The reflector used by an imaging atmospheric Cherenkov telescope (IACT) consists of a tessellated array of mirrors mounted on a large frame. This arrangement allows for a very large reflecting surface with sufficient optical quality for the implementation of the IACT technique at a moderate price. The main challenge presented by such a reflector is maintaining the optical quality, which depends on the individual alignment of several hundred mirror facets. We describe a method of measuring and correcting the alignment of the mirror facets of the reflectors used by the VERITAS telescopes. This method employs a CCD camera, placed at the focal point of the reflector, which acquires a series of images of the reflector while the telescope performs a raster scan about a star. Well-aligned facets appear bright when the telescope points directly at the star while misaligned facets appear bright when the angle between the telescope pointing direction and the star is twice the misalignment angle of the mirror. Data from these scans can therefore be used to produce a set of corrections which can be applied to the facets. In this contribution we report on initial experience with an alignment system based on this principle.
Identification of Cherenkov light generated by muons has been suggested as a promising way to dramatically improve the background rejection power of Imaging Atmospheric Cherenkov Telescope (IACT) arrays at high energies. However, muon identification remains a challenging task, for which efficient algorithms are still being developed. We present an approach in which, rather than identifying Cherenkov light from muons, we simply consider the presence of Cherenkov light other than the main shower image in IACTs with large mirror area. We show that in the case of the H.E.S.S. array of five telesco…
The presence of muons in air-showers initiated by cosmic ray protons and nuclei is well established as a powerful tool to separate such showers from those initiated by gamma rays. However, so far this approach has been fully exploited only for ground level particle detecting arrays. We explore the feasibility of using Cherenkov light from muons as a background rejection tool for imaging atmospheric Cherenkov telescope arrays at the highest energies. We adopt an analytical model of the Cherenkov light from individual muons to allow rapid simulation of a large number of showers in a hybrid mode.…
The presence of muons in air-showers initiated by cosmic ray protons and nuclei is well established as a powerful tool to separate such showers from those initiated by gamma-rays. However, so far this approach has been fully exploited only for ground level particle detecting arrays. In this contribution, we explore the feasibility of using Cherenkov light from muons as a background rejection tool for imaging atmospheric Cherenkov telescope arrays at the highest energies. We adopt an analytical model of the Cherenkov light from individual muons to allow rapid simulation of a large number of sho…
Reconstruction of energies of very-high-energy gamma-rays observed by imaging atmospheric Cherenkov telescopes is affected by changes in the atmospheric conditions and the performance of telescope components. Reliable calibration schemes aimed at these effects are necessary for the forthcoming Cherenkov Telescope Array (CTA) to achieve its goals on the maximally allowed systematic uncertainty of the global energy scale. A possible means of estimating the atmospheric attenuation of Cherenkov light is the method of the Cherenkov transparency coefficient (CTC). The CTC is calculated using the tel…
The last 20 years have seen the development of new techniques in Astroparticle Physics providing access to the highest end of the electromagnetic spectrum. It has been shown that some sources emit photons up to energies close to 100 TeV. Yet the fluxes of these photons are incredibly low and new detection techniques are needed to go higher in energy. A new technique that would use the new generation of Cherenkov Telescopes, i.e., the Cherenkov Telescope Array (CTA), is proposed to push further the energy frontier. It is based on the detection of the fluorescence radiation emitted in extensive …
We introduce a new simulation and analysis paradigm for Imaging Atmospheric Cherenkov Telescope (IACT) arrays, simulating the actual observation conditions as well as individual telescope configuration for each observation unit. Compared to existing frameworks, where simulations are usually generated using pre-defined settings, this run-wise simulation approach implies more realistic simulations and hence reduced systematic uncertainties. The computational effort of this dedicated simulation concept is notably independent of the amount of different observation configurations but just scales li…
Machine learning, through the use of convolutional and recurrent neural networks is a promising avenue for the improvement of background rejection performance in imaging atmospheric Cherenkov telescopes. However, it is of paramount importance for science analysis that their performance remains stable against a wide range of observing conditions and instrument states. We investigate the stability of convolutional recurrent networks by applying them to background rejection in a toy Monte Carlo simulation of a Cherenkov telescope array. We then vary a range of observation and instrument parameter…
The potential of an array of imaging atmospheric Cherenkov telescopes to detect gamma-ray sources in complex regions has been investigated. The basic characteristics of the gamma-ray instrument have been parametrized using simple analytic representations. In addition to the ideal (Gaussian form) point spread function (PSF), the impact of more realistic non-Gaussian PSFs with tails has been considered. Simulations of isolated point-like and extended sources have been used as a benchmark to test and understand the response of the instrument. The capability of the instrument to resolve multiple s…
In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of the latest techniques in machine learning, namely recurrent and convolutional neural networks, to the background rejection problem. Use of these machine-learning techniques addresses some of the key challenges encountered in the currently implemented algorithms and helps to significantly increase the background rejection performance at all energies. We apply …
Determination of the energy and flux of the gamma photons by Imaging Atmospheric Cherenkov Technique is strongly dependent on optical properties of the atmosphere. Therefore, atmospheric monitoring during the future observations of the Cherenkov Telescope Array (CTA) as well as anticipated long-term monitoring in order to characterize overal properties and annual variation of atmospheric conditions are very important. Several instruments are already installed at the CTA sites in order to monitor atmospheric conditions on long-term. One of them is a Sun/Moon photometer CE318-T, installed at the…
In this paper we have introduced a novel method for gamma hadron separation in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine Learning. IACTs captures images of Extensive Air Showers (EAS) produced from very high energy gamma rays. We have used the QML Algorithms, Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) for binary classification of the events into signals (Gamma) and background(hadron) using the image parameters. MAGIC Gamma Telescope dataset is used for this study which was generated from Monte Carlo Software Coriska. These quan…
We present a sophisticated likelihood reconstruction algorithm for shower-image analysis of imaging Cherenkov telescopes. The reconstruction algorithm is based on the comparison of the camera pixel amplitudes with the predictions from a Monte Carlo based model. Shower parameters are determined by a maximisation of a likelihood function. Maximisation of the likelihood as a function of shower fit parameters is performed using a numerical non-linear optimisation technique. A related reconstruction technique has already been developed by the CAT and the H.E.S.S. experiments, and provides a more pr…
It is anticipated that forthcoming, next generation, atmospheric Cherenkov telescope arrays will include a number of medium-sized telescopes that are constructed using a dual-mirror Schwarzschild-Couder configuration. These telescopes will sample a wide ($8^{\circ}$) field of view using a densely pixelated camera comprising over $10^{4}$ individual readout channels. A readout frequency congruent with the expected single-telescope trigger rates would result in substantial data rates. To ameliorate these data rates, a novel, hardware-level Distributed Intelligent Array Trigger (DIAT) is envision…
Variable emission is one of the defining characteristic of active galactic nuclei (AGN). While providing precious information on the nature and physics of the sources, variability is often challenging to observe with time- and field-of-view-limited astronomical observatories such as Imaging Atmospheric Cherenkov Telescopes (IACTs). In this work, we address two questions relevant for the observation of sources characterized by AGN-like variability: what is the most time-efficient way to detect such sources, and what is the observational bias that can be introduced by the choice of the observing…
A detailed case study of $γ$-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers,Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for $γ$-hadron seg…
The stereoscopic imaging atmospheric Cherenkov technique, developed in the 1980s and 1990s, is now used by a number of existing and planned gamma-ray observatories around the world. It provides the most sensitive view of the very high energy gamma-ray sky (above 30 GeV), coupled with relatively good angular and spectral resolution over a wide field-of-view. This Chapter summarizes the details of the technique, including descriptions of the telescope optical systems and cameras, as well as the most common approaches to data analysis and gamma-ray reconstruction.
Imaging Atmospheric Cherenkov Telescopes (IACTs) represent a class of instruments which are dedicated to the ground-based observation of cosmic VHE gamma ray emission based on the detection of the Cherenkov radiation produced in the interaction of gamma rays with the Earth atmosphere. One of the key elements of such instruments is a pixelized focal-plane camera consisting of photodetectors. To date, photomultiplier tubes (PMTs) have been the common choice given their high photon detection efficiency (PDE) and fast time response. Recently, silicon photomultipliers (SiPMs) are emerging as an alt…
Atmospheric monitoring is an integral part of the design of the Cherenkov Telescope Array (CTA), as atmospheric conditions affect the observations by Imaging Atmospheric Cherenkov Telescopes (IACT) in multiple ways. The variable optical properties of the atmosphere are a major contribution to the systematic uncertainty in the determination of the energy and flux of the gamma photons. Both the development of the air-shower and the production of Cherenkov light depend on the molecular profile of the atmosphere. Additionally, the rapidly changing aerosol profile, affecting the transmission of the…
An Approach to Gamma-Hadron Separation via Measurements of the Middle-UV Fraction of EAS emission by an Imaging Atmospheric Cherenkov Telescope with UV-Sensitive SiPM detectors
The next generation of imaging atmospheric Cherenkov telescopes will be composed of hundreds of telescopes working together to attempt to unveil some fundamental physics of the high-energy Universe. Along with the scientific data, a large volume of housekeeping and auxiliary data coming from weather stations, instrumental sensors, logging files, etc., will be collected as well. Driven by supervised and reinforcement learning algorithms, such data can be exploited for applying predictive maintenance and failure type detection to these astrophysical facilities. In this paper, we present the proj…
The Imaging Atmospheric Cherenkov Technique (IACT) is unusual in astronomy as the atmosphere actually forms an intrinsic part of the detector system, with telescopes indirectly detecting very high energy particles by the generation and transport of Cherenkov photons deep within the atmosphere. This means that accurate measurement, characterisation and monitoring of the atmosphere is at the very heart of successfully operating an IACT system. The Cherenkov Telescope Array (CTA) will be the next generation IACT observatory with an ambitious aim to improve the sensitivity of an order of magnitude…
Imaging Atmospheric Cherenkov Telescopes (IACTs) need imaging optics with large apertures and high image intensities to map the faint Cherenkov light emitted from cosmic ray air showers onto their image sensors. Segmented reflectors fulfill these needs, and composed from mass production mirror facets they are inexpensive and lightweight. However, as the overall image is a superposition of the individual facet images, alignment remains a challenge. Here we present a simple, yet extendable method, to align a segmented reflector using its Bokeh. Bokeh alignment does not need a star or good weathe…
The Cherenkov Telescope Array (CTA) is the future ground-based observatory for gamma-ray astronomy at very high energies. The atmosphere is an integral part of every Cherenkov telescope. Different atmospheric conditions, such as clouds, can reduce the fraction of Cherenkov photons produced in air showers that reach ground-based telescopes, which may affect the performance. Decreased sensitivity of the telescopes may lead to misconstructed energies and spectra. This study presents the impact of various atmospheric conditions on CTA performance. The atmospheric transmission in a cloudy atmospher…
The simulation of atmospheric showers through Monte-Carlo processes as well as their projection into Imaging Atmospheric Cherenkov Telescopes (IACT) is long and very computing intensive. As these simulations are the most advanced ones from a physics point of view, they are not suited for simple tests. Here we present a Python package developed in order to model atmospheric showers using different profiles and to image them with an array of IACT. This allows for first order studies of the influence of the primary photon energy and angular direction on the stereoscopic images. Its simplicity als…
We present a new paradigm for the simulation of arrays of Imaging Atmospheric Cherenkov Telescopes (IACTs) which overcomes limitations of current approaches. Up to now, all major IACT experiments rely on the same Monte-Carlo simulation strategy, using predefined observation and instrument settings. Simulations with varying parameters are generated to provide better estimates of the Instrument Response Functions (IRFs) of different observations. However, a large fraction of the simulation configuration remains preserved, leading to complete negligence of all related influences. Additionally, th…
Imaging Atmospheric Cherenkov Telescopes (IACTs) need imaging optics with large apertures and high image intensities to map the faint Cherenkov light emitted from cosmic ray air showers onto their image sensors. Segmented reflectors fulfill these needs, and as they are composed from mass production mirror facets they are inexpensive and lightweight. However, as the overall image is a superposition of the individual facet images, alignment is a challenge. Here we present a computer vision based star tracking alignment method, which also works for limited or changing star light visibility. Our m…
Deep convolutional neural networks (DCNs) are a promising machine learning technique to reconstruct events recorded by imaging atmospheric Cherenkov telescopes (IACTs), but require optimization to reach full performance. One of the most pressing challenges is processing raw images captured by cameras made of hexagonal lattices of photo-multipliers, a common layout among IACT cameras which topologically differs from the square lattices conventionally expected, as their input data, by DCN models. Strategies directed to tackle this challenge range from the conversion of the hexagonal lattices ont…
The Cherenkov Telescope Array (CTA) will be the next generation observatory employing different types of Cherenkov telescopes for the detection of particle showers initiated by very-high-energy gamma rays. A good knowledge of the Earth's atmosphere, which acts as a calorimeter in the detection technique, will be crucial for calibration in CTA. Variations of the atmosphere's transparency to Cherenkov light and not correctly performed calibration of individual telescopes in the array result in large systematic uncertainties on the energy scale. The Cherenkov Transparency Coefficient (CTC), devel…
Imaging atmospheric Cherenkov telescopes (IACTs) such as the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes endeavor to reach the lowest possible energy threshold. In doing so the trigger system is a key element. Reducing the trigger threshold is hampered by the rapid increase of accidental triggers generated by ambient light, the so-called Night Sky Background (NSB). In this paper we present a topological trigger, dubbed Topo-trigger, which rejects events on the basis of their relative orientation in the telescope cameras. We have simulated and tested the trigger selection a…
The IceCube Neutrino Observatory has revealed the existence of sources of high-energy astrophysical neutrinos. However, identification of the sources is challenging because astrophysical neutrinos are difficult to separate from the background of atmospheric neutrinos produced in cosmic-ray-induced particle cascades in the atmosphere. The efficient detection of air showers in coincidence with detected neutrinos can greatly reduce those backgrounds and increase the sensitivity of neutrino telescopes. Imaging Air Cherenkov Telescopes (IACTs) are sensitive to gamma-ray-induced (and cosmic-ray-indu…
Calibration of a PMT matrix is crucial for the treatment of the data obtained with Cherenkov tracking detector. Furthermore, due to high variability of the aerosol abundance in the atmosphere depending on season, weather etc. A constant monitoring of the atmospheric transparency is required during the measurements. For this purpose, besides traditional methods, a station for laser atmospheric probing is used.
TARGET~5 is a new application-specific integrated circuit (ASIC) of the TARGET family, designed for the readout of signals from photosensors in the cameras of imaging atmospheric Cherenkov telescopes (IACTs) for ground-based gamma-ray astronomy. TARGET~5 combines sampling and digitization on 16 signal channels with the formation of trigger signals based on the analog sum of groups of four channels. We describe the ASIC architecture and performance. TARGET~5 improves over the performance of the first-generation TARGET ASIC, achieving: tunable sampling frequency from $<0.4$~GSa/s to $>1$~GSa/s; …
The Cherenkov Telescope Array (CTA) is the next generation facility of Imaging Atmospheric Cherenkov Telescopes. It will reach unprecedented sensitivity and energy resolution in very-high-energy gamma-ray astronomy. CTA will detect Cherenkov light emitted within an atmospheric shower of particles initiated by cosmic-gamma rays or cosmic rays entering the Earth's atmosphere. From the combination of images the Cherenkov light produces in the telescopes, one is able to infer the primary particle energy and direction. A correct energy estimation can be thus performed only if the local atmosphere i…
Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学斯科别利岑核物理研究所）；Applied Physics Institute of Irkutsk State University（伊尔库茨克国立大学应用物理研究所）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）
High-energy particles hitting the upper atmosphere of the Earth produce extensive air showers that can be detected from the ground level using imaging atmospheric Cherenkov telescopes. The images recorded by Cherenkov telescopes can be analyzed to separate gamma-ray events from the background hadron events. Many of the methods of analysis require simulation of massive amounts of events and the corresponding images by the Monte Carlo method. However, Monte Carlo simulation is computationally expensive. The data simulated by the Monte Carlo method can be augmented by images generated using faste…
The Cherenkov Telescope Array (CTA) is the next generation of Imaging Atmospheric Cherenkov Telescopes. It will reach a sensitivity and energy resolution never obtained until now by any other high energy gamma-ray experiment. Understanding the systematic uncertainties in general will be a crucial issue for the performance of CTA. It is well known that atmospheric conditions contribute particularly in this aspect.Within the CTA consortium several groups are currently building Raman LIDARs to be installed on the two sites. Raman LIDARs are devices composed of a powerful laser that shoots into th…
A FRAM (F/(Ph)otometric Robotic Atmospheric Monitor) telescope is a system of a robotic mount, a large-format CCD camera and a fast telephoto lens that can be used for atmospheric monitoring at any site when information about the atmospheric transparency is required with high spatial or temporal resolution and where continuous use of laser-based methods for this purpose would interfere with other observations. The original FRAM has been operated at the Pierre Auger Observatory in Argentina for more than a decade, while three more FRAMs are foreseen to be used by the Cherenkov Telescope Array (…
