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Register a new userGamma Hadron Separation using Pairwise Compactness Method with HAWC
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Added by
Zigfried Hampel-Arias
Publication date
2015
fields
Physics
and research's language is
English
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The High-Altitude Water Cherenkov (HAWC) Observatory is a ground based air-shower array deployed on the slopes of Volcan Sierra Negra in the state of Puebla, Mexico. While HAWC is optimized for the detection of gamma-ray induced air-showers, the background flux of hadronic cosmic-rays is four orders of magnitude greater, making background rejection paramount for gamma-ray observations. On average, gamma-ray and cosmic-ray showers are characterized by different topologies at ground level. We will present a method to identify the primary particle type in an air-shower that uses the spatial relationship of triggered PMTs (or hits) in the detector. For a given event hit-pattern on the HAWC array, we calculate the mean separation distance of the hits for a subset of hit pairs weighted by their charges. By comparing the mean charge and mean separating distance for the selected hits, we infer the identity of the events primary. We will report on the efficiency for identifying gamma-rays and the performance of the technique with simulation.
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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 showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.
In recent years, Imaging Atmospheric Cherenkov Telescopes (IACTs) have discovered a rich diversity of very high energy (VHE, > 100 GeV) gamma-ray emitters in the sky. These instruments image Cherenkov light emitted by gamma-ray induced particle cascades in the atmosphere. Background from the much more numerous cosmic-ray cascades is efficiently reduced by considering the shape of the shower images, and the capability to reduce this background is one of the key aspects that determine the sensitivity of a IACT. In this work we apply a tree classification method to data from the High Energy Stereoscopic System (H.E.S.S.). We show the stability of the method and its capabilities to yield an improved background reduction compared to the H.E.S.S. Standard Analysis.
In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join.
A new approach to Gamma/Hadron separation algorithms is proposed. The differences between Gamma and Hadron showers are notorious in two main aspects. The first is the wideness of the shower, and the second is the distribution of the angles of emission of Cherenkov photons in respect to the shower main axis. Using more than one IAC telescope, and their respective bi-dimensional images of arrival directions of the Cherenkov photons, the 3D geometrical characteristics of the shower can be reconstructed.
The High-Altitude Water Cherenkov Gamma Ray Observatory (HAWC) is under construction 4100 meters above sea level at Sierra Negra, Mexico. We describe the design and cabling of the detector, the characterization of the photomultipliers, and the timing calibration system. We also outline a next-generation detector based on the water Cherenkov technique.
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