arXiv subjects

A. Cammi

Publications and source records attributed to A. Cammi.

16 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

Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors

Magnetohydrodynamic (MHD) phenomena play a pivotal role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten salts employed in reactor blankets) interact with magnetic fields of varying intensity and orientation, influencing the resulting flow dynamics. The numerical solution of MHD models entails the resolution of highly nonlinear, multiphysics systems of equations, which can become computationally demanding, particularly in multi-query, parametric, or real-time contexts. This study investigates a fully data-driven framework for MHD state reconstruction that integrates dimensionality reduction through Singular Value Decomposition (SVD) with the SHallow REcurrent Decoder (SHRED), a neural network architecture designed to reconstruct the full spatio-temporal state from sparse time-series measurements of selected observables, including previously unseen parametric configurations. The SHRED methodology is applied to a three-dimensional geometry representative of a portion of a WCLL blanket cell, in which lead-lithium flows around a water-cooled tube. Multiple magnetic field configurations are examined, including constant toroidal fields, combined toroidal-poloidal fields, and time-dependent magnetic fields. Across all considered scenarios, SHRED achieves high reconstruction accuracy, robustness, and generalization to magnetic field intensities, orientations, and temporal evolutions not seen during training. Notably, in the presence of time-varying magnetic fields, the model accurately infers the temporal evolution of the magnetic field itself using temperature measurements alone. Overall, the findings identify SHRED as a computationally efficient, data-driven, and flexible approach for MHD state reconstruction, with significant potential for real-time monitoring, diagnostics and control in fusion reactor systems.

cs.LG↗

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

Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics

Magnetohydrodynamic (MHD) effects play a key role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten salts in reactor blankets) interact with magnetic fields of varying intensity and orientation, which affect the resulting flow. The numerical resolution of MHD models involves highly nonlinear multiphysics systems of equations and can become computationally expensive, particularly in multi-query, parametric, or real-time contexts. This work investigates a fully data-driven framework for MHD state reconstruction that combines dimensionality reduction via Singular Value Decomposition (SVD) with the SHallow REcurrent Decoder (SHRED), a neural network architecture designed to recover the full spatio-temporal state from sparse time-series measurements of a limited number of observables. The methodology is applied to a parametric MHD test case involving compressible lead-lithium flow in a stepped channel subjected to thermal gradients and magnetic fields spanning a broad range of intensities. To improve efficiency, the full-order dataset is first compressed using SVD, yielding a reduced representation used as reference truth for training. Only temperature measurements from three sensors are provided as input, while the network reconstructs the full fields of velocity, pressure, and temperature. To assess robustness with respect to sensor placement, thirty randomly generated sensor configurations are tested in ensemble mode. Results show that SHRED accurately reconstructs the full MHD state even for magnetic field intensities not included in the training set. These findings demonstrate the potential of SHRED as a computationally efficient surrogate modeling strategy for fusion-relevant multiphysics problems, enabling low-cost state estimation with possible applications in real-time monitoring and control.

cs.LG↗

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↗

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

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

Distillation and Stripping purification plants for JUNO liquid scintillator

The optical and radiochemical purification of the scintillating liquid, which will fill the central detector of the JUNO experiment, plays a crucial role in achieving its scientific goals. Given its gigantic mass and dimensions and an unprecedented target value of about 3% @ 1 MeV in energy resolution, JUNO has set severe requirements on the parameters of its scintillator, such as attenuation length (Lat>20 m at 430 nm), transparency, light yield, and content of radioactive contaminants (238U,232Th<10-15 g/g). To accomplish these needs, the scintillator will be processed using several purification methods, including distillation in partial vacuum and gas stripping, which are performed in two large scale plants installed at the JUNO site. In this paper, layout, operating principles, and technical aspects which have driven the design and construction of the distil- lation and gas stripping plants are reviewed. The distillation is effective in enhancing the optical properties and removing heavy radio-impurities (238U,232Th, 40K), while the stripping process exploits pure water steam and high-purity nitrogen to extract gaseous contaminants (222Rn, 39Ar, 85Kr, O2) from the scintillator. The plant operating parameters have been tuned during the recent com- missioning phase at the JUNO site and several QA/QC measurements and tests have been performed to evaluate the performances of the plants. Some preliminary results on the efficiency of these purification processes will be shown.

physics.ins-det↗

arXiv2024

Refractive index in the JUNO liquid scintillator

In the field of rare event physics, it is common to have huge masses of organic liquid scintillator as detection medium. In particular, they are widely used to study neutrino properties or astrophysical neutrinos. Thanks to its safety properties (such as low toxicity and high flash point) and easy scalability, linear alkyl benzene is the most common solvent used to produce liquid scintillators for large mass experiments. The knowledge of the refractive index is a pivotal point to understand the detector response, as this quantity (and its wavelength dependence) affects the Cherenkov radiation and photon propagation in the medium. In this paper, we report the measurement of the refractive index of the JUNO liquid scintillator between 260-1064 nm performed with two different methods (an ellipsometer and a refractometer), with a sub percent level precision. In addition, we used an interferometer to measure the group velocity in the JUNO liquid scintillator and verify the expected value derived from the refractive index measurements.

physics.ins-det↗

arXiv2023

Analysis of reactor burnup simulation uncertainties for antineutrino spectrum prediction

Nuclear reactors are a source of electron antineutrinos due to the presence of unstable fission products that undergo $\beta^-$ decay. They will be exploited by the JUNO experiment to determine the neutrino mass ordering and to get very precise measurements of the neutrino oscillation parameters. This requires the reactor antineutrino spectrum to be characterized as precisely as possible both through high resolution measurements, as foreseen by the TAO experiment, and detailed simulation models. In this paper we present a benchmark analysis utilizing Serpent Monte Carlo simulations in comparison with real pressurized water reactor spent fuel data. Our objective is to study the accuracy of fission fraction predictions as a function of different reactor simulation approximations. Then, utilizing the BetaShape software, we construct fissile antineutrino spectra using the summation method, thereby assessing the influence of simulation uncertainties on reactor antineutrino spectrum.

physics.ins-det↗

arXiv2020

Optimization of the JUNO liquid scintillator composition using a Daya Bay antineutrino detector

To maximize the light yield of the liquid scintillator (LS) for the Jiangmen Underground Neutrino Observatory (JUNO), a 20 t LS sample was produced in a pilot plant at Daya Bay. The optical properties of the new LS in various compositions were studied by replacing the gadolinium-loaded LS in one antineutrino detector. The concentrations of the fluor, PPO, and the wavelength shifter, bis-MSB, were increased in 12 steps from 0.5 g/L and <0.01 mg/L to 4 g/L and 13 mg/L, respectively. The numbers of total detected photoelectrons suggest that, with the optically purified solvent, the bis-MSB concentration does not need to be more than 4 mg/L. To bridge the one order of magnitude in the detector size difference between Daya Bay and JUNO, the Daya Bay data were used to tune the parameters of a newly developed optical model. Then, the model and tuned parameters were used in the JUNO simulation. This enabled to determine the optimal composition for the JUNO LS: purified solvent LAB with 2.5 g/L PPO, and 1 to 4 mg/L bis-MSB.

physics.ins-det↗

arXiv2020

Embedded Readout Electronics R&D for the Large PMTs in the JUNO Experiment

Jiangmen Underground neutrino Observatory (JUNO) is a next generation liquid scintillator neutrino experiment under construction phase in South China. Thanks to the anti-neutrinos produced by the nearby nuclear power plants, JUNO will primarily study the neutrino mass hierarchy, one of the open key questions in neutrino physics. One key ingredient for the success of the measurement is to use high speed, high resolution sampling electronics located very close to the detector signal. Linearity in the response of the electronics in another important ingredient for the success of the experiment. During the initial design phase of the electronics, a custom design, with the Front-End and Read-Out electronics located very close to the detector analog signal has been developed and successfully tested. The present paper describes the electronics structure and the first tests performed on the prototypes. The electronics prototypes have been tested and they show good linearity response, with a maximum deviation of 1.3% over the full dynamic range (1-1000 p.e.), fulfilling the JUNO experiment requirements.

physics.ins-det↗

arXiv2020

A coupled neutronics shock physics solver: implementation of an SN neutron transport module

A multiphysics finite volume method (FVM) solver, coupling neutronics and shock physics, is under development at Politecnico di Milano for the analysis of shock imploding fissile materials [1]. The proposed solver can be a useful tool to make preliminary safety assessment of subcritical plutonium experiments [2] and, more in general, to perform criticality safety evaluations in case of strongly energetic events (such as chemical explosions) involving fissile materials [3]. To this aim, a multi-group SP3 neutron transport model is coupled with a hydrodynamic shock physics model [4], suitable to describe the propagation of strong shockwaves in solid materials. The shock physics module implements a dynamic mesh to reproduce material deformations and its governing equations are written in an Arbitrary Lagrangian Eulerian (ALE) formulation to preserve the mesh quality in case of large distortions.

physics.comp-ph↗

arXiv2019

$^{222}$Rn contamination mechanisms on acrylic surfaces

In this work, the $^{222}$Rn contamination mechanisms on acrylic surfaces have been investigated. $^{222}$Rn can represent a significant background source for low-background experiments, and acrylic is a suitable material for detector design thanks to its purity and transparency. Four acrylic samples have been exposed to a $^{222}$Rn rich environment for different time periods, being contaminated by $^{222}$Rn and its progenies. Subsequently, the time evolution of radiocontaminants activity on the samples has been evaluated with $α$ and $γ$ measurements, highlighting the role of different decay modes in the contamination process. A detailed analysis of the alpha spectra allowed to quantify the implantation depth of the contaminants. Moreover, a study of both $α$ and $γ$ measurements pointed out the $^{222}$Rn diffusion inside the samples.

physics.ins-det↗

arXiv2015

JUNO Conceptual Design Report

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$σ$, and determine neutrino oscillation parameters $\sin^2θ_{12}$, $Δm^2_{21}$, and $|Δm^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.

physics.ins-det↗