Related papers: CNN-Based Online Trigger for QGP Event Selection
The formation of quark-gluon plasma (QGP) in relativistic heavy ion collision, is expected to be accompanied by a background of ordinary collision events without phase transition. In this short note an algorithm is proposed to select the…
We present an overview of selected aspects of ultrarelativistic nucleus-nucleus collisions, a research program devoted to the study of strongly interacting matter at high energy densities and in particular to the characterization of the…
Understanding the properties of the quark-gluon plasma (QGP) offers insights into the strong interaction and the conditions of the early universe.Since the QGP cannot be observed directly, its properties must be inferred from the particles…
The method of smoothed particle hydrodynamics (SPH) is applied for ultra-relativistic heavy-ion collisions. The SPH method has several advantages in studying event-by-event fluctuations, which attract much attention in looking for quark…
In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, consequently, greater complexity in…
In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we propose a graph neural network enhanced by…
Event classifiers are the most fundamental observables to probe the event topology of hadronic and nuclear collisions at relativistic energies. Over the last five decades, significant progress has been made to establish suitable event…
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been…
In this review, we present an up-to-date phenomenological summary of research developments in the physics of the Quark--Gluon Plasma (QGP). A short historical perspective and theoretical motivation for this rapidly developing field of…
The implementation of convolutional neural networks in programmable logic, for applications in fast online event selection at hadron colliders is studied. In particular, an approach based on full event images for classification is studied,…
We revisit the D-measure of event-by-event net-electric charge fluctuations, an idea first introduced over 20 years ago as a potential signature for the presence of quark-gluon plasma (QGP) in heavy-ion collisions. We developed a…
It is shown that the acoustic scaling patterns of anisotropic flow for different event shapes at a fixed collision centrality (shape-engineered events), provide robust constraints for the event-by-event fluctuations in the initial-state…
This study investigates Quark-Gluon Plasma (QGP) in heavy-ion collisions through two avenues: high-$p_{\perp}$ frameworks and hydrodynamic modeling. Using the T$_{\text{R}}$ENTo model, we find that IP-Glasma mimicking $p=0$ value aligns…
In collider experiments, the kinematic reconstruction of heavy, short-lived particles is vital for precision tests of the Standard Model and in searches for physics beyond it. Performing kinematic reconstruction in collider events with many…
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models…
The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained…
We posit a unified hydrodynamic and microscopic description of the quark-gluon plasma (QGP) produced in ultrarelativistic $p$-Pb and Pb-Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$ TeV and evaluate our assertion using Bayesian inference.…
We develop a neural network model, based on the processes of high-energy heavy-ion collisions, to study and predict several experimental observables in Au+Au collisions. We present a data-driven deep learning framework for predicting…
Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction…
High-energy heavy-ion physics and low-energy nuclear structure physics have historically been disconnected fields. The hydrodynamic description of the quark-gluon plasma (QGP) requires input from nuclear structure to model the initial…