Related papers: Analysis of Fluorescence Telescope Data Using Mach…
Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to…
Machine learning is applied to derive microscopically parameters of the interacting boson model for nuclear spectroscopy. A physics-guided neural network is proposed, which is trained to map the potential energy landscapes that are…
Cherenkov Telescopes (as Magic, Hess and Veritas), while pointing horizontally should reveal also the fluorescence flare tails of nearby down-going air-showers. Such air-showers, born at higher (tens km) altitudes, are growing and extending…
The next generation of ultra high energy cosmic ray experiments will probably detect several dozens of events clustered around the direction towards each of the most powerful extragalactic sources. We develop a method which could make…
The JEM-EUSO observatory on board of the International Space Station (ISS) is a proposed pioneering space mission devoted to the investigation of Ultra High Energy Cosmic Rays (UHECRs). Looking downward at the earth's atmosphere with a…
The Cherenkov Telescope Array is the next generation of observatory using imaging air Cherenkov technique for very-high-energy gamma-ray astronomy. Its first prototype telescope is operational on-site at La Palma and its data acquisitions…
The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2), successfully launched from Wanaka, New Zealand on May 13, 2022, is a precursor for a space-based astroparticle observatory such as the Probe Of Extreme…
In this paper we review the search for astrophysical neutrinos. We begin by summarizing the various theoretical predictions which correlate the expected neutrino flux with data from other messengers, specifically gammas and ultra-high…
In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of…
We consider energy-dispersive X-ray Fluorescence (EDXRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are…
The Penning-trap electronic-detection technique that offers the precision and sensitivity requested in mass spectrometry for fundamental studies in nuclear and particle physics has not been proven yet to be universal. This has motivated the…
Fluorescence spectroscopy and modeling provide powerful means to characterize biomacromolecular structures, dynamics, and interactions. F\"orster resonance energy transfer serves as a key technique for this due to its nanometer-scale…
The Telescope Array (TA) observatory, located in midwest Utah, USA, is designed to detect ultra high energy cosmic rays whose energy is greater than 1 EeV. TA mainly consists of two types of detector. The first type is the atmospheric…
Ground based Cherenkov telescope systems measure astrophysical gamma-ray emission against a background of cosmic-ray induced air showers. The subtraction of this background is a major challenge for the extraction of spectra and morphology…
Multi-messenger astronomy is becoming the key to understanding the Universe from a comprehensive perspective. In most cases, the data and the technology are already in place, therefore it is important to provide an easily-accessible package…
This chapter presents deep neural network based methods for enhancing the sensitivity of X-ray telescopic observations with imaging polarimeters. Deep neural networks can be used to determine photoelectron emission directions, photon…
Few-Shot Learning is the challenge of training a model with only a small amount of data. Many solutions to this problem use meta-learning algorithms, i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger dataset, we…
The determination of the primary energy of extensive air showers using the fluorescence detection technique requires an estimation of the energy carried away by particles that do not deposit all their energy in the atmosphere. This…
Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be…
Ultra-high-energy cosmic rays (UHECRs) are the most energetic particles ever detected. Cosmic rays that achieve the highest energies are rare, and their flux at Earth is extremely low. As a result, next-generation experiments with large…