM algorithm iteratively evaluates the membership probability of each discrete data point, then uses those probabilities to update parameter estimates for member and contaminant distributions. The EM approach has wide applicability to the analysis of astronomical data. Here we tailor an EM algorithm to operate on spectroscopic samples obtained with the Michigan-MIKE Fiber System (MMFS) as part of our Magellan survey of stellar radial velocities in nearby dwarf spheroidal (dSph) galaxies. These samples are presented in a companion paper and contain discrete measurements of line-of-sight velocity, projected position, and Mg index for ~1000 - 2500 stars per dSph, including some fraction of contamination by foreground Milky Way stars. The EM algorithm quantifies both dSph and contaminant distributions, returning maximum-likelihood estimates of the means and variances, as well as the probability that each star is a dSph member. Applied to our MMFS data, the EM algorithm identifies more than 5000 probable dSph members. We test the performance of the EM algorithm on simulated data sets that represent a range of sample size, level of contamination, and amount of overlap between dSph and contaminant velocity distributions. The simulations establish that for samples ranging from large (N ~3000) to small (N~30), the EM algorithm distinguishes members from contaminants and returns accurate parameter estimates much more reliably than conventional methods of contaminant removal (e.g., sigma clipping).