Related papers: Developing a Machine Learning Algorithm-Based Clas…
Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the…
CTLearn is a new Python package under development that uses the deep learning technique to analyze data from imaging atmospheric Cherenkov telescope (IACT) arrays. IACTs use the Cherenkov light emitted from air showers, initiated by…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
The identification of $\gamma$-rays from the predominant hadronic-background is a key aspect in their ground-based detection using Imaging Atmospheric Cherenkov Telescopes (IACTs). While current methods are limited in their ability to…
Radio Cherenkov radiation is arguably the most efficient mechanism for detecting showers from ultra-high energy particles of 1 PeV and above. Showers occuring in Antarctic ice should be detectable at distances up to 1 km. We report on…
Measurements of the lateral distribution of Cherenkov photons with the wide-angle atmospheric Cherenkov light detector array AIROBICC and of the charged particle lateral distribution with the scintillator matrix of the HEGRA air-shower…
The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative parton showers. Rooted in the Neyman-Pearson lemma, this method is theoretically designed…
The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high-resolution model output, but it…
Correctly identifying the nature and properties of outgoing particles from high energy collisions at the Large Hadron Collider is a crucial task for all aspects of data analysis. Classical calorimeter-based classification techniques rely on…
Context. The response of imaging atmospheric Cherenkov telescopes to incident {\gamma}-ray-initiated showers in the atmosphere changes as the telescopes age due to exposure to light and weather. These aging processes affect the…
Wide-angle, non-imaging air Cherenkov detectors provide a way to observe cosmic gamma-rays which is complementary to observations by imaging Cherenkov telescopes. Their particular strength lies in the multi-TeV to ultra high energy range (E…
The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not…
We present a new method for the reconstruction of the longitudinal profile of extensive air showers induced by ultra-high energy cosmic rays. In contrast to the typically considered shower size profile, this method employs directly the…
The optimization of the observation schedule of gamma-ray emitters by the new generation of Cherenkov Telescopes to extract cosmological parameters from the measurement of the Gamma Ray Horizon at different redshifts is discussed. It is…
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…
We present a macroscopic calculation of coherent electro-magnetic radiation from air showers initiated by ultra-high energy cosmic rays, based on currents obtained from three-dimensional Monte Carlo simulations of air showers in a realistic…
We present a deep learning, computer vision algorithm constructed for the purposes of identifying and classifying charged particles in camera image sensors. We apply our algorithm to data collected by the Distributed Electronic Cosmic-ray…
The system of light electroweakinos and heavy squarks gives rise to one of the most challenging signatures to detect at the LHC. It consists of missing transverse energy recoiled against a few hadronic jets originating either from QCD…
Surface radio antenna-based measurements of cosmic-ray air showers present significant computational challenges in accurately reconstructing physics observables, in particular, the depth of shower maximum, X$_{max}$. State-of-the-art…
The stereoscopic reconstruction of air showers viewed by multiple imaging atmospheric Cherenkov telescopes (IACTs) allows a more precise reconstruction of shower energies and hence an improved determination of energy spectra. Reconstruction…