Related papers: Boosted top quark tagging and polarization measure…
Convolutional neural networks are basic structures using jet images as input for the jet tagging problems. However, what they have learned during the training process is always difficult to understand just through feature maps. Inspired by…
The identification of b jets is a crucial issue to study and characterize various channels like top quark events and many new physics scenarios. Different b-tagging techniques are defined in CMS which benefit from the long life time, high…
We present an application of a particular machine-learning method (Boosted Decision Trees, BDTs using AdaBoost) to separate stars and galaxies in photometric images using their catalog characteristics. BDTs are a well established machine…
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle…
We study the boosted Higgs tagging using the Lund jet plane. The convolutional neural network is used for the Lund images data set to classify hadronically decaying Higgs from the QCD background. We consider $H\to b \bar{b}$ and $H \to gg$…
A measurement is reported of the jet mass distribution in hadronic decays of boosted top quarks produced in pp collisions at $\sqrt{s} =$ 13 TeV. The data were collected with the CMS detector at the LHC and correspond to an integrated…
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…
Top taggers are established analysis tools to reconstruct boosted hadronically decaying top quarks for example in searches for heavy resonances. We first present a dedicated study of signal efficiency versus background rejection, allowing…
We explore the usage of boosted as well as semi-boosted topologies in all-hadronic $t\bar{t}$ final states in simulated $pp$ collisions at $\sqrt{s} = 14\,$TeV, with top quarks decaying into a boosted hadronic top-jet or a $W$-jet and an…
Deep neural networks have rightfully won the place of one of the most accurate analysis tools in high energy physics. In this paper we will cover several methods of improving the performance of a deep neural network in a classification task…
Top polarization is an important probe of new physics that couples to the top sector, and which may be discovered at the 14 TeV LHC. Taking the example of the MSSM, we argue that top polarization measurements can put a constraint on the…
In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on attention-based Transformer networks. We report the…
Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and…
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…
Tagging jets of strongly interacting particles initiated by energetic strange quarks is one of the few largely unexplored Standard Model object classification problems remaining in high energy collider physics. In this paper we investigate…
Studying heavy-flavor jets in pp collision is important since they can test pQCD calculations and be used as a reference for heavy-ion collisions. Jets in this analysis are reconstructed from charged particles using the…
At the Large Hadron Collider, numerous physics processes expected within the standard model and theories beyond it give rise to very high momentum particles decaying to multihadronic final states. Development of algorithms for efficient…
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…
The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. This work demonstrates the feasibility and advantages of applying…
The ability to identify jets containing B hadrons is important for the high-pT physics program of a general-purpose experiment such as ATLAS. b-tagging is in particular useful for selecting very pure top quark samples, for studying standard…