Related papers: Improved Energy Reconstruction in NOvA with Regres…

Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high energy physics. In this paper, we attempt to understand the potential of CNNs for event…

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep…

High precision vertex and energy reconstruction is crucial for large liquid scintillator detectors such as JUNO, especially for the determination of the neutrino mass ordering by analyzing the energy spectrum of reactor neutrinos. This…

Available estimates for the energy resolution of DUNE vary by as much as a factor of four. To address this controversy, and to connect the resolution to the underlying physical processes, we build an independent simulation pipeline for…

To employ a Convolutional Neural Network (CNN) in an energy-constrained embedded system, it is critical for the CNN implementation to be highly energy efficient. Many recent studies propose CNN accelerator architectures with custom…

A dramatic progress in the field of computer vision has been made in recent years by applying deep learning techniques. State-of-the-art performance in image recognition is thereby reached with Convolutional Neural Networks (CNNs). CNNs are…

Ultra high energy neutrinos ($E_\nu > 10^{16.5}$eV$)$ are efficiently measured via radio signals following a neutrino interaction in ice. An antenna placed $\mathcal{O}$(15 m) below the ice surface will measure two signals for the vast…

Recently Convolutional Neural Networks (CNN) have been used to reconstruct hyperspectral information from RGB images. Moreover, this spectral reconstruction problem (SR) can often be solved with good (low) error. However, these methods are…

Electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, normal power system operation, and the management. However, electricity consumption prediction for the mineral company is…

The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be…

The tracks left by charged particles in a gaseous time projection chamber~(TPC) incorporate important information about the interaction process and drift of electrons in gas. The electron diffusion information carried by the tracks is an…

Deep convolution Neural Network (DCNN) has been widely used in computer vision tasks. However, for edge devices even inference has too large computational complexity and data access amount. The inference latency of state-of-the-art models…

Liquid scintillators are commonly used to detect low energy neutrinos from the reactors, sun, and earth. It is a challenge to reconstruct deposited energies for a large liquid scintillator detector. For detectors with multiple optical…

In the present study, the capabilities of a new Convolutional Neural Network (CNN) model are explored with the paramount objective of reconstructing the temperature field of wall-bounded flows based on a limited set of measurement points…

Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models…

The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment with a broad physical program. The main goals of JUNO are the determination of the neutrino mass ordering and high precision investigation of neutrino oscillation…

Deep learning has made significant improvements at many image processing tasks in recent years, such as image classification, object recognition and object detection. Convolutional neural networks (CNN), which is a popular deep learning…

With the improvement of the pattern recognition and feature extraction of Deep Neural Networks (DPNNs), image-based design and optimization have been widely used in multidisciplinary researches. Recently, a Reconstructive Neural Network…

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in…

Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible. To understand this approximate invertibility phenomenon and how to leverage it more effectively, we focus on a theoretical…