On the spatial distribution of the Large-Scale structure: An Unsupervised search for Parity Violation

TL;DR

This paper applies machine learning techniques to analyze the Large-Scale Structure of the universe for signs of parity violation, aiming to verify previous claims of chirality detection and find no such violations in the BOSS data.

Contribution

It introduces a novel machine learning approach inspired by collider physics to search for parity violations in cosmological data, reproducing and testing prior claims.

Findings

No parity violation detected in BOSS data

Machine learning methods effectively evaluate parity odd functions

Reproduces previous chirality detection claims

Abstract

We use machine learning methods to search for parity violations in the Large-Scale Structure (LSS) of the Universe, motivated by recent claims of chirality detection using the 4-Point Correlation Function (4PCF), which would suggest new physics during the epoch of inflation. This work seeks to reproduce these claims using methods originating from high energy collider analyses. Our machine learning methods optimise some underlying parity odd function of the data, and use it to evaluate the parity odd fraction. We demonstrate the effectiveness and suitability of these methods and then apply them to the Baryon Oscillation Spectroscopic Survey (BOSS) catalogue. No parity violation is detected at any significance.