Performance of the Cherenkov Telescope Array

Maier, Gernot; Arrabito, L.; Bregeon, J.; Cumani, P.; Hassan, Tarek; Moralejo, A.; Bernlöhr, K.

doi:10.22323/1.301.0846

Cited by 22 publications

(26 citation statements)

References 9 publications

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“…This work uses the latest CTA instrument response functions (IRFs) to compute PeVatron search sensitivities. The high-energy bound of CTA IRFs is 200 TeV, due to Monte Carlo simulation limitations [6]. The study of the impact of the limitation of the IRFs on the results is on-going.…”

Section: The Methodsmentioning

confidence: 99%

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Searching for PeVatrons in the CTA Galactic Plane Survey

Trichard¹

2017

Proceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017)

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The Cherenkov Telescope Array (CTA) will perform a survey of the whole Galactic disk with unprecedented sensitivity at energies up to 300 TeV. One of the key science projects of the CTA consortium is the discovery of Galactic PeVatrons (cosmic ray accelerators to PeV energies). The determination of efficient criteria to identify PeVatron candidates during the Galactic plane survey observations is essential in order to trigger deeper observations. This contribution presents a method which relies on the broadband spectrum of the source to investigate high energy spectral features. The application of this method to specific sources will also be presented.

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Section: The Methodsmentioning

confidence: 99%

“…These telescopes have a large field of view (∼8 • ) and are widely spaced to provide a large array area. This sub-array is therefore the major contributor to the sensitivity of the instrument above few TeV, and will provide measurements up to 300 TeV [6].…”

Section: Ctamentioning

confidence: 99%

Searching for PeVatrons in the CTA Galactic Plane Survey

Trichard¹

2017

Proceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017)

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“…The energy resolution is defined as the one standard deviation of a Gaussian function fit of the distribution of the difference between true and reconstructed energy divided by true energy for a given energy range. The expected energy resolution performance of CTA [23] based on combination of Hillas parametrization and multivariate classification methods is slightly better with about an energy resolution of 9% at 300 GeV. Table 1 compares the energy resolution of the baseline algorithm with the results of this work for three distinct energy bins.…”

Section: Convolutional Neural Network For Simulated Cherenkov Telescmentioning

confidence: 96%

“…A Monte Carlo simulation has been used [22,23] to produce a large artificial data set 1 and to examine the performance of different CNN architectures. As presented in [22] the Monte Carlo generated gamma-ray data has been verified against real gamma-ray data from the existing Cherenkov telescopes.…”

Section: Monte Carlo Simulation and Preselectionmentioning

confidence: 99%

Extracting Gamma-Ray Information from Images with Convolutional Neural Network Methods on Simulated Cherenkov Telescope Array Data

Mangano

Delgado

Bernardos

et al. 2018

Lecture Notes in Computer Science

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The Cherenkov Telescope Array (CTA) will be the world's leading ground-based gamma-ray observatory allowing us to study very high energy phenomena in the Universe. CTA will produce huge data sets, of the order of petabytes, and the challenge is to find better alternative data analysis methods to the already existing ones. Machine learning algorithms, like deep learning techniques, give encouraging results in this direction. In particular, convolutional neural network methods on images have proven to be effective in pattern recognition and produce data representations which can achieve satisfactory predictions. We test the use of convolutional neural networks to discriminate signal from background images with high rejections factors and to provide reconstruction parameters from gamma-ray events. The networks are trained and evaluated on artificial data sets of images. The results show that neural networks trained with simulated data can be useful to extract gamma-ray information. Such networks would help us to make the best use of large quantities of real data coming in the next decades.

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“…The multimessenger approach is the next frontier for DM searches. Signatures of DM annihilation or decay events in astrophysical sources may be observed in cosmic-ray fluxes by Cerenkov telescopes such as VERITAS [36], HESS [37], MAGIC [38], HAWC [39] and CTA [40]; neutrino telescopes such as ANTARES [41] or IceCube [42]; satellites such as PAMELA [43], AMS [44] and Fermi [45] or ballon experiments like CAPRICE [46] or BESS [47]( for a general overview see e.g. [48]).…”

Section: Multimessenger Approach To Indirect Searches Of Dmmentioning

confidence: 99%

Multimessenger Multi-TeV Dark Matter

Gammaldi

2019

Front. Astron. Space Sci.

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We briefly review the general insight into the indirect searches of dark matter. We discuss the primary equation in a three-level multimessenger approach (gamma rays, neutrinos and antiprotons), and we introduce the reader to the main topics and related uncertainties (e.g. dark matter density distribution, cosmic rays, particle physics). As an application of the general concept, we focus on the multi-TeV dark matter candidate among other weak interactive massive particles. We present the state-of-the-art on this sub-field, and we discuss open questions and experimental limitations.

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Performance of the Cherenkov Telescope Array

Cited by 22 publications

References 9 publications

Searching for PeVatrons in the CTA Galactic Plane Survey

Searching for PeVatrons in the CTA Galactic Plane Survey

Extracting Gamma-Ray Information from Images with Convolutional Neural Network Methods on Simulated Cherenkov Telescope Array Data

Multimessenger Multi-TeV Dark Matter

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