# Spectral Decomposition of Missing Transverse Energy at Hadron Colliders

## TL;DR

This paper introduces a spectral decomposition method to analyze missing transverse energy data at hadron colliders, enabling the extraction of dark matter properties and differentiation of interaction models.

## Contribution

It presents a novel spectral decomposition approach that expresses MET distributions as a combination of basis functions linked to sub-processes, aiding dark matter model discrimination.

## Key findings

- Spectral decomposition can differentiate dark matter-mediator interactions.
- Basis functions correspond to subprocess differential cross sections.
- Method provides a systematic way to analyze MET data for dark matter signals.

## Abstract

We propose a spectral decomposition to systematically extract information of dark matter at hadron colliders. The differential cross section of events with missing transverse energy (MET) can be expressed by a linear combination of basis functions. In the case of $s$-channel mediator models for dark matter particle production, basis functions are identified with the differential cross sections of sub-processes of virtual mediator and visible particle production while the coefficients of basis functions correspond to dark matter invariant mass distribution in the manner of the K\"all\'en-Lehmann spectral decomposition. For a given MET data set and mediator model, we show that one can differentiate a certain dark matter-mediator interaction from another through spectral decomposition.