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Finn Mayhew (Michigan State University)15/06/2026, 09:50
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...
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Jianming Bian (University of California, Irvine)15/06/2026, 10:55
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...
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Fabio Cufino (ETH Zurich)15/06/2026, 13:30
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...
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Chuyue Fang (University of California, Santa Barbara)15/06/2026, 13:55
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...
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Toni Mäkelä (University of California, Irvine)15/06/2026, 14:20
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...
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Dr Peter Gaemers (SLAC)15/06/2026, 14:40Experimental ApplicationsApplications in Experiments
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...
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Félix Cormier (TRIUMF)15/06/2026, 15:05Experimental ApplicationsApplications in Experiments
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...
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Ms Sanya Arora (University of California, Berkeley)15/06/2026, 15:50Experimental ApplicationsApplications in Experiments
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,...
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Po-Wei Huang (University of Oxford)15/06/2026, 16:15Experimental ApplicationsApplications in Experiments
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...
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Harry Hausner (Fermilab)15/06/2026, 16:40Experimental ApplicationsApplications in Experiments
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...
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Dr Saul Alonso Monsalve (ETH Zurich)16/06/2026, 09:00
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...
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Jiaxi Liu (University of California, Irvine)16/06/2026, 09:40
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...
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Xiangyu Qin (University of Alberta)16/06/2026, 10:00
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...
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Qiyu Yan (University of Chinese Academy of Sciences)16/06/2026, 11:00
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...
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LIANGBO HE (Tsinghua University)16/06/2026, 11:20
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....
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Mihir Tare (St. Mark's School of Texas)16/06/2026, 13:30
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,...
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Dowling Wong (Karlsruhe Institute of Technology)16/06/2026, 13:50
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...
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Zeviel Imani (Tufts University)16/06/2026, 14:15
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...
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Dr V Hewes (University of Cincinnati)16/06/2026, 15:00
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...
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Hilary Utaegbulam (University of Rochester)16/06/2026, 15:25
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...
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Dr Jose Luis Bonilla Ramirez (University of Wroclaw)16/06/2026, 16:05
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...
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Junjie Xia (SLAC)17/06/2026, 09:00
Liquid Argon Time Projection Chambers (LArTPCs) are a leading detector technology for precise 3D imaging and reconstruction of neutrino interactions, offering millimeter-scale spatial resolution. The SPINE (Scalable Particle Imaging with Neural Embeddings) reconstruction chain employs Sparse Convolutional Neural Networks for voxel-level feature extraction and Graph Neural Networks for...
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Sam Young (SLAC)17/06/2026, 09:40
Previously, we presented Panda (arXiv:2512.01324), a scalable point cloud architecture and training method that learns reusable sensor-level representations directly from raw, unlabeled 3D TPC data. We showed that this method–a UNet-like point cloud encoder coupled with a multi-view self-distillation objective–produces an encoder with foundation-model-like behavior: its self-taught...
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Carolyn Smith (SLAC)17/06/2026, 10:40
The DUNE near detector will face high interaction rates that stress traditional optical reconstruction pipelines relying on heuristic metrics for interaction detection. To achieve a flexible, data-driven solution and narrow sim-to-real gaps, we explore self-supervised pretraining for optical waveform reconstruction in liquid argon time projection chambers. We pretrain a Conformer-based masked...
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Mr Dikshant Sagar (University of California, Irvine)17/06/2026, 11:00
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...
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Rishi Babu (Michigan State University)17/06/2026, 13:30Novel Computational MethodsAccelerated, Scalable Computing Methods
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...
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Yifan Chen (SLAC)17/06/2026, 13:50Lessons LearnedLessons Learned and Challenges
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...
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Yousen Zhang (Brookhaven National Laboratory)17/06/2026, 14:15Novel Computational MethodsAccelerated, Scalable Computing Methods
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...
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Gabor Galgoczi (BNL)17/06/2026, 15:00Novel Computational MethodsAccelerated, Scalable Computing Methods
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...
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Omar Alterkait (Tufts University/ IAIFI)17/06/2026, 15:20
Liquid argon time projection chambers (LArTPCs) at DUNE and SBN need fast simulation, automated event reconstruction, and precise detector calibration, which are usually handled by separate tools. We built JAXTPC, a single differentiable detector simulator that unifies all of three tasks. Powered by JAX for efficient parallelization, JAXTPC models the full LArTPC response, from charge...
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Christopher Sauer (UCSB)18/06/2026, 09:00
Numerous new physics models predict massive long-lived particles that can decay to muon pairs. However, searching for such dimuon beyond standard model signals in liquid argon time projection chamber-based neutrino experiments is challenging because of the nearly irreducible neutrino background that includes one muon and one pion in the final states. The primary limiting factor is the...
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Vinicius Da Silva18/06/2026, 09:25
"Liquid argon time projection chambers (LArTPC) detector technology has been at the forefront of some recent (MicroBooNE, ICARUS) and future (Dune) large-scale Neutrino experiments. Due to LArTPC data’s interpretability as a collection of images, it has benefited from advances in ML methods, particularly in the field of computer vision. Yet, how to best apply these methods to 3D LArTPC...
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Matteo Vicenzi (Brookhaven National Laboratory (US))18/06/2026, 09:50
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...
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Roger Huang18/06/2026, 10:35
Modern neutrino analyses are generally simulation-based, requiring some choice of underlying neutrino event generator. However, since different neutrino event generators vary significantly in their predictions, it is useful to test simulation-based analyses with different event generators to check for any dependencies on the choice of generator. This is often practically difficult to do due to...
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Gregor Krzmanc (SLAC)18/06/2026, 11:00Applications in Experiments
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...
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Dr Jessie Micallef (Tufts University)18/06/2026, 11:30Sharable AI Tools
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...
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Karla Tame (Fermilab)18/06/2026, 13:30Sharable AI Tools
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...
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David Caratelli (UC Santa Barbara)18/06/2026, 14:00
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...
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Benda Xu (Tsinghua University)18/06/2026, 14:25
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...
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Linyan Wan (Fermilab)18/06/2026, 14:50
Neutrino public datasets are increasingly important for advancing neutrino physics by broadening participation, enabling independent cross-checks, and supporting method development beyond large experimental collaborations. However, their scientific utility is limited when users lack access to detector simulation or the detailed detector knowledge needed to interpret reconstructed quantities....
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César Jesús Valls (CERN)18/06/2026, 15:30
Machine learning is increasingly shaping analysis strategies in high-energy physics, a field characterised by large data volumes and complex detector responses. Progress in this direction depends on accessible, well-documented datasets that enable method development and provide common benchmarks for comparison.
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We present WAND, a public dataset of simulated events in a water Cherenkov... -
Kazuhiro Terao (SLAC)18/06/2026, 15:55
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Veronika Shalamova (University of California, Riverside)19/06/2026, 09:00Experimental ApplicationsApplications in Experiments
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...
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Max Dornfest (University of Hawaii at Manoa - Physics and Astronomy Department)19/06/2026, 09:25Experimental ApplicationsApplications in Experiments
"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...
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Grant Parker (Lawrence Livermore National Laboratory)19/06/2026, 10:25Experimental ApplicationsApplications in Experiments
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...
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Andrew Atta (Monash University)19/06/2026, 10:50
Large water Cherenkov detector experiments aim to shed light on violations in the neutrino sector by extracting the oscillation parameters from observed neutrino events. However, this is made challenging by the complexity of the detector dataset, which consists of charge and timing measurements from tens of thousands of photomultiplier tubes. In this talk, I describe recent progress toward a...
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Chao Zhang (Brookhaven National Laboratory)19/06/2026, 11:15Experimental ApplicationsApplications in Experiments
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...
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Xuefeng Ding (Institute of High Energy Physics, Chinese Academy of Sciences)19/06/2026, 11:40Experimental ApplicationsApplications in Experiments
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...
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Jiangmei Yang (Hong Kong University of Science and Technology)19/06/2026, 13:00
Liquid argon time projection chambers (LArTPCs) 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 leads to incomplete event reconstruction and limits the precision of neutrino energy...
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Larry Zhao (University of California Irvine)19/06/2026, 13:20Experimental ApplicationsApplications in Experiments
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...
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Weijun Li (Institute of High Energy Physics, Chinese Academy of Science)19/06/2026, 13:40Experimental ApplicationsApplications in Experiments
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...
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Kaiwen Yu (University of California-Irvine)Poster presentation
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...
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Geting Qin (Duke University)Poster presentation
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...
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Hilary Utaegbulam (University of Rochester)Poster presentation
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...
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Castaly Fan (University of Florida)Poster presentation
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...
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Thomas Schwemberger (QUP / KEK)Poster presentation
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...
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Daeun Jung (Chonnam National University)Poster presentation
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.
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We propose extending the Simulation-Based Inference... -
Mr Nived Puthumana Meleppattu (IJCLab)Poster presentation
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...
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Dr Sang Yong KIM (Chonnam National University)
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...
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Shuoyu Chen (ICRR, University of Tokyo)Applications in Experiments
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...
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Grant Parker (Lawrence Livermore National Laboratory)Applications in Experiments
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...
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