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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.
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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.
Arrays of imaging atmospheric Cherenkov telescopes (IACT) are superb instruments to probe the very-high-energy gamma-ray sky. This type of telescope focuses the Cherenkov light emitted from air showers, initiated by very-high-energy gamma rays and cosmic rays, onto the camera plane. Then, a fast camera digitizes the longitudinal development of the air shower, recording its spatial, temporal, and calorimetric information. The properties of the primary very-high-energy particle initiating the air shower can then be inferred from those images: the primary particle can be classified as a gamma ray or a cosmic ray and its energy and incoming direction can be estimated. This so-called full-event reconstruction, crucial to the sensitivity of the array to gamma rays, can be assisted by machine learning techniques. We present a deep-learning driven, full-event reconstruction applied to simulated IACT events using CTLearn. CTLearn is a Python package that includes modules for loading and manipulating IACT data and for running deep learning models with TensorFlow, using pixel-wise camera data as input.
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.
Composite mirrors for gamma-ray astronomy have been developed to fulfill the specifications required for the next generation of Cherenkov telescopes represented by CTA (Cherenkov Telescope Array). In addition to the basic requirements on focus and reflection efficiency, the mirrors have to be stiff, lightweight, durable and cost efficient. In this paper, the technology developed to produce such mirrors is described, as well as some tests that have been performed to validate them. It is shown that these mirrors comply with the needs of CTA, making them good candidates for use on a significant part of the array.
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