Related papers: Developing a Machine Learning Algorithm-Based Clas…

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not…

A cross-benchmark has been done on three critical aspects, data imputing, feature selection and regression algorithms, for machine learning based chemical vapor deposition (CVD) virtual metrology (VM). The result reveals that linear feature…

Ground-based arrays of imaging atmospheric Cherenkov telescopes have emerged as the most sensitive gamma-ray detectors in the energy range of about 100 GeV and above. The strengths of these arrays are a very large effective collection area…

The simulation of Cherenkov photon's lateral density and arrival time distributions in Extensive Air Showers (EASs) was performed with the CORSIKA code in the energy range: 100 GeV to 100 TeV. On the basis of this simulation we obtained a…

The details of Cerenkov light produced by a gamma ray or a cosmic ray incident at the top of the atmosphere is best studied through systematic simulations of the extensive air showers. Recently such studies have become all the more…

Using air showers induced by TeV gamma-rays from Mrk 501, the radial distribution of Cherenkov light is investigated. The shower geometry is reconstructed from the stereoscopic shower images obtained with the telescopes of the HEGRA IACT…

Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of…

Gravitational microlensing of gamma-ray bursts (GRBs) provides a unique opportunity to probe compact dark matter and small-scale structures in the Universe. However, identifying such microlensed GRBs within large data sets is a significant…

Atmospheric fluorescence is usually neglected in the reconstruction of the signals registered by Cherenkov telescopes, both IACTs and wide-angle detector arrays. In this paper we quantify the fluorescence contribution to the total light…

With the ongoing increase in the worldwide population and escalating consumption habits,there's a surge in the amount of waste produced.The situation poses considerable challenges for waste management and the optimization of recycling…

We present a method to reconstruct the longitudinal profile of electrons in showers using Cherenkov telescopes. We show how the Cherenkov light collected by an array of telescopes can be transformed into the number of electrons as a…

The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy. Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data. In this paper,…

The CoMET R&D project focuses on the development of a new technique for the observation of very high-energy (VHE) $\gamma$-rays from the ground at energies above ~200 GeV, thus covering emission from soft-spectrum sources. The CoMET array…

The trajectory of a primary gamma-ray detected with an array of at least four atmospheric Cherenkov imaging telescopes can be reconstructed from the shower image centroid positions and geometrical considerations independent of the primary…

Deep neural networks are a powerful technique that have found ample applications in several branches of Physics. In this work, we apply machine learning algorithms to a specific problem of Cosmic Ray Physics: the estimation of the muon…

The study of Ultra High Energy Cosmic Rays represents one of the most challenging topic in the Cosmic Rays and in the Astroparticle Physics fields. The interaction of primary particles with atmospheric nuclei produces a huge Extensive Air…

A toy detector array is designed to detect a shower generated by the interaction between a TeV cosmic ray and the atmosphere. In the present paper, the primary energies of showers detected by the detector array are reconstructed with the…

Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. The Support Vector Machine learning algorithm is a…

Particles produced in high energy collisions that are charged under one of the fundamental forces will radiate proportionally to their charge, such as photon radiation from electrons in quantum electrodynamics. At sufficiently high…

Halide perovskites exhibit unpredictable properties in response to environmental stressors, due to several composition-dependent degradation mechanisms. In this work, we apply data visualization and machine learning (ML) techniques to…