Estimation of the chances to find new phenomena at the LHC in a model-agnostic combinatorial analysis

Abstract

In this paper, we estimate the number of event topologies that have the potential to be produced in $pp$ collisions at the Large Hadron Collider (LHC) without violating kinematic and other constraints. We use numerical calculations and combinatorics, guided by large-scale Monte Carlo simulations of Standard Model (SM) processes. Then, we set the upper limit on the probability that new physics may escape detection, assuming a model-agnostic approach. The calculated probability is unexpectedly large, and the fact that the LHC has not found new physics until now is not entirely surprising. We argue that the optimal direction for maximizing the chances of finding new physics is to use unsupervised machine learning for anomaly detection or algorithms designed for event classification.

arXiv comment13 pages,1 figure, submitted to a journal

References in corpus (8)

• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• Parton distributions for the LHC Run II
• LHAPDF6: parton density access in the LHC precision era
• A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
• Precision searches in dijets at the HL-LHC and HE-LHC
• Search for phenomena beyond the Standard Model in events with large $b$-jet multiplicity using the ATLAS detector at the LHC

Cited by in corpus (3)

• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks
• Discovering the Underlying Analytic Structure Within Standard Model Constants Using Artificial Intelligence