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While the potential of the Jiangmen Underground Neutrino Observatory (JUNO) to measure solar neutrinos is known, realizing this potential requires new techniques to reduce detector backgrounds. One of the most serious backgrounds is due to…
The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton multi-purpose liquid scintillator detector currently being built in a dedicated underground laboratory in Jiangmen (PR China). Data-taking is expected to start in 2023.…
NOvA is a long-baseline neutrino oscillation experiment. It is optimized to measure $\nu_e$ appearance and $\nu_{\mu}$ disappearance at the Far Detector in the $\nu_{\mu}$ beam produced by the NuMI facility at Fermilab. NOvA uses a…
Recent advances in photoacoustic (PA) imaging have enabled detailed images of microvascular structure and quantitative measurement of blood oxygenation or perfusion. Standard reconstruction methods for PA imaging are based on solving an…
We address the challenge of parameter-efficient fine-tuning (PEFT) for three-dimensional (3D) U-Net-based denoising diffusion probabilistic models (DDPMs) in magnetic resonance imaging (MRI) image generation. Despite its practical…
The reconstruction of event-level information, such as the direction or energy of a neutrino interacting in IceCube DeepCore, is a crucial ingredient to many physics analyses. Algorithms to extract this high level information from the…
The main goal of the Jiangmen Underground Neutrino Observatory (JUNO) under construction in China is to determine the neutrino mass hierarchy and to measure oscillation parameters to the sub-percent level. The detector consists of 20 ktons…
Turbulence-degraded image frames are distorted by both turbulent deformations and space-time-varying blurs. To suppress these effects, we propose a multi-frame reconstruction scheme to recover a latent image from the observed image…
Photomultiplier tubes (PMTs) are widely used in particle and nuclear physics experiments. The reconstruction of PMT waveforms is a fundamental task in these experiments, where accurate extraction of photoelectron (PE) multiplicities and…
Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between…
Pulse shape discrimination (PSD) is widely used in particle and nuclear physics. Specifically in liquid scintillator detectors, PSD facilitates the classification of different particle types based on their energy deposition patterns. This…
We propose a modification of Wigner Distribution Deconvolution (WDD) to support live processing ptychography. Live processing allows to reconstruct and display the specimen transfer function gradually while diffraction patterns are…
This paper presents a comprehensive review of recent advances in deploying convolutional neural networks (CNNs) for object detection, classification, and tracking on Field Programmable Gate Arrays (FPGAs). With the increasing demand for…
The current event display system in the offline software of Jiangmen Underground Neutrino Observatory Experiment(JUNO) is based on the ROOT EVE package. We use Unity, a renowned game engine, to improve its performance and make it available…
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal…
The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in…
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm. In this paper, we propose a new…
Pneumonia poses a significant global health challenge, demanding accurate and timely diagnosis. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in medical image analysis for pneumonia detection,…
The use of deep learning is facilitating a wide range of data processing tasks in many areas. The analysis of fusion data is no exception, since there is a need to process large amounts of data collected from the diagnostic systems attached…
We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end…