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Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement.…
In time for the first tests on LHC data we introduce a set of improvements and tests of purely kinematic top tagging algorithms. First, we show how different jet algorithms can be used for different transverse momentum regimes. Combining…
A measurement is performed of Higgs bosons produced with high transverse momentum ($p_\mathrm{T}$) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV…
Particle physicists at the Large Hadron Collider investigate the properties of matter at subatomic length scales by colliding together bunches of high-energy protons and observing the decay products of the collisions. ATLAS is one of two…
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…
The ATLAS collaboration will replace its tracking detector with new all silicon pixel and strip systems. This will allow to cope with the higher radiation and occupancy levels expected after the 5-fold increase in the luminosity of the LHC…
International Linear Collider (ILC) is a next-generation $e^+e^-$ linear collider to explore Beyond-Standard-Models by precise measurements of Higgs bosons. Jet flavor tagging plays a vital role in the ILC project by identification of $H\to…
Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this work, we…
Identification of jets originating from b quarks (b-tagging) is a key element of many physics analyses at the LHC. Various algorithms for b-tagging have been developed by the CMS experiment to identify b-tagged jets with a typical…
Distinguishing hadronically decaying boosted top quarks from massive QCD jets is an important challenge at the Large Hadron Collider. In this paper we use the power counting method to study jet substructure observables designed for top…
We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving…
The ATLAS experiment is one of two large general-purpose particle detectors at the Large Hadron Collider (LHC) at the CERN laboratory in Geneva, Switzerland. ATLAS has been collecting data from the collisions of protons since December 2009,…
A short run of proton-oxygen and oxygen-oxygen collisions is planned to take place at the Large Hadron Collider during LHC Run 3. The primary goal of this run is to improve the modeling of Cosmic-Ray interactions and to reduce the…
Pixel detectors currently in use by high energy physics experiments such as ATLAS, CMS, LHCb, etc., are critical systems for tagging $B$ hadrons within particle jets. However, the performance of standard tagging algorithms begins to fall in…
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern…
Distinguishing between quark- and gluon-initiated jets is a critical and challenging task in high-energy physics, pivotal for improving new physics searches and precision measurements at the Large Hadron Collider. While deep learning,…
In searches for new physics in the energy regime of the LHC, it is becoming increasingly important to distinguish single-jet objects that originate from the merging of the decay products of W bosons produced with high transverse momenta…
Preliminary results of two differential cross-section measurements in the ATLAS experiment at CERN are presented. Measurements are focused on the top-antitop quark pair production in the~lepton+jets and the~all-hadronic channels in the…
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top…
Measurements and searches performed with the ATLAS detector at the CERN Large Hadron Collider often involve signatures with one or more prompt leptons. Such analyses are subject to `fake/non-prompt' lepton backgrounds, where either a hadron…