The Intrinsic Dimension of Collider Events and Model-Independent Searches in 100 Dimensions

TL;DR

This paper introduces a new method to analyze high-dimensional collider event data by measuring the intrinsic dimensionality of the data manifold, enabling model-independent searches for new physics without the look-elsewhere effect.

Contribution

It presents a novel technique to determine the intrinsic dimension of collider event manifolds, facilitating model-independent new physics searches in high-dimensional phase space.

Findings

Successfully identified new physics signals in simulated 100-dimensional data.

Method is insensitive to energy scale uncertainties and avoids the look-elsewhere effect.

Applicable to single-particle data, paving the way for global collider data analysis.

Abstract

The phase space of hadron collider events spans hundreds of dimensions, generating an intricate geometry that we are just starting to explore. The number of possible new physics signals is exponential in the number of dimensions and detecting all of them is currently impossible for any human or artificial intelligence. In this work we introduce a method to search for new physics model-independently in this high-dimensional space. It is based on the measurement of the most basic property of the manifold of collider events, its dimensionality. Our proposed technique does not suffer from a look-elsewhere effect that grows exponentially with the number of dimensions of the dataset, and by construction is insensitive to energy scale uncertainties. We illustrate its potential by finding new physics in simulated events with hundreds of phase space dimensions, taking as input single particles rather than jets. This study sets the stage for new model-independent search strategies based on global properties of collider data manifolds.