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Julia Velkovska

Publications and source records attributed to Julia Velkovska.

12 recordsLinked to original sources
arXiv2025

Development and characterization of MPGD-based transition radiation detectors

Transition Radiation Detectors (TRDs) are useful for electron identification and hadron suppression in high energy nuclear and particle physics experiments. Conventional wire-chamber TRDs face operational limitations due to space charge effects, motivating the replacement of the amplification stage with MicroPattern Gaseous Detectors (MPGDs). This work explores different MPGD technologies - Gas Electron Multiplier (GEM), Micro-Mesh Gaseous Structure (Micromegas), and Resistive Micro-Well ($\mu$RWELL) - as alternative TRD amplification stages. We report on the design, construction, and in-beam characterization of multiple MPGD-based TRD prototypes exposed to 3-20 GeV mixed electron-hadron beams at the Fermilab Test Beam Facility and at the CERN SPS H8 beamline. Each detector consisted of a multi-layered radiator, an approximately 2 cm deep drift region, an MPGD amplification stage optimized for X-ray transition radiation detection in a Xe:CO$_{2}$ (90:10) gas mixture, and a two-dimensional readout. The GEM-based TRD prototype achieved a pion suppression factor of about 8 at 90% electron efficiency, while the Micromegas-based prototype - with an added GEM preamplification layer - demonstrated improved operational stability and clear TR photon discrimination. The $\mu$RWELL prototype achieved stable operation but limited signal gain. Geant4-based studies confirmed the observed trends and highlighted the sensitivity of the TR yield to cathode material and radiator configuration. These studies represent the first in-beam measurements of Micromegas- and $\mu$RWELL-based TRDs, along with discussion of the performance capabilities of a triple-GEM-TRD. The results demonstrate the feasibility of MPGDs as scalable, high-rate amplification structures for next-generation TRD applications.

physics.ins-det↗
arXiv2025

Validating a Machine Learning Approach to Identify Quenched Jets in Heavy-Ion Collisions

Jet quenching is a phenomenon in heavy-ion collisions arising from jet interactions with the quark-gluon plasma (QGP). Its study is complicated by the interplay of multiple physics processes that affect jet observables. In addition, detector effects may influence the results and must be accounted for when identifying quenched jets. We employ a Long Short-Term Memory (LSTM) neural network trained on jet substructure, incorporating parton shower history, to predict jet-by-jet quenching levels. Using photon-jet samples from the \textsc{Jewel} event generator, we show that the LSTM predictions strongly correlate with true jet energy loss. This validates that the model effectively learns the features of jet-QGP interaction. We simulate detector effects using \textsc{Delphes} simulation framework and demonstrate that the method identifies quenching effects in a realistic environment. We test the approach with photon-jet momentum imbalance, jet fragmentation function, and jet shape, which were not included in the training, confirming its ability to distinguish true quenching features.

hep-ph↗
arXiv2025

Interplay of prompt and non-prompt photons in photon-triggered jet observables

Prompt photons are important yet challenging to observe in relativistic heavy-ion collisions, as they are produced in the early stages and traverse almost the entire QGP medium without interaction. Experimental analyses typically employ isolation cuts, in the hope to identify prompt photons. Most theoretical studies consider only events with actual prompt photons, assuming no contribution from isolated non-prompt photons to reduce computational cost. For the first time, we present a study that compares simulation results generated using inclusive (bremsstrahlung) and prompt-photon events with multiple experimental observables for both $p-p$ and $Pb-Pb$ collisions at $5.02$ TeV. Simulations are carried out using the multi-stage JETSCAPE framework tuned to describe the quenching of jets and hadrons. Isolated non-prompt photons are generated in hard photon bremsstrahlung, where the photon is radiated at a sufficient angle to the jet. Several photon triggered jet and jet substructure observables show significant contributions from inclusive photons, yielding an improvement in comparison with experimental data. Novel photon triggered jet substructure observables are also expected to show new structures, yet to be detected in experiment. This effort examines the significance of isolated non-prompt photons using parameters tuned for a simultaneous description of the leading hadron and jet spectrum, and thus provides an independent verification of the multistage evolution framework.

hep-ph↗
arXiv2025

Effects of hadronic reinteraction on jet fragmentation from small to large systems

We investigate the impact of the hadronic phase on jet quenching in nuclear collider experiments, an open question in heavy-ion physics. Previous studies in a simplified setup suggest that hadronic interactions could have significant effects, but a systematic analysis is needed. Using the X-SCAPE event generator with the SMASH afterburner, we study the role of hadronic rescattering on jet fragmentation hadrons. Applying this framework to $e^++e^-$ collisions, we demonstrate that even in small systems with limited particle production, hadronic interactions lead to measurable modifications in final-state hadronic and jet observables by comparing scenarios with and without afterburner rescattering.

hep-ph↗
arXiv2023

3D Multi-system Bayesian Calibration with Energy Conservation to Study Rapidity-dependent Dynamics of Nuclear Collisions

Considerable information about the early-stage dynamics of heavy-ion collisions is encoded in the rapidity dependence of measurements. To leverage the large amount of experimental data, we perform a systematic analysis using three-dimensional hydrodynamic simulations of multiple collision systems -- large and small, symmetric and asymmetric. Specifically, we perform fully 3D multi-stage hydrodynamic simulations initialized by a parameterized model for rapidity-dependent energy deposition, which we calibrate on the hadron multiplicity and anisotropic flow coefficients. We utilize Bayesian inference to constrain properties of the early- and late- time dynamics of the system, and highlight the impact of enforcing global energy conservation in our 3D model.

nucl-th↗
arXiv2023

Hybrid Hadronization of Jet Showers from $e^++e^-$ to $A+A$ with JETSCAPE

In this talk we review jet production in a large variety of collision systems using the JETSCAPE event generator and Hybrid Hadronization. Hybrid Hadronization combines quark recombination, applicable when distances between partons in phase space are small, and string fragmentation appropriate for dilute parton systems. It can therefore smoothly describe the transition from very dilute parton systems like $e^++e^-$ to full $A+A$ collisions. We test this picture by using JETSCAPE to generate jets in various systems. Comparison to experimental data in $e^++e^-$ and $p+p$ collisions allows for a precise tuning of vacuum baseline parameters in JETSCAPE and Hybrid Hadronization. Proceeding to systems with jets embedded in a medium, we study in-medium hadronization for jet showers. We quantify the effects of an ambient medium, focusing in particular on the dependence on the collective flow and size of the medium. Our results clarify the effects we expect from in-medium hadronization of jets on observables like fragmentation functions, hadron chemistry and jet shape.

hep-ph↗
arXiv2023

A multistage framework for studying the evolution of jets and high-$p_T$ probes in small collision systems

Understanding the modification of jets and high-$p_T$ probes in small systems requires the integration of soft and hard physics. We present recent developments in extending the JETSCAPE framework to build an event generator, which includes correlations between soft and hard partons, to study jet observables in small systems. The multi-scale physics of the collision is separated into different stages. Hard scatterings are first sampled at binary collision positions provided by the Glauber geometry. They are then propagated backward in space-time following an initial-state shower to obtain the initiating partons' energies and momenta before the collision. These energies and momenta are then subtracted from the incoming colliding nucleons for soft-particle production, modeled by the 3D-Glauber + hydrodynamics + hadronic transport framework. This new hybrid approach (X-SCAPE) includes non-trivial correlations between jet and soft particle productions in small systems. We calibrate this framework with the final state hadrons' $p_T$-spectra from low to high $p_T$ in $p$-$p$, and and then compare with the spectra in $p$-$Pb$ collisions from the LHC. We also present results for additional observables such as the distributions of event activity as a function of the hardest jet $p_T$ in forward and mid-rapidity for both $p$-$p$ and $p$-$Pb$ collisions.

hep-ph↗
arXiv2022

Effective gain and Ion Back Flow study of triple and quadruple gem detector

Gas electron Multipliers (GEM) are a new generation of gaseous avalanche devices in the Micro Patterned Gaseous Detector category. GEMs are widely used in both nuclear and high energy experiments as well as in medical science. Several parameters define the performance of these types of devices under various experimental conditions. This article focuses on the study of effective gain and Ion Back Flow (IBF) in both triple and quadruple GEM detectors. Effective gain and IBF are two of the most important parameters in determining the performance of GEM detectors.

physics.ins-det↗
arXiv2022

Identifying quenched jets in heavy ion collisions with machine learning

Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with quark gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a machine learning approach to the identification of quenched jets is designed. Jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy ion collisions.

hep-ph↗
arXiv2021

Reduction of ion back flow using a quadruple GEM detector with various gas mixtures

In gaseous tracking detectors with a large gaseous volume multiple layers of Gas Electron Multipliers (GEM) can be used to block positive ions from flowing back into the active volume, which is detrimental to the tracking performance. We report on studies of effective gain, ion backflow (IBF), and energy resolution in quadruple GEM detectors, and on strategies for minimizing IBF by optimizing the operating voltages of the individual GEM layers and the potential differences between different layers.

physics.ins-det↗
arXiv2004

Production and Flow of Identified Hadrons at RHIC

We review the production and flow of identified hadrons at RHIC with a main emphasis on the intermediate transverse momentum region ($2< p_{T} <5$ GeV/$c$). The goal is to unravel the dynamics of baryon production and resolve the anomalously large baryon yields and elliptic flow observed in the experiments.

nucl-ex↗