# Constraining compressed versions of MUED and MSSM using soft tracks at the LHC

## TL;DR

This paper demonstrates that incorporating soft track observables significantly enhances the detection of compressed spectrum signals in MUED and MSSM models at the LHC, surpassing traditional monojet plus missing energy searches.

## Contribution

It introduces a novel analysis method using $p_T$-binned track observables to improve discrimination of new physics signals in compressed spectra at the LHC.

## Key findings

- Compressed MUED with $ ext{Λ} R=2$ can be excluded with current data.
- Multivariate analysis outperforms cut-based methods in signal discrimination.
- Soft track observables provide uncorrelated information, enhancing detection sensitivity.

## Abstract

A compressed spectrum is an anticipated hideout for many beyond standard model scenarios. Such a spectrum naturally arises in the minimal universal extra dimension framework and also in supersymmetric scenarios. Low $p_T$ leptons and jets are characteristic features of such situations. Hence, a monojet with $\not E_T$ has been the conventional signal at the Large Hadron Collider (LHC). However, we stress that inclusion of $p_T$-binned track observables from such soft objects provide very efficient discrimination of new physics signals against various SM backgrounds. We consider two benchmark points each for minimal universal extra dimension (MUED) and minimal supersymmetric standard model (MSSM) scenarios. We perform a detailed cut-based and multivariate analysis (MVA) to show that the new physics parameter space can be probed in the ongoing run of LHC at 13 TeV center-of-mass energy with an integrated luminosity $\sim$ 20-50 fb$^{-1}$. When studied in conjunction with the dark matter relic density constraint assuming standard cosmology, we find that compressed MUED (with $\Lambda R=2$) can be already excluded from the existing data. Also, MVA turns out to be a better technique than regular cut-based analysis since tracks provide uncorrelated observables which would extract more information from an event.