Reconstructing events with missing transverse momentum at the LHC and its application to spin measurement

TL;DR

This paper introduces a probabilistic method for reconstructing missing transverse momentum at the LHC to improve spin measurement accuracy, effectively distinguishing between different spin hypotheses despite uncertainties and backgrounds.

Contribution

It presents a novel probabilistic reconstruction technique for unknown momenta that enhances spin hypothesis discrimination at the LHC.

Findings

The method improves spin hypothesis discrimination accuracy.

It remains effective despite mass uncertainties and Standard Model backgrounds.

Compared to observable-only methods, it reduces error probability.

Abstract

In this article we discuss the measurement of spin at the LHC, in events with two unknown four-momenta. Central to this problem is the identification of spin-dependent kinematic variables and the construction of a statistical test that can distinguish between different spin hypotheses. We propose a method for reconstructing kinematic variables that depend upon the unknown momenta. The method is based upon a probabilistic reconstruction of each event, given the masses of the final and intermediate states and the cross-section of the assumed hypothesis. We demonstrate that this method can distinguish between two spin hypotheses for a specific process, even after mass uncertainties and Standard Model backgrounds are taken into account. We compare our method with another that only utilises the observable momenta of each event. We will show that our method permits an improved discrimination between hypotheses, with a reduced probability of error.