MATHUSLA: A Detector Proposal to Explore the Lifetime Frontier at the HL-LHC

Abstract

The observation of long-lived particles at the LHC would reveal physics beyond the Standard Model, could account for the many open issues in our understanding of our universe, and conceivably point to a more complete theory of the fundamental interactions. Such long-lived particle signatures are fundamentally motivated and can appear in virtually every theoretical construct that address the Hierarchy Problem, Dark Matter, Neutrino Masses and the Baryon Asymmetry of the Universe. We describe in this document a large detector, MATHUSLA, located on the surface above an HL-LHC $pp$ interaction point, that could observe long-lived particles with lifetimes up to the Big Bang Nucleosynthesis limit of 0.1 s. We also note that its large detector area allows MATHUSLA to make important contributions to cosmic ray physics. Because of the potential for making a major breakthrough in our conceptual understanding of the universe, long-lived particle searches should have the highest level of priority.

arXiv comment8 pages, 5 figures. Input to the update process of the European Strategy for Particle Physics by the MATHUSLA collaboration (http://mathusla.web.cern.ch). See also CERN-PBC-REPORT-2018-007 for the ESPP contribution of the Physics Beyond Colliders working group, which contains a discussion of low-energy simplified models as well as some comments on MATHUSLA's budget and timelines

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Cited by in corpus (12)

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• Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2
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• Enhancing Sensitivities to Long-lived Particles with High Granularity Calorimeters at the LHC
• Twin Higgs Portal Dark Matter
• Freeze-in and Freeze-out of Dark Matter with Charged Long-lived Partners
• Collider search of light dark matter model with dark sector decay
• LHC experiments for long-lived particles of the dark sector
• Muon Detector for Underground Tomography
• Liquid Scintillators for Large Area Tracking System
• Precision Higgs Couplings in Neutral Naturalness Models: an Effective Field Theory Approach
• Particle Track Reconstruction using Geometric Deep Learning