Optimal filters for detecting cosmic bubble collisions

TL;DR

This paper introduces an improved optimal filter algorithm for detecting cosmic bubble collision signatures in CMB data, significantly enhancing sensitivity and identifying new candidates in WMAP observations.

Contribution

The paper presents a novel optimal filter algorithm that doubles detection sensitivity for cosmic bubble collisions in CMB data, outperforming previous methods.

Findings

Enhanced detection sensitivity by a factor of two.

Identified eight new candidate bubble collision signatures in WMAP data.

Proved the algorithm's superiority through theoretical analysis and simulations.

Abstract

A number of well-motivated extensions of the LCDM concordance cosmological model postulate the existence of a population of sources embedded in the cosmic microwave background (CMB). One such example is the signature of cosmic bubble collisions which arise in models of eternal inflation. The most unambiguous way to test these scenarios is to evaluate the full posterior probability distribution of the global parameters defining the theory; however, a direct evaluation is computationally impractical on large datasets, such as those obtained by the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck. A method to approximate the full posterior has been developed recently, which requires as an input a set of candidate sources which are most likely to give the largest contribution to the likelihood. In this article, we present an improved algorithm for detecting candidate sources using optimal filters, and apply it to detect candidate bubble collision signatures in WMAP 7-year observations. We show both theoretically and through simulations that this algorithm provides an enhancement in sensitivity over previous methods by a factor of approximately two. Moreover, no other filter-based approach can provide a superior enhancement of these signatures. Applying our algorithm to WMAP 7-year observations, we detect eight new candidate bubble collision signatures for follow-up analysis.