Signals of New Physics in the Underlying Event
Roni Harnik, Tommer Wizansky

TL;DR
This paper proposes analyzing the anomalous underlying event as a new soft signal for detecting certain new physics scenarios at the LHC, such as quirks and hidden valleys, by studying soft energy patterns and their deviations from expected backgrounds.
Contribution
It introduces the concept of using underlying event analysis as a novel approach to identify new physics signals at the LHC, supported by a detailed simulation and multipole analysis.
Findings
An excess of anomalous underlying events could indicate new physics.
Soft unclustered energy patterns can distinguish signals from backgrounds.
Simulation shows potential for detecting quirks via calorimetric and tracking signals.
Abstract
LHC searches for new physics focus on combinations of hard physics objects. In this work we propose a qualitatively different soft signal for new physics at the LHC - the "anomalous underlying event". Every hard LHC event will be accompanied by a soft underlying event due to QCD and pile-up effects. Though it is often used for QCD and monte carlo studies, here we propose the incorporation of an underlying event analysis in some searches for new physics. An excess of anomalous underlying events may be a smoking-gun signal for particular new physics scenarios such as "quirks" or "hidden valleys" in which large amounts of energy may be emitted by a large multiplicity of soft particles. We discuss possible search strategies for such soft diffuse signals in the tracking system and calorimetry of the LHC experiments. We present a detailed study of the calorimetric signal in a concrete…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEarthquake Detection and Analysis · Computational Physics and Python Applications · Dark Matter and Cosmic Phenomena
