arXiv subjects

I. Karakoulias

Publications and source records attributed to I. Karakoulias.

5 recordsLinked to original sources

arXiv2026

Study of Supernova Neutrinos at ESSnuSB

In this paper, we have studied the sensitivity of the ESSnuSB far detector to supernova neutrinos. ESSnuSB is a proposed long-baseline neutrino experiment in Sweden, which will use a 538 kt water Cherenkov detector to probe the leptonic phase $δ_{\rm CP}$ by studying the second oscillation maximum. However, given the very large detector volume, it will have an excellent sensitivity to supernova neutrinos if a supernova explosion occurs during the run-time of ESSnuSB. Motivated by this, we first estimate the expected event rates at the ESSnuSB far detector for three different supernova flux models and then we probe its capability to distinguish these flux models. Additionally, we also investigate the impact of systematic errors and detector efficiency. Our results show that depending on the model of the supernova neutrinos, the expected number of events detected at Earth varies significantly. Our results also show that the ESSnuSB far detector may have excellent potential in distinguishing these flux models depending upon the distance of the supernova explosion, systematic errors and detector efficiency.

hep-ex↗

arXiv2026

Exploring ESS$ν$SB Near Water Cherenkov Detector Designs Through Graph Neural Network Flavour Identification

The ESS$ν$SB experiment aims to measure CP violation in the leptonic sector with high precision, necessitating robust reconstruction of neutrino events in the water Cherenkov (WC) detectors. In this work, we investigate the flavour identification potential of the proposed near WC detector using graph neural network (GNN)-based classification, with a focus on variations of key detector design parameters. In particular, we study whether a smaller and/or less instrumented detector can achieve the required classification performance. Using detailed Monte Carlo simulations of charged-current (CC) neutrino interactions, we train GNN classifiers to distinguish electron and muon neutrino CC events. We find that GNN-based classification remains accurate even for detector configurations with volumes up to a factor of eight smaller than the nominal design, with only moderate degradation in classification efficiency at fixed background rejection. The resulting loss in efficiency can largely be compensated by increased exposure time. Furthermore, we demonstrate that reduced photomultiplier tube (PMT) coverage in the nominal detector has a limited impact on classification performance, provided that coverage is maintained in regions of highest signal yield, in particular near the forward end-cap.

hep-ex↗

arXiv2026

Advances in photocathode development for PICOSEC Micromegas precise-timing detectors

The PICOSEC Micromegas detector is a~precise-timing gaseous detector that combines a Cherenkov radiator, a~semi-transparent photocathode and a Micromegas amplification stage, targeting time resolutions of tens of picoseconds for minimum ionising particles (MIPs). Initial single-pad prototypes achieved time resolutions of $σ<25$ ps, demonstrating strong potential for High Energy Physics (HEP) applications and beyond. The objective of this paper is a comprehensive characterisation of photocathodes, with a strong focus on robust materials while preserving excellent timing performance. The study includes laboratory measurements of optical and resistive properties, along with beam tests using 150 GeV/$c$ muons to evaluate the time resolution and photoelectron yield for various photocathodes. The best performance was obtained by a 5 nm Cesium Iodide (CsI) photocathode, reaching $σ= 10.9 \pm 0.3$ ps with more than 30 extracted photoelectrons, representing the most precise time resolution achieved by PICOSEC Micromegas to date. Metallic and carbon-based photocathodes, including Titanium (Ti), Boron Carbide (B$_4$C) and Diamond-Like Carbon (DLC), were also tested, with Ti and B$_4$C emerging as the most promising alternatives, achieving $σ\approx 30$ ps with about 5 extracted photoelectrons. These results demonstrate that improved robustness can be achieved while maintaining excellent time resolution, supporting the feasibility of using the PICOSEC Micromegas concept in future experiments.

physics.ins-det↗

arXiv2026

Spatial resolution improvement of PICOSEC Micromegas precise timing detectors

The combination of a Cherenkov radiator with a semi-transparent photocathode and a Micromegas based amplification stage allows PICOSEC Micromegas detectors to achieve a time resolution of better than 15ps. While tileable prototypes with 10x10 channels feature 1x1 cm^2 readout pads, finer readout granularity can be used to improve the spatial resolution. We report on the study of high readout granularity PICOSEC Micromegas prototypes which achieve around 0.5mm spatial resolution with 3.5mm large pads. No significant improvement was found when readout pad size was further reduced to 2.2mm. The timing resolution of the leading pad was found to be slightly degraded but remained better than 20ps for a medium granularity prototype. The achieved spatial resolution can enable PICOSEC Micromegas to be used as precise timing and moderate resolution tracking detector simultaneously.

physics.ins-det↗

arXiv2025

Classification of Electron and Muon Neutrino Events for the ESS$ν$SB Near Water Cherenkov Detector using Graph Neural Networks

In the effort to obtain a precise measurement of leptonic CP-violation with the ESS$ν$SB experiment, accurate and fast reconstruction of detector events plays a pivotal role. In this work, we examine the possibility of replacing the currently proposed likelihood-based reconstruction method with an approach based on Graph Neural Networks (GNNs). As the likelihood-based reconstruction method is reasonably accurate but computationally expensive, one of the benefits of a Machine Learning (ML) based method is enabling fast event reconstruction in the detector development phase, allowing for easier investigation of the effects of changes to the detector design. Focusing on classification of flavour and interaction type in muon and electron events and muon- and electron neutrino interaction events, we demonstrate that the GNN reconstructs events with greater accuracy than the likelihood method for events with greater complexity, and with increased speed for all events. Additionally, we investigate the key factors impacting reconstruction performance, and demonstrate how separation of events by pion production using another GNN classifier can benefit flavour classification.

hep-ex↗