Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDeep learning reconstruction in ANTARES
226
0
0.0
(
0
)
Authors
J. Garcia-Mendez
Ask ChatGPT about the research
No Arabic abstract
ANTARES is currently the largest undersea neutrino telescope, located in the Mediterranean Sea and taking data since 2007. It consists of a 3D array of photo sensors, instrumenting about 10Mt of seawater to detect Cherenkov light induced by secondary particles from neutrino interactions. The event reconstruction and background discrimination is challenging and machine-learning techniques are explored to improve the performance. In this contribution, two case studies using deep convolutional neural networks are presented. In the first one, this approach is used to improve the direction reconstruction of low-energy single-line events, for which the reconstruction of the azimuth angle of the incoming neutrino is particularly difficult. We observe a promising improvement in resolution over classical reconstruction techniques and expect to at least double our sensitivity in the low-energy range, important for dark matter searches. The second study employs deep learning to reconstruct the visible energy of neutrino interactions of all flavors and for the multi-line setup of the full detector.
rate research
Read More
Muons are the most abundant charged particles arriving at sea level originating from the decay of secondary charged pions and kaons. These secondary particles are created when high-energy cosmic rays hit the atmosphere interacting with air nuclei initiating cascades of secondary particles which led to the formation of extensive air showers (EAS). They carry essential information about the extra-terrestrial events and are characterized by large flux and varying angular distribution. To account for open questions and the origin of cosmic rays, one needs to study various components of cosmic rays with energy and arriving direction. Because of the close relation between muon and neutrino production, it is the most important particle to keep track of. We propose a novel tracking algorithm based on the Geometric Deep Learning approach using graphical structure to incorporate domain knowledge to track cosmic ray muons in our 3-D scintillator detector. The detector is modeled using the GEANT4 simulation package and EAS is simulated using CORSIKA (COsmic Ray SImulations for KAscade) with a focus on muons originating from EAS. We shed some light on the performance, robustness towards noise and double hits, limitations, and application of the proposed algorithm in tracking applications with the possibility to generalize to other detectors for astrophysical and collider experiments.
Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presented. The algorithm uses the charge signal (S2) light distribution obtained by simulating events using a waveform generator. To deal with the computational effort required by the likelihood-free approach, we employ the Bayesian Optimization for Likelihood-Free Inference (BOLFI) algorithm. Together with BOLFI, prior distributions for the parameters of interest (x; y; e) and highly informative discrepancy measures to perform the analyses are introduced. We evaluate the quality of the proposed algorithm by a comparison against the currently existing alternative methods using a large-scale simulation study. BOLFI provides a natural probabilistic uncertainty measure for the reconstruction and it improved the accuracy of the reconstruction over the next best algorithm by up to 15% when focusing on events over a large radii (R > 30 cm, the outer 37% of the detector). In addition, BOLFI provides the smallest uncertainties among all the tested methods.
The ANTARES deep-sea neutrino telescope comprises a three-dimensional array of photomultipliers to detect the Cherenkov light induced by upgoing relativistic charged particles originating from neutrino interactions in the vicinity of the detector. The large scattering length of light in the deep sea facilitates an angular resolution of a few tenths of a degree for neutrino energies exceeding 10 TeV. In order to achieve this optimal performance, the time calibration procedures should ensure a relative time calibration between the photomultipliers at the level of about 1ns. The methods developed to attain this level of precision are described.
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.
Muons created by $ u_mu$ charged current (CC) interactions in the water surrounding the ANTARES neutrino telescope have been almost exclusively used so far in searches for cosmic neutrino sources. Due to their long range, highly energetic muons inducing Cherenkov radiation in the water are reconstructed with dedicated algorithms that allow the determination of the parent neutrino direction with a median angular resolution of about unit{0.4}{degree} for an $E^{-2}$ neutrino spectrum. In this paper, an algorithm optimised for accurate reconstruction of energy and direction of shower events in the ANTARES detector is presented. Hadronic showers of electrically charged particles are produced by the disintegration of the nucleus both in CC and neutral current (NC) interactions of neutrinos in water. In addition, electromagnetic showers result from the CC interactions of electron neutrinos while the decay of a tau lepton produced in $ u_tau$ CC interactions will in most cases lead to either a hadronic or an electromagnetic shower. A shower can be approximated as a point source of photons. With the presented method, the shower position is reconstructed with a precision of about unit{1}{metre}, the neutrino direction is reconstructed with a median angular resolution between unit{2}{degree} and unit{3}{degree} in the energy range of SIrange{1}{1000}{TeV}. In this energy interval, the uncertainty on the reconstructed neutrino energy is about SIrange{5}{10}{%}. The increase in the detector sensitivity due to the use of additional information from shower events in the searches for a cosmic neutrino flux is also presented.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
