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Nowadays, there has been a growing trend in the field of high-energy physics (HEP), in both its experimental and phenomenological studies, to incorporate machine learning (ML) and its specialized branch, deep learning (DL). This review…
The encoder-decoder is the typical framework for Neural Machine Translation (NMT), and different structures have been developed for improving the translation performance. Transformer is one of the most promising structures, which can…
The Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily. The multi-head attention network performs the scaled dot-product attention function in parallel, empowering the model by…
Machine learning (ML) has become an integral component of high energy physics data analyses and is likely to continue to grow in prevalence. Physicists are incorporating ML into many aspects of analysis, from using boosted decision trees to…
This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervised machine learning. We discuss the…
Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL), where models adapt to new tasks through example-based prompts without requiring parameter updates. However, understanding how tasks are…
A central challenge in high-energy nuclear physics is to extract informative features from the high-dimensional final-state data of heavy-ion collisions (HIC) in order to enable reliable downstream analyses. Traditional approaches often…
We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical…
Charged Higgs bosons are predicted in variety of theoretically well-motivated new physics models with extended Higgs sectors. In this study, we focus on a type-II two Higgs doublet model (2HDM-II) and consider a heavy charged Higgs with its…
Recently, radical progress in machine learning (ML) has revolutionized computational materials science, enabling unprecedentedly rapid materials discovery and property prediction, but the quantum many-body problem -- which is the key to…
Many physics signals presently studied at the high energy collision experiments lead to final states with jets originating from heavy flavor quarks. This report reviews the algorithms for heavy flavor jets identification developed by the…
Unlike other realisations of the 2-Higgs Doublet Model (2HDM), the so-called Type-I allows for a very light Higgs boson spectrum. Specifically, herein, the heaviest of the two CP-even neutral Higgs states, $H$, can be the one discovered at…
A study of Underlying Events (UE) and Minimum Bias (MB) at Large Hadron Collider (LHC)with CMS and ATLAS detector under nominal conditions is discussed. Using charged particle and charged particle jets, it will be possible to discriminate…
Temporal convolutions have been the paradigm of choice in action segmentation, which enhances long-term receptive fields by increasing convolution layers. However, high layers cause the loss of local information necessary for frame…
Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a GEANT-based…
We present a novel hybrid quantum-classical vision transformer architecture incorporating quantum orthogonal neural networks (QONNs) to enhance performance and computational efficiency in high-energy physics applications. Building on…
We illustrate how the Matrix Element Method at Next-to-Leading Order (MEM@NLO) can be used to discriminate between events arising from the production of a Higgs boson, which subsequently decays to a final state consisting of…
We study lepton-flavor-violating (LFV) decays of a heavy Higgs boson, $H \to \mu\tau$, in the Type-III two-Higgs-doublet model by recasting the CMS search at $\sqrt{s} = 13$ TeV with 35.9 fb$^{-1}$ using fast detector simulation in the mass…
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…
We present a scalable technique for the simulation of collider events with multi-jet final states, based on an improved parton-level event file format. The method is implemented for both leading- and next-to-leading order QCD calculations.…