Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPhoton Reconstruction for H.E.S.S. Using a Semi-Analytical Shower Model
130
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The High Energy Stereoscopic System (H.E.S.S.) is an array of five Imaging Atmospheric Cherenkov Telescopes (IACTs) designed to detect cosmogenic gamma-rays with very high energies. Originally consisting of just four identical IACTs (CT1-4) with an effective mirror diameter of 12$,$m each, it was expanded with a fifth IACT (CT5) with a mirror diameter of 28$,$m in 2012. Being the largest IACT worldwide, CT5 allows to lower the energy threshold of H.E.S.S., making the array sensitive at energies where space-based detectors run out of statistics. Events can be analysed either monoscopically (i.e. using only information of CT5) or stereoscopically (requiring at least two triggered telescopes per event). To achieve a good performance, a sophisticated event reconstruction and analysis framework is indispensable. This is particularly important for H.E.S.S. since it is now the first IACT array that consists of different telescope types. An advanced reconstruction method is based on a semi-analytical model of electromagnetic particle showers in the atmosphere (model analysis). The properties of the primary particle are reconstructed by comparing the image recorded by each triggered telescope with the Cherenkov emission from the shower model using a log-likelihood maximisation. Due to its performance, this method has become one of the standard analysis techniques applied to CT1-4 data. Now it has been modified for use with the five-telescope array. We present the adapted model analysis and its performance in both monoscopic and stereoscopic analysis mode.
rate research
Read More
We introduce a new Monte Carlo template-based reconstruction method for air shower arrays, with a focus on shower core and energy reconstruction of $gamma$-ray induced air showers. The algorithm fits an observed lateral amplitude distribution of an extensive air shower against an expected probability distribution using a likelihood approach. A full Monte Carlo air shower simulation in combination with the detector simulation is used to generate the expected probability distributions. The goodness of fit can be used to discriminate between $gamma$-ray and hadron induced air showers. As an example, we apply this method to the High Altitude Water Cherenkov $gamma$-ray Observatory and its recently installed high-energy upgrade. The performance of this method and the applicability to air shower arrays with mixed detector types makes it a promising reconstruction approach for current and future instruments.
I present the first public releases (v3.4 and v3.5) of the USINE code for cosmic-ray propagation in the Galaxy (https://lpsc.in2p3.fr/usine). It contains several semi-analytical propagation models previously used in the literature (leaky-box model, 2-zone 1D and 2D diffusion models) for the calculation of nuclei ($Z=1-30$), anti-protons, and anti-deuterons. For minimisations, the geometry, transport, and source parameters of all models can be enabled as free parameters, whereas nuisance parameters are enabled on solar modulation levels, cross sections (inelastic and production), and systematics of the CR data. With a single ASCII initialisation file to configure runs, its many displays, and the speed associated to semi-analytical approaches, USINE should be a useful tool for beginners, but also for experts to perform statistical analyses of high-precision cosmic-ray data.
The atmospheric depth of the air shower maximum $X_{mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed $X_{mathrm{max}}$ using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than $25~mathrm{g/cm^{2}}$ at energies above $2times 10^{19}~mathrm{eV}$.
The mass composition of cosmic rays contains important clues about their origin. Accurate measurements are needed to resolve long-standing issues such as the transition from Galactic to extragalactic origin, and the nature of the cutoff observed at the highest energies. Composition can be studied by measuring the atmospheric depth of the shower maximum Xmax of air showers generated by high-energy cosmic rays hitting the Earths atmosphere. We present a new method to reconstruct Xmax based on radio measurements. The radio emission mechanism of air showers is a complex process that creates an asymmetric intensity pattern on the ground. The shape of this pattern strongly depends on the longitudinal development of the shower. We reconstruct Xmax by fitting two-dimensional intensity profiles, simulated with CoREAS, to data from the LOFAR radio telescope. In the dense LOFAR core, air showers are detected by hundreds of antennas simultaneously. The simulations fit the data very well, indicating that the radiation mechanism is now well-understood. The typical uncertainty on the reconstruction of Xmax for LOFAR showers is 17 g/cm^2.
The emergence of optical interferometers with three and more telescopes allows image reconstruction of astronomical objects at the milliarcsecond scale. However, some objects contain components with very different spectral energy distributions (SED; i.e. different temperatures), which produces strong chromatic effects on the interferograms that have to be managed with care by image reconstruction algorithms. For example, the gray approximation for the image reconstruction process results in a degraded image if the total (u, v)-coverage given by the spectral supersynthesis is used. The relative flux contribution of the central object and an extended structure changes with wavelength for different temperatures. For young stellar objects, the known characteristics of the central object (i.e., stellar SED), or even the fit of the spectral index and the relative flux ratio, can be used to model the central star while reconstructing the image of the extended structure separately. Methods. We present a new method, called SPARCO (semi-parametric algorithm for the image reconstruction of chromatic objects), which describes the spectral characteristics of both the central object and the extended structure to consider them properly when reconstructing the image of the surrounding environment. We adapted two image-reconstruction codes (Macim, Squeeze, and MiRA) to implement this new prescription. SPARCO is applied using Macim, Squeeze and MiRA on a young stellar object model and also on literature data on HR5999 in the near-infrared with the VLTI. This method paves the way to improved aperture-synthesis imaging of several young stellar objects with existing datasets. More generally, the approach can be used on astrophysical sources with similar features such as active galactic nuclei, planetary nebulae, and asymptotic giant branch stars.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
