The detectors of the Kamiokande saga relies on a well-established detection method using Cherenkov effect in ultra-pure water. Consequently, the reconstruction algorithms, especially of the Super-Kamiokande detector, have been developed since several decades, allowing to reach unparallel results in neutrino sector, from the constraints on the solar neutrino upturn to the atmospheric neutrino...
"The Hyper-Kamiokande Detector represents the next generation of neutrino observatories, following in the lineage of the Kamiokande and Super-Kamiokande experiments. With significantly enhanced sensitivity, Hyper-Kamiokande will support a diverse and ambitious physics program, including searches for proton decay, studies of solar neutrinos under non-standard scenarios, and the potential first...
A key goal of neutrino physics is to measure CP violation in neutrino oscillations, which may explain why the universe is dominated by matter over antimatter. The upcoming Hyper-Kamiokande experiment will be central to this effort. With a much larger detector volume and more photomultipliers than Super-K, Hyper-K will collect data at kilohertz rates. Traditional reconstruction tools are...
Hyper-Kamiokande (Hyper-K) is a next-generation water Cherenkov neutrino experiment currently under construction, designed to address key questions in particle physics, including leptonic CP violation and proton decay. Convolutional neural networks (CNNs) have previously been applied to water Cherenkov detectors by treating PMTs as pixels, with charge and timing information serving as input...
The Water Cherenkov Test Experiment (WCTE) is a 30-ton water Cherenkov detector that received 100-1200 MeV electrons, muons, charged pions and protons from the CERN East Area T9 beam, as well as observing secondary neutrons captured on Gadolinium and tagged photons through operation in a dedicated setup. With a suite of beamline detectors to characterise and tag the particles entering the...
The Water Cherenkov Test Experiment is a powerful detector for studying water Cherenkov detectors, demonstrating new detection technologies testing reconstruction algorithms with controlled data. In particular, the relatively small scale of the detector allows for a fast and lightweight exploration of machine learning methods. We will present here the performances of Graph Neural Networks for...
Traditional Monte Carlo (MC) simulations rely on accurate modeling of both physics processes and detector responses. However, optimization with calibration data is often challenging,or even impractical, due to stochastic effects and the high dimensionality of correlated parameters.
Within the CIDeR-ML Collaboration, we are developing differentiable surrogate models that enable automated...
Machine learning methods were explored for particle identification (PID) in the SuperFGD detector, which part of the recent upgrade to the T2K near detector. Baseline models were established to provide benchmarks, followed by a classical neural network where feature engineering was applied to improve separation. A transformer-based architecture was then developed, with data augmentation,...
We present a dual application of conditional normalizing flows to (i) accelerate Monte Carlo sampling of exclusive neutrino–nucleus cross-sections and (ii) model uncertainty distributions within bayesian fits of systematics, illustrated on the T2K near-detector fit. Although aimed at different applications and uses, both efforts share a nearly identical implementation: an iterative training...
The Jiangmen Underground Neutrino Observatory (JUNO) is the world's
largest 20-kiloton liquid scintillator (LS) detector in south China. It
will precisely measure the oscillation of reactor antineutrinos from two
commercial nuclear power plants 53km away, with the goal of determining
the neutrino mass ordering and measuring three oscillation parameters to
sub-percent precisions. This...
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation multipurpose
liquid scintillator detector with a 20-kiloton target mass, located in southern China. One of its primary goals is to determine the neutrino mass ordering (NMO) with a significance of at least 3σ by precisely measuring the oscillation pattern of reactor antineutrinos over a 53 km baseline. Beyond reactor...
The Jiangmen Underground Neutrino Observatory (JUNO), which has recently begun operations, is an ambitious experiment designed for precision neutrino physics. To achieve its main objectives — the determination of neutrino mass ordering, and the sub-percent measurement of the θ_12, Δm_21^2 and Δm_31^2 neutrino oscillation parameters — the experiment demands highly accurate Monte Carlo (MC)...
Characterizing the astrophysical neutrino flux with the IceCube Neutrino Observatory traditionally relies on a binned forward-folding likelihood approach. Insufficient Monte Carlo (MC) statistics in each bin limits the granularity and dimensionality of the binning scheme. A neural network can be employed to optimize a summary statistic that serves as the input for data analysis, yielding the...
The IceCube Neutrino Observatory issues a variety of real-time alerts that identify high-energy neutrino events with a high probability of astrophysical origin. These alerts rely on rapid reconstruction of the incident particle's direction and energy, enabling follow-up observations by other telescopes and observatories in the context of multi-messenger astronomy. Traditionally, reconstruction...
The KM3NeT collaboration is building two neutrino telescopes in the Mediterranean Sea: ORCA for low-energy oscillation studies and ARCA for the detection of high-energy astrophysical neutrinos. Both detectors are three-dimensional arrays of photomultiplier tubes that record Cherenkov light from secondary particles produced in neutrino interactions. High-level variables - such as the...
Baikal-GVD is a large-scale underwater neutrino telescope in Lake Baikal designed to study the properties of high-energy neutrinos. In this talk, I will present the neural-network–based data processing chain currently under development for Baikal-GVD data analysis. This pipeline addresses the following goals: suppression of extensive air shower background, rejection of optical module...
This report presents a neural network–based approach for the Baikal-GVD experiment to separate neutrino-induced events from background events caused by extensive air showers (EAS). Two Transformer encoder models were developed for different stages of the data processing pipeline. The first, trained on Monte Carlo simulated raw optical module hits with spatial, temporal, and charge information,...
Graph neural networks (GNNs) have proven to excel at Cherenkov detector event reconstruction, where the signal is sparse, varying in size, and non-euclidian in structure. For the European Spallation Source Neutrino Super Beam (ESSnuSB) - which aims to measure CP-violation in the leptonic sector with an accuracy less impaired by systematic uncertainties than other proposed experiments - we have...
The observation of neutrinoless double-beta decay (0νββ) would confirm the Majorana nature of neutrinos, violate lepton number conservation, and offer deep insights into the origin of neutrino mass and the matter-antimatter asymmetry of the universe. The LEGEND (Large Enriched Germanium Experiment for Neutrinoless ββ Decay) program builds upon the success of its predecessors, the MAJORANA...
SNO+ is an operational multi-purpose neutrino detector located 2km underground at SNOLAB in Sudbury, Ontario, Canada. 780 tonnes of linear alkylbenzene-based liquid scintillator are observed by ~9300 photomultiplier tubes (PMTs) mounted outside the spherical scintillator volume. SNO+ has a broad physics program which will include a search for the neutrinoless double beta decay of...
We developed machine learning methods for background rejection in the 0νββ decay search of the KamLAND-Zen experiment. Using CNN-based KamNet and Transformer-based ViViT for particle identification from PMT hit maps, we compared the rejection efficiency of both models. The results showed equivalent performance with high correlation (0.85-0.95) in output scores. Performance improvement through...
"The observation of neutrinoless double-beta decay (0νββ) would demonstrate that the
Neutrino is Majorana in nature (its own antiparticle),establish the violation of lepton number conservation, and provide insight into the absolute neutrino mass scale. The NEXT experiment (Neutrino Experiment with a Xenon TPC) is searching for 0νββ decay in high-pressure xenon gas at the Laboratorio...
The Deep Underground Neutrino Experiment (DUNE) is the flagship next-generation neutrino experiment in the United States, designed to decisively measure neutrino CP violation and determine the neutrino mass hierarchy. DUNE employs Liquid Argon Time Projection Chamber (LArTPC) technology, which provides exceptional spatial resolution and enables detailed reconstruction of final-state particles...
The Deep Underground Neutrino Experiment (DUNE) will deploy massive liquid argon time projection chamber (LArTPC) detectors to measure the properties of neutrinos with unprecedented precision. Currently, two full-scale prototypes of the DUNE far detectors (FDs) are at CERN, which are called the ProtoDUNE detectors. Recent studies indicate that these prototypes could have the potential to...
"Particle imaging detectors have been central to particle physics for more than a century, providing an unrivaled level of detail that has enabled numerous discoveries. Liquid argon time projection chambers (LArTPCs), a dense and scalable realization of this detection paradigm, constitute the core technology of the Deep Underground Neutrino Experiment (DUNE). Automating the reconstruction of...
In the global scientific effort to better understand how neutrinos fit (or don’t) within the bounds of the Standard Model, the Deep Underground Neutrino Experiment (DUNE) aims to make precise neutrino oscillation measurements to determine the neutrino mass ordering and establish the value of neutrino Charge-Parity (CP) violation. To accomplish this, DUNE has a host of near detectors that will...
DUNE is a next-generation long-baseline neutrino experiment aiming to determine
the neutrino mass ordering, study CP violation in the leptonic sector, observe
supernova neutrinos, and search for physics beyond the Standard Model. It will
feature a Near Detector 547 m from the source and a Far Detector ~1300 km away.
Within the Near Detector, the System for on Axis Neutrino Detection...
Liquid Argon Time Projection Chambers (LAr TPCs) provide detailed imaging of neutrino interactions, making them an ideal environment for machine learning based classification tasks. While conventional neural networks have achieved strong performance in this domain, they do not naturally account for uncertainties, an essential requirement for robust physics analyses. Bayesian Neural Networks...
We present what to our knowledge is the first sensor-level foundation model (FM) for neutrino detectors, trained directly on simulated 3D LArTPC charge data without manual labels. The goal is simple: build a scalable model that learns the underlying physics automatically from data, then adapts to event reconstruction, PID, calibration, and other tasks using only a small labelled sample. I will...
NOvA is a long-baseline neutrino experiment studying neutrino oscillations with Fermilab's NuMI beam. Prong reconstruction, the task of matching detector hits to their source particles and identifying the type of each particle, is a crucial and resource-intensive step in the event reconstruction process. This task has commonly been done using traditional clustering approaches or convolutional...
The ICARUS detector, a LArTPC (Liquid Argon Time Projection Chamber) of 476 tons fiducial volume, serves as the Far Detector of the SBN (Short Baseline Neutrino) program. ICARUS is situated on-axis with respect to the BNB and off-axis to the NuMI neutrino beams at Fermilab. LArTPC is a powerful detector technology for achieving precise neutrino interaction imaging and reconstruction in 3D,...
The Short-Baseline Neutrino (SBN) Program at Fermilab consists of two liquid argon time projection chamber (LArTPC) detectors: the Short-Baseline Near Detector (SBND) located 110 meters downstream from the Booster Neutrino Beam (BNB) target, and the ICARUS detector positioned 600 meters downstream from the BNB target. The program is designed to probe short-baseline neutrino anomalies,...
The MiniBooNE Low-Energy Excess (LEE) of electron-like events from the Booster Neutrino Beam (BNB) has puzzled neutrino physicists for decades. One possible explanation has been an unpredicted excess of neutral current (NC) ∆ resonance interactions with a subsequent radiative decay. An increase in the rate of NC ∆→Nγ events by a factor of 3.18 could explain the LEE seen by MiniBooNE. ICARUS...
ICARUS is a liquid argon time projection chamber (LArTPC) neutrino experiment at Fermilab. Located ~600 m from the Booster Neutrino Beam (BNB) target, it serves as the far detector in the Short-Baseline Neutrino (SBN) Program. The primary objective of the SBN Program is to probe neutrino oscillation physics at short baselines using multiple detectors, including measurements of both muon...
The Short-Baseline Near Detector (SBND) is a 100-ton scale Liquid Argon Time Projection Chamber (LArTPC) neutrino detector positioned in the Booster Neutrino Beam (BNB) at Fermilab, as part of the Short-Baseline Neutrino (SBN) program. Recent inroads in Computer Vision (CV) and Machine Learning (ML) have motivated a new approach to the analysis of particle imaging detector data. SBND data can...
We present initial results on nuclear recoil detection based on the fluorescence of color centers created by nuclear recoils in lithium fluoride. We use gamma rays, fast and thermal neutrons, and study the difference in responses they induce, showing that this type of detector is rather insensitive to gamma rays. We use light-sheet fluorescence microscopy to image nuclear recoil tracks from...
Applying deep learning to neutrino physics offers powerful new capabilities for tasks such as event reconstruction, but it also exposes unique technical challenges. In this talk, I'll discuss lessons learned from the deep learning perspective: handling sparse, high-dimensional detector data; not limiting the capacity of our models; and ensuring model robustness and interpretability. These...
The COherent Neutrino–Nucleus Interaction Experiment (CONNIE), located 30 m from the core of the Angra 2 reactor at the Almirante Álvaro Alberto Nuclear Power Plant in Brazil, is the first reactor neutrino experiment to employ silicon Skipper-CCDs with the aim of detecting coherent elastic neutrino-nucleus scattering (CEvNS), searching for physics beyond the Standard Model, and monitoring...
Noble element time projection chambers (TPCs) have enabled searches for neutrino physics both within and beyond the Standard Model, but these rare event searches are limited by electric field distortions on event position reconstruction. Traditional data-driven field distortion corrections lack smoothness and differentiability, require extensive calibration data, and often do not follow...
Event reconstruction in large liquid scintillator neutrino detectors, such as SNO+, rely on hit times from large numbers of photomultiplier tubes (PMTs). Standard methods of PMT timing calibrations involve dedicated hardware with deployed or in situ light sources. A calibration using in situ radioactive backgrounds present in regular physics data would allow vastly more frequent calibrations...
Next-generation monolithic Water Cherenkov detectors aim to probe fundamental questions in neutrino physics. These measurements demand unprecedented precision in detector calibration and event reconstruction, pushing beyond the capabilities of traditional techniques. We present a novel framework for differentiable simulation of Water Cherenkov detectors that enables end-to-end optimization...
Liquid argon time projection chambers (LArTPCs) are highly attractive for particle detection because of their tracking resolution and calorimetric reconstruction capabilities. Developing high-quality simulators for such detectors is very challenging because conventional approaches to describe different detector parameters or processes ignore their entanglement (ie, calibrations are done one at...
Missing correlations and potential biases in detector calibrations are one major challenge mitigating data—simulation differences. To address this, we propose to use gradients, enabled by differentiable simulation, for efficient and effective optimization of the detector physics parameters. We developed an auto-differentiation enabled simulation of a liquid argon time projection chamber using...
The FASERCal detector, proposed as an off-axis neutrino detector for the FASER experiment to operate during LHC Run 4, will produce sparse, 3D voxelized data, demanding advanced deep learning for neutrino event reconstruction. We present a hybrid architecture that uses a Sparse Submanifold Convolutional Network (SSCN) to efficiently tokenize voxel hits into patch embeddings. These are...
Given the challenges in LArTPC event reconstruction, we have taken the first steps towards creating an end-to-end inference pipeline to go from 2D images to event properties. Inspired by the success of denoising diffusion probabilistic models (DDPMs), we have developed a method of conditionally generating 2D LArTPC images. By utilizing a modified latent diffusion model, we have demonstrated...
We present a study of proton–deuteron separation in the Liquid Argon In A Testbeam (LArIAT) experiment at Fermilab. LArIAT, which operated from 2015-2017, was designed to study and characterise charged particles in liquid argon that commonly occur in neutrino-Ar interaction final-states. Deuterons are rare and subject to significant background contamination in the LArIAT dataset, and their...
The efficient classification of electromagnetic activity from $\pi^0$ and electrons is a notoriously challenging problem in the reconstruction of neutrino interactions in Liquid Argon Time Projection Chamber (LArTPC) detectors. We address this problem using the mathematical framework of Optimal Transport (OT), which has been successfully employed for event classification in other HEP contexts...
Following the completion of its neutrino mass measurement program at the end of 2025, the KATRIN experiment aims to probe keV-scale sterile neutrinos by analyzing the full tritium beta decay spectrum with a novel detector system, TRISTAN. Leveraging KATRIN’s high source activity, this search is sensitive to mixing amplitudes at the parts-per-million level. However, extracting a potential...
Real-time anomaly detection provides a model-independent way to search for unexpected phenomena, complementing traditional model-driven triggers. At the LHC, autoencoder-based anomaly triggers in CMS such as CICADA (on raw calorimeter data) and AXOL1TL (on reconstructed objects) have already demonstrated the power of these methods, with similar efforts underway in ATLAS through GELATO....
Neutrino interactions are poorly understood and modeled, with some processes entirely missing from simulation. This sometimes leads to to surprising observations from rudimentary data analysis techniques such as hand-scanning. Here, the use of contrastive learning and unsupervised clustering is investigated as a more systematic way to explore event types in data. Although the aim is to...
In this talk, I will review recent work from our group on developing deep learning models for lepton–nucleus interactions. I will begin by applying generative adversarial networks (GANs) to simulate neutrino and antineutrino collisions with nucleons. Our models encompass both charged-current quasielastic and inclusive interactions of muon neutrinos with a carbon target, offering detailed...
Final-state interactions (FSI) are a significant source of systematic uncertainty in neutrino event generators, particularly for LArTPC-based experiments. These interactions occur when particles produced in the initial neutrino-nucleus collision scatter or are absorbed while exiting the nucleus, complicating event reconstruction and interpretation. While advanced models such as GiBUU offer...
It is well-known that classifiers can be trained to approximate the likelihood ratio between two distributions, and that machine learning based classifiers in particular can learn this likelihood ratio in high-dimensional spaces. This provides a method to reweight events from different distributions as functions of many features. OmniFold is an unfolding technique that uses this concept to...
Quantifying systematic uncertainties in particle physics analyses is complicated by the need to estimate detector responses through Monte Carlo (MC) simulations. Conventional approaches compute variations in reconstructed variables under fixed physics assumptions, which can bias the parameters being measured. We present a likelihood-free inference method that uses neural networks to derive...
