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Register a new userDeep Learning for Energy Estimation and Particle Identification in Gamma-ray Astronomy
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Evgeny Postnikov
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Deep learning techniques, namely convolutional neural networks (CNN), have previously been adapted to select gamma-ray events in the TAIGA experiment, having achieved a good quality of selection as compared with the conventional Hillas approach. Another important task for the TAIGA data analysis was also solved with CNN: gamma-ray energy estimation showed some improvement in comparison with the conventional method based on the Hillas analysis. Furthermore, our software was completely redeveloped for the graphics processing unit (GPU), which led to significantly faster calculations in both of these tasks. All the results have been obtained with the simulated data of TAIGA Monte Carlo software; their experimental confirmation is envisaged for the near future.
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Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.
CTLearn is a new Python package under development that uses the deep learning technique to analyze data from imaging atmospheric Cherenkov telescope (IACT) arrays. IACTs use the Cherenkov light emitted from air showers, initiated by very-high-energy gamma rays, to form an image of the longitudinal development of the air shower on the camera plane. The spatial, temporal, and calorimetric information of the originating high-energy particle is then recorded electronically. The sensitivity of IACTs to astrophysical sources depends strongly on the efficient rejection of the background of much more numerous cosmic-ray showers. CTLearn includes modules for running machine learning models with TensorFlow, using pixel-wise camera data as input. Its high-level interface provides a configuration-file-based workflow to drive reproducible training and prediction. We illustrate the capabilities of CTLearn by presenting some results using IACT simulated data.
Following the discovery of the cosmic rays by Victor Hess in 1912, more than 70 years and numerous technological developments were needed before an unambiguous detection of the first very-high-energy gamma-ray source in 1989 was made. Since this discovery the field on very-high-energy gamma-ray astronomy experienced a true revolution: A second, then a third generation of instruments were built, observing the atmospheric cascades from the ground, either through the atmospheric Cherenkov light they comprise, or via the direct detection of the charged particles they carry. Present arrays, 100 times more sensitive than the pioneering experiments, have detected a large number of astrophysical sources of various types, thus opening a new window on the non-thermal Universe. New, even more sensitive instruments are currently being built; these will allow us to explore further this fascinating domain. In this article we describe the detection techniques, the history of the field and the prospects for the future of ground-based very-high-energy gamma-ray astronomy.
We describe a straightforward modification of frequently invoked methods for the determination of the statistical significance of a gamma-ray signal observed in a counting process. A simple criterion is proposed to decide whether a set of measurements of the numbers of photons registered in the source and background regions is consistent with the assumption of a constant source activity. This method is particularly suitable for immediate evaluation of the stability of the observed gamma-ray signal. It is independent of the exposure estimates, reducing thus the impact of systematic inaccuracies, and properly accounts for the fluctuations in the number of detected photons. The usefulness of the method is demonstrated on several examples. We discuss intensity changes for gamma-ray emitters detected at very high energies by the current gamma-ray telescopes (e.g. 1ES 0229+200, 1ES 1959+650 and PG 1553+113). Some of the measurements are quantified to be exceptional with large statistical significances.
In principle, diffractive optics, particularly Phase Fresnel Lenses (PFLs), offer the ability to construct large, diffraction-limited, and highly efficient X-ray/$gamma$-ray telescopes, leading to dramatic improvement in angular resolution and photon flux sensitivity. As the diffraction limit improves with increasing photon energy, gamma-ray astronomy would offer the best angular resolution over the entire electromagnetic spectrum. A major improvement in source sensitivity would be achieved if meter-size PFLs can be constructed, as the entire area of these optics focuses photons. We have fabricated small, prototype PFLs using Micro-Electro-Mechanical Systems (MEMS) fabrication techniques at the University of Maryland and measured near diffraction-limited performance with high efficiency using 8 keV and higher energy X-rays at the GSFC 600-meter Interferometry Testbed. A first generation, 8 keV PFL has demonstrated imaging corresponding to an angular resolution of approximately 20 milli-arcseconds with an efficiency $sim$70$%$ of the theoretical expectation. The results demonstrate the superior imaging potential in the X-ray/$gamma$-ray energy band for PFL-based optics in a format that is scalable for astronomical instrumentation. Based upon this PFL development, we have also fabricated a `proof-of-principle refractive-diffractive achromat and initial measurements have demonstrated nearly uniform imaging performance over a large energy range. These results indicate that the chromaticity inherent in diffractive optics can be alleviated.
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