Related papers: Machine Learning Tools for the IceCube-Gen2 Optica…
The IceCube neutrino observatory is composed of more than five thousand light sensors, Digital Optical Modules (DOMs), installed on the surface and at depths between 1450 and 2450 m in clear ice at the South Pole. Each DOM incorporates a…
Neural networks are a prominent tool for identifying and modeling complex patterns, which are otherwise hard to detect and analyze. While machine learning and neural networks have been finding applications across many areas of science and…
The discovery of high-energy astrophysical neutrinos by IceCube has opened a new window to the Universe. However, the origin of these neutrinos is still a mystery, and some of them could be a result of dark matter interactions such as…
The IceCube Neutrino Observatory instruments about 1 km$^3$ of deep, glacial ice at the geographic South Pole using 5160 photomultipliers to detect Cherenkov light from relativistic, charged particles. Most IceCube science goals rely on…
Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing. This paper provides a comprehensive review of the latest developments in this field, focusing on…
The IceCube Neutrino Observatory includes low energy extensions such as the existing DeepCore subarray and the upcoming IceCube Upgrade, which will consist of seven new strings of photosensors with denser instrumentation than the existing…
With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we…
Neural networks as well as other methods of machine learning (ML) are known to be highly efficient in different classification tasks, including classification of images and videos. Mini- EUSO is a wide-field-of-view imaging telescope that…
We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as…
IceCube is a cubic-kilometer neutrino telescope under construction at the geographic South Pole. Once completed it will comprise 4800 optical sensors deployed on 80 vertical strings at depths in the ice between 1450 and 2450 meters. Part of…
Searches for astrophysical neutrino sources in IceCube rely on an unbinned likelihood that consists of an energy and spatial component. Accurate modeling of the detector, ice, and spatial distributions leads to improved directional and…
We study several methods for timing the neutrino signal of a Galactic supernova (SN) for different detectors via Monte Carlo simulations. We find that, for the methods we studied, at a distance of $10\,$kpc both Hyper-Kamiokande and IceCube…
This talk summarizes the main physics goals and basic methods of telescopes for high energy neutrinos. It reviews the present status of deep underwater telescopes and sketches the ICECUBE project as an example for a cube kilometer detector.…
Visual exploration of large multidimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are…
Deep learning using Convolutional Neural Networks (CNNs) has been shown to significantly out-performed many conventional vision algorithms. Despite efforts to increase the CNN efficiency both algorithmically and with specialized hardware,…
Photomultiplier tubes (PMTs) are a central component of neutrino telescopes such as IceCube and KM3NeT, and an accurate understanding and measurement of their properties is indispensable for improvements of these experiments. In this…
The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted…
Neural networks (NNs) provide a powerful and flexible tool for selecting a signal from a larger background. The D0 collaboration has used them extensively in studying t-tbar decays. NNs were essential to the measurement of the t-tbar…
This paper introduces an Interpretable Neural Network (INN) incorporating spatial information to tackle the opaque parameterization process of random weighted neural networks. The INN leverages spatial information to elucidate the…
Neutrino telescopes are large-scale detectors designed to observe Cherenkov radiation produced from neutrino interactions in water or ice. They exist to identify extraterrestrial neutrino sources and to probe fundamental questions…