Related papers: Deep learning reconstruction in ANTARES

IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of…

The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or Atlas experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used…

The ANTARES Collaboration is building a neutrino telescope 2400 m below the Mediterranean sea close to the Southern French coast. The site is already linked to the shore station by a 40 km-long electro-optical cable (EOC) which transmits…

Using the data recorded by the ANTARES neutrino telescope during 2007 and 2008, a search for high energy neutrinos coming from the direction of the Sun has been performed. The neutrino selection criteria have been chosen so as to maximize…

Completed in May 2008, the ANTARES neutrino telescope is located 40 km off the coast of Toulon, at a depth of about 2500 m. The telescope consists of 12 detect or lines housing a total of 884 optical modules. Each line is anchored to the…

A method to search for neutrino induced showers from gamma-ray bursts in the ANTARES detector is presented. ANTARES consists of a three-dimensional array of photosensitive devices that measure Cherenkov light induced by charged particles…

The volume of space debris currently orbiting the Earth is reaching an unsustainable level at an accelerated pace. The detection, tracking, identification, and differentiation between orbit-defined, registered spacecraft, and rogue/inactive…

Measurements of neutrinos at and below 10 GeV provide unique constraints of neutrino oscillation parameters as well as probes of potential Non-Standard Interactions (NSI). The IceCube Neutrino Observatory's DeepCore array is designed to…

One of the most significant challenges involved in efforts to understand the equation of state of dense neutron-rich matter is the uncertain density dependence of the nuclear symmetry energy. Because of its broad impact, pinning down the…

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of…

A method for reconstructing the direction of a fast neutron source using a segmented organic scintillator-based detector and deep learning model is proposed and analyzed. The model is based on recurrent neural network, which can be trained…

ANTARES is the largest undersea neutrino telescope and it has been taking data in its final configuration for more than ten years. On their journey to the Earth, cosmic rays can be absorbed by celestial objects, like the Sun, leading to a…

Large underwater telescopes have been proposed as a challenging method to measure high energy neutrinos from astrophysical objects. In recent years, The Antares collaboration has designed and realized the first detector of this type in the…

The main goal of the ANTARES neutrino telescope is the identification of neutrinos from cosmic accelerators. The good visibility towards the Southern sky for neutrino energies below 100 TeV and the good angular resolution for reconstructed…

Position calibration in the deep sea is typically done by means of acoustic multilateration using three or more acoustic emitters installed at known positions. Rather than using hydrophones as receivers that are exposed to the ambient…

The ANTARES detector is at present the most sensitive neutrino telescope in the Northern Hemisphere. The highly significant cosmic neutrino excess observed by the Antarctic IceCube detector can be studied with ANTARES, exploiting its…

In this study we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex…

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…

Deep learning holds great promise in the reconstruction of undersampled Magnetic Resonance Imaging (MRI) data, providing new opportunities to escalate the performance of rapid MRI. In existing deep learning-based reconstruction methods,…

The use of conventional neutrino telescope methods and technology for detecting neutrinos with energies above 1 EeV from astrophysical sources would be prohibitively expensive and may turn out to be technically not feasible. Acoustic…