Saul Alonso-Monsalve

Last Name

Alonso-Monsalve

First Name

Saul

ORCID

0000-0002-9678-7121

Organisational unit

03503 - Rubbia, André / Rubbia, André

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Publications 1 - 10 of 16

Artificial intelligence for improved fitting of trajectories of elementary particles in dense materials immersed in a magnetic field
Alonso-Monsalve, Saul; Sgalaberna, Davide; Zhao, Xingyu; et al. (2023)
Communications Physics
Particle track fitting is crucial for understanding particle kinematics. In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the design of future particle physics experiments and their data exploitation.
Contrastive learning for robust representations of neutrino data
Wilkinson, Alex; Radev, Radi; Alonso-Monsalve, Saul (2025)
Physical Review D
In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalize effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field.
Measurements of neutrino oscillation parameters from the T2K experiment using 3.6 × 10²¹ protons on target
T2K Collaboration; Abe, K.; Alonso-Monsalve, Saul; et al. (2023)
The European Physical Journal C
The T2K experiment presents new measurements of neutrino oscillation parameters using 19.7(16.3)×10²⁰ protons on target (POT) in (anti-)neutrino mode at the far detector (FD). Compared to the previous analysis, an additional 4.7×10²⁰ POT neutrino data was collected at the FD. Significant improvements were made to the analysis methodology, with the near-detector analysis introducing new selections and using more than double the data. Additionally, this is the first T2K oscillation analysis to use NA61/SHINE data on a replica of the T2K target to tune the neutrino flux model, and the neutrino interaction model was improved to include new nuclear effects and calculations. Frequentist and Bayesian analyses are presented, including results on sin² θ₁₃ and the impact of priors on the δ_CP measurement. Both analyses prefer the normal mass ordering and upper octant of sin² θ₂₃ with a nearly maximally CP-violating phase. Assuming the normal ordering and using the constraint on sin² θ₁₃ from reactors, sin² θ₂₃ = 0.561⁺⁰.⁰²¹₋₀.₀₃₂ using Feldman–Cousins corrected intervals, and Δm²₃₂ = 2.494⁺⁰.⁰⁴¹₋₀.₀₅₈×10−3 eV² using constant Δχ² intervals. The CP-violating phase is constrained to δCP=−1.97⁺⁰.⁹⁷₋₀.₇₀ using Feldman–Cousins corrected intervals, and δ_CP = 0,π is excluded at more than 90% confidence level. A Jarlskog invariant of zero is excluded at more than 2σ credible level using a flat prior in δCP, and just below 2σ using a flat prior in sin δ_CP. When the external constraint on sin² θ₁₃ is removed, sin² θ₁₃ = 28.0+2.8−6.5×10−3, in agreement with measurements from reactor experiments. These results are consistent with previous T2K analyses.
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
DUNE Collaboration; Abed Abud, Adam; Abi, Babak; et al. (2022)
The European Physical Journal C
Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.
Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors
Alonso-Monsalve, Saul; Douq, Dana; Jesús-Vall, César; et al. (2021)
Physical Review D
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 set 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.
Measurements of the $\nu_{\mu}$ and $\bar{\nu}_{\mu}$-induced Coherent Charged Pion Production Cross Sections on $^{12}C$ by the T2K experiment
T2K Collaboration; Koya, Abe; Alonso-Monsalve, Saul; et al. (2023)
Physical Review D
Searching for solar KDAR with DUNE
Dune Collaboration; Alonso-Monsalve, Saul; Alt, Christoph; et al. (2021)
Journal of Cosmology and Astroparticle Physics
The observation of 236 MeV muon neutrinos from kaon-decay-at-rest (KDAR) originating in the core of the Sun would provide a unique signature of dark matter annihilation. Since excellent angle and energy reconstruction are necessary to detect this monoenergetic, directional neutrino flux, DUNE with its vast volume and reconstruction capabilities, is a promising candidate for a KDAR neutrino search. In this work, we evaluate the proposed KDAR neutrino search strategies by realistically modeling both neutrino-nucleus interactions and the response of DUNE. We find that, although reconstruction of the neutrino energy and direction is difficult with current techniques in the relevant energy range, the superb energy resolution, angular resolution, and particle identification offered by DUNE can still permit great signal/background discrimination. Moreover, there are non-standard scenarios in which searches at DUNE for KDAR in the Sun can probe dark matter interactions.
Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC
DUNE Collaboration; Abed Abud, Adam; Alonso-Monsalve, Saul; et al. (2022)
Journal of Instrumentation
The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.
Adversarial methods to reduce simulation bias in neutrino interaction event filtering at liquid argon time projection chambers
Babicz, Marta; Alonso-Monsalve, Saul; Dolan, Stephen; et al. (2022)
Physical Review D
For current and future neutrino oscillation experiments using large liquid argon time projection chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D submanifold sparse convolutional network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the short baseline neutrino program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying domain adversarial neural networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adversarial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.
First Joint Oscillation Analysis of Super-Kamiokande Atmospheric and T2K Accelerator Neutrino Data
Super-Kamiokande Collaboration; T2K Collaboration; Abe, Kou; et al. (2025)
Physical Review Letters
The Super-Kamiokande and T2K Collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of 19.7(16.3)×1020 protons on target in (anti)neutrino mode, the analysis finds a 1.9σ exclusion of CP conservation (defined as JCP=0) and a 1.2σ exclusion of the inverted mass ordering.

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Saul Alonso-Monsalve

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