Related papers: Deep learning techniques for energy clustering in …
A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from…
A Shashlyk-type electromagnetic calorimeter (ECal) will be used in the Multi-purpose Detector at Nuclotron-based Ion Collider facility to study the properties of nuclear matter. In this experiment, the ECal detector is responsible for…
Clouds play a major role in Earth's energy budget and hydrological cycle. Clouds dynamical structure and mixing with the ambient air have a large impact on their vertical mass and energy fluxes and on precipitation. Most of the cloud…
A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015-2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25…
Accurate particle shower simulation remains a critical computational bottleneck for high-energy physics. Traditional Monte Carlo methods, such as Geant4, are computationally prohibitive, while existing machine learning surrogates are tied…
The Particle Flow Algorithms adopted for future $e^{+}e^{-}$ colliders detectors and phase-II CMS upgrade require very high granularity calorimeters to deconvolve the individual contributions of particles in jets. This is especially true…
Energy being the very key concern area with sensor networks, so the main focus lies in developing a mechanism to increase the lifetime of a sensor network by energy balancing. To achieve energy balancing and maximizing network lifetime we…
The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup and electronic noise make the inference of energy from…
Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we…
We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's…
When electrons with energies of $O(100)$ MeV pass through a liquid argon time projection chamber (LArTPC), they deposit energy in the form of electromagnetic showers. Methods to reconstruct the energy of these showers in LArTPCs often rely…
Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering…
Photovoltaics (PV) are widely used to harvest solar energy, an important form of renewable energy. Photovoltaic arrays consist of multiple solar panels constructed from solar cells. Solar cells in the field are vulnerable to various…
Electrodynamical coupled cluster (CC) methodologies have been formulated employing standard QED Hamiltonian that is written in Coulomb gauge while using the DF and the MCDF pictures of the matter field for closed-shell and open-shell cases…
Low-loss electron energy loss spectroscopy (EELS) has emerged as a technique of choice for exploring the localization of plasmonic phenomena at the nanometer level, necessitating analysis of physical behaviors from 3D spectral data sets.…
Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn the final clustering by only utilizing a single layer of…
A particle flow event-reconstruction algorithm has been successfully deployed in the CMS experiment and is nowadays used by most of the analyses. It aims at identifying and reconstructing individually each particle arising from the LHC…
Traditional methods for enhancing tropical cyclone (TC) intensity from climate model outputs or projections have primarily relied on either dynamical or statistical downscaling. With recent advances in deep learning (DL) techniques, a…
In the particle-flow approach information from all available sub-detector systems is combined to reconstruct all stable particles. The global event reconstruction has been shown to improve, in particular, the resolution of jet energy and…
Deep optical images are often crowded with overlapping objects. This is especially true in the cores of galaxy clusters, where images of dozens of galaxies may lie atop one another. Accurate measurements of cluster properties require…