The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory with more than 100 telescopes located in the northern and southern hemispheres. It will be the major global instrument for very high energy astronomy over the next decade, offering one order of magnitude better flux sensitivity than current generation ground-based gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing their Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for Convolutional Neural Networks (CNNs) to determine the energy and direction of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA.
Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we will generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy and arrival direction reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to reduce the depth of our neural network to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance.
CTA;gamma ray;machine learning;
|Subcategory||Experimental Methods & Instrumentation|