English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We consider the application of machine learning techniques to searches at theLarge Hadron Collider (LHC) for pair-produced lepton partners which decay toleptons and invisible particles. This scenario can arise in the MinimalSupersymmetric Standard Model (MSSM), but can be realized in many otherextensions of the Standard Model (SM). We focus on the case of intermediatemass splitting ($\sim 30$ GeV) between the dark matter (DM) and the scalar. Forthese mass splittings, the LHC has made little improvement over LEP due tolarge electroweak backgrounds. We find that the use of machine learningtechniques can push the LHC well past discovery sensitivity for a benchmarkmodel with a lepton partner mass of $\sim 110$ GeV, for an integratedluminosity of $300~{\rm fb}^{-1}$, with a signal-to-background ratio of $\sim0.3$. The LHC could exclude models with a lepton partner mass as large as $\sim160$ GeV with the same luminosity. The use of machine learning techniques insearches for scalar lepton partners at the LHC could thus definitively probethe parameter space of the MSSM in which scalar muon mediated interactionsbetween SM muons and Majorana singlet DM can both deplete the relic densitythrough dark matter annihilation and satisfy the recently measured anomalousmagnetic moment of the muon. We identify several machine learning techniqueswhich can be useful in other LHC searches involving large and complexbackgrounds.

Applying Machine Learning Techniques to Searches for Lepton-Partner Pair-Production with Intermediate Mass Gaps at the Large Hadron Collider