Related papers: Advances in Multi-Variate Analysis Methods for New…
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event…
Selected results from searches for new physics with unconventional signatures using the ATLAS and CMS detectors are presented. Such signatures include emerging jets, heavy charged particles, displaced or delayed objects, and disappearing…
Searches for resonances in final states with leptons and photons have always been a powerful tool for discovery in high energy physics. We present here the latest results from the ATLAS and CMS experiments, based on up to 36.1 fb$^{-1}$ of…
In this paper we document the current analysis software training and onboarding activities in several High Energy Physics (HEP) experiments: ATLAS, CMS, LHCb, Belle II and DUNE. Fast and efficient onboarding of new collaboration members is…
The research of innovative methods aimed at reducing costs and shortening the time needed for simulation, going beyond conventional approaches based on Monte Carlo methods, has been sparked by the development of collision simulations at the…
Searches for new resonances decaying into two photons in the ATLAS experiment at the CERN Large Hadron Collider are described. The analysis is based on proton--proton collision data corresponding to an integrated luminosity of 3.2 fb$^{-1}$…
A model-independent search for new physics leading to final states containing $H\rightarrow\gamma\gamma$ decays is performed with 139 fb$^{-1}$ of $\sqrt{s}$ = 13 TeV pp collision data recorded by the ATLAS detector at the Large Hadron…
All experimental measurements of particle physics today are beautifully described by the Standard Model. However, there are good reasons to believe that new physics may be just around the corner at the TeV energy scale. This energy range is…
This article presents a brief overview of the CMS experiment capabilities to study the hot and dense matter created in relativistic heavy-ion collisions. The CERN Large Hadron Collider will provide collisions of Pb nuclei at 5.5 TeV per…
The strong force is responsible for a rich set of phenomena that can be probed using a variety of techniques over a wide energy and angular range at the Large Hadron Collider. This talk reports on the latest results from the ATLAS…
The ATLAS Collaboration at CERN's Large Hadron Collider is at the forefront of particle physics research and is equally committed to bridging the gap between cutting-edge science and the wider public. Since 2010, the ATLAS Virtual Visits…
Searches for vector-like quarks and $t\bar{t}$ resonances are performed with the ATLAS experiment at the Large Hadron Collider using an integrated luminosity of 14.3~fb$^{-1}$ of proton-proton collisions recorded in 2012 at a center-of-mass…
Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for…
Some of the studies performed by the ATLAS and CMS collaborations to establish the future sensitivity of the experiments to extra dimension signals are reviewed. The discrimination of those signals from other new physics signals and the…
Accelerator searches for new resonances have a long-standing history of discoveries that have driven advances in our understanding of nature. Since 2010, the Large Hadron Collider (LHC) has probed previously inaccessible energy scales,…
Physics analysis in astroparticle experiments requires the capability of recognizing new phenomena; in order to establish what is new, it is important to develop tools for automatic classification, able to compare the final result with data…
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 application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review…
Many theories beyond the Standard Model predict new phenomena which decay to well isolated, high-$p_{\text{T}}$ leptons. Searches for new physics with these signatures are performed using the ATLAS experiment at the LHC. The results…
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training…