arXiv subjects

G. Ferrante

Publications and source records attributed to G. Ferrante.

5 recordsLinked to original sources

arXiv2026

Ultra-trace analysis of 40K in organic liquid scintillators

Rare-event searches require exceptionally low background levels, motivating the development of increasingly sophisticated screening methods to push sensitivity limits. Liquid scintillators are particularly attractive detector media due to their intrinsic radiopurity and the ability to scale to large target masses. In this work, we present a screening strategy capable of measuring ultra-trace concentrations of $^{40}\text{K}$ with sensitivities below $10^{-15}$g/g. The method combines neutron activation analysis with a dedicated radiochemical treatment, followed by low-background HPGe gamma spectroscopy. Using this approach, we achieved a minimum detectable concentration of $2.9\cdot10^{-16}$g/g for $^{40}\text{K}$, placing this technique among the most sensitive currently available.

physics.ins-det↗

arXiv2026

Real-Time Wiener Deconvolution for feature reconstruction in JUNO

In particle physics, experiments generate substantial amounts of data that can be difficult to process without preliminary scaling. To avoid losing potentially crucial data, experimental collaborations are studying novel techniques for real-time data processing to extract features for further physics analysis. A common approach, especially in neutrino physics, is to use FPGAs for data acquisition and pre-processing. This paper presents an advanced Real-Time Wiener deconvolution algorithm designed to leverage the processing capabilities of the FPGA integrated into the readout boards of the Jiangmen Underground Neutrino Observatory (JUNO). The goal is to enable real-time reconstruction of the signal generated by photomultiplier tubes (PMTs) when neutrino interactions are detected. By exploiting online reconstruction of the signal generated by PMTs, we expect to improve the detection of low-energy depositions, such as those produced by transient astrophysical phenomena. These depositions are usually not saved because of the significant background that affects the low end of the energy spectrum, which would result in a large trigger rate, hence a large amount of data required for storage. This paper presents the features of the algorithm, including its ability to manage high-throughput data streams with minimal latency, adaptability, and resilience in discerning the characteristics of input data. Performance is evaluated on a JUNO electronic board. This study further demonstrates the potential of FPGA-based solutions for neutrino physics.

physics.ins-det↗

arXiv2025

Ultra-trace analysis of U and Th in organic liquid scintillators with high sensitivity

Rare event searches demand extremely low background levels, necessitating ever-advancing screening techniques to enhance sensitivity. Liquid scintillators are highly attractive as detector media due to their inherent radiopurity and scalability in mass. In this work, we present a screening procedure to measure ultra-trace concentrations of natural contaminants -- $^{238}$U and $^{232}$Th -- with sensitivities at the \qty{E-15}{g/g} level. Our method combines neutron activation analysis with radiochemical techniques, followed by \bg\ coincidence spectroscopy to minimize interference backgrounds. This approach achieves sensitivities of \qty{0.65E-15}{g/g} for $^{238}$U and \qty{2.3E-15}{g/g} for $^{232}$Th, among the best reported worldwide. Potential pathways for further sensitivity improvements are outlined in the conclusions.

physics.ins-det↗

arXiv2025

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning

Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators that model likelihoods of calibration data: conditional normalizing flows and a transformer-based regressor. We adopt JUNO - a large neutrino experiment - as a case study. The energy response of JUNO depends on several parameters, all of which should be tuned, given their non-linear behavior and strong correlations in the calibration data. To this end, we integrate the modeled likelihoods with Bayesian nested sampling for parameter inference, achieving uncertainties limited only by statistics with near-zero systematic biases. The normalizing flows model enables unbinned likelihood analysis, while the transformer provides an efficient binned alternative. By providing both options, our framework offers flexibility to choose the most appropriate method for specific needs. Finally, our approach establishes a template for similar applications across experimental neutrino and broader particle physics.

physics.data-an↗

arXiv2004

Langevin Approach to Understand the Noise of Microwave Transistors

A Langevin approach to understand the noise of microwave devices is presented. The device is represented by its equivalent circuit with the internal noise sources included as stochastic processes. From the circuit network analysis, a stochastic integral equation for the output voltage is derived and from its power spectrum the noise figure as a function of the operating frequency is obtained. The theoretical results have been compared with experimental data obtained by the characterization of an HEMT transistor series (NE20283A, by NEC) from 6 to 18 GHz at a low noise bias point. The reported procedure exhibits good accuracy, within the typical uncertainty range of any experimental determination. The approach allows to extract all the information required for understanding the noise performance of the device without any restriction on the statistics of the noise sources. The results show the relevant noise phenomena from a new angle.

cond-mat.stat-mech↗