Related papers: Machine-learning techniques for model-independent …
This Letter describes a model-agnostic search for pairs of jets (dijets) produced by resonant and non-resonant phenomena beyond the Standard Model in 3.6 fb$^{-1}$ of proton-proton collisions with a centre-of-mass energy of $\sqrt{s}=13$…
A search for resonances produced in 13 TeV proton-proton collisions and decaying into top-quark pairs is presented. In this study events where the top-quark decay produces a single isolated charged lepton, missing transverse momentum and…
A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which…
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant…
In this article we present the application of classical and quantum-classical hybrid anomaly detection schemes to explore exotic configuration with anomalous features. We consider the Anderson model as a prototype where we define two types…
We present an application of unsupervised learning for zero-bias detection of rare particle decays and exotic hadrons in low-background environments such as those characteristic of diffractive events and ultraperipheral pp, p--A, or A--A…
A search for new physics using events containing an imbalance in transverse momentum and one or more energetic jets arising from initial-state radiation or the hadronic decay of W or Z bosons is presented. A data sample of proton-proton…
A variety of models of physics beyond the standard model predict new particles that decay to leptons, jets, or both together. These models include axigluons, colorons, diquarks, excited quarks, heavy long-lived charged particles,…
This study explores the potential of unsupervised anomaly detection for identifying physics beyond the Standard Model that may appear at proton collisions at the Large Hadron Collider. We introduce a novel quantum autoencoder circuit ansatz…
This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for…
We review the experimental searches for new particles in the dijet mass spectrum conducted at the CERN SppS, the Fermilab Tevatron Collider, and the CERN Large Hadron Collider. The theory of the QCD background and new particle signals is…
The results of several recent CMS searches for exotic phenomena beyond the Standard Model are presented in this talk. Two searches look for new physics in a final state with a vector boson and missing transverse energy. Three searches…
Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of…
A novel deep neural network classifier, a ``Particle transformer'' (PaRT), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the…
Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information…
This paper explores different strategies for enhancing sensitivity to new heavy resonances that decay into two or more Higgs bosons. This is achieved using two neural network architectures: an unsupervised autoencoder for anomaly detection…
First searches for new physics phenomena using the LHC 7 TeV proton-proton collision data collected by the CMS detector in 2010 are reviewed. Results are presented of searches for new physics in events with hadronic jet pairs, and for heavy…
Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning…
The possible detection of massive quasi-stable exotic particles at the high luminosity hadronic colliders is discussed. In the coming ten years the LHC, now under preparation, has the best opportunity to observe them at the TeV scale. The…
Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer…