Related papers: Machine-learning-based particle identification wit…
The ALICE detector has excellent Particle IDentification (PID) capabilities in the central barrel ($\lvert \eta \rvert <$ 0.9). This allows identified hadron production to be measured over a wide transverse momentum ($p_{\rm{T}}$) range,…
This study demonstrates a proof-of-concept application of a deep neural network for particle identification in simulated high transverse momentum proton-proton collisions, with a focus on evaluating model performance under controlled…
An incomplete particle identification distorts the observed event-by-event fluctuations of the hadron chemical composition in nucleus-nucleus collisions. A new experimental technique called the {\em identity method} was recently proposed.…
Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent advances in representation learning give rise to distance-based OOD detection, where testing samples are detected as OOD if they are relatively far…
A Large Ion Collider Experiment (ALICE) is built to study the properties of the strongly interacting matter created in heavy-ion collisions at the LHC. With the upgrade of its Inner Tracking System (ITS), the ALICE experiment is going to…
One of the main goals in the study of hadronic interactions at LHC energies is the attempt to characterize the mechanisms involved in particle production in different regimes. The charged-particle multiplicity is one of the most interesting…
In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more…
Particle-in-cell codes are the most widely used simulation tools for kinetic studies of ultra-intense laser-plasma interactions. Using the motion of a single electron in a plane electromagnetic wave as a benchmark problem, we show…
Class incremental learning (CIL) is a challenging setting of continual learning, which learns a series of tasks sequentially. Each task consists of a set of unique classes. The key feature of CIL is that no task identifier (or task-id) is…
The ALICE experiment is equipped with a wide range of detectors providing excellent tracking and particle identification in the central region, as well as forward detectors with extended pseudorapidity coverage, which are well suited for…
Detection and localization of image manipulations like splices are gaining in importance with the easy accessibility of image editing softwares. While detection generates a verdict for an image it provides no insight into the manipulation.…
In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis. Typically, algorithms select individual collision events for preservation and store…
The measurement of identified charged-hadron production at mid-rapidity ($|y| < 0.5$) performed with the ALICE experiment at the LHC is presented for pp collisions at $\sqrt{s}$ = 900 GeV and 7 TeV and for Pb--Pb collisions at…
We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $\gamma$, $\mu^-$, $\pi^\pm$, and…
Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to capture solution-related features through neural networks.…
Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering (CS&E) world. At least two challenges exist for PINNs at…
This paper studies the problem of Person Re-Identification (ReID)for large-scale applications. Recent research efforts have been devoted to building complicated part models, which introduce considerably high computational cost and memory…
From AlexNet to Inception, autoencoders to diffusion models, the development of novel and powerful deep learning models and learning algorithms has proceeded at breakneck speeds. In part, we believe that rapid iteration of model…
Real-world data is often ambiguous; for example, human annotation produces instances with multiple conflicting class labels. Partial-label learning (PLL) aims at training a classifier in this challenging setting, where each instance is…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…