Related papers: Neural networks for boosted di-$\tau$ identificati…
In proton-proton collisions at LHC energies, Z and low mass Higgs bosons would be produced with high and predominantly longitudinal boost with respect to the beam axis. This note describes a new analysis tool devised to handle this…
Boosted decision trees are applied to particle identification in the MiniBooNE experiment operated at Fermi National Accelerator Laboratory (Fermilab) for neutrino oscillations. Numerous attempts are made to tune the boosted decision trees,…
The use of neural networks for signal vs.~background discrimination in high-energy physics experiment has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top quark…
Boosted objects - particles whose transverse momentum is greater than twice their mass - are becoming increasingly important as the LHC continues to explore energies in the TeV range. The sensitivity of searches for new phenomena beyond the…
More often than not in benchmark supervised ML, tabular data is flat, i.e. consists of a single $m \times d$ (rows, columns) file, but cases abound in the real world where observations are described by a set of tables with structural…
Many extensions of the standard model (SM) predict the existence of axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic decays, are…
Identification of boosted, hadronically-decaying top quarks is a problem of central importance for physics goals of the Large Hadron Collider. We present a theoretical analysis of top quark tagging, establishing zeroth-order, minimal…
We investigate photon--pion discrimination in regimes where electromagnetic showers overlap at the scale of calorimeter granularity. Using full detector simulations with fine-grained calorimeter segmentation of approximately…
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…
We have studied the application of different classification algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data analysis, the performance of other…
The success of Large Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and dataset size, is the primary driver of performance in modern machine learning. While machine learning has long…
We present the report of the hadronic working group of the BOOST2010 workshop held at the University of Oxford in June 2010. The first part contains a review of the potential of hadronic decays of highly boosted particles as an aid for…
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…
We show that the tracking system in a collider detector can be used to efficiently identify boosted massive particles from their QCD backgrounds. We examine variables defined with tracking information which are sensitive to jet radiation…
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…
Since the machine learning techniques are improving rapidly, it has been shown that the image recognition techniques in deep neural networks can be used to detect jet substructure. And it turns out that deep neural networks can match or…
Time-series data classification is central to the analysis and control of autonomous systems, such as robots and self-driving cars. Temporal logic-based learning algorithms have been proposed recently as classifiers of such data. However,…
We demonstrate the utility of leptons which fail standard isolation criteria in searches for new physics at the LHC. Such leptons can arise in any event containing a highly boosted particle which decays to both leptons and quarks. We begin…
Gradient Boosted Decision Trees (GBDTs) are dominant machine learning algorithms for modeling discrete or tabular data. Unlike neural networks with millions of trainable parameters, GBDTs optimize loss function in an additive manner and…
A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which…