The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation neutrino experiment currently under construction in southern China. It is designed with a 20 kton liquid scintillator detector and 78% photomultiplier tube (PMT) coverage. The primary physics goal of JUNO is to determine the neutrino mass ordering and measure oscillation parameters with unprecedented precision. JUNO’s...
The Jiangmen Underground Neutrino Observatory (JUNO) is a state-of-the-art 20 kton liquid scintillator detector designed to achieve an unprecedented energy resolution of 3% @ 1 MeV. The energy resolution is of paramount importance for the measurement of neutrino mass ordering (NMO) through the study of reactor neutrinos at JUNO. A key factor contributing to the energy resolution in JUNO is the...
The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment under construction with a broad physics program. The main goals of JUNO are the determination of the neutrino mass ordering and the high-precision measurement of neutrino oscillation properties with anti-neutrinos produced in commercial nuclear reactors. JUNO's central detector is an acrylic sphere 35.4 meters in...
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation large (20 kton) liquid-scintillator neutrino detector, designed to determine the neutrino mass ordering from its precise reactor neutrino spectrum measurement. Additionally, high-energy (GeV-level) atmospheric neutrino measurements could also improve its sensitivity to mass ordering via matter effects on oscillations,...
The Jiangmen Underground Neutrino Observation (JUNO), located at Southern China, is a multi-purpose neutrino experiment that consist of a 20-kton liquid scintillator detector. The primary goal of the experiment is to measure the neutrino mass ordering (NMO) and measure the relevant oscillation parameters to a high precision. Atmospheric neutrinos are sensitive to NMO via matter effects and can...
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...
MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and...
MicroBooNE is a short baseline neutrino oscillation experiment that employs a Liquid Argon Time Projection Chamber (LArTPC) together with an array of Photomultiplier Tubes (PMTs), which detect scintillation light. This light detection is necessary for providing a means to reject cosmic ray background and trigger on beam-related interactions. Thus, accurate modeling of the expected optical...
Training neural networks for analyzing three-dimensional trajectories in particle detectors presents challenges due to the high combinatorial complexity of the data. Incorporating networks with Euclidean Equivariance could significantly reduce the reliance on data augmentation. To achieve Euclidean Equivariance, we construct neural networks that primarily represent data and perform...
The ICARUS T600 detector is a liquid argon time projection chamber (LArTPC) installed at Fermilab, aimed towards a sensitive search for possible electron neutrino excess in the 200-1000 MeV region. To investigate nue appearance signals in ICARUS, a fast and accurate algorithm for selecting electron neutrino events from a background of cosmic interactions is required. We present an application...
The ICARUS T600 Liquid Argon Time Projection Chamber (LArTPC) detector is the far detector of the Short Baseline Neutrino (SBN) Program located at Fermilab National Laboratory (FNAL). The data collection for ICARUS began in May 2021, utilizing neutrinos from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector off-axis beam (NuMI). The SBN Program has been designed to...
The NOvA experiment uses the ~1 MW NuMI beam from Fermilab to study neutrino oscillations: electron neutrino appearance and muon neutrino disappearance in a baseline of 810 km, with a 300-ton near detector and a 14-kiloton far detector. NOvA was the first experiment in high-energy physics to apply convolutional neural networks to classify neutrino interactions and composite particles in a...
The NOvA experiment is a long-baseline accelerator neutrino experiment utilizing Fermilab's upgraded NuMI beam. It measures the appearance of electron neutrinos and the disappearance of muon neutrinos at its Far Detector in Ash River, Minnesota. NOvA is the first neutrino experiment to use convolutional neural networks (CNNs) for event identification and reconstruction. Recently, we introduced...
Imaging is one of the main pillars of clinical protocols for cancer care that provides essential non-invasive biomarkers for detection, diagnosis and response assessment. The development of Artificial Intelligence (AI) tools, and Machine Learning (ML) in particular, have proven potential to transform the analysis of radiological images, by significantly reducing processing time, by increasing...
The NEXT experiment is an international collaboration that searches for the neutrinoless double-beta decay using $^{136}\mathrm{Xe}$. It features an entirely gaseous TPC, which allows for the resolution of individual electron tracks. This opens up the possibility to employ machine learning techniques to distinguish between signal and background events based on their topological signature. In...
The LEGEND experiment is dedicated to the search for neutrinoless double beta decay using $^{76}Ge$-enriched High Purity Germanium detectors. While LEGEND has excellent energy resolution and ultra-low background levels, noise from readout electronics can make identifying events of interest more challenging. An efficient signal denoising algorithm can further enhance LEGEND’s energy resolution,...
The XENONnT detector is a dual-phase xenon time projection chamber to search for rare low-energy events. While its main purpose is the direct detection of Dark Matter, XENONnT is also sensitive to neutrino interactions for example from solar 8B neutrinos. To fully utilize the fiducialization and background reduction capabilities of the XENONnT detector, it is important to know the exact...
At the forefront of investigating neutrinoless double beta decay (0νββ) using 76Ge-enriched detectors, the LEGEND experiment is driven by the quest to unravel the mysteries of neutrinos and explore physics beyond the Standard Model. In its initial phase, LEGEND-200 deploys 200 kg of germanium at INFN Gran Sasso National Laboratory, aiming for a discovery half-life sensitivity surpassing...
The Karlsruhe Tritium Neutrino (KATRIN) experiment probes the effective electron anti-neutrino mass by precisely measuring the tritium beta-decay spectrum close to its kinematic endpoint.
A world-leading upper limit of $0.8 \,$eV$\,$c$^{-2}$ (90$\,$\% CL) has been set with the first two measurement campaigns.
Subsequent improvements in operational conditions and a substantial increase in...
The DUNE near detector is employing new technologies in Liquid Argon Time Projection Chamber (LArTPC) detection methods, including a 3D charge pixel readout, and is modularized into a 5x7 rectangular grid of TPCs. A smaller 2x2 prototype is nearing testing in the NuMI neutrino beam at Fermilab and we are faced with reconstructing the modularized, 3D LArTPC images. While a chain of machine...
Generating simulation data for future and current LArTPC experiments requires addressing several challenges, such as reducing computation time and the expression of detector model uncertainties. Inspired by the success of recently developed generative models to produce complex, high-dimensional data such as natural images, we are exploring how these methods might be applied to LArTPCs. Initial...
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...
The Deep Underground Neutrino Experiment (DUNE) will measure all long-baseline neutrino oscillation parameters, including the CP-violating phase and the neutrino mass ordering, in one single experiment. DUNE will also study astrophysical neutrinos and perform a broad range of new physics searches. This ambitious programme is enabled by the very high-resolution imaging capabilities of...
One of the primary oscillation physics goals of the Deep Underground Neutrino Experiment (DUNE) far detector (FD) is the measurement of CP violation in the neutrino sector. To achieve this, DUNE plans to employ large-scale liquid-argon time-projection chamber technology to capture neutrino interactions in unprecedented detail. Such fine-grain images demand a highly sophisticated automated...
The highly detailed images produced by liquid argon time projection chamber (LArTPC) technology hold the promise of an unprecedented window into neutrino interactions; however, traditional reconstruction techniques struggle to efficiently use all available information. This is especially true for complicated interactions produced by tau neutrinos, which are typically large, consist of many...
The Deep Underground Neutrino Experiment will operate large-scale Liquid-Argon Time-Projection Chambers at the far site in South Dakota, producing high-resolution images of neutrino interactions. Extracting the maximum value from the images requires sophisticated pattern-recognition to interpret detector signals as physically meaningful objects for physics analyses. Identifying the neutrino...
Deep Learning (DL) techniques for background event rejection in Liquid Argon Time Projection Chambers (LArTPCs) have been extensively studied for various physics channels [1,2], yielding promising results. However, the potential of massive LArTPCs in the low-energy regime remains to be fully exploited, particularly in the classification of few-hits events that encode information hardly...
A key challenge in the application of deep learning to neutrino experiments is overcoming the discrepancy between data and Monte Carlo simulation used in training. In order to mitigate bias when deep learning models are used as part of an analysis they must be made robust to mismodelling of the detector simulation. We demonstrate that contrastive learning can be applied as a pre-training step...
Hyper-Kamiokande (HK) is the next generation neutrino observatory in Japan and the successor of Super-Kamiokande (SK) detector. It has been designed to extend the legacy of its predecessor into new realms of neutrino physics ranging from MeV (Solar or Supernovae neutrino) to several GeV energy scales, and in particular, discover CP violation for the very first time in the lepton sector. To...
KM3NeT/ARCA and KM3NeT/ORCA are the new generation of neutrino telescopes located in the depths of the Mediterranean Sea. Each comprises a grid of optical sensors that capture the Cherenkov light emitted by charged particles produced in neutrino interactions. KM3NeT/ARCA, sensitive to interactions with energies ranging from TeV to PeV, focuses on cosmic neutrinos, while KM3NeT/ORCA...
The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section modeling uncertainties in the analysis. A key issue is the dimensionality used, as the kinematics of all outgoing particles in an event typically affects the reconstruction performance in a neutrino detector. OmniFold is...
In this preliminary study we consider and explore the application of Machine Learning algorithms for reconstruction in Super-Kamiokande. To do so simulated event samples have been used. The aim is the development of a tool to be employed in proton decay analysis along with the official reconstruction software (fiTQun). The final goal will be to improve Cherenkov ring detection and...
Building upon the LiquidO detection paradigm, the CLOUD detector represents a significant evolution in neutrino detection, offering rich capabilities in capturing both spatial and temporal information of low-energy particle interactions. With a 5–10-ton opaque scintillator inner detector volume, CLOUD is the byproduct of the EIC/UKRI funded AntiMatter-OTech project, whose main objective is to...
Modeling the light propagation in LArTPC with sinusoidal representation networks (SIREN) is scalable and capable of being calibrated using data.
In this talk, I will demonstrate a few applications of the SIREN in position reconstruction and charge-light signal correlation.
Drawing statistical conclusions out of experimental data entails to compare it to the physics model predictions of interest. In modern particle physics experiments, producing predictions often requires of three subtasks: 1) Simulating the particles interactions and propagation within the detector, 2) Describing the detector response and tuning its description to calibration data and 3)...
In high energy physics, the detection of rare events and the computation of their properties require precise and reliable statistical methods, with uncertainty quantification playing a crucial role. Nowadays, most research relies on machine learning methods, where the calibration of output probabilities is not always straightforward. How can we then draw conclusions with the required five...
We shall review the results of our recent work on the developments of the NuWro Monte Carlo generator of events. We are working on applying deep learning techniques to optimize the NuWro generator. In the first step, we work on the neural network model that generates the lepton-nucleus cross-sections. We obtained a deep neural network model that predicts the electron-carbon cross-section over...
With the rising importance of large sequential models in neutrino imaging and reconstruction tasks, a robust estimation of uncertainty at each stage of reconstruction is essential. These chained models produce valuable physical representations of the evolution of a neutrino interaction, which are of interest to many disparate fields of study. This talk will discuss methods available for...
