Unsupervised Searches for Cosmological Parity-Violation: An Investigation with Convolutional Neural Networks

TL;DR

This paper introduces an unsupervised convolutional neural network approach to detect cosmological parity-violation signals in large-scale structure data, overcoming limitations of traditional methods reliant on simulations and covariance modeling.

Contribution

The work presents a novel unsupervised CNN-based method for identifying parity-violation signals in cosmological data, capable of analyzing higher-order N-point functions without extensive simulations.

Findings

Successfully detects parity-violation signals in toy models

Complementary to existing 4PCF methods

Applicable to upcoming large-scale surveys

Abstract

Recent measurements of the $4$-point correlation functions (4PCF) from spectroscopic surveys provide evidence for parity-violations in the large-scale structure of the Universe. If physical in origin, this could point to exotic physics during the epoch of inflation. However, searching for parity-violations in the 4PCF signal relies on a large suite of simulations to perform a rank test, or an accurate model of the 4PCF covariance to claim a detection, and this approach is incapable of extracting parity information from the higher-order $N$-point functions. In this work we present an unsupervised method which overcomes these issues, before demonstrating the approach is capable of detecting parity-violations in a few toy models using convolutional neural networks. This technique is complementary to the 4-point method and could be used to discover parity-violations in several upcoming surveys including DESI, Euclid and Roman.