arXiv subjects

F. Petrucci

Publications and source records attributed to F. Petrucci.

At least 19 recordsLinked to original sources

arXiv2026

Dark Matter Sensitivity of the CYGNO Detector with HFO-1234ze Enhanced Gas Mixtures

The CYGNO collaboration introduces an innovative approach to direct dark matter detection, proposing a high-resolution optical Time Projection Chamber. It operates at atmospheric pressure with a He:CF$_{4}$ (60:40) gas mixture and uses a triple Gas Electron Multiplier stage for signal amplification. A key feature is its optical readout system, which captures the scintillation light produced during the electron avalanche. This setup allows 3D event reconstruction by combining the time profile of the light detected by photomultiplier tubes with high-granularity, pixelated X-Y tracking recorded by a scientific camera. The CYGNO experiment's projected sensitivity to both spin-independent and spin-dependent interactions is competitive in the framework of directional dark matter detectors. However, incorporating a hydrogen-based gas would introduce an even lighter target, further improving the detection potential at low dark matter masses. In this work, we present the performance characterization of one of the CYGNO experiment prototypes, MANGO, operated with the standard gas mixture enriched with varying concentrations of HFO-1234ze, a gas with a promising low global warming potential. The study includes measurements of the detector charge gain and scintillation yield for each configuration. In addition, to evaluate the impact of HFO-1234ze on scintillation light quenching, the secondary scintillation spectrum was collected for each gas mixture tested.

physics.ins-det↗

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

He-CF4-CH4 ternary mixtures as target gas for the CYGNO directional dark matter experiment

The CYGNO collaboration is advancing a high-resolution optical Time Projection Chamber (TPC) for directional dark matter searches and solar neutrino spectroscopy at LNGS. The detector uses a He-40%CF4 gas mixture at atmospheric pressure and a triple-GEM cascade for ionization signal amplification. Scintillation light from GEM electron avalanches is read out using sCMOS cameras, enabling high sensitivity to interactions in the few keV range, alongside precise ionization event tracking and particle identification. This study investigates the effects of adding 3-10% methane to the He-40%CF4 mixture. Methane improves the electrical stability of the TPC, allowing for higher GEM voltages before discharge onset, which compensates for its scintillation quenching and leads to enhanced overall scintillation yield. Compared to prior studies performed with isobutane, methane demonstrates a lower quenching effect on visible and UV photons while maintaining good energy resolution. Importantly, the inclusion of methane lowers the dark matter detection threshold by providing a lighter target and extending track lengths of light nuclear recoils, thus enhancing directional discrimination. These results establish methane as a promising additive for optimizing CYGNO's performance in detecting low-mass dark matter candidates.

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↗

arXiv2026

The CYGNO experiment: a gaseous TPC with optical readout for rare events searches

The CYGNO collaboration is developing a novel strategy for directional Dark Matter searches based on a gaseous Time Projection Chamber (TPC). The detector is optimized for the exploration of light (0.5-50 GeV) WIMPs-like particles and employs a He/CF4 gas mixture at atmospheric pressure, sensitive to both spin-dependent and spin-independent interactions. A key feature of the project is its optical readout, which relies on photon detection rather than charge collection. In CYGNO detectors, electrons released by ionizing tracks drift toward an amplification stage of three Gas Electron Multipliers (GEMs). The electron avalanches generate scintillation light that is captured by scientific CMOS (sCMOS) cameras for high-resolution two-dimensional imaging and by Photomultiplier Tubes (PMTs) that provide a precise time profile along the drift direction. This allows a 3D event reconstruction, detailed energy deposition mapping, and effective topology and head-to-tail discrimination. Building on the achievements of the 50 L prototype (LIME), which successfully operated underground at LNGS, the next step is the deployment of a 0.4 m3 demonstrator, CYGNO-04, to be completed in 2026. The demonstrator will validate scalability and confirm the advantages of the proposed technique. Recent results from LIME highlight strong progress in 3D tracking and particle identification. The current status of CYGNO-04 and its role in advancing the program will be presented as well.

physics.ins-det↗

arXiv2026

Upgrade of the Trigger and Data Acquisition System for Continuous Imaging and Multi-Camera Operation in CYGNO

The CYGNO experiment employs an optical readout to image particle interactions in a gaseous Time Projection Chamber (TPC), combining cameras and photomultiplier tubes (PMTs) to achieve high spatial resolution and timing information. This approach enables detailed track reconstruction but poses significant challenges for data acquisition, particularly in view of the next experimental phase, CYGNO-04, which will operate multiple cameras simultaneously. In this paper, we present an upgrade of the CYGNO Trigger and Data Acquisition (T-DAQ) system, developed starting from the LIME configuration and validated on the MANGO prototype. The upgrade introduces a continuous imaging acquisition mode, substantially reducing the camera dead time, together with an extended trigger time-tagging scheme that provides a robust global time reference for PMT signals. A synchronous multi-camera DAQ architecture is also implemented and tested, enabling coordinated operation of multiple optical sensors without a master camera. The performance of the upgraded system is validated through dedicated tests, demonstrating stable continuous acquisition, reliable time-tagging, and consistent synchronization across multiple cameras. These results establish a solid and scalable foundation for the CYGNO-04 DAQ and represent a key step toward efficient data acquisition in future large-scale optical TPC detectors.

physics.ins-det↗

arXiv2026

First Optical Observation of Negative Ion Drift at Surface Pressure

We report the first observation of Negative Ion Drift (NID) at surface pressure of $900 \pm 7$ mbar at Laboratori Nazionali del Gran Sasso in a He:CF$_4$:SF$_6$ mixture using an optically read out Time Projection Chamber (TPC) within the CYGNO/INITIUM project. We present the first PMT waveform analysis in the NID regime, interpreting the temporal light pattern through a model that combines track geometry and charge transport. The inferred drift velocities correspond to mobilities of O(cm$^2$ V$^{-1}$ s$^{-1}$), consistent with negative ion transport. The observed linear scaling of the time extension mean with drift distance reveals the presence of a faster minority charge carrier population in addition to the dominant SF$_6^-$ species, drifting at a $\sim$25\% higher velocity under external inputs. These results demonstrate multi-species negative ion drift operation at surface pressure in a He:CF$_4$:SF$_6$ mixture and open a concrete path toward large scale, low diffusion optical TPCs for rare event searches.

physics.ins-det↗

arXiv2026

Trigger Optimization and Event Classification for Dark Matter Searches in the CYGNO Experiment Using Machine Learning

The CYGNO experiment employs an optical-readout Time Projection Chamber (TPC) to search for rare low-energy interactions using finely resolved scintillation images. While the optical readout provides rich topological information, it produces large, sparse megapixel images that challenge real-time triggering, data reduction, and background discrimination. We summarize two complementary machine-learning approaches developed within CYGNO. First, we present a fast and fully unsupervised strategy for online data reduction based on reconstruction-based anomaly detection. A convolutional autoencoder trained exclusively on pedestal images (i.e. frames acquired with GEM amplification disabled) learns the detector noise morphology and highlights particle-induced structures through localized reconstruction residuals, from which compact Regions of Interest (ROIs) are extracted. On real prototype data, the selected configuration retains (93.0 +/- 0.2)% of reconstructed signal intensity while discarding (97.8 +/- 0.1)% of the image area, with ~25 ms per-frame inference time on a consumer GPU. Second, we report a weakly supervised application of the Classification Without Labels (CWoLa) framework to data acquired with an Americium--Beryllium neutron source. Using only mixed AmBe and standard datasets (no event-level labels), a convolutional classifier learns to identify nuclear-recoil-like topologies. The achieved performance approaches the theoretical limit imposed by the mixture composition and isolates a high-score population with compact, approximately circular morphologies consistent with nuclear recoils.

physics.ins-det↗

arXiv2026

Simulation of the CYGNO Gaseous TPC Optical Readout

Gaseous Time Projection Chambers with Optical Readout are sensitive detectors suitable for 3D measurement of low-energy O(1 keV) particles and are proposed for detecting rare events such as Dark Matter particle interactions. The CYGNO collaboration is developing such a detector with a high spatial and energy resolution, leveraging an innovative optical readout system. A reliable simulation of the detector response is needed to properly assess the physics reach of this technique and to better understand the performance of the detector in the development phase. Such a simulation cannot entirely rely on existing software packages; indeed, none of the available tools is capable of properly and reliably treating the different phenomena occurring in the detector, from the primary interaction in the gas volume throughout the whole detector response model, including charge transport, light production and propagation, and the response of the optical sensors. In this paper, we present a modeling of the detector response tuned on the CYGNO Optical TPC case; a description of the method is reported together with comparisons with experimental data from the LIME prototype to demonstrate the simulation performances.

physics.ins-det↗

arXiv2025

Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC

Optical-readout Time Projection Chambers (TPCs) produce megapixel-scale images whose fine-grained topological information is essential for rare-event searches, but whose size challenges real-time data selection. We present an unsupervised, reconstruction-based anomaly-detection strategy for fast Region-of-Interest (ROI) extraction that operates directly on minimally processed camera frames. A convolutional autoencoder trained exclusively on pedestal images learns the detector noise morphology without labels, simulation, or fine-grained calibration. Applied to standard data-taking frames, localized reconstruction residuals identify particle-induced structures, from which compact ROIs are extracted via thresholding and spatial clustering. Using real data from the CYGNO optical TPC prototype, we compare two pedestal-trained autoencoder configurations that differ only in their training objective, enabling a controlled study of its impact. The best configuration retains (93.0 +/- 0.2)% of reconstructed signal intensity while discarding (97.8 +/- 0.1)% of the image area, with an inference time of approximately 25 ms per frame on a consumer GPU. The results demonstrate that careful design of the training objective is critical for effective reconstruction-based anomaly detection and that pedestal-trained autoencoders provide a transparent and detector-agnostic baseline for online data reduction in optical TPCs.

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

Characterization of cutting-edge CMOS Active Pixel sensors within the CYGNO Experiment

Time Projection Chambers equipped with Gas Electron Multipliers and optical readout by scientific CMOS cameras are a promising technology for low-energy particle detection, as demonstrated by the CYGNO experiment. To help identify the optimal CYGNO detector configuration, we performed a detailed characterization of two state-of-the-art scientific CMOS sensors, focusing on dark-signal behavior across different exposure times and on detection sensitivity, assessed using the well-defined X-ray emissions from a 55Fe source, which reproduce the low-light conditions expected in CYGNO. CYGNO currently employs a very low-noise Hamamatsu sensor, the ORCA-Fusion, for testing and validation of its detection system. Hamamatsu has recently introduced two new sensors that may be of interest for future upgrades. The first is an improved version of the current model, the ORCA-Fusion-BT, featuring a back-illuminated design that reaches a quantum efficiency of up to 95% at 550 nm. The second is a next-generation sensor, the ORCA-Quest. Although its peak quantum efficiency is not as high as that of the Fusion-BT, it offers high sensitivity over a broader spectral range, extending into the ultraviolet region, and provides ultra-low readout noise of 0.27 electrons, about 2.6 times lower than that of the Fusion family. These two sensors therefore represent a significant opportunity to enhance the performance of scientific experiments, including those conducted by the CYGNO collaboration. This document presents a comprehensive characterization of these sensors to evaluate their relevance for experiments operating in photon-limited environments and their suitability for integration into the CYGNO detector system.

physics.ins-det↗

arXiv2025

Characterization of field cage and cathode for low radioactivity operation with the CYGNO experiment

Dark matter, which is considered to account for approximately the 27% of the Universe's energy-mass content, remains an open issue in modern particle physics along with its composition. The CYGNO Experiment aims to exploit an innovative approach applied to the direct detection search of low energy nuclear recoils possibly induced by cold particle-like dark matter candidates. CYGNO employs a directional detector based on a Time Projection Chamber (TPC) filled with a He:CF$_{4}$ gas mixture and equipped with an optical readout. Currently, the CYGNO Collaboration is constructing the detector demonstrator, CYGNO-04, in Hall F at Laboratori Nazionali del Gran Sasso (LNGS). This 0.4 m$^3$ detector has the goal of proving the scalability of the technology and assessing the physics and radiopurity capabilities. Given the low radioactivity requirements, especially in internal components such as field cage and cathode, the reduction of material while keeping the correct electrical behavior is paramount. In this paper, we present the validation of several internal components, mainly focusing on the field cage material and support structure. The tests included geometrical asymmetries in the electric field response, collection efficiency as well as measurement of known physical quantities. A preferred configuration is found with a structure based on Nylon material which supports a PET or Kapton sheet with copper strips deposited on.

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↗

arXiv2025

Fluorescence emission of the JUNO liquid scintillator

JUNO is a huge neutrino detector that will use 20 kton of organic liquid scintillator as its detection medium. The scintillator is a mixture of linear alkyl benzene (LAB), 2.5 g/L of 2,5-diphenyloxazole (PPO) and 3 mg/L of 1,4-Bis(2-methylstyryl)benzene (Bis-MSB). The main goal of JUNO is to determine the Neutrino Mass Ordering [1, 2, 3]. In order to achieve this purpose, good energy and position reconstruction is required, hence a complete understanding of the optical characteristics of the liquid scintillator is mandatory. In this paper we present the measurements on the JUNO scintillator emission spectrum, absorption length and fluorescence time distribution performed respectively with a spectrofluorimeter, a spectrophotometer and a custom made setup

physics.ins-det↗

arXiv2024

Resistive Fine Granularity Micromegas: Characterization and Performance for Different Spark Protection Resistive Schemes

The aim of the presented work is the development of single-stage amplification resistive Micro Pattern Gas Detectors (MPGD) based on Micromegas technology with the following characteristics: ability to efficiently operate up to 10 MHz/cm$^2$ counting rate; scalability to large areas; fine granularity readout with small pads of the order of mm$^2$; good spatial and time resolutions (below 100 $\mu$m and 10 ns, respectively). The miniaturization of the readout elements and the optimization of the spark protection system, as well as the stability and robustness under operation, are the primary challenges of the project. Two families of resistive patterns were realized using different techniques: pad-patterned embedded resistors and double-layer of Diamond Like Carbon (DLC) structures foils. Their main difference is that for the embedded resistors the charge is evacuated through independent pads, for double-layer DLC the resistive layers are continuous and uniform and the charge is evacuated through a network of dot-connections, several millimetres apart. Using the DLC technique, a medium-size detector with an active area of 400 cm$^2$ was recently built and tested, with the main results reported in this paper. Additionally, a large module (50x40 cm$^2$ active area), suitable for tiling large systems in future experiments, has been successfully realised and is currently undergoing testing and performance studies. The characterization and performance studies of the detectors were conducted using radioactive sources and an X-rays generator, with the detectors operated with various gas mixtures. A comparison of the results obtained with different resistive layouts and configurations is provided, with a particular focus on the response under high-rate exposure. Key results on tracking and timing performance from test-beam data for the latest constructed medium-size detector are also presented.

physics.ins-det↗

arXiv2024

Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector

Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.

physics.ins-det↗

arXiv2024

Enhancing the light yield of He:CF$_4$ based gaseous detector

The CYGNO experiment aims to build a large ($\mathcal{O}(10)$ m$^3$) directional detector for rare event searches, such as nuclear recoils (NRs) induced by dark matter (DM), such as weakly interactive massive particles (WIMPs). The detector concept comprises a time projection chamber (TPC), filled with a He:CF$_4$ 60/40 scintillating gas mixture at room temperature and atmospheric pressure, equipped with an amplification stage made of a stack of three gas electron multipliers (GEMs) which are coupled to an optical readout. The latter consists in scientific CMOS (sCMOS) cameras and photomultipliers tubes (PMTs). The maximisation of the light yield of the amplification stage plays a major role in the determination of the energy threshold of the experiment. In this paper, we simulate the effect of the addition of a strong electric field below the last GEM plane on the GEM field structure and we experimentally test it by means of a 10$\times$10 cm$^2$ readout area prototype. The experimental measurements analyse stacks of different GEMs and helium concentrations in the gas mixture combined with this extra electric field, studying their performances in terms of light yield, energy resolution and intrinsic diffusion. It is found that the use of this additional electric field permits large light yield increases without degrading intrinsic characteristics of the amplification stage with respect to the regular use of GEMs.

physics.ins-det↗