arXiv subjects

Laszlo Papp

Publications and source records attributed to Laszlo Papp.

5 recordsLinked to original sources

arXiv2026

Procurement and Purification of Liquid Argon for the LEGEND-200 Experiment

LEGEND-200 requires high-purity liquid argon for effective background discrimination. In this paper, we present the design, construction, and performance of a dedicated liquid argon purification system, along with the procurement and purification of liquid argon for filling the LEGEND-200 cryostat to its total capacity of 91 t. The purifier is based on copper catalyst and molecular sieve to remove oxygen and water. Starting with liquid argon of 5.5 quality, featuring an effective scintillation light triplet lifetime $\tau_t$ of about 0.9 $\mu$s, we achieved a final purity corresponding to $\tau_t$ = 1.3 $\mu$s. After complete filling of the LEGEND-200 cryostat, the measured effective triplet lifetime was 1.16 $\mu$s. The notable reduction is caused by a residual nitrogen impurity introduced by an accidentally spoiled liquid argon delivery. An excessive nitrogen influx was prevented by the LEGEND Liquid Argon Monitoring Apparatus (LLAMA), which served as one of the three independent purity monitors during the filling campaign.

physics.ins-det↗

arXiv2026

Genetic algorithm vs. gradient descent for training a neural network architecture dedicated to low data regimes in small medical datasets

Aim/Introduction: Distance-encoding biomorphic-informational neural network (DEBI-NN) is a recently proposed architecture in which connection weights are defined by the distances between neurons positioned in a Euclidian space. This approach drastically reduces the number of trainable parameters compared to classical neural networks in which weights are directly trained. The training process for DEBI-NN is based on a genetic algorithm (GA), rather than gradient descent (GD) which remains the prevailing optimization algorithm in deep learning. We aim to design and implement a GD learner for DEBI-NN and assess its performance compared to GA. Materials and Methods: We designed a spatial backpropagation scheme tailored to DEBI-NN and carried out a comparison between GD and GA for classification tasks, using a synthetic non-linear "two-moons" dataset, two clinical medical imaging radiomic datasets and a fetal cardiotocography dataset with a sample sizes ranging from n=85 to n=2126. Each optimizer was tuned through targeted hyperparameter searches adapted to each dataset. Results: Across all experiments, GA consistently produced superior decision boundaries and classification performance (Synthetic: 100% vs 83%; DLBCL: 83% vs 78%; HECKTOR: 80% vs 67%; Fetal: 81% vs 66%), whereas GD exhibited instability and failed to fully capture the non-linear patterns inherent to DEBI-NN's spatial encoding. The entangled gradients resulting from neuron interdependencies limit the effectiveness of classical backpropagation. Conclusion: These findings highlight fundamental limitations of gradient-based methods in architectures with highly interdependent spatial parameters and confirm the suitability of evolutionary strategies for training DEBI-NN.

cs.NE↗

arXiv2023

STRAW-b (STRings for Absorption length in Water-b): the second pathfinder mission for the Pacific Ocean Neutrino Experiment

Since 2018, the potential for a high-energy neutrino telescope, named the Pacific Ocean Neutrino Experiment (P-ONE), has been thoroughly examined by two pathfinder missions, STRAW and STRAW-b, short for short for Strings for Absorption Length in Water. The P-ONE project seeks to install a neutrino detector with a one cubic kilometer volume in the Cascadia Basin's deep marine surroundings, situated near the western shores of Vancouver Island, Canada. To assess the environmental conditions and feasibility of constructing a neutrino detector of that scale, the pathfinder missions, STRAW and STRAW-b, have been deployed at a depth of 2.7 km within the designated site for P-ONE and were connected to the NEPTUNE observatory, operated by Ocean Networks Canada (ONC). While STRAW focused on analyzing the optical properties of water in the Cascadia Basin, \ac{strawb} employed cameras and spectrometers to investigate the characteristics of bioluminescence in the deep-sea environment. This report introduces the STRAW-b concept, covering its scientific objectives and the instrumentation used. Furthermore, it discusses the design considerations implemented to guarantee a secure and dependable deployment process of STRAW-b. Additionally, it showcases the data collected by battery-powered loggers, which monitored the mechanical stress on the equipment throughout the deployment. The report also offers an overview of STRAW-b's operation, with a specific emphasis on the notable advancements achieved in the data acquisition (DAQ) system and its successful integration with the server infrastructure of ONC.

astro-ph.IM↗

arXiv2022

Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers

Kernel methods are the basis of most classical machine learning algorithms such as Gaussian Process (GP) and Support Vector Machine (SVM). Computing kernels using noisy intermediate scale quantum (NISQ) devices has attracted considerable attention due to recent progress in the design of NISQ devices. However noise and errors on current NISQ devices can negatively affect the predicted kernels. In this paper we utilize two quantum kernel machine learning (ML) algorithms to predict the labels of a Breast Cancer dataset on two different NISQ devices: quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM). We estimate the quantum kernels on the 11 qubit IonQ and the 5 qubit IBMQ Belem quantum devices. Our results demonstrate that the predictive performances of the error mitigated quantum kernel machine learning algorithms improve significantly compared to their non-error mitigated counterparts. On both NISQ devices the predictive performances became comparable to those of noiseless quantum simulators and their classical counterparts

quant-ph↗

arXiv2021

Two-Year Optical Site Characterization for the Pacific Ocean Neutrino Experiment P-ONE in the Cascadia Basin

The STRings for Absorption length in Water (STRAW) are the first in a series of pathfinders for the Pacific Ocean Neutrino Experiment (P-ONE), a future large-scale neutrino telescope in the north-eastern Pacific Ocean. STRAW consists of two 150 m long mooring lines instrumented with optical emitters and detectors. The pathfinder is designed to measure the attenuation length of the water and perform a long-term assessment of the optical background at the future P-ONE site. After two years of continuous operation, measurements from STRAW show an optical attenuation length of about 28 metres at 450 nm. Additionally, the data allow a study of the ambient undersea background. The overall optical environment reported here is comparable to other deep-water neutrino telescopes and qualifies the site for the deployment of P-ONE.

astro-ph.IM↗