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Register a new userOptimization of multivariate analysis for IACT stereoscopic systems
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Multivariate methods have been recently introduced and successfully applied for the discrimination of signal from background in the selection of genuine very-high energy gamma-ray events with the H.E.S.S. Imaging Atmospheric Cerenkov Telescope. The complementary performance of three independent reconstruction methods developed for the H.E.S.S. data analysis, namely Hillas, model and 3D-model suggests the optimization of their combination through the application of a resulting efficient multivariate estimator. In this work the boosted decision tree method is proposed leading to a significant increase in the signal over background ratio compared to the standard approaches. The improved sensitivity is also demonstrated through a comparative analysis of a set of benchmark astrophysical sources.
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Imaging Atmospheric Cherenkov Telescopes for very-high energy gamma-ray astronomy need mirror with high reflectance roughly in the wavelength between 300 and 550 nm. The current standard reflective layer of such mirrors is aluminum. Being permanently exposed to the environment they show a constant degradation over the years. New and improved dielectric coatings have been developed to enhance their resistance to environmental impact and to extend their possible lifetime. In addition, these customized coatings have an increased reflectance of over 95% and are designed to significantly lower the night-sky background contribution. The development of such coatings for mirrors with areas up to 2 m2 and low application temperatures to suite the composite materials used for the new mirror susbtrates of the Cherenkov Telescope Array (CTA) and the results of extensive durability tests are presented.
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.
In astronomy, we are witnessing an enormous increase in the number of source detections, precision, and diversity of measurements. Additionally, multi-epoch data is becoming the norm, making time-series analyses an important aspect of current astronomy. The Gaia mission is an outstanding example of a multi-epoch survey that provides measurements in a large diversity of domains, with its broad-band photometry; spectrophotometry in blue and red (used to derive astrophysical parameters); spectroscopy (employed to infer radial velocities, v sin(i), and other astrophysical parameters); and its extremely precise astrometry. Most of all that information is provided for sources covering the entire sky. Here, we present several properties related to the Gaia time series, such as the time sampling; the different types of measurements; the Gaia G, G BP and G RP-band photometry; and Gaia-inspired studies using the CORrelation-RAdial-VELocities data to assess the potential of the information on the radial velocity, the FWHM, and the contrast of the cross-correlation function. We also present techniques (which are used or are under development) that optimize the extraction of astrophysical information from the different instruments of Gaia, such as the principal component analysis and the multi-response regression. The detailed understanding of the behavior of the observed phenomena in the various measurement domains can lead to richer and more precise characterization of the Gaia data, including the definition of more informative attributes that serve as input to (our) machine-learning algorithms.
The MAGIC gamma-ray observatory has recently been upgraded by a second Cherenkov telescope at a distance of 85 m from the first one. Simultaneous observation of air showers with the two MAGIC telescopes (stereoscopic mode) will improve the reconstruction of the shower axis and solve the ambiguity in the impact point occurring in single-telescope mode. Also, the stereo observation will result in a better angular resolution, energy estimation and cosmic-ray background rejection. It is expected that the sensitivity of MAGIC improves significantly over the full energy range (60 GeV - 20 TeV). Here, we present the performance estimated from Monte Carlo simulations.
A filament eruption, accompanied by a B9.5 flare, coronal dimming and an EUV wave, was observed by the Solar TERrestrial Relations Observatory (STEREO) on 19 May 2007, beginning at about 13:00 UT. Here, we use observations from the SECCHI/EUVI telescopes and other solar observations to analyze the behavior and geometry of the filament before and during the eruption. At this time, STEREO A and B were separated by about 8.5 degrees, sufficient to determine the three-dimensional structure of the filament using stereoscopy. The filament could be followed in SECCHI/EUVI 304 A stereoscopic data from about 12 hours before to about 2 hours after the eruption, allowing us to determine the 3D trajectory of the erupting filament. From the 3D reconstructions of the filament and the chromospheric ribbons in the early stage of the eruption, simultaneous heating of both the rising filamentary material and the chromosphere directly below is observed, consistent with an eruption resulting from magnetic reconnection below the filament. Comparisons of the filament during eruption in 304 A and Halpha show that when it becomes emissive in He II, it tends to disappear in Halpha, indicating that the disappearance probably results from heating or motion, not loss, of filamentary material.
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