Non-parametric estimation of morphological lopsidedness

TL;DR

This paper introduces a non-parametric, automated method for classifying galaxy asymmetries in neutral hydrogen gas distributions, accounting for observational effects, to facilitate large-scale statistical studies in upcoming HI surveys.

Contribution

It presents a novel non-parametric approach that corrects for noise, resolution, and inclination effects, enabling efficient and objective classification of galaxy asymmetries in large surveys.

Findings

Method effectively accounts for observational biases.

Provides estimates of classification precision.

Outlines expected performance for future HI surveys.

Abstract

Asymmetries in the neutral hydrogen gas distribution and kinematics of galaxies are thought to be indicators for both gas accretion and gas removal processes. These are of fundamental importance for galaxy formation and evolution. Upcoming large blind HI surveys will provide tens of thousands of galaxies for a study of these asymmetries in a proper statistical way. Due to the large number of expected sources and the limited resolution of the majority of objects, detailed modelling is not feasible for most detections. We need fast, automatic and sensitive methods to classify these objects in an objective way. Existing non-parametric methods suffer from effects like the dependence on signal to noise, resolution and inclination. Here we show how to correctly take these effects into account and show ways to estimate the precision of the methods. We will use existing and modelled data to give an outlook on the performance expected for galaxies observed in the various sky surveys planned for e.g. WSRT/APERTIF and ASKAP.