Related papers: Optimization of multivariate analysis for IACT ste…
We present studies for optimizing the next generation of ground-based imaging atmospheric Cherenkov telescopes (IACTs). Results focus on mid-sized telescopes (MSTs) for CTA, detecting very high energy gamma rays in the energy range from a…
The reconstruction of broad resonances is important for understanding the dynamics of heavy ion collisions. However, large combinatorial background makes this objective very challenging. In this work an innovative iterative method which…
This work is a methodical study on hybrid reconstruction techniques for hybrid imaging/timing Cherenkov observations. This type of hybrid array is to be realized at the gamma-observatory TAIGA intended for very high energy gamma-ray…
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizing an appropriate background dataset, the method transforms a…
The high energy end of gamma-ray source spectra might provide important clues regarding the nature of the processes involved in gamma-ray emission. Several galactic sources with hard emission spectra extending up to more than 30TeV have…
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…
Imaging Atmospheric Cherenkov Telescopes (IACTs) are ground-based indirect detectors for cosmic gamma rays with energies above tens of GeV. The major backgrounds for gamma-ray observations in IACTs are cosmic-ray charged particles. The…
Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles like…
The MAGIC experiment was upgraded to a two-telescope system in 2009. Unlike other Imaging Air Cherenkov Telescope arrays, MAGIC has operated for five years exclusively in monoscopic mode, and the single telescope analysis was optimized…
We propose a novel hyperspectral (HS) anomaly detection method that is robust to various types of noise. Most existing HS anomaly detection methods are designed without explicit consideration of noise or are based on the assumption of…
Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular,…
The High Energy Stereoscopic System (H.E.S.S.) is a southern hemisphere array of four Imaging Atmospheric Cherenkov Telescopes observing the sky in the very high energy gamma-ray range (E $>$ 100 GeV). VHE observations are an invaluable…
The Earth's atmosphere is an integral part of the detector in ground-based imaging atmospheric Cherenkov telescope (IACT) experiments and has to be taken into account in the calibration. Atmospheric and hardware-related deviations from…
Gamma ray observations from a few hundred MeV up to tens of TeV are a valuable tool for studying particle acceleration and diffusion within our galaxy. Constructing a coherent physical picture of particle accelerators such as supernova…
Over the last decade, the Imaging Air Cerenkov technique has proven itself to be an extremely powerful means to study very energetic gamma-radiation from a number of astrophysical sources in a regime which is not practically accessible to…
Machine learning (ML) has been widely applied in high energy physics to help the physical community in particle classification and data analysis. Here we describe the application of machine learning to solve the problem of classifying…
We study the binary discrimination problem of identification of boosted $H\to gg$ decays from massive QCD jets in a systematic expansion in the strong coupling. Though this decay mode of the Higgs is unlikely to be discovered at the LHC, we…
In this paper we have introduced a novel method for gamma hadron separation in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine Learning. IACTs captures images of Extensive Air Showers (EAS) produced from very high…
A novel solution is presented for the problem of estimating the backgrounds of a signal search using observed data while simultaneously maximizing the sensitivity of the search to the signal. The ``ABCD method'' provides a reliable…
The HEGRA--collaboration is operating a system of imaging atmospheric Cherenkov telescopes to search for sources of TeV-gamma-rays. Air showers are observed in stereoscopic mode with several telescopes simultaneously. To trigger the…