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Register a new userNeural networks for gamma-hadron separation in MAGIC
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Oriana Mansutti Dr.
Publication date
2005
fields
Physics
and research's language is
English
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Neural networks have proved to be versatile and robust for particle separation in many experiments related to particle astrophysics. We apply these techniques to separate gamma rays from hadrons for the MAGIC Cerenkov Telescope. Two types of neural network architectures have been used for the classi cation task: one is the MultiLayer Perceptron (MLP) based on supervised learning, and the other is the Self-Organising Tree Algorithm (SOTA), which is based on unsupervised learning. We propose a new architecture by combining these two neural networks types to yield better and faster classi cation results for our classi cation problem.
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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.
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.
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.
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.
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 on the order of 100,000 square meter, combined with excellent single photon angular and energy resolutions. The sensitivity of such detectors is limited by statistical fluctuations in the number of Cosmic Ray initiated air showers that resemble gamma-ray air showers in many ways. In this paper, we study the performance of simple event reconstruction methods when applied to simulated data of the Very Energetic Radiation Imaging Telescope Array System (VERITAS) experiment. We review methods for reconstructing the arrival direction and the energy of the primary photons, and examine means to improve on their performance. For a software threshold energy of 300 GeV (100 GeV), the methods achieve point source angular and energy resolutions of sigma[63%]= 0.1 degree (0.2 degree) and sigma[68%]= 15% (22%), respectively. The main emphasis of the paper is the discussion of gamma-hadron separation methods for the VERITAS experiment. We find that the information from several methods can be combined based on a likelihood ratio approach and the resulting algorithm achieves a gamma-hadron suppression with a quality factor that is substantially higher than that achieved with the standard methods used so far.
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