Aobo Li

The next generation of particle physics experiments will face an era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing these data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning, silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing toward a successful transition to the data frontier of fundamental science.

We report a 3.3 σ measurement of coherent elastic neutrino-nucleus scattering from solar B 8 neutrinos using a 6.77 t × yr exposure from the XENONnT experiment, inferring a solar B 8 neutrino flux of ( 5 − 2 + 3 ) × 10 6 cm − 2 s − 1 , consistent with previous measurements. In the presence of the B 8 “neutrino fog,” we find no evidence for light dark matter, and observe diminishing returns in sensitivity with increasing exposure. A 93% increase in exposure from the previous search improves the median sensitivity to the nucleon scattering cross section for a 5 GeV / c 2 spin-independent weakly interacting massive particle by 10%. The dataset was also used to measure the weak mixing angle at ∼ 0.02 GeV / c momentum transfer and constrain physics beyond the Standard Model.

High-purity germanium (HPGe) detectors are a key technology for rare-event searches such as neutrinoless double-beta decay ( 0 ν β β ) and dark matter experiments. Pulse shapes from these detectors vary with interaction topology and thus encode information critical for event classification. Pulse shape simulations (PSSs) are essential for modeling analysis cuts that distinguish signal events from backgrounds and for generating reliable simulations of energy spectra. Traditional PSS methods rely on a series of first-principles corrections to replicate the effect of readout electronics, requiring challenging fits over large parameter spaces and often failing to accurately model the data. We present a neural network architecture, the Cyclic Positional U-Net ( CPU-Net ) ⁷ 7 https://github.com/aobol/CPU-Net . , that performs translations of simulated pulses so that they closely resemble measured detector signals. Using a cycle generative adversarial network framework, this response emulation network learns a data-driven mapping between simulated and measured pulses with high fidelity, without requiring a predetermined response model. We use data from a HPGe detector with an inverted-coaxial point contact geometry to show that CPU-Net effectively captures and reproduces critical pulse shape features, allowing more realistic simulations without detector-specific tuning. CPU-Net achieves up to a factor-of-four improvement in distribution-level agreement for pulse shape parameter reconstruction, while preserving the topology-dependent information required for pulse-shape discrimination.

Ge 76 can β β decay into three possible excited states of Se 76 , with the emission of two or, if the neutrino is Majorana, zero neutrinos. None of these six transitions have yet been observed. The was designed to study β β decay of Ge 76 using a low background array of high purity germanium detectors. With 98.2 kg-y of isotopic exposure, the emonstrator sets the strongest half-life limits to date for all six transition modes. For 2 ν β β to the 0 1 + state of Se 76 , this search has begun to probe for the first time half-life values predicted using modern many-body nuclear theory techniques, setting a limit of T 1 / 2 > 1.5 × 10 24 y (90% CL). Published by the American Physical Society 2025

The next generation of searches for neutrinoless double beta decay (0ν\b{eta}\b{eta}) are poised to answer deep questions on the nature of neutrinos and the source of the Universe's matter-antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic 0ν\b{eta}\b{eta} is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. In this work, we describe the performance of generative models designed for monolithic liquid scintillator detectors like KamLAND to produce highly accurate simulation data without a predefined physics model. We demonstrate its ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.

The enclosed data release consists of a subset of the calibration data from the Majorana Demonstrator experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimination cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release is specifically designed to support the training and testing of Artificial Intelligence (AI) and Machine Learning (ML) algorithms upon our data. This document is structured as follows. Section I provides an overview of the dataset's content and format; Section II outlines the location of this dataset and the method for accessing it; Section III presents the NPML Machine Learning Challenge associated with this dataset; Section IV contains a disclaimer from the Majorana collaboration regarding the use of this dataset; Appendix A contains technical details of this data release. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li).