Line Profiles from Discrete Kinematic Data
N. C. Amorisco, N. W. Evans (Cambridge)

TL;DR
This paper introduces a new maximum likelihood method for analyzing line profiles from discrete stellar velocity data, outperforming traditional Gauss-Hermite expansions, and applies it to dwarf spheroidals revealing insights into their orbital structures.
Contribution
The paper develops a novel maximum likelihood approach using symmetric and asymmetric distribution families for line profile analysis from discrete data, improving over Gauss-Hermite methods.
Findings
Sculptor, Carina, Sextans show peaked velocity distributions.
Fornax exhibits a transition from peaked to flat-topped distributions outward.
Results suggest different orbital anisotropies and evolutionary histories among the dwarf spheroidals.
Abstract
We develop a method to extract the shape information of line profiles from discrete kinematic data. The Gauss-Hermite expansion, which is widely used to describe the line of sight velocity distributions extracted from absorption spectra of elliptical galaxies, is not readily applicable to samples of discrete stellar velocity measurements, accompanied by individual measurement errors and probabilities of membership. We introduce two parameter families of probability distributions describing symmetric and asymmetric distortions of the line profiles from Gaussianity. These are used as the basis of a maximum likelihood estimator to quantify the shape of the line profiles. Tests show that the method outperforms a Gauss-Hermite expansion for discrete data, with a lower limit for the relative gain of approx 2 for sample sizes N approx 800. To ensure that our methods can give reliable…
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