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Register a new userApplication of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data
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The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 - 10 x better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced 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 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 generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance.
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The Cherenkov Telescope Array (CTA) will be the worlds 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.
Very High Energy gamma-ray astronomy with the Cherenkov Telescope Array (CTA) is evolving towards the model of a public observatory. Handling, processing and archiving the large amount of data generated by the CTA instruments and delivering scientific products are some of the challenges in designing the CTA Data Management. The participation of scientists from within CTA Consortium and from the greater worldwide scientific community necessitates a sophisticated scientific analysis system capable of providing unified and efficient user access to data, software and computing resources. Data Management is designed to respond to three main issues: (i) the treatment and flow of data from remote telescopes; (ii) big-data archiving and processing; (iii) and open data access. In this communication the overall technical design of the CTA Data Management, current major developments and prototypes are presented.
The Cherenkov Telescope Array (CTA) observatory will be one of the largest ground-based very high-energy gamma-ray observatories. The On-Site Analysis will be the first CTA scientific analysis of data acquired from the array of telescopes, in both northern and southern sites. The On-Site Analysis will have two pipelines: the Level-A pipeline (also known as Real-Time Analysis, RTA) and the level-B one. The RTA performs data quality monitoring and must be able to issue automated alerts on variable and transient astrophysical sources within 30 seconds from the last acquired Cherenkov event that contributes to the alert, with a sensitivity not worse than the one achieved by the final pipeline by more than a factor of 3. The Level-B Analysis has a better sensitivity (not be worse than the final one by a factor of 2) and the results should be available within 10 hours from the acquisition of the data: for this reason this analysis could be performed at the end of an observation or next morning. The latency (in particular for the RTA) and the sensitivity requirements are challenging because of the large data rate, a few GByte/s. The remote connection to the CTA candidate site with a rather limited network bandwidth makes the issue of the exported data size extremely critical and prevents any kind of processing in real-time of the data outside the site of the telescopes. For these reasons the analysis will be performed on-site with infrastructures co-located with the telescopes, with limited electrical power availability and with a reduced possibility of human intervention. This means, for example, that the on-site hardware infrastructure should have low-power consumption. A substantial effort towards the optimization of high-throughput computing service is envisioned to provide hardware and software solutions with high-throughput, low-power consumption at a low-cost.
The Cherenkov Telescope Array (CTA) Observatory must be capable of issuing fast alerts on variable and transient sources to maximize the scientific return. This will be accomplished by means of a Real-Time Analysis (RTA) pipeline, a key system of the CTA observatory. The latency and sensitivity requirements of the alarm system impose a challenge because of the large foreseen data flow rate, between 0.5 and 8 GB/s. As a consequence, substantial efforts toward the optimization of this high-throughput computing service are envisaged, with the additional constraint that the RTA should be performed on-site (as part of the auxiliary infrastructure of the telescopes). In this work, the functional design of the RTA pipeline is presented.
The Cherenkov Telescope Array (CTA), the new generation very high-energy gamma-ray observatory, will improve the flux sensitivity of the current Cherenkov telescopes by an order of magnitude over a continuous range from about 10 GeV to above 100 TeV. With tens of telescopes distributed in the Northern and Southern hemispheres, the large effective area and field of view coupled with the fast pointing capability make CTA a crucial instrument for the detection and understanding of the physics of transient, short-timescale variability phenomena (e.g. Gamma-Ray Bursts, Active Galactic Nuclei, gamma-ray binaries, serendipitous sources). The key CTA system for the fast identification of flaring events is the Real-Time Analysis (RTA) pipeline, a science alert system that will automatically detect and generate science alerts with a maximum latency of 30 seconds with respect to the triggering event collection and ensure fast communication to/from the astrophysics community. According to the CTA design requirements, the RTA search for a true transient event should be performed on multiple time scales (from minutes to hours) with a sensitivity not worse than three times the nominal CTA sensitivity. Given the CTA requirement constraints on the RTA efficiency and the fast response ability demanded by the transient science, we perform a preliminary evaluation of the RTA sensitivity as a function of the CTA high-level technical performance (e.g. effective area, point spread function) and the observing time. This preliminary approach allows the exploration of the complex parameter space defined by the scientific and technological requirements, with the aim of defining the feasibility range of the input parameters and the minimum background rejection capability of the RTA pipeline.
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