Related papers: Identification of b-jets using QCD-inspired observ…
We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as…
The Lund jet plane (LJP) is measured for the first time in $t\overline{t}$ events, using 140 fb$^{-1}$ of $\sqrt{s}$ = 13 TeV pp collision data collected with the ATLAS detector at the LHC. The LJP is a two-dimensional observable of the…
We develop taggers for multi-pronged jets that are simple functions of jet substructure (so-called `subjettiness') variables. These taggers can be approximately decorrelated from the jet mass in a quite simple way. Specifically, we use a…
Jet substructure tools have proven useful in a number of high-energy particle-physics studies. A particular case is the discrimination, or tagging, between a boosted jet originated from an electroweak boson (signal), and a standard QCD…
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are…
Several observables sensitive to the fragmentation of $b$-quarks into $b$-hadrons are measured using 36 fb${}^{-1}$ of $\sqrt s = 13$ TeV proton-proton collision data collected with the ATLAS detector at the LHC. Jets containing $b$-hadrons…
I present a new scheme for tagging boosted heavy flavor jets called "$\mu_x$ tagging" and its application to TeV-scale physics beyond the Standard Model. Using muons from B hadron decay to define a particular combination "x" of angular…
Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of particle physics collider data. One such challenging task is…
Hadronic jets are extremely abundant at the LHC, and testing QCD in various corners of phase-space is important to understand backgrounds and some specific signatures of new physics. In this article, various measurements aiming at probing…
Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJetTransformer, is presented, which exploits a transformer-based neural network that is substantially faster to train than state-of-the-art graph…
N-subjettiness is a jet shape designed to identify boosted hadronic objects such as top quarks. Given N subjet axes within a jet, N-subjettiness sums the angular distances of jet constituents to their nearest subjet axis. Here, we…
Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective…
We introduce a new jet shape -- N-subjettiness -- designed to identify boosted hadronically-decaying objects like electroweak bosons and top quarks. Combined with a jet invariant mass cut, N-subjettiness is an effective discriminating…
Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton-proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a…
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…
Discriminating quark and gluon jets is a long-standing topic in collider phenomenology. In this paper, we address this question using the Lund jet plane substructure technique introduced in recent years. We present two complementary…
We present a quantum enhanced tagger to identify jets with large Lorentz boost at colliders. For the first time, a convolutional quantum graph neural network (QGNN) is designed to discriminate boosted jets arising from hadronic decays of…
In these proceedings, we report on recent results related to vector boson-tagged jet production in heavy ion collisions and the related modification of jet substructure, such as jet shapes and jet momentum sharing distributions.…
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark…
Jet tagging has become an essential tool for new physics searches at the high-energy frontier. For jets that contain energetic charged leptons we introduce Feature Extended Supervised Tagging (FEST) which, in addition to jet substructure,…