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Georgia Karagiorgi

Publications and source records attributed to Georgia Karagiorgi.

At least 19 recordsLinked to original sources
arXiv2026

Low-Noise SiPM Light Readout and ASIC-Based Charge Readout of a Liquid Argon Time Projection Chamber for MeV Gamma-Ray Measurements

We have developed a compact liquid argon time projection chamber (LArTPC), NanoGRAMS, as a technology demonstrator for the Gamma-Ray and AntiMatter Survey (GRAMS). LArTPCs have the potential to enable Compton cameras with unprecedented effective area in the MeV gamma-ray band. NanoGRAMS has an active volume of $5.12 \times 5.12 \times 10~\mathrm{cm^3}$ and is equipped with a low-noise scintillation and charge readout system. The scintillation light is detected by an array of 16 SiPMs ($6 \times 6~\mathrm{mm^2}$ each), whose signals are summed and amplified by a low-noise transimpedance amplifier operable at liquid argon temperature. Ionization electrons are read out with $3.2\,\mathrm{mm}$-pitch pixels and processed by VATA-SGD ASICs, with synchronization provided by an FPGA-based data acquisition system. We irradiated the detector with a $^{60}\mathrm{Co}$ source (1173 and $1332\,\mathrm{keV}$) and successfully detected both 1-hit and 2-hit events. The collected charge was converted to deposited energy using a phenomenological recombination model, and the detector response was evaluated with a Geant4-based Monte Carlo simulation. The reconstructed energy spectrum shows Compton edges at 963 and $1118\,keV$, consistent with the expected values. For 2-hit events, the sequence of interactions was identified, and the reconstructed back-projection image agrees with the source position. These results demonstrate the feasibility of NanoGRAMS as a Compton camera for MeV gamma-ray imaging spectroscopy.

astro-ph.IM↗
arXiv2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

physics.ins-det↗
arXiv2025

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers

We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector or the future Deep Underground Neutrino Experiment. These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation, to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays, GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.

hep-ex↗
arXiv2025

IsoDAR@Yemilab: Preliminary Design Report -- Volume II: Medium Energy Beam Transport, Neutrino Source, and Shielding

This Preliminary Design Report (PDR) describes the IsoDAR electron-antineutrino source in two volumes which are mostly site-independent and describe the cyclotron driver providing a 60 MeV, 10 mA proton beam (Volume I); and the medium energy beam transport line (MEBT) and target (this Volume). The IsoDAR driver and target will produce about $1.15\cdot10^{23}$ electron-antineutrinos over five calendar years. Paired with a kton-scale liquid scintillator detector, this will enable a broad particle physics program including searches for new symmetries, new interactions and new particles. Here in Volume II, we describe the medium energy beam transport line, the antineutrino source beam-target and surrounding sleeve, shielding, and plans for monitoring and installation.

hep-ex↗
arXiv2024

IsoDAR@Yemilab: Preliminary Design Report -- Volume I: Cyclotron Driver

This Preliminary Design Report (PDR) describes the IsoDAR electron-antineutrino source in two volumes which are mostly site-independent and describe the cyclotron driver providing a 60 MeV, 10 mA proton beam (this Volume); and the medium energy beam transport line (MEBT) and target (Volume II). The IsoDAR driver and target will produce about 1.15e23 electron-antineutrinos over five years. Paired with a kton-scale liquid scintillator detector, it will enable a broad particle physics program including searches for new symmetries, new interactions and new particles. Here in Volume I, we describe the driver, which includes the ion source, low energy beam transport, and cyclotron. The latter features Radio-Frequency Quadrupole (RFQ) direct axial injection and represents the first accelerator purpose-built to make use of so-called vortex motion.

physics.acc-ph↗
arXiv2022

Snowmass Neutrino Frontier Report

This report summarizes the current status of neutrino physics and the broad and exciting future prospects identified for the Neutrino Frontier as part of the 2021 Snowmass Process.

hep-ex↗
arXiv2022

Reconstruction of multiple Compton scattering events in MeV gamma-ray Compton telescopes towards GRAMS: the physics-based probabilistic model

Aimed at progress in mega-electron volt (MeV) gamma-ray astronomy, which has not yet been well-explored, Compton telescope missions with a variety of detector concepts have been proposed so far. One of the key techniques for these future missions is an event reconstruction algorithm that is able to determine the scattering orders of multiple Compton scattering events and to identify events in which gamma rays escape from the detectors before they deposit all of their energies. We revisit previous event reconstruction methods and propose a modified algorithm based on a probabilistic method. First, we present a general formalism of the probabilistic model of Compton scattering describing physical interactions inside the detector and measurement processes. Then, we also introduce several approximations in the calculation of the probability functions for efficient computation. For validation, the developed algorithm has been applied to simulation data of a Compton telescope using a liquid argon time projection chamber, which is a new type of Compton telescope proposed for the GRAMS project. We have confirmed that it works successfully for up to 8-hit events, including correction of incoming gamma-ray energies for escape events. The proposed algorithm can be used for next-generation MeV gamma-ray missions featured by large-volume detectors, e.g., GRAMS.

astro-ph.IM↗
arXiv2022

Physics Community Needs, Tools, and Resources for Machine Learning

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.

cs.LG↗
arXiv2022

Real-time Inference with 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-rate Particle Imaging Detectors

We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end Field Programmable Gate Arrays (FPGAs). To meet FPGA resource constraints, a two-layer CNN is optimized for accuracy and latency with KerasTuner, and network \textit{quantization} is further used to minimize the computing resource utilization of the network. We use "High Level Synthesis for Machine Learning" (\textit{hls4ml}) tools to test CNN deployment on a Xilinx UltraScale+ FPGA, which is a proposed FPGA technology for the front-end readout system of the future Deep Underground Neutrino Experiment (DUNE) far detector. We evaluate network accuracy and estimate latency and hardware resource usage, and comment on the feasibility of applying CNNs for real-time data selection within the proposed DUNE data acquisition system.

physics.ins-det↗
arXiv2022

Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors

This paper presents the custom implementation, optimization, and performance evaluation of convolutional neural networks on field programmable gate arrays, for the purposes of accelerating deep neural network inference on large, two-dimensional image inputs. The targeted application is that of data selection for high-resolution particle imaging detectors, and in particular liquid argon time projection chamber detectors, such as that employed by the future Deep Underground Neutrino Experiment. We motivate this particular application based on the excellent performance of deep neural networks on classifying simulated raw data from the DUNE LArTPC, combined with the need for power-efficient data processing in the case of remote, long-term, and limited-access operating detector conditions.

physics.ins-det↗
arXiv2021

Machine Learning in the Search for New Fundamental Physics

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

hep-ph↗
arXiv2021

Cross-match between the latest Swift-BAT and Fermi-LAT catalogs

We report the results of a cross-match study between the hard X-ray and GeV gamma-ray catalogs, by making use of the latest 105-month Swift-BAT and 10-yr Fermi-LAT catalogs, respectively. The spatial cross-matching between the two catalogs results in the matching of 132 point-like sources, including ~5% of false-match sources. Additionally, 24 sources that have been identified as the same identifications are matched. Among the 75 extended sources in the Fermi-LAT catalog, 31 sources have spatial coincidences with at least one Swift-BAT source inside their extent. All the matched sources consist of blazars (>60%), pulsars and pulsar wind nebulae (~13%), radio galaxies (~7%), binaries (~5%), and others. Compared to the original catalogs, the matched sources are characterized by a double-peaked photon index distribution, higher flux, and larger gamma-ray variability index. This difference arises from the different populations of sources, particularly the large proportion of blazars (i.e., FSRQ and BL Lac). We also report 13 cross-matched and unidentified sources. The matched sources in this study would be promising in the intermediate energy band between the hard X-ray and GeV gamma-ray observations, that is the unexplored MeV gamma-ray domain.

astro-ph.HE↗
arXiv2020

Snowmass 2021 Letter of Interest: The GRAMS Project: MeV Gamma-Ray Observations and Antimatter-Based Dark Matter Searches

The Gamma-Ray and AntiMatter Survey (GRAMS) project is a proposed next-generation balloon/satellite mission targeting both MeV gamma-ray observations and antimatter-based dark matter searches. A cost-effective, large-scale Liquid Argon Time Projection Chamber (LArTPC) detector technology will allow GRAMS to have a significantly improved sensitivity to MeV gamma rays while extensively probing dark matter parameter space via antimatter measurements.

astro-ph.HE↗
arXiv2020

Grid-based minimization at scale: Feldman-Cousins corrections for SBN

We present a computational model for the construction of Feldman-Cousins (FC) corrections frequently used in High Energy Physics (HEP) analysis. The program contains a grid-based minimization and is written in C++. Our algorithms exploit vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by using DIY. We demonstrate the application to scale very well at High Performance Computing (HPC) sites. We use HDF5 in conjunction with HighFive to write results of the calculation to file.

physics.comp-ph↗
arXiv2019

Dual MeV Gamma-Ray and Dark Matter Observatory -- GRAMS Project

GRAMS (Gamma-Ray and AntiMatter Survey) is a novel project that can simultaneously target both astrophysical observations with MeV gamma rays and an indirect dark matter search with antimatter. The GRAMS instrument is designed with a cost-effective, large-scale LArTPC (Liquid Argon Time Projection Chamber) detector surrounded by plastic scintillators. The astrophysical observations at MeV energies have not yet been well-explored (the so-called "MeV-gap") and GRAMS can improve the sensitivity by more than an order of magnitude compared to previous experiments. While primarily focusing on MeV gamma-ray observations, GRAMS is also optimized for cosmic ray antimatter surveys to indirectly search for dark matter. In particular, low-energy antideuterons will provide an essentially background-free dark matter signature. GRAMS will be a next generation experiment beyond the current GAPS (General AntiParticle Spectrometer) project for antimatter survey.

astro-ph.HE↗
arXiv2019

Liquid Argon TPC Trigger Development with SBND

The Short Baseline Near Detector (SBND) is a 112 ton active mass liquid argon time projection chamber (LArTPC) that will begin operations in the Booster Neutrino Beamline at Fermilab in 2020. Its main physics goals include high-statistics measurements of neutrino-argon interaction cross-sections and searches for sterile neutrino oscillations as part of three LArTPCs that make up the Short Baseline Neutrino (SBN) Program at Fermilab. In addition, SBND serves as an R\&D platform for future LArTPC detectors such as those employed by the Deep Underground Neutrino Experiment (DUNE). One of the technical challenges of DUNE that SBND aims to address is that of efficient self-triggering utilizing TPC signal information. Such capability will enable searches for rare processes in the DUNE far detector, for example neutrino interactions from a potential galactic supernova burst, or proton decay. These proceedings describe the SBND TPC readout system and ongoing R\&D efforts to develop and demonstrate efficient TPC-based self-triggering.

physics.ins-det↗
arXiv2019

Finding the remnants of the Milky Way's last neutron star mergers

The discovery of a binary neutron star merger (NSM) through both its gravitational wave and electromagnetic emission has revealed these events to be key sites of r-process nucleosynthesis. Here, we evaluate the prospects of finding the remnants of Galactic NSMs by detecting the gamma-ray decay lines from their radioactive r-process ejecta. We find that $^{126}$Sn, which has several lines in the energy range 415-695 keV and resides close to the second r-process peak, is the most promising isotope, because of its half-life $t_{1/2}=2.30(14)\times 10^{5}$ yr being comparable to the ages of recent NSMs. Using a Monte Carlo procedure, we predict that multiple remnants are detectable as individual sources by next-generation gamma-ray telescopes which achieve sub-MeV line sensitivities of $\sim 10^{-8}$-$10^{-6}$ $γ$ cm$^{-2}$ s$^{-1}$. However, given the unknown locations of the remnants, the most promising search strategy is a systematic survey of the Galactic plane and bulge extending to high Galactic latitudes. Individual known supernova remnants which may be mis-classified NSM remnants could also be targeted, especially those located outside the Galactic plane. Detection of a moderate sample of Galactic NSM remnants would provide important clues to unresolved issues such as the production of actinides in NSMs, properties of merging NS binaries, and even help distinguish them from rare supernovae as current Galactic r-process sources. We also investigate the diffuse flux from longer-lived nuclei (e.g. $^{182}$Hf) that could in principle trace the Galactic spatial distribution of NSMs over longer timescales, but find that the detection of the diffuse flux appears challenging even with next-generation telescopes.

astro-ph.HE↗