Related papers: Interpretable Machine Learning Methods Applied to …
Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models are often complicated and hardly interpretable, which poses…
Studies of fully-reconstructed jets in heavy-ion collisions aim at extracting thermodynamical and transport properties of hot and dense QCD matter. Recently, a plethora of new jet substructure observables have been theoretically and…
Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we…
When we deploy machine learning models in high-stakes medical settings, we must ensure these models make accurate predictions that are consistent with known medical science. Inherently interpretable networks address this need by explaining…
Thin film processing by means of sputter deposition inherently depends on the interaction of energetic particles with a target surface and the subsequent particle transport. The length and time scales of the underlying physical phenomena…
We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwork from the original full model, with compressed structure…
Jet quenching in heavy-ion collisions probes parton energy loss in the quark--gluon plasma (QGP), but the extracted transport properties may not be universally constrained across centrality, beam energy, and observable class. In this work,…
Full jet reconstruction in heavy-ion collisions is expected to provide more sensitive measurements of jet quenching in hot QCD matter at RHIC. In this paper we review recent studies of jets utilizing modern jet reconstruction algorithms and…
Jet quenching has long been regarded as one of the key signatures for the formation of quark-gluon plasma in heavy-ion collisions. Despite significant efforts, the separate identification of quark and gluon jet quenching has remained as a…
Jet quenching has become an essential signal for the characterization of the medium formed in experiments of heavy-ion collisions. After a brief introduction to the field, we present the full derivation of the medium-induced gluon radiation…
Supersymmetry with hadronic R-parity violation in which the lightest neutralino decays into three quarks is still weakly constrained. This work aims to further improve the current search for this scenario by the boosted decision tree method…
These lectures were presented at the 2024 QCD Masterclass in Saint-Jacut-de-la-Mer, France. They introduce and review fundamental theorems and principles of machine learning within the context of collider particle physics, focused on…
Hard scattered partons are predicted to be well calibrated probes of the hot and dense medium produced in heavy ion collisions. Interactions of these partons with the medium w ill result in modifications of internal jet structure in Au+Au…
High energetic particles traversing a dense medium lose a sizable part of their energy in form of gluon radiation. As a result, the rate of high-$p_t$ particles is expected to be suppressed in heavy ion collisions with respect to the proton…
This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance…
We explore the potential to use machine learning methods to search for heavy neutrinos, from their hadronic final states including a fat-jet signal, via the processes $pp \rightarrow W^{\pm *}\rightarrow \mu^{\pm} N \rightarrow \mu^{\pm}…
We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model,…
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 review recent theoretical developments in the study of the structure of jets that are produced in ultra relativistic heavy ion collisions. The core of the review focusses on the dynamics of the parton cascade that is induced by the…
Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective…