Constraining the ratio of flavors of astrophysical neutrinos at Earth informs neutrino production and oscillation scenarios. The IceCube Neutrino Observatory measures the neutrino flux by recording the Cherenkov light deposited by neutrino secondaries with around five thousand PMTs distributed within a cubic kilometer of ice at the South Pole. Tau neutrinos are difficult to distinguish from...
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...
Precise reconstruction of high-energy neutrino interactions at the LHC is critical for the physics program of the proposed FASERCal detector, an off-axis neutrino detector for the FASER experiment during LHC Run 4, enabling precision measurements of TeV-scale neutrino interactions in the far-forward region. The detector's highly granular, 3D voxelized geometry produces sparse data that...
The Liquid Argon Time Projection Chamber (LArTPC) technology provides high-resolution spatial and calorimetric information which can lead to great capabilities for particle identification. MicroBooNE was one of the first large LArTPC experiments to operate in a neutrino beam. This talk presents the integration of NuGraph2, a reconstruction tool based on machine learning (ML), into MicroBooNE’s...
Most existing and proposed high energy neutrino experiments have excellent muon charge identification capabilities, enabling the distinction of $\nu_\mu$ and $\overline{\nu}_\mu$ charged current interactions. In contrast, distinguishing $e^\pm$ from $\nu_e$ and $\overline{\nu}_e$ interactions is typically impossible, as they interact quickly within the characteristically dense detector...
Accurate position reconstruction in noble-element time-projection chambers is critical for rare-event searches in astroparticle physics, yet is systematically limited by electric field distortions arising from charge accumulation on detector surfaces. Conventional data-driven field corrections suffer from three fundamental limitations: discretization artifacts that break smoothness and...
Water Cherenkov neutrino detectors present unique opportunities and challenges for the application of machine learning techniques. The Super Kamiokande detector is a 50 kiloton detector set up for a multitude of neutrino and non-neutrino physics, including that of detecting neutrinos from the T2K experiment 295km away. The flavour of neutrino as well as its interaction vertex and direction are...
Monolithic liquid scintillator detectors are searching for neutrinoless double beta decay, a theorized process that would confirm the Majorana nature of neutrinos. The production and propagation of photons in the detection medium depend on various optical properties, such as light yield, attenuation lengths and re-emission/absorption spectra. These must all be separately characterized,...
Accurate characterization of scintillation emission profiles is essential for robust event reconstruction in liquid scintillator detectors. The SNO+ Collaboration has developed an innovative framework employing Bayesian optimization (BO) to calibrate these timing profiles using internal radioactive backgrounds. Our results demonstrate that BO achieves convergence in at least an order of...
Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire...
High-resolution neutrino detectors typically rely on fine detector segmentation to obtain detailed spatial information, which introduces substantial complexity, channel count, and cost. In this talk, I will present the deep-learning reconstruction developed for PLATON, a detector concept based on plenoptic imaging of scintillation light in an unsegmented scintillator volume. The central...
NOvA is a long-baseline neutrino experiment studying neutrino oscillations by detecting neutrinos from the NuMI beam at Fermilab. Its physics analysis relies on accurate prong segmentation, which involves matching each hit to its source particle and identifying the particle type. This task has commonly been addressed using a combination of traditional clustering algorithms and convolutional...
Direction reconstruction in liquid scintillator detectors is challenging because the directionality is due to Cherenkov light, which is typically a small fraction of scintillation light especially with high concentrations of the wavelength shifter. We present a deep learning framework tailored to this regime, based on a purpose-built Dual-Temporal Attention architecture that combines...
Accurate photon timing in large liquid-scintillator neutrino detectors requires precise modeling of complex optical processes. These include complex scattering, absorption and re-emission, boundary effects, steel-frame shadowing, and multi-path reverberation. These effects draw a complex picture that is yet intractable for analytic methods and too detector-specific to learn end-to-end. We...
The Jiangmen Underground Neutrino Observatory (JUNO) is the largest liquid scintillator detector in the world, aiming to determine the neutrino mass ordering with an energy resolution of $3\%/\sqrt{E~[\mathrm{MeV}]}$. Accurate analysis of photomultiplier tube (PMT) waveforms is essential for energy resolution. We present a deep learning framework for PMT waveform denoising and reconstruction....
I will present a search for a hypothetical heavy neutral gauge boson (Z′) decaying to dilepton pairs using machine-learning classifiers trained exclusively on low-mass kinematic sidebands. A gradient boosted decision tree (BDT) and a multi-layer perceptron deep neural network (DNN) are trained to distinguish Z-peak events from Drell-Yan background events using only 11 raw kinematic features,...
Cryogenic detectors aiming to push detection thresholds to ever lower energies must operate close to the noise limit, where reducing the threshold further risks increasing false triggers. The DELight experiment will use a superfluid helium detector instrumented with large area cryogenic microcalorimeters to probe sub-GeV dark matter via faint quasiparticle and photon signals.
We formulate...
We present a data-driven approach for the generation and inference of 2D LArTPC events. Using a conditional latent diffusion model trained on LArTPC images, we have demonstrated the generation of physically realistic protons. Combining this conditional model with Earth Mover’s Distance (EMD) enables us to perform stochastic gradient descent to efficiently infer the 3D momentum of an input...
NuGraph3 is a hierarchical Graph Neural Network (GNN) architecture for event reconstruction in neutrino physics experiments, utilized across a range of Liquid Argon Time Projection Chamber (LArTPC) experiments including MicroBooNE, ICARUS, SBND and DUNE. This third-generation architecture leverages a range of different decoders to predict multiple outputs simultaneously on a heterogeneous...
The 2x2 Demonstrator is a prototype of ND-LAr, the liquid argon time-projection chamber of the Deep Underground Neutrino Experiment's Near Detector complex. Both the 2x2 Demonstrator and ND-LAr are modular detectors with pixelated charge readouts and inactive regions wherein there is no sensitivity to energy depositions in the liquid argon. These inactive regions are located between the active...
As we enter the era of precision in neutrino physics, it is essential to better understand and limit systematic uncertainties in experimental setups. With the improvement of statistics and measurement quality in detectors, the uncertainties arising from Monte Carlo (MC) generators, used throughout the data analysis process, become more relevant. These uncertainties stem from limitations in the...
Recent advances in machine learning, particularly in multimodal models, have created new opportunities for analyzing complex data in high-energy physics, where accurate identification of particle interactions is critical for scientific discovery. However, existing approaches rely heavily on convolutional neural networks, which lack interpretability and do not fully leverage multimodal...
The IceCube Neutrino Observatory is a neutrino detector located at the South Pole consisting of a three-dimensional array of optical sensors embedded deep within the Antarctic ice. It records the Cherenkov radiation produced by charged particles that are generated when neutrinos interact in the ice. The trajectory of the interacting neutrino can be inferred by analyzing the spatio-temporal...
Bridging the gap between data and simulation is one of the most persistent challenges in modern particle physics experiments. Detector calibration aims to shrink this gap by improving the fidelity of detector modeling. Conventional calibration workflows address individual detector effects sequentially. Differentiable simulation offers a compelling alternative: by propagating gradients through...
We present a GPU-native simulation framework for liquid-argon neutrino detectors that enables efficient simulation and signal processing of sparse data in dense GPU-oriented workflows. Traditional dense FFT-based methods in liquid-argon detector simulation are adapted to sparse signals through blockwise processing and analytic tensor operations. We develop an efficient block-sparse binned...
Optical photon tracking in Geant4 is the dominant cost in simulating large neutrino detectors that rely on scintillation or Cherenkov light, and it caps the size of training samples available for ML-based reconstruction. Simphony (old name eic-opticks) is a GPU optical simulation framework built on Opticks (originally developed for JUNO) that runs inside a standard Geant4 job and delivers two...
Wire-Cell is a mature, production-level LArTPC reconstruction framework that integrates physics-driven models with targeted machine learning to perform signal processing and 3D tomographic reconstruction. Current reconstruction approaches in Wire-Cell primarily exploit geometry, charge, and local connectivity, while higher-level topological information remains only partially encoded. In...
Future AI-based studies in particle physics will likely start from a foundation model to accelerate training and enhance sensitivity. As a step towards a general-purpose foundation model for particle physics, we investigate whether the OmniLearned foundation model pre-trained on diverse high-$Q^2$ simulated and real $pp$ and $ep$ collisions can be effectively transferred to a few-GeV...
The Deep Underground Neutrino Experiment (DUNE) aims to accomplish precision measurements of neutrino oscillation. DUNE will use the world’s most intense neutrino beam, expecting over 100 neutrino interactions in the near-site Liquid Argon detector per spill. Resolving the overlapping particle signatures in the near detector will be vital for providing precision neutrino oscillation...
To enable an accurate determination of oscillation parameters, accelerator-based neutrino experiments require detailed simulations of nuclear interaction physics in the GeV regime. While substantial effort from both theory and experiment is currently being invested to improve the fidelity of these simulations, their present deficiencies typically oblige experimental collaborations to resort to...
Public datasets from high energy physics experiments can help spur the development of new analysis methods and techniques. This is particularly true in the Liquid Argon Time Projection Chamber (LArTPC) community, where many detectors sharing the same technology have been operating concurrently at Fermilab and CERN, enhancing the benefit of shared, broadly-available datasets for...
Next-generation large-scale liquid scintillator detectors (JUNO, KamLAND2 , SNO+ and JNE) rely on tens of thousands of Photomultiplier Tubes (PMTs) to capture single optical photons. Pushing the energy resolution to the physical limit requires extracting sub-nanosecond timing and precise charge from highly piled-up and noisy PMT waveforms. However, the lack of standardized, publicly accessible...
Supernova neutrinos provide a unique probe of neutrino physics under extreme conditions and core-collapse dynamics while simultaneously enabling multimessenger observations of supernovae. The DarkSide-20k experiment, a dual-phase liquid argon time projection chamber, is sensitive to supernova neutrinos through both coherent elastic neutrino-nucleus scattering (CE$\nu$NS) and the...
"We present a potential improvement over the standard method developed to determine antineutrino directionality in inverse-beta-decay detectors via a new directionality algorithm utilizing the Frobenius norm for the ``distance'' between two matrices. The previously developed method in monolithic and segmented detectors underestimated angular uncertainty in the low-count regime. We discuss the...
This study presents a denoising algorithm trained using machine learning to improve the energy resolution of a single-phase liquid xenon time projection chamber for neutrinoless double beta decay detection. Supervised, unsupervised, and semi-supervised models are demonstrated to significantly remove noise from simulated measurements while preserving signal information. The supervised model...
One of the great mysteries in modern particle physics is why there is more matter than antimatter. The phenomenon of CP violation–where particles behave differently from their antiparticles–is one of the key ingredients necessary to explain this asymmetry. The Hyper-Kamiokande (Hyper-K) experiment aims to shed light on this enigma by measuring CP violation in the neutrino sector. However, this...
Quality control of front-end electronics is a critical but labor-intensive step in large-scale neutrino detector deployments. Expert knowledge must be applied consistently across thousands of channels, often in remote or underground environments where turnaround time directly impacts commissioning schedules. We present an agentic LLM-based workflow that automates this process...
Modern neutrino experiments depend on complex and highly iterative analysis workflows involving reconstruction, simulation, calibration, background studies, validation, and documentation. In many cases, the bottleneck is not a single algorithm, but the efficient, reproducible, and auditable execution of expert-defined procedures. This talk presents the application of the Dr.Sai agentic...
Liquid argon time projection chambers (LArTPCs), such as those that will be used in the Deep Underground Neutrino Experiment (DUNE), provide high-resolution, three-dimensional imaging of neutrino interactions. A persistent challenge in these detectors is neutron reconstruction, as neutrons do not produce direct ionization tracks and are instead inferred through secondary interactions. This...
NOvA (NuMI Off-Axis $\nu_e$ Appearance) is a long baseline neutrino experiment designed to measure neutrino oscillations over a distance of 810 km. NOvA employs a near and far detector to observe $\nu_\mu$ disappearance and $\nu_e$ appearance of neutrinos produced by the NuMI beam at Fermilab. Energy reconstruction is critical for precise measurements of neutrino oscillation parameters and...
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment located in southern China, featuring a 20-kton liquid scintillator detector with excellent energy resolution and large target mass. JUNO has been collecting full liquid scintillator data since August 2025. JUNO has strong potential to observe atmospheric neutrino oscillations. Such measurements would...
Neutron-antineutron transitions are a baryon number violating process with ΔB=2, providing a unique insight into potential explanations of the baryon asymmetry of the universe, particularly via post-sphaleron baryogenesis scenarios. The Deep Underground Neutrino Experiment (DUNE), whose primary physics program includes neutrino oscillation measurements, searches for proton decay, and detection...
The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino experiment utilizing large liquid argon time-projection chambers (LArTPCs). It is highly sensitive to the electron neutrino burst from a galactic core-collapse supernova. This neutrino burst can be used to point back to the supernova. The primary directional information comes from elastic scattering on...
The 2x2 Demonstrator is a prototype of ND-LAr, the liquid argon time-projection chamber of the Deep Underground Neutrino Experiment's Near Detector complex. Both the 2x2 Demonstrator and ND-LAr are modular detectors with pixelated charge readouts and inactive regions wherein there is no sensitivity to energy depositions in the liquid argon. These inactive regions are located between the active...
The Short-Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) neutrino detector in the Short-Baseline Neutrino (SBN) program at Fermilab. SBND is designed to investigate the Low-Energy Excess (LEE), an unexplained excess of electron-like events observed by previous short-baseline neutrino experiments that may point to physics beyond the Standard Model. In LArTPC...
We perform a hierarchical simulation-based inference analysis of beyond the Standard Model (BSM) neutrino interactions affecting the propagation of high-energy astrophysical neutrinos over cosmological distances. We solve the full cosmological transport equation, including Hubble expansion, energy redistribution, regeneration, and possible resonant scattering, and fit to the diffuse...
Accurate modeling of optical parameters in Water Cherenkov detector including absorption length, scattering length, and PMT quantum efficiency, is essential for reliable event reconstruction, yet their simultaneous calibration remains challenging due to strong inter-parameter correlations and the absence of tractable analytical likelihoods.
We propose extending the Simulation-Based Inference...
SPINE (Scalable Particle Imaging with Neural Embeddings) is rapidly emerging as the machine learning based reconstruction pipeline of choice across all the LArTPC experiments. Following the remarkable icarus results and successful implementation in SBND and ND-LAR, the next step is it's integration into the DUNE far detectors and 2 of its large scale prototypes at cern. This collaborative work...
The Reactor Experiment for Neutrinos and Exotics (RENE) is a pioneering initiative aimed at investigating the existence of sterile neutrinos within the Δm²≈2 eV² parameter space, motivated by the observed Reactor Antineutrino Anomaly. The experimental setup consists of a cylindrical target volume containing 270 liters of gadolinium-loaded liquid scintillator (Gd-LS), surrounded by a...
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...
This study presents a denoising algorithm trained using machine learning to improve the energy resolution of a single-phase liquid xenon time projection chamber for neutrinoless double beta decay detection. Supervised, unsupervised, and semi-supervised models are demonstrated to significantly remove noise from simulated measurements while preserving signal information. The supervised model...
