arXiv subjects

César Jesús-Valls

Publications and source records attributed to César Jesús-Valls.

11 recordsLinked to original sources

arXiv2026

A Novel Approach to Short Baseline Oscillation Searches Using Neutrino Tagging with nuSCOPE

We present the first study of short-baseline neutrino oscillation searches using a tagged neutrino beamline, taking the proposed nuSCOPE facility at CERN as a benchmark. In this Letter we demonstrate that tagged neutrino beams, where the neutrino flavor, energy, and propagation distance are determined with exceptional event-by-event precision, provide a new experimental approach to search for non-standard neutrino oscillations. We evaluate the sensitivity to sterile-neutrino-induced oscillations in the $ν_μ$ disappearance, $ν_μ\rightarrow ν_e$ appearance, and $ν_e$ disappearance channels, demonstrating the ability to probe multiple flavor transitions and both appearance and disappearance modes within a single experiment. Our results show that tagged beams enable sensitivity improvements to mass-squared splittings spanning several orders of magnitude while substantially reducing the dependence on neutrino flux predictions that limits conventional searches. We find that nuSCOPE can probe a broad region of parameter space motivated by existing anomalies and extend coverage into previously unexplored territory, demonstrating the strong potential of tagged neutrino beams for precision oscillation physics.

hep-ex↗

arXiv2026

Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physics

Foundation models are increasingly being pursued in particle and nuclear physics, but existing approaches remain strongly tied to individual experiments through detector-specific architectures or pre-training objectives, limiting their reuse across sensing modalities. We show that a point cloud self-distillation framework yields a substantially more general sensor-level pre-training recipe. We show that the same refined architecture and objective can be independently pre-trained with minimal changes on three qualitatively different detector modalities: liquid argon time projection chamber (LArTPC), collider TPC, and water Cherenkov. Using 1,000 labeled images for downstream task adaptation, Panda V2 matches or exceeds specialized foundation-model baselines trained with orders of magnitude more supervision, matching state-of-the-art particle-clustering performance with 70x fewer labeled events on sPHENIX while substantially improving particle identification, and on LArTPC data matching Panda (arXiv:2512.01324) particle reconstruction with up to 1,000x fewer labels. Beyond reconstruction, simple linear probes reveal physically meaningful latent structure associated with particle causality and track curvature.

hep-ex↗

arXiv2026

ADoNIS: A Differentiable generatOr of Neutrino Interaction Samples

Neutrino interaction generators are central to precision oscillation analyses, but conventional implementations do not directly provide derivatives of their predictions with respect to physics parameters, leaving analyses to obtain this dependence through reweighting, finite differences or external response models. We present ADoNIS, a fully differentiable neutrino interaction event generator that makes event reweighting and its gradients directly available, propagating exact derivatives through the nuclear ground state, hard-scattering amplitudes and stochastic intranuclear cascade. Following the physics choices of ACHILLES, we demonstrate agreement with its predictions across neutrino, electron and hadron probes, showing that differentiability is achieved without loss of physical fidelity. We then show how the resulting Jacobian exposes which measurements and regions of phase space constrain each parameter and how different probes break degeneracies, providing a quantitative tool for fit design, model tuning, measurement design and experiment design. The same differentiable predictions can be used directly in frequentist and Bayesian inference and carried through detector response to reconstructed observables for unfolding, while also yielding computational gains. In sum, ADoNIS provides a practical recipe for constructing differentiable neutrino event generators, applicable beyond the particular physics models implemented here, and this paper demonstrates their use across a wide range of tasks relevant to both theory and experiment.

hep-ex↗

arXiv2026

End-to-end Differentiable Calibration and Reconstruction for Optical Particle Detectors

Large-scale homogeneous detectors with optical readouts are widely used in particle detection, with Cherenkov and scintillator neutrino detectors as prominent examples. Analyses in experimental physics rely on high-fidelity simulators to translate sensor-level information into physical quantities of interest. This task critically depends on accurate calibration, which aligns simulation behavior with real detector data, and on tracking, which infers particle properties from optical signals. We present the first end-to-end differentiable optical particle detector simulator, enabling simultaneous calibration and reconstruction through gradient-based optimization. Our approach unifies simulation, calibration, and tracking, which are traditionally treated as separate problems, within a single differentiable framework. We demonstrate that it achieves smooth and physically meaningful gradients across all key stages of light generation, propagation, and detection while maintaining computational efficiency. We show that gradient-based calibration and reconstruction greatly simplify existing analysis pipelines while matching or surpassing the performance of conventional non-differentiable methods in both accuracy and speed. Moreover, the framework's modularity allows straightforward adaptation to diverse detector geometries and target materials, providing a flexible foundation for experiment design and optimization. The results demonstrate the readiness of this technique for adoption in current and future optical detector experiments, establishing a new paradigm for simulation and reconstruction in particle physics.

hep-ex↗

arXiv2025

Early Career Researcher Input to the European Strategy for Particle Physics Update: White Paper

This document, written by early career researchers (ECRs) in particle physics, aims to represent the perspectives of the European ECR community and serves as input for the 2025--2026 update of the European Strategy for Particle Physics. With input from a community-wide survey, it highlights key challenges faced by ECRs -- career stability, funding access and long-term research opportunities -- while proposing policy recommendations and targeted initiatives. It underscores the importance of practices fostering diverse, equitable, inclusive and healthy workplaces, as well as of stronger ECR communities, and highlights how effective communication and interdisciplinary collaborations reinforce the societal relevance of particle physics and promote continued support for large-scale and long-term projects. Finally, the future of both collider and beyond-collider experiments is addressed, emphasising the critical role of ECRs in shaping future projects. The ECR contribution is formed of two parts: the ten-page executive summary submitted as input to the European Strategy for Particle Physics Update and, as backup document, this extended white paper providing additional context.

hep-ex↗

arXiv2025

Neutrino Oscillation Tomography of the Earth with the Hyper-Kamiokande Detector

Using PREM as a reference model for the Earth density distribution we investigate the sensitivity of the Hyper-Kamiokande (HK) detector to deviations of the Earth i) core average density $\barρ_C$, ii) lower mantle average density $\barρ_{lman}$) and iii) upper mantle average density $\barρ_{uman}$, from their respective PREM densities. The analysis is performed by studying the effects of the Earth matter on the oscillations of atmospheric $ν_μ$, $ν_e$, $\barν_μ$ and $\barν_e$. We implement the constraints on the variations of $ρ_C$, $ρ_{lman}$ and $ρ_{uman}$ following from the precise knowledge of the Earth mass $M_\oplus$ and moment of inertia $I_\oplus$, as well as from the requirement that the Earth be in hydrostatic equilibrium (EHE). These constraints limit in the case of the three layer Earth density structure we are considering the maximal positive deviation of $\barρ_C$ from its PREM value to $10\%$. Considering the case of normal ordering (NO) of neutrino masses, we present results which illustrate the dependence of sensitivity to the core, lower and upper mantle average densities on the energy and zenith angle resolutions and on the value of $θ_{23}$. We show, in particular, that in the ''nominal'' case of neutrino energy resolution $E_{res} = 30\%$ and zenith angle resolution $θ_{zres} = 20^\circ$ and for, e.g., $\sin^2θ_{23}=0.45~(0.58)$, HK can determine the average core density $\barρ_C$ at $2σ$ C.L. after 6500 days of operation with an uncertainty of (-14.5\%)/+39.5\% ((-9.3\%/+31.7\%). In the ''more favorable'' case of $E_{res}= 20\%$ and $θ_{zres} = 10^\circ$, and if $\sin^2θ_{23}=0.58~(0.45)$, the core density would be determined at $2σ$ C.L. with an uncertainty of (-8.3\%)/+9.8\% ((-9.2\%)/+11.3\%).

hep-ex↗

arXiv2023

Uncovering the neutrino mass ordering with the next galactic core-collapse supernova neutrino burst using water Cherenkov detectors

A major challenge of particle physics is determining the neutrino mass ordering (MO). Due to matter effects, the flavor content of the neutrino flux from a Core-Collapse Supernova (CCSN) depends on the true neutrino MO resulting in markedly different energy and angle distributions for the measured lepton in water Cherenkov neutrino detectors. In this article, those distributions are compared for eight different CCSN models and used to study how their differences affect the determination of the neutrino mass ordering. In all cases, the inferred neutrino mass ordering is found to be either correct or inconclusive, with no significant false positives. However, the substantial variation observed among model predictions emphasizes the criticality of ongoing research in CCSN modeling.

hep-ex↗

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↗

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↗