Related papers: The Physics Behind ML-based Quark-Gluon Taggers
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…
We study the problem of distinguishing $b$-jets stemming from the decay of a colour singlet, such as the Higgs boson, from those originating from the abundant QCD background. In particular, as a case study, we focus on associate production…
The objective is to provide clear and well-motivated guidance to Machine Learning (ML) teams, founded on our experience in empirical turbulence modeling. Guidance is also needed for modeling outside ML. ML is not yet successful in…
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…
Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in…
We derive an expression relating the transport parameter $\hat{q}$ and the shear viscosity $\eta$ of a weakly coupled quark-gluon plasma. A deviation from this relation can be regarded as a quantitative measure of ``strong coupling'' of the…
The study of the differences of the fragmentation of quarks and gluons to jets of hadrons gives insight into the fundamental structure of QCD. Results from different approaches to properties of quarks and gluons are shown. The colour factor…
Interpretability is becoming an active research topic as machine learning (ML) models are more widely used to make critical decisions. Tabular data is one of the most commonly used modes of data in diverse applications such as healthcare…
The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration,…
We present a topology-informed approach for classifying particle jets using persistent homology, a framework that captures the structural properties of point clouds. Particle jets produced in proton-proton collisions consist of cascades of…
The phenomenology of dark sector is complicated if dark sector is charged under a confined hidden gauge group. In such kind of model, a dark parton produced at a high energy collider showers and hadronize to a cluster of dark mesons. Dark…
Explainable machine learning methods have been accompanied by substantial development. Despite their success, the existing approaches focus more on the general framework with no prior domain expertise. High-stakes financial sectors have…
Discriminating quark jets from gluon jets is an important but challenging problem in jet substructure. In this paper, we use the concept of mutual information to illuminate the physics of quark/gluon tagging. Ideal quark/gluon separation…
An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP.…
A method is introduced for distinguishing top jets (boosted, hadronically decaying top quarks) from light quark and gluon jets using jet substructure. The procedure involves parsing the jet cluster to resolve its subjets, and then imposing…
To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g.…
Jet quenching studies play a prominent role in our current understanding of ultra-relativistic heavy ion collisions. In this review I first present the available formalism to compute medium-induced gluon radiation. Then I discuss its effect…
Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives…
Jet physics provides a powerful tool to investigate interaction properties of quarks and gluons. These studies have been possible at an energy never investigated before at LHC. In this proceedings we review the main characteristics of…
High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex…