Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA

The Cherenkov Telescope Array (CTA) will consist of two arrays of Imaging Atmospheric Cherenkov Telescopes (IACTs) at the northern and southern hemispheres. CTA will feature IACTs with mirrors of three different sizes optimized to cover different energy ranges. The pro- posed sub-arrays of four Large Size Telescopes (LST) at CTA-North and CTA-South target the lowest energy range between around 20 GeV and 100 GeV. Thanks to their low weight of around 110 tons the LSTs can move by 180 deg in azimuth in 20 seconds for Gamma Ray Burst (GRB) follow-up. An LST has a tessellated parabolic mirror of 23 m diameter equipped with a system of actuators to correct for gravity-induced deformations during data taking. Its low-weight 2 ton camera at the prime focus has a 4.5 deg diameter, 1855 high QE PMTs and an embedded readout with 1 GSps sampling speed designed for data acquisition rates exceeding 10 kHz. A fully equipped LST has been installed at the CTA-North site in 2018 and is expected to be finished commissioning during 2019. The remaining three LSTs in the north will be installed by 2022. We will review the status of the LSTs, describe the installation of the first LST and report on the first results of the commissioning tests.

Ground-based observatories allow for the detection of the most energetic forms of cosmic radiation in a multi-messenger context, including cosmic rays with energies above 10 15 eV and gamma rays with energies above 10 10 eV. Current experiments, such as the Pierre Auger Observatory, and forthcoming ones, such as the Cherenkov Telescope Array (CTA), have to deal with interpreting extensive air showers to reconstruct the fluxes of particles arriving at Earth. In this scenario, the general purpose of this thesis is to contribute to the improvement of the techniques used to reconstruct the properties of the primary particle in measurements of extensive air showers. In particular, four individual studies based on the simulation of air showers are presented. First, a study of the fluctuations in the atmospheric depth at which a shower reaches its maximum (X max ) is performed. The best functional form to describe the simulated X max distributions is selected and, along with two alternative functions, parametrized. This study aims to improve the reconstruction of the relative abundances of primary masses in the ultra-high-energy regime (E 0 > 10 17 eV) and to provide a practical tool for any other studies depending on X max distributions. Second, the angular distribution of Cherenkov light emitted from extensive air showers is parametrized using simulations performed in a broad energy range: from 100 GeV to 1 EeV. For this, a functional form to describe the angular distribution of Cherenkov photons is built using analytical arguments, and its parameters are constrained to describe the simulated showers. The resulting parametrization can be used to reconstruct the properties of air showers both through fluorescence detectors and Imaging Atmospheric Cherenkov Telescopes (IACT). Third, a technique to measure X max through IACTs is developed. The proposed technique is shown to provide a resolution comparable to that of fluorescence detectors, allowing for mass composition studies around the knee in the cosmic-ray spectrum. Fourth, the problem of modeling hadronic interactions is visited in the context of the CTA observatory. An analysis based on simulations is performed to determine the extent to which image parameters reconstructed by IACTs are influenced by uncertainties in hadronic interaction models. Then, a computation of the uncertainty on the estimated CTA sensitivity curve due to hadronic interaction models is performed.

We provide an updated assessment of the power of the Cherenkov Telescope Array (CTA) to search for thermally produced dark matter at the TeV scale, via the associated gamma-ray signal from pair-annihilating dark matter particles in the region around the Galactic centre. We find that CTA will open a new window of discovery potential, significantly extending the range of robustly testable models given a standard cuspy profile of the dark matter density distribution. Importantly, even for a cored profile, the projected sensitivity of CTA will be sufficient to probe various well-motivated models of thermally produced dark matter at the TeV scale. This is due to CTA's unprecedented sensitivity, angular and energy resolutions, and the planned observational strategy. The survey of the inner Galaxy will cover a much larger region than corresponding previous observational campaigns with imaging atmospheric Cherenkov telescopes. CTA will map with unprecedented precision the large-scale diffuse emission in high-energy gamma rays, constituting a background for dark matter searches for which we adopt state-of-the-art models based on current data. Throughout our analysis, we use up-to-date event reconstruction Monte Carlo tools developed by the CTA consortium, and pay special attention to quantifying the level of instrumental systematic uncertainties, as well as background template systematic errors, required to probe thermally produced dark matter at these energies.

Riccardo Rando∗ab, Daniele Cortia, Francesco Dazzic, Alessandro De Angelisd, Antonios Dettlaffc, Daniela Dornere, David Finkc, Nadia Fouque f , Felix Grundnerc, Werner Habererc, Alexander Hahnc, Richard Hermel f , Samo Korparg, Gasper Kukec Mezekh, Ronald Maierc, Christian Maneaa, Mose Mariottiab, Daniel Mazinci, Fatima Mehrez f , Razmik Mirzoyanc, Sergey Podkladkinc, Ignasi Reichardta, Wolfgang Rhode j, Sylvie Rosier f , Cornelia Schultzab, Carlo Stellak, Masahiro Teshimaci, Holger Wetteskindc and Marko Zavrtanikgh for the CTA Consortium† E-mail: riccardo.rando@pd.infn.it a INFN Section of Padova, Italy; b University of Padova, Italy; c Max Planck Institute for Physics, Munich, Germany; d LIP/IST Lisboa, Portugal; e University of Wurzburg, Germany; f LAPP, CNRS-IN2P3, Annecy, France; g Jožef Stefan Institute, Ljubljana, Slovenia; h University of Nova Gorica, Slovenia; i University of Tokyo and ICRR, Tokyo, Japan; j Technische Universitat, Dortmund, Germany; k University of Udine and INFN, Italy. The Cherenkov Telescope Array (CTA) is the the next generation facility of imaging atmospheric Cherenkov telescopes; two sites will cover both hemispheres. CTA will reach unprecedented sensitivity, energy and angular resolution in very-high-energy gamma-ray astronomy. Each CTA array will include four Large Size Telescopes (LSTs), designed to cover the low-energy range of the CTA sensitivity (∼20 GeV to 200 GeV). In the baseline LST design, the focal-plane camera will be instrumented with 265 photodetector clusters; each will include seven photomultiplier tubes (PMTs), with an entrance window of 1.5 inches in diameter. The PMT design is based on mature and reliable technology. Recently, silicon photomultipliers (SiPMs) are emerging as a competitor. Currently, SiPMs have advantages (e.g. lower operating voltage and tolerance to high illumination levels) and disadvantages (e.g. higher capacitance and cross talk rates), but this technology is still young and rapidly evolving. SiPM technology has a strong potential to become superior to the PMT one in terms of photon detection efficiency and price per square mm of detector area. While the advantage of SiPMs has been proven for high-density, small size cameras, it is yet to be demonstrated for large area cameras such as the one of the LST. We are working to develop a SiPM-based module for the LST camera, in view of a possible camera upgrade. We will describe the solutions we are exploring in order to balance a competitive performance with a minimal impact on the overall LST camera design. The 34th International Cosmic Ray Conference, 30 July6 August, 2015 The Hague, The Netherlands

The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy. Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data. In this paper, we present for the first time the development of a full-event reconstruction based on deep convolutional neural networks and its application to real data. We show that it outperforms the standard analysis, both on simulated and on real data, thus validating the deep approach for the CTA data analysis. This work also illustrates the difficulty of moving from simulated data to actual data.

In recent years, a new generation of optical intensity interferometers has emerged, leveraging the existing infrastructure of Imaging Atmospheric Cherenkov Telescopes (IACTs). The MAGIC telescopes host the MAGIC-SII system (Stellar Intensity Interferometer), implemented to investigate the feasibility and potential of this technique on IACTs. After the first successful measurements in 2019, the system was upgraded and now features a real-time, dead-time-free, 4-channel, GPU-based correlator. These hardware modifications allow seamless transitions between MAGIC’s standard very-high-energy gamma-ray observations and optical interferometry measurements within seconds. We establish the feasibility and potential of employing IACTs as competitive optical Intensity Interferometers with minimal hardware adjustments. The measurement of a total of 22 stellar diameters are reported, 9 corresponding to reference stars with previous comparable measurements, and 13 with no prior measurements. A prospective implementation involving telescopes from the forthcoming Cherenkov Telescope Array Observatory’s Northern hemisphere array, such as the first prototype of its Large-Sized Telescopes, LST-1, is technically viable. This integration would significantly enhance the sensitivity of the current system and broaden the UV-plane coverage. This advancement would enable the system to achieve competitive sensitivity with the current generation of long-baseline optical interferometers over blue wavelengths.

The Cherenkov Telescope Array (CTA) observatory will probe the non-thermal universe above 20 GeV up to several hundreds of TeV with a significant improvement in sensitivity and angular resolution compared to current experiments. Its outstanding capabilities will allow to increase the number of extragalactic cosmic accelerators detected at very high energy (VHE) and therefore to better constrain the population of VHE accelerators and the gamma-ray absorption processes in the intergalactic medium. For the first time in the history of imaging atmospheric Cherenkov telescopes (IACTs), CTA will be an open observatory and high-level data will be made available to the astronomical community. Gammapy is an open-source Python package developed by the Cherenkov telescope community that provides tools to simulate the gamma-ray sky and analyse IACT data. The versatile architecture of, and steady user contributions to Gammapy enable a large variety of high-level data analyses. Examples of Gaammapy applications are presented, particularly in the context of extragalactic science with CTA.

Night Sky Background (NSB) is a complex phenomenon, consisting of all light detected by Imaging Atmospheric Cherenkov Telescopes (IACTs) not attributable to Cherenkov light emission. Understanding the effect of NSB on cameras for the next-generation Cherenkov Telescope Array (CTA) is important, as it affects the systematic errors on observations, the energy threshold, the thermal control of the cameras and the ability of the telescopes to operate under partial moonlight conditions. This capacity to observe under partial moonlight conditions is crucial for the CTA transient science programme, as it substantially increases the potential observing time. Using tools initially developed for H.E.S.S. (in combination with the prototype CTA analysis package ctapipe) we will present predictions for the NSB present in images taken by the CTA Small Sized Telescope Camera (SSTCAM), showing that SSTCAM will likely be able to meet the associated CTA requirements. Additionally, we calculate the potential observing time gain by operating under high NSB conditions.

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.

The Cherenkov Telescope Array (CTA) is the next generation ground-based observatory for very high-energy (E>100 GeV) gamma-ray astronomy. It will integrate several tens of imaging atmospheric Cherenkov telescopes (IACTs) with different apertures into a single astronomical instrument. The US part of the CTA collaboration has proposed and is developing a novel IACT design with a Schwarzschild-Couder (SC) aplanatic two-mirror optical system. In comparison with the traditional single mirror Davies-Cotton IACT the SC telescope, by design, can accommodate a wider field-of-view, with significantly improved imaging resolution. In addition, the reduced plate scale of an SC telescope makes it compatible with highly integrated cameras assembled from silicon photo multipliers. In this submission we report on the status of the development of the SC optical system, which is part of the e ort to construct a full-scale prototype telescope of this type at the Fred Lawrence Whipple Observatory in southern Arizona.

Earth magnetic field effects on the cosmic electron flux as background for Cherenkov Telescopes at low energies

Muon ring images observed with Imaging Atmospheric Cherenkov Telescopes (IACTs) provide a powerful means to calibrate the optical throughput of IACTs and monitor their optical point spread function. We investigate whether muons ring images can be used as the primary optical throughput calibration method for the telescopes of the future Cherenkov Telescope Array (CTA) and find several additional systematic effects in comparison to previous works. To ensure that the method achieves the accuracy required by CTA, these systematic effects need to be taken into account and minor modifications to the hardware and analysis are necessary. We derive analytic estimates for the expected muon data rates to be used for optical throughput calibration, monitor- ing of the optical point spread function, with achievable statistical and systematic uncertainties, and explore the potential of muon ring images as a secondary method of camera pixel flat-fielding.

Very-high-energy (VHE, E > 100 GeV) photons are a powerful probe for studying astrophysics and fundamental physics under extreme conditions.During recent years, the research field of gamma-ray astronomy brought a continuous increase in the knowledge of the most violent phenomena in the Universe via the detection of copious amounts of VHE gamma rays in a variety of environments.The advent of new and more powerful detectors, designed to detect the short Cherenkov light pulse produced in the air showers initiated by VHE gamma rays interacting with the Earth's atmosphere, enabled to tremendously go well beyond initial discoveries.VHE gamma rays are also efficiently exploited to probe fundamental physics such as the nature of Dark Matter (DM).This elusive component, required to address several open questions in cosmology and astrophysics, could be entirely made of Weakly Interacting Massive Particles (WIMPs).Imaging Atmospheric Cherenkov telescopes (IACT), such as the High Energy Stereoscopic System (H.E.S.S.), could observe gamma-ray products in the final state of WIMP self-annihilation.A wealth of constraints on dark matter particle properties has been produced by IACTs.A number of reports have been published on dark matter searches with gamma rays.It is now relevant to present a review on this topic for graduate students with astrophysics and particle physics backgrounds in light of the recent achievements in the VHE astrophysics field and the new observatories to come by the end of the decade.We present current achievements, the technique at the base of the functioning of IACTs, the framework developed to search for DM particle models and distribution, and how to use the collected datasets to obtain meaningful insights on the particle nature of DM.We also provide detailed examinations of the results obtained so far with IACT observations on the search for DM annihilation signals, together with outlooks on the expectations for future observations in this field, which is timely given the near-future advent of the Cherenkov Telescope Array.

Colon cancer is cancer that is present on the inner side of colon walls or the rectum walls in the large intestine. Most of these types of cancer begin as abnormal growth of tissue called as polyp. Colonography uses low dose radiation Computed tomography (CT) scanning to obtain an interior view of the colon making use of special x-ray machine to view the large intestine for cancer and abnormal growths known as polyps. Radiologists examine these images to find polyp like structure using computer tools. As CT Colonography image contain noise such as lungs, small intestine, instruments during image capturing; segmenting colon from noise is the key task. Polyp occurrence can be detected mainly using shape feature; eliminating shapes similar to polyp is challenging. Hence, to tackle above issues, Image processing techniques are used by applying deep learning algorithm - Convolution Neural Network (CNN) and the results are compared with classical machine learning algorithm. In proposed method, each image is pre-processed to filter air filled dark region that includes colon, lungs etc. Next, each pre-processed CT Image separated into fixed number of blocks. Using pre-trained CNN, each block of ROI is classified as Type 1 (Usually Ascending and descending colon), Type 2 (Usually Traversal and sigmoidal colon) and Type3 (Noise such as lungs, instruments) to segment colon blocks by eliminating noise. Classified Blocks is further diagnosed for polyp like structure using pre-trained CNN by classifying each colon block as normal or abnormal. The experiment is setup with classical machine learning algorithms - Random Forest (RF) and k-nearest neighbor (KNN) by extracting texture feature - Local binary pattern (LBP) and shape feature - Histogram oriented gradient (HOG) for comparison. The experiment results showed the accuracy of proposed method for colon segmentation using CNN (87%) outperforms RF (85%) and KNN (83%). In, addition, the polyp detection accuracy of CNN (88%) is better than Random forest (85%) and KNN (80%). Hence, in the proposed method, there is significant accuracy improvement using deep learning algorithm compared to classical machine learning algorithms. It also provide baseline for automated colon cancer diagnosis using Deep learning algorithms for further research.

The Review of [formula omitted]-ray Astronomical Observing Techniques