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Cherenkov light from cosmic-ray muons is a significant source of background for the Imaging Atmospheric Cherenkov Technique. However, muon events are also valuable as a diagnostic tool because they produce distinctive ring images, and the…

Astrophysics · Physics 2019-08-14 T. B. Humensky

We describe a process for cross-calibrating the effective areas of X-ray telescopes that observe common targets. The targets are not assumed to be "standard candles" in the classic sense, in that we assume that the source fluxes have…

Instrumentation and Methods for Astrophysics · Physics 2021-12-01 Herman L. Marshall , Yang Chen , Jeremy J. Drake , Matteo Guainazzi , Vinay L. Kashyap , Xiao-Li Meng , Paul P. Plucinsky , Peter Ratzlaff , David A. van Dyk , Xufei Wang

The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand…

Computational Physics · Physics 2020-11-25 Michele Invernizzi , Pablo Miguel Piaggi , Michele Parrinello

In the absence of a standard source of gamma rays or hadrons of known energy one has to study the details of production of \v Cerenkov light at the observation level only through detailed simulation studies. Recently such studies have…

Astrophysics · Physics 2007-05-23 P. N. Bhat

Context. The increase in sensitivity of Imaging Atmospheric Cherenkov Telescopes (IACTs) has lead to numerous detections of extended $\gamma$-ray sources at TeV energies, sometimes of sizes comparable to the instrument's field of view…

Instrumentation and Methods for Astrophysics · Physics 2018-11-07 Ie. Vovk , M. Strzys , C. Fruck

General characterization of physical measurements is discussed within the framework of a classical information theory. Uncertainty relation for simultaneous measurements of two physical observables is defined in this framework for…

Quantum Physics · Physics 2012-12-18 Yoshimasa Kurihara

The brisk progression of the industrial digital innovation, leading to high degree of automation and big data transfer in manufacturing technologies, demands continuous development of appropriate off-line metrology methods to support…

Methodology · Statistics 2021-07-29 Danilo Quagliotti

Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the underlying physical processes. Operational ensemble forecasts aim to represent these…

Machine Learning · Computer Science 2026-05-26 Birgit Kühbacher , Daan Crommelin , Niki Kilbertus

The sensitivity of an Imaging Atmospheric Cherenkov telescope is calibrated by shining, from a distant pulsed monochromatic light source, a defined photon flux onto the mirror. The light pulse is captured and reconstructed by the telescope…

Astrophysics · Physics 2009-10-30 A. Frass , C. Koehler , G. Hermann , M. Hess , W. Hofmann

Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and climate modelling, and approaches are often developed separately for different timescales. Here, we introduce a unified framework…

Atmospheric and Oceanic Physics · Physics 2025-12-01 Laura A. Mansfield , Hannah M. Christensen

New Vapnik and Chervonenkis type concentration inequalities are derived for the empirical distribution of an independent random sample. Focus is on the maximal deviation over classes of Borel sets within a low probability region. The…

Statistics Theory · Mathematics 2022-04-26 Stéphane Lhaut , Anne Sabourin , Johan Segers

In his constructive and well-informed commentary, Andrei Khrennikov acknowledges a privileged status of classical probability theory with respect to statistical analysis. He also sees advantages offered by the Contextuality-by-Default…

Quantum Physics · Physics 2018-12-14 Ehtibar N. Dzhafarov , Maria Kon

Uncertainties in successive measurements of general canonically conjugate variables are examined. Such operators are approached within a limiting procedure of the Pegg-Barnett type. Dealing with unbounded observables, we should take into…

Quantum Physics · Physics 2017-01-02 Alexey E. Rastegin

As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from being an ill-posed optimization problem). A robust…

Methodology · Statistics 2023-12-20 Soumya Chakraborty , Ayanendranath Basu , Abhik Ghosh

Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has been paid to exploring the uncertainty of these generated…

Machine Learning · Computer Science 2021-07-22 Eoin Delaney , Derek Greene , Mark T. Keane

For studying variable sources like blazars, it is crucial to achieve unbiased monitoring, either with dedicated telescopes in pointing mode or survey instruments. At TeV energies, the High Altitude Water Cherenkov (HAWC) observatory…

Machine learning-based Deepfake detection models have achieved impressive results on benchmark datasets, yet their performance often deteriorates significantly when evaluated on out-of-distribution data. In this work, we investigate an…

Computer Vision and Pattern Recognition · Computer Science 2025-07-09 Haroon Wahab , Hassan Ugail , Lujain Jaleel

Determination of the energy and flux of the gamma photons by Imaging Atmospheric Cherenkov Technique is strongly dependent on optical properties of the atmosphere. Therefore, atmospheric monitoring during the future observations of the…

High Energy Astrophysical Phenomena · Physics 2018-01-22 Jakub Juryšek , Michael Prouza

We present a sophisticated gamma-ray likelihood reconstruction technique for Imaging Atmospheric Cerenkov Telescopes. The technique is based on the comparison of the raw Cherenkov camera pixel images of a photon induced atmospheric particle…

Instrumentation and Methods for Astrophysics · Physics 2015-03-13 Mathieu de Naurois , Loic Rolland
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