Estimating the distribution of Galaxy Morphologies on a continuous space

TL;DR

This paper introduces a continuous space representation for galaxy morphologies using dictionary learning and sparse coding, enabling statistical analysis of galaxy shape distributions.

Contribution

It proposes a novel approach to model galaxy shapes in a continuous space, capturing the full diversity without discrete classification.

Findings

Reduced high-dimensional shape data to a low-dimensional continuous space.

Enabled statistical inference and distribution estimation in the shape space.

Facilitated analysis of galaxy morphology variations.

Abstract

The incredible variety of galaxy shapes cannot be summarized by human defined discrete classes of shapes without causing a possibly large loss of information. Dictionary learning and sparse coding allow us to reduce the high dimensional space of shapes into a manageable low dimensional continuous vector space. Statistical inference can be done in the reduced space via probability distribution estimation and manifold estimation.