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Infrared-blocking scattering aerogel filters have a broad range of potential applications in astrophysics and planetary science observations in the far-infrared, sub-millimeter, and microwave regimes. Successful dielectric modeling of…
Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information…
Imaging atmospheric Cherenkov telescopes (IACTs) detect gamma rays by measuring the Cherenkov light emitted by secondary particles in the air shower when the gamma rays hit the atmosphere. At low energies, the limited amount of Cherenkov…
Computer vision based fine-grained recognition has received great attention in recent years. Existing works focus on discriminative part localization and feature learning. In this paper, to improve the performance of fine-grained…
Hyper-Kamiokande (Hyper-K) is a proposed next generation underground water Cherenkov (WCh) experiment. The far detector will measure the oscillated neutrino flux from the long-baseline neutrino experiment using 0.6 GeV neutrinos produced by…
AMS-02 is a detector currently in operation onboard the International Space Station (ISS). One of the main scientific goals of the spectrometer is the measurement of charged particle fluxes. The detector design makes possible the…
The use of a nonhomogeneous aerogel radiator, i.e. one consisting of layers with different refractive indices, has been shown to improve the resolution of the Cherenkov angle measured with a proximity focusing RICH detector. In order to…
Water Cherenkov detectors offer a robust and economical solution for real-time radiation monitoring by detecting Cherenkov light from charged particles moving faster than light in water. This work presents a novel two-stage classification…
Cherenkov gamma telescope observes high energy gamma rays, taking advantage of the radiation emitted by charged particles produced inside the electromagnetic showers initiated by the gammas, and developing in the atmosphere. The detector…
When dense granular gases are continuously excited under microgravity conditions, spatial inhomogeneities of the particle number density can emerge. A significant share of particles may collect in strongly overpopulated regions, called…
In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known. Canonical approaches to this problem include…
A detailed case study of $\gamma$-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial…
We present initial results on nuclear recoil detection based on the fluorescence of color centers created by nuclear recoils in lithium fluoride. We use gamma rays, fast and thermal neutrons, and study the difference in responses they…
We demonstrate how one can use machine learning techniques to bypass the technical difficulties of designing an experiment and translating its outcomes into concrete claims about fundamental features of quantum fields. In practice, all…
Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles. The effectiveness of these…
Isotopic composition measurements of singly charged cosmic rays (CR) provide essential insights into CR transport in the Galaxy. The Alpha Magnetic Spectrometer (AMS-02) can identify singly charged isotopes up to about 10 GeV/n. However,…
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…
Cosmic rays interacting with the atmosphere allow for the probing of fundamental interactions at ultra-high energies. We thus obtain limits on strongly coupled new physics models via their imprints on cosmic-ray air showers. Using the Monte…
Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered…
The study explores machine learning methods for revealing chemical sensitivity in Helium spin-echo spectroscopy, in order to obtain ultra-sensitive surface analytic technique. We model bi-species co-adsorbed systems and demonstrate that by…