Extreme data compression while searching for new physics
Alan Heavens, Elena Sellentin, Andrew Jaffe

TL;DR
This paper introduces a novel data compression method that preserves the ability to detect and analyze new physics in high-dimensional datasets, enabling rapid, precise constraints on both standard and non-standard models.
Contribution
It extends the MOPED algorithm with a generalized PCA approach, creating MOPED-PC, to efficiently scout for new physics during data compression without losing sensitivity.
Findings
Enables analytic Bayesian evidence computation for new physics detection.
Provides fast estimates of non-standard model parameters.
Preserves standard model parameter precision when no new physics is present.
Abstract
Bringing a high-dimensional dataset into science-ready shape is a formidable challenge that often necessitates data compression. Compression has accordingly become a key consideration for contemporary cosmology, affecting public data releases, and reanalyses searching for new physics. However, data compression optimized for a particular model can suppress signs of new physics, or even remove them altogether. We therefore provide a solution for exploring new physics \emph{during} data compression. In particular, we store additional agnostic compressed data points, selected to enable precise constraints of non-standard physics at a later date. Our procedure is based on the maximal compression of the MOPED algorithm, which optimally filters the data with respect to a baseline model. We select additional filters, based on a generalised principal component analysis, which are carefully…
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