Deep learning reconstruction in ANTARES

  title={Deep learning reconstruction in ANTARES},
  author={J. Garc'ia-M'endez and Nicole Geisselbrecht and T Eberl and Miguel Ardid and Salva Ardid},
  journal={Journal of Instrumentation},
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… Expand
1 Citations
A Living Review of Machine Learning for Particle Physics
This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments. Expand


Muon energy reconstruction in the ANTARES detector
Abstract The energy reconstruction of both neutrino-induced muons from neutrino interactions in the vicinity of the detector and of muons from cosmic ray air showers contributes indispensableExpand
A Fast Algorithm for Muon Track Reconstruction and its Application to the ANTARES Neutrino Telescope
An algorithm is presented, that provides a fast and robust reconstruction of neutrino induced upward-going muons and a discrimination of these events from downward-going atmospheric muon backgroundExpand
ANTARES: the first undersea neutrino telescope
The ANTARES Neutrino Telescope was completed in May 2008 and is the first operational Neutrino Telescope in the Mediterranean Sea. The main purpose of the detector is to perform neutrino astronomyExpand
An algorithm for the reconstruction of neutrino-induced showers in the ANTARES neutrino telescope
Muons created by $\nu_\mu$ 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.Expand
Recent advances in convolutional neural networks
This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing. Expand
Event Classification and Energy Reconstruction for ANTARES using Convolutional Neural Networks
  • 2021
Event classification and energy reconstruction for ANTARES using convolutional neural networks, master's thesis
  • 2021
Event classification and energy reconstruction for ANTARES using convolutional neural networks, master’s thesis, Friedrich-Alexander Universität, Erlangen-Nürnberg, Germany (2021)
  • 2021
Event reconstruction for KM3NeT/ORCA using convolutional neural networks
It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches. Expand
Monte Carlo simulations for the ANTARES underwater neutrino telescope
Monte Carlo simulations are a unique tool to check the response of a detector and to monitor its performance. For a deep-sea neutrino telescope, the variability of the environmental conditions thatExpand