arXiv subjects

Federico Sánchez

Publications and source records attributed to Federico Sánchez.

18 recordsLinked to original sources

arXiv2026

Probing Neutrino-Energy Reconstruction with New Superscaling-Based Observables

In this study, we explore the superscaling variable $ψ'$ within the context of neutrino-nucleus charged-current quasielastic interactions. Building on the neutrino-oriented definition of the superscaling variable $ψ'$, we reparametrize it in terms of the neutrino-energy reconstruction bias, thereby establishing a direct conceptual and analytical connection between the superscaling formalism and neutrino-energy reconstruction. This approach allows the hadronic part of the reconstructed $ψ'$ to be expanded around the quasielastic point, yielding new observables that depend only on the outgoing-muon kinematics. At the same time, the resulting distributions remain sensitive to the underlying nuclear dynamics and vary across nuclear models. Among them, a newly identified quantity $L_1$, constructed solely from the outgoing-muon kinematics, provides a nuclear-physics constraint on the neutrino-energy reconstruction bias. This observable largely inherits the scaling properties of $ψ'$ across different neutrino fluxes and nuclear targets. The nuclear-model dependence is investigated using ND280-like $ν_μ$ and $\barν_μ$ Monte Carlo samples on carbon, generated with several nuclear models implemented in NEUT, NuWro, and GENIE, while the stability across experimental conditions is tested using additional carbon- and argon-based flux-target configurations.

hep-ph↗

arXiv2026

Flow-Based Surrogates for High-Dimensional Likelihoods in Experimental Neutrino Physics

Precision long-baseline neutrino experiments use near-detector data to constrain systematic uncertainties on the unoscillated neutrino flux, a prerequisite for accurate oscillation parameter measurements at the far detector. When the constrained likelihood is high-dimensional and non-Gaussian, this procedure demands advanced statistical treatment. Here we show that normalizing flows provide faithful and portable likelihood models for this problem. Leveraging an initial Gaussian approximation of the likelihood, we train a hybrid architecture combining coupling transformations and autoregressive spline flows. We demonstrate the method on a representative near-detector likelihood replica with 110 systematic uncertainty parameters, 10 of which explicitly introduce non-Gaussianities in the posterior. The trained model achieves a relative effective sample size of 98%, compared with about 5% for the Gaussian approximation, and reproduces a Markov chain Monte Carlo reference while remaining closed-form, samplable, and pointwise evaluable, making it suited to downstream uncertainty propagation and future near-detector to far-detector fits.

hep-ex↗

arXiv2026

A data-driven method for measuring corner-clipping probabilities in segmented particle detectors

The accuracy of particle counting in highly segmented detectors is limited by the corner-clipping effect, in which a single ionizing particle generates signals in adjacent detection elements. This phenomenon introduces a direction-dependent overcounting bias that distorts reconstructed observables and is commonly corrected using Monte-Carlo simulations, thereby inheriting modeling uncertainties. We present a fully data-driven method to directly measure the single-particle corner-clipping probability, exploiting the nanosecond timing resolution of modern detectors to statistically distinguish genuine corner-clipping events from random coincidences, with non-neighboring detection elements serving as an intrinsic control sample. The technique is validated using detailed simulations of the Underground Muon Detector of the Pierre Auger Observatory, reproducing the true angular dependence of the corner-clipping probability with absolute deviations below 0.01. To parameterize the results, we introduce a compact analytical model incorporating detector geometry, minimum detectable path length, and orientation-independent contributions. The proposed methodology and parameterization enable the direct incorporation of data-driven corner-clipping corrections into reconstruction algorithms, mitigating the overcounting bias and ultimately yielding a more accurate determination of the muonic component of extensive air showers. These developments are broadly applicable to any segmented detector with sufficient timing resolution, making them relevant to a wide range of experiments in high-energy and astroparticle physics.

astro-ph.IM↗

arXiv2026

Exclusive Hadron Observables in Neutrino Induced $2p2h$ Multinucleon Knockout

We explore the combined lepton and hadron kinematic observables from the exclusive Valencia $2p2h$ model. We present variables of interest which are available due to the exclusive kinematics and compare them with the democratically distributed outgoing nucleon kinematics as currently treated in neutrino event generators. We also show the effect of nuclear re-scattering based on the NEUT semi classical cascade. We comment on the observability of these variables in current and future long baseline neutrino detectors.

hep-ph↗

arXiv2025

Characterizing Beam Profiles in Accelerator Neutrino Experiments through Off-Axis Neutrino Interactions

We introduce a novel approach that utilizes neutrino events from the off-axis near detector to investigate the beam profile in long-baseline neutrino experiments. Understanding the dynamics of the neutrino beam is crucial for improving the precision of neutrino oscillation measurements. We demonstrate that certain observables related to the azimuthal angle of the neutrino direction are useful for determining the average neutrino production point from experimental data, providing a valuable cross-check against Monte Carlo simulations. Additionally, these observables can help identify potential alignment issues between the detector and the decay volume. In future neutrino experiments with significantly higher statistics, these observables will become essential to ensure the accuracy and stability of the beam profile.

hep-ex↗

arXiv2025

Efficient Monte Carlo Event Generation for Neutrino-Nucleus Exclusive Cross Sections

Modern neutrino-nucleus cross section predictions need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing Normalizing Flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than $98.4 \%$, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.

hep-ex↗

arXiv2024

Performance tests and hardware qualification of the FEBs for the Super-FGD of T2K Phase II

T2K is a long baseline neutrino experiment, entering Phase II with a Near Detector upgrade. The T2K near detector (ND280) upgrade consists of the installation of three new detector systems: a plastic scintillator neutrino active target (Super-FGD), two time projection chambers (HA-TPC) and a time of flight detector (TOF). The Super-FGD is composed of 2-million 1 cm-cube scintillating cubes read by almost 60 thousand wavelength-shifting (WLS) fibers coupled to an MPPC on one end. Given the large number of channels, the limited space inside magnetic environment, and the limited time from production to installation, the development and testing of the Front-end electronics boards (FEB) for the read-out of the Super-FGD channels represented a challenging task for the success of the upgrade. This work presents the performance tests confirming that the FEB aligns with detector requirements, and the hardware qualification of 240 FEBs through a custom QC test bench designed to detect and locate hardware failures to speed up the repairing process. Installation of the electronics in the detector took place in March 2024, one year after the beginning of the FEB mass production, and the first successful neutrino beam run took place in June of the same year.

physics.ins-det↗

arXiv2024

Neutrino mass and nature through its mediation in atomic clock interference

The absolute mass of neutrinos and their nature are presently unknown. Aggregate matter has a coherent weak charge leading to a repulsive interaction mediated by a neutrino pair. Near its range at micron distances the virtual neutrinos are non-relativistic, giving a distinct behavior for Dirac versus Majorana mass terms. The magnitude and the distance dependence of the effective potential disentangle these fundamental properties of neutrinos. We propose an experiment to search for this potential based on the concept that the density dependent interaction of an atomic probe with a material source in one arm of an atomic clock interferometer generates a differential phase. The appropriate geometry of the device is selected using the saturation of the weak potential as a guide. The proposed experiment has the added benefit of being sensitive to gravity at micron distances. A strategy to suppress the competing Casimir-Polder interaction, depending on the electronic structure of the material source, as well as a way to compensate the gravitational interaction in the two arms of the interferometer is discussed.

physics.atom-ph↗

arXiv2024

Fast Posterior Probability Sampling with Normalizing Flows and Its Applicability in Bayesian analysis in Particle Physics

In this study, we use Rational-Quadratic Neural Spline Flows, a sophisticated parametrization of Normalizing Flows, for inferring posterior probability distributions in scenarios where direct evaluation of the likelihood is challenging at inference time. We exemplify this approach using the T2K near detector as a working example, focusing on learning the posterior probability distribution of neutrino flux binned in neutrino energy. The predictions of the trained model are conditioned at inference time by the momentum and angle of the outgoing muons released after neutrino-nuclei interaction. This conditioning allows for the generation of personalized posterior distributions, tailored to the muon observables, all without necessitating a full retraining of the model for each new dataset. The performances of the model are studied for different shapes of the posterior distributions.

physics.data-an↗

arXiv2024

The Superscaling Variable and Neutrino Energy Reconstruction, From Theoretical Predictions to Experimental Limitations

We introduce the novel approach of using the superscaling variable as an observable and an analysis tool in the context of charged current neutrino-nucleus interactions. We study the relation between the superscaling variable and the removal energy, in addition to other fundamental parameters of the neutrino-nucleus interaction models. In the second half of the paper, we discuss the experimental viability of this measurement following a study of neutrino energy and missing momentum reconstruction. We show that the superscaling variable is measurable in neutrino interaction experiments provided that the proton is detected in the final state. We discuss the resolution of this measurement, and the limitation imposed by the proton's detection threshold.

hep-ph↗

arXiv2023

Lead perovskites as CE$ν$NS detectors

The recent discovery of Coherent Elastic neutrino-Nucleus Scattering (CE$ν$NS) has created new opportunities to detect and study neutrinos. The interaction cross-section in CE$ν$NS scales quadratically with the number of neutrons, making heavy-nuclei targets such as active lead-based detectors ideal. In this Letter, we discuss for the first time the potential of semiconductor lead perovskites for building neutrino detectors. Lead perovskites have emerged in the last decade as revolutionary materials for radiation detection due to their heavy and flexible element composition and their unique optoelectronic properties that result in an excellent energy resolution at an economic cost. While dedicated research and development will be necessary, we find great benefits and no inherent obstacles for the development of lead perovskites as CE$ν$NS detectors.

physics.ins-det↗

arXiv2022

Data-driven detector signal characterization with constrained bottleneck autoencoders

A common technique in high energy physics is to characterize the response of a detector by means of models tunned to data which build parametric maps from the physical parameters of the system to the expected signal of the detector. When the underlying model is unknown it is difficult to apply this method, and often, simplifying assumptions are made introducing modeling errors. In this article, using a waveform toy model we present how deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The results show that excellent performance results can be achieved even when the signals are significantly affected by random noise. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.

physics.ins-det↗

arXiv2022

Benchmarking intra-nuclear cascade models for neutrino scattering with relativistic optical potentials

The description of final-state interactions (FSI) in the large phase space probed in neutrino experiments poses a great challenge. In neutrino experiments, which operate under semi-inclusive conditions, cascade models are commonly used for this task, while under exclusive conditions FSI can be treated with relativistic optical potentials (ROP). We formulate conditions under which the ROP approach and cascade model can be directly compared. We feed the NEUT cascade with events from a relativistic distorted-wave impulse approximation calculation that uses the real part of an optical potential. Cuts on the missing energy of the resulting events are applied to define a set of events that can be directly compared to RDWIA calculations with the full optical potential. The NEUT cascade and ROP agree for proton kinetic energies $T_p > 150$ MeV for carbon, oxygen and calcium nuclei when a realistic nuclear density is used to introduce events in the cascade. For $T_p < 100$ MeV the ROP and NEUT cross sections differ in shape and differences in magnitude are larger than 50 \%. Single transverse variables allow to distinguish different approaches to FSI, but due to a large non-QE contribution the comparison to T2K data does not give an unambiguous view of FSI. We discuss electron scattering and argue that with a cut in missing energy FSI can be studied with minimal confounding factors in e.g. $e4ν$. The agreement of the ROP and NEUT for T2K conditions lends confidence to these models as a tool in oscillation analyses for sufficiently large nucleon kinetic energies. These results urge for caution when a cascade model is applied for small nucleon energies. The assessment of model assumptions relevant to this region are strongly encouraged. This paper provides novel constraints on cascade models from proton-nucleus scattering that can be easily applied to other neutrino event generators.

nucl-th↗

arXiv2021

A novel range telescope concept for proton CT

Proton beam therapy can potentially offer improved treatment for cancers of the head and neck and in paediatric patients. There has been a sharp uptake of proton beam therapy in recent years as improved delivery techniques and patient benefits are observed. However, treatments are currently planned using conventional x-ray CT images due to the absence of devices able to perform high quality proton computed tomography (pCT) under realistic clinical conditions. A new plastic-scintillator-based range telescope concept, named ASTRA, is proposed here to measure the proton's energy loss in a pCT system. Simulations conducted using GEANT4 yield an expected energy resolution of 0.7\%. If calorimetric information is used the energy resolution could be further improved to about $0.5\%$. In addition, the ability of ASTRA to track multiple protons simultaneously is presented. Due to its fast components, ASTRA is expected to reach unprecedented data collection rates, similar to $10^8$ protons/s. The performance of ASTRA has also been tested by simulating the imaging of phantoms. The results show excellent image contrast and relative stopping power reconstruction.

physics.med-ph↗

arXiv2021

Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors

Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, we report on the performance of a graph neural network in assisting with particle flow event reconstruction. The three-dimensional reconstruction of particle tracks produced in neutrino interactions can be subject to ambiguities due to high multiplicity signatures in the detector or leakage of signal between neighboring active detector volumes. Graph neural networks potentially have the capability of identifying all these features to boost the reconstruction performance. As an example case study, we tested a graph neural network, inspired by the GraphSAGE algorithm, on a novel 3D-granular plastic-scintillator detector, that will be used to upgrade the near detector of the T2K experiment. The developed neural network has been trained and tested on diverse neutrino interaction samples, showing very promising results: the classification of particle track voxels produced in the detector can be done with efficiencies and purities of 94-96% per event and most of the ambiguities can be identified and rejected, while being robust against systematic effects.

physics.data-an↗

arXiv2020

Exhaustive Neural Importance Sampling applied to Monte Carlo event generation

The generation of accurate neutrino-nucleus cross-section models needed for neutrino oscillation experiments require simultaneously the description of many degrees of freedom and precise calculations to model nuclear responses. The detailed calculation of complete models makes the Monte Carlo generators slow and impractical. We present Exhaustive Neural Importance Sampling (ENIS), a method based on normalizing flows to find a suitable proposal density for rejection sampling automatically and efficiently, and discuss how this technique solves common issues of the rejection algorithm.

hep-ex↗

arXiv2020

Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows

In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the likelihood-free inference problem of the measurement of neutrino oscillation parameters in Long Baseline neutrino experiments. A method adapted to physics parameter inference is developed and applied to the case of the disappearance muon neutrino analysis at the T2K experiment.

hep-ph↗

arXiv2020

Efficient sampling generation from explicit densities via Normalizing Flows

For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challenging through the inverse transform. In these cases, rejection and importance sampling require suitable proposal densities, which can be evaluated and sampled from efficiently. We will present a method based on normalizing flows, proposing a solution for the common problem of exploding reverse Kullback-Leibler divergence due to the target density having values of 0 in regions of the flow transformation. The performance of the method will be demonstrated using a multi-mode complex density function.

cs.LG↗