The MicroBooNE experiment consists of liquid argon time projection chamber(LArTPC) situated in the path of the Booster Neutrino Beam (BNB) at Fermilab. The goals of the experiment are to (1) investigate the observation of an excess of a possible electron-neutrino and anti-neutrino events by the MiniBooNE experiment, (2) measure argon-nucleus cross sections, and (3) perform R&D for LArTPCs. The...
The MicroBooNE experiment employs a Liquid Argon Time Projection Chamber (LArTPC) detector to measure sub-GeV neutrino interactions from the muon neutrino beam produced by the Booster Neutrino Beamline at Fermilab. Neutrino oscillation measurements, such as those performed in MicroBooNE, rely on the capability to distinguish between different flavors of neutrino interactions. Deep...
MicroBooNE has accumulated data in a 1E21 POT neutrino beam over five years to test the excess of low energy electron neutrino-like events observed by MiniBooNE. To this end, we have explored the use of a new hybrid analysis chain that includes both conventional and machine learning reconstruction algorithms to identify events with the exclusive 1-proton-1-electron signal topology. The...
Theia is a proposed 25-100 kiloton multi-purpose neutrino detector using novel target materials and advanced light detection techniques to address a wide range of neutrino and rare event physics. Key to this is the ability to separate scintillation and Cherenkov light using high-precision timing photo-sensors. Water-based-liquid-scintillator (WBLS) can be used to optimise the relative ratio...
The field of experimental neutrino physics has entered an era of high precision measurements. In order to capture the details of neutrino interactions, high resolution particle imaging detectors, such as time projection chambers, have been developed. However, the analysis of images containing highly detailed particle trajectories and a large number of pileups remains challenging. Deep learning...
Particle imaging detectors such as Liquid Argon Time Projection Chambers offer high resolution imaging of charged particle trajectories. They are used and will be used in current and future neutrino experiments to maximize physics output from neutrino interactions. In order to understand the physics behind the neutrino-nucleus interactions, which remain poorly known today, the SLAC machine...
High resolution particle imaging detectors can record full details of charged particle interactions, and opens a door to high precision neutrino oscillation measurements. In order to maximize the physics output, however, development of high quality data reconstruction techniques is critical. One of the challenging data reconstruction task is clustering of pixels to identify individual...
Liquid Argon Time Projection Chamber (LArTPC) offers high resolution (~3mm/pixel) 2D or 3D imaging of charged particles' trajectories. Deep neural networks (DNN) have been successfully applied to the data reconstruction of LArTPC. At SLAC we are building an end-to-end 3D LArTPC data reconstruction chain of algorithms, specifically designed for sparse LArTPC data. However LArTPCs come in two...
Graph neural networks (GNNs) are a category of neural networks which operate on graph-structured inputs, instead of the grid-structured inputs required by a CNN. Building on work developed for the HL-LHC for particle tracking with GNNs as part of the Exa.TrkX collaboration, this talk presents work to develop GNN-based techniques for hit-level reconstruction in Liquid Argon Time Projection...
Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. Some neutrino experiments are already using them, for example, for distinguishing among different neutrino topologies.
In this talk, we report on the performance of a graph neural network (GNN) approach in assisting with...
Normalizing flows present a powerful framework to sample and evaluate probability density functions via neural networks. In this work we address two applications of them in the domain of neutrino physics: i) Perform likelihood-free inference of the measurement of neutrino oscillation parameters in Long Baseline neutrino experiments. A method adapted to physics parameter inference is developed...
PROSPECT is an antineutrino detector located above ground at the High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). The energy spectrum of antineutrinos emitted from the reactors is measured by using a delayed coincidence technique through the inverse-beta-decay reaction (IBD). The ORNL group is currently exploring several applications of machine learning techniques for...
The COHERENT collaboration utilizes a suite of detectors to search for coherent elastic neutrino-nucleus scattering (CEvNS) and associated backgrounds at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. Measurement of the low-energy nuclear recoil signature of CEvNS events necessitates the identification and rejection of environmental and detector-intrinsic backgrounds....
An inclusive measurement of the cross section of the neutrino charged-current interactions on 127I will help study the quenching of gA , the axial-vector coupling constant, which determines the rate of neutrinoless double beta decays. At the Los Alamos Meson Production Facility (LAMPF), an exclusive measurement was made but with a large statistical error. To make an inclusive and more accurate...
Project 8 is developing Cyclotron Radiation Emission Spectroscopy (CRES) on the beta-decay spectrum of tritium for the measurement of the absolute neutrino mass scale. CRES is a frequency-based technique that aims to probe the endpoint in the tritium energy spectrum with a final sensitivity of 0.04 eV. Current studies are performed on monoenergetic electrons from a gaseous...
Neutrinoless Double Beta Decay (0νββ) is one of the major research thrusts in neutrino physics. The discovery of 0νββ would answer persistent puzzles in the standard model. KamLAND-Zen experiment is one of the leading efforts in the search of 0νββ. The current data is collected from 745kg of Xe136 dissolved in liquid scintillator, a medium that emits isotropic light when particles deposit...
DIDACTS (Data-Intensive Discovery Accelerated by Computational Techniques for Science) is a collaboration of physics and machine learning experts with an overall goal of incorporate scientific knowledge into machine learning and data science methods in the context of scientific disciplines. As part of DIDACTS’ research program, we are looking into the challenging problem of Dark Matter direct...
The NEXT (Neutrino Experiment with a Xenon TPC) experiment searches for neutrinoless double-beta decay in 136Xe using a time projection chamber (TPC) filled with enriched xenon gas at high pressure. NEXT can reconstruct the extended ionization tracks left by electrons in the gas. Using this information we can select events with two electrons with a common vertex (double beta decay) from the...
The NEXT Collaboration is currently designing and performing R&D for a ton-scale detector capable of observing neutrinoless double beta decay. NEXT utilizes a high pressure gaseous xenon TPC with an electroluminescent region to amplify the signal from the drift electrons, and has successfully built and collected data with several smaller scale prototypes. The current expected sensitivity of...
nEXO is a proposed 5 tonne liquid xenon experiment which seeks to detect neutrinoless double beta decay $0\nu\beta\beta$ in Xe-136 using Time Projection Chamber (TPC) technology. The experiment will use the combination of scintillation and ionization signals to reconstruct events with an energy resolution of 1\% $\sigma/E$ at the \gls{onbb} Q-value. The scintillation light will be collected...
Deep neural networks are becoming increasingly pervasive in science and engineering applications. These networks are often treated as high-fidelity models with accurate predictive powers by end users. However, even predictions from a trained neural network may contain significant
errors and uncertainties due to bias, noise and complexity of the data; the volume of the training data; the...
NOvA is a long-baseline neutrino experiment primarily studying neutrino oscillations in the NuMI beam from Fermi National Laboratory (FNAL), USA. It consists of two functionally identical, finely granulated detectors which are separated by 809 km and situated 14.6 mrad off the NuMI beam axis from FNAL. A new set of oscillation results were shown at the Neutrino 2020 conference. Key to these...
NOvA is an accelerator neutrino experiment with an 810 km baseline. Using the NuMI beam from Fermilab, it measures electron neutrino appearance and muon neutrino disappearance at its far detector. NOvA has embraced a wide range of deep learning methods. Here we will focus on energy regression CNNs. NOvA has developed a regression CNN that takes the raw cells from the detector as inputs, and...
In this talk we discuss application of the recurrent neural networks to the
task of energy reconstruction at the NOvA experiment. NOvA is a long-baseline
accelerator based neutrino oscillation experiment that holds a leading
measurement of the $\Delta m_{32}^2$ oscillation parameter. In order to achieve
good estimation of the oscillation parameters it is imperative to have a good
neutrino...
The NOvA experiment is a long baseline neutrino oscillation experiment measuring neutrino oscillations and cross sections using the NuMI beam at Fermilab. Reconstructing particles produced in neutrino interactions provides the basis for neutrino energy estimation and final state identification for cross section measurements and interaction model tuning. This talk will present an end-to-end...
The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions....
In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering and the octant of $\theta_{23}$ remain unknown. The primary goal of DUNE is to address these questions by measuring the oscillation patterns of $\nu_\mu$ and $\bar\nu_\mu$ over a range of energies spanning the first and second oscillation maxima, which requires precisely reconstructed neutrino...
Next generation long-baseline experiments will measure neutrino mixing parameters with unprecedented precision, requiring stringent constraints on systematic uncertainties. We present the methods used in the recently published DUNE technical design report to test the robustness of the experiment with respect to variations of the neutrino interaction model. A multivariate method was used to...
In this talk we will show the potential improvements in neutrino event reconstruction that a 3D pixelated readout could offer over a 2D projective wire readout for liquid argon time projection chambers. We simulated and studied events in two generic, idealized detector configurations for these two designs, classifying events in each sample with deep convolutional neural networks to compare the...
The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton Gd-doped water Cherenkov detector installed in the Booster Neutrino Beam (BNB) at Fermilab. The experiment aims to make a unique measurement of neutron yield from neutrino-nucleus interactions and to perform R&D for the next generation of water-based neutrino detectors. To realise these goals the ANNIE collaboration...
The employment of machine learning (ML) techniques has now become commonplace in the offline reconstruction workflows of modern neutrino experiments. Since such workflows are typically run on CPU-based high-througput computing (HTC) clusters with limited or no access to ML accelerators like GPU or FPGA coprocessors, the ML algorithms, for which CPUs are not the best suited platform, tend to...
Machine learning in neutrino physics leverages many tools and techniques from the more mainstream areas of computer vision, but also brings new and interesting challenges. Notably, neutrino experiments have large images, typically with very high resolution, and often sparse or irregular data. In this talk I'll present several techniques that are successfully shown to accelerate machine...
We present initial developments of ML based event reconstruction for water Cherenkov detectors in the context of Hyper Kamiokande. Using ML, we aim to exploit additional spatial and directional information from higher granularity PMTs developed for HyperK to improve on existing reconstruction performance and to enable new measurements that are very challenging in conventional...
Inverse beta decay is the primary interaction mode for low energy electron anti-neutrinos, producing two signals in a water Cherenkov detector like Super-Kamiokande: a low energy positron and, ~200 µs later, a neutron capture on hydrogen producing a 2.2 MeV photon. These result in only ~10 of SK’s 11,000+ photomultiplier tubes being hit by light, making them difficult to differentiate from...
Hyper-Kamiokande is the proposed next generation Water Cherenkov neutrino detector in Kamioka, Japan. Based on the design of Super-Kamiokande, Hyper-K will have an order of magnitude larger fiducial mass, enabling the survey of topics in neutrino physics on a broader scale. The intermediate Water Cherenkov detector (IWCD) near the J-PARC beam in Tokai aims at reducing systemic uncertainty in...
Deep neural networks are an area of very active research in neutrino event reconstruction. On the other hand, state-of-the-art reconstruction methods for water Cherenkov detectors use more traditional maximum-likelihood approaches. Here we present initial studies for a convolutional neural network that generates probability density functions for the data (hit charge and time) observed at each...
The field of machine learning has become increasingly important over the last years and now constitutes a vital contribution to the physics output of experiments such as IceCube. IceCube is a neutrino telescope situated at the geographic South Pole, instrumenting a cubic kilometer of glacial ice. Atmospheric and astrophysical neutrinos are indirectly measured via Cherenkov radiation of charged...
Tau appearance from neutrino oscillations of atmospheric muon neutrinos is studied by the DeepCore subarray, the densely-instrumented region of IceCube, an ice-Cherenkov neutrino detector 1.5 kilometers below the surface of the South Pole. These studies probe the unitarity of the PMNS matrix. Distinguishable event signatures in this region include track-like and shower-like events. Because the...
The IceCube Neutrino Observatory, located at the South Pole, instruments a cubic kilometer of ice with 5160 optical modules that are used to detect astrophysical and atmospheric neutrinos. Near the lowest energies that IceCube can resolve, at the 10s of GeV-scale, these events leave a Cherenkov signature that only a few optical modules will record. Thus, these events are difficult to...
Machine learning in neutrino physics leverages many tools and techniques from the more mainstream areas of computer vision, but also brings new and interesting challenges. Notably, neutrino experiments have large images, typically with very high resolution, and often sparse or irregular data. In this talk I'll present several techniques that are successfully shown to accelerate machine...
