Related papers: Deep-learned Top Tagging with a Lorentz Layer

Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural…

We demonstrate that the classification of boosted, hadronically-decaying weak gauge bosons can be significantly improved over traditional cut-based and BDT-based methods using deep learning and the jet charge variable. We construct binary…

Top taggers which identify and reconstruct boosted top quarks have been established as novel tools for a multitude of LHC analyses. We show how single top production in association with a light-flavor or bottom jet can be observed in the…

The ubiquity of top-rich final states in the context of beyond the Standard Model (BSM) searches has led to their status as extensively studied signatures at the LHC. Over the past decade, numerous endeavours have been undertaken in the…

The identification of hadronically decaying heavy states, such as vector bosons, the Higgs, or the top quark, produced with large transverse boosts has been and will continue to be a central focus of the jet physics program at the Large…

High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their largest decay mode and to assuaging resolution difficulties as…

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…

Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize the top quark across its various production modes and to…

We apply gradient boosting machine learning techniques to the problem of hadronic jet substructure recognition using classical subjettiness variables available within a common parameterized detector simulation package DELPHES. Per-jet…

This paper describes a method for detecting a rare top quark decay into a charm quark and a Higgs boson (H), which decays further into b quarks, at the Large Hadron Collider (LHC), and introduces a tagging algorithm to identify boosted tops…

Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential…

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…

At the LHC, a generic supersymmetric cascade can be a source of top quark. Specifically third generation squarks and gluino are the major sources of top quark which could also be boosted. In this article, we have shown that jet substructure…

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The…

The production at the LHC of boosted top quarks (top quarks with a transverse momentum that greatly exceeds their rest mass) is a promising process to search for phenomena beyond the Standard Model. In this contribution several examples are…

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…

The Large Hadron Collider (LHC) is the first machine that provides high enough energy to produce large numbers of boosted top quarks. The decay products of these top quarks are confined to a cone in the top quark flight direction and can be…

We study the reconstruction of high p_T hadronically-decaying top quarks at the LHC. The main challenge in identifying energetic top quarks is that the decay products become increasingly collimated. This reduces the efficacy of conventional…

Machine learning algorithms have the capacity to discern intricate features directly from raw data. We demonstrated the performance of top taggers built upon three machine learning architectures: a BDT that uses jet-level variables…

For highly energetic top quarks, the products of the decay t->bqq' are collimated. The 3-prong decay structure can no longer be resolved using calorimeter information alone if the particle jet separation approaches the calorimeter…