Related papers: Machine Learning Tools for the IceCube-Gen2 Optica…
High-energy neutrinos are uniquely suited to study a large variety of physics as they traverse the universe almost untouched, in contrast to conventional astronomical messengers like photons or cosmic rays which are limited by interactions…
The cosmic neutrino flux recently discovered by IceCube will be instrumental in probing the highest-energy astrophysical processes. Nevertheless, the origin of these neutrinos is still unknown. While it would be more straightforward to…
The IceCube Neutrino Observatory is currently the largest and most sensitive detector for astrophysical neutrinos and has pioneered the field of high-energy neutrino astronomy. Despite being designed with the primary goal of identifying…
The scientific interest in studying high-energy transient phenomena in the Universe has largely grown for the last decade. Now, multiple ground-based survey projects have emerged to continuously monitor the optical (and multi-messenger)…
The IceCube Upgrade Camera System is a novel calibration system designed to calibrate the IceCube detector by measuring the optical properties of the Antarctic ice. The system comprises nearly 2,000 cameras and illumination LEDs, which are…
Embedded distributed inference of Neural Networks has emerged as a promising approach for deploying machine-learning models on resource-constrained devices in an efficient and scalable manner. The inference task is distributed across a…
As we enter the era of large-scale imaging surveys with the up-coming telescopes such as LSST and SKA, it is envisaged that the number of known strong gravitational lensing systems will increase dramatically. However, these events are still…
The IceCube Neutrino Observatory observes neutrinos interacting deep within the South Pole ice. It consists of 5,160 digital optical modules embedded within a cubic kilometer of ice, over depths of 1,450 m to 2,450 m. At the lower center of…
IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. Its goal is to detect astrophysical neutrinos and identify their sources. High-energy muon neutrinos are identified through the secondary muons produced via…
We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end…
Deep learning algorithms offer a powerful means to automatically analyze the content of medical images. However, many biological samples of interest are primarily transparent to visible light and contain features that are difficult to…
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…
Since the end of the 2005-2006 austral summer, the IceCube detector consists of an array of 9 strings, deployed between 1450 m and 2450 m of depth and containing 540 digital optical sensors and 16 IceTop surface stations with 64 sensors.…
One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of…
Kilometer-scale neutrino detectors such as IceCube are discovery instruments covering nuclear and particle physics, cosmology and astronomy. Examples of their multidisciplinary missions include the search for the particle nature of dark…
The next galactic supernova presents a once-in-a-lifetime opportunity to obtain detailed information about the explosion of a star and the extreme conditions found within its core. A core-collapse supernova will produce a neutrino burst…
The IceCube Upgrade will augment the existing IceCube Neutrino Observatory by deploying 700 additional optical sensor modules and calibration devices within its center at a depth of 1.5 to 2.5 km in the Antarctic ice. One goal of the…
Convolutional Neural Networks (CNNs) have revolutionized image classification by extracting spatial features and enabling state-of-the-art accuracy in vision-based tasks. The squeeze and excitation network proposed module gathers…
Neutrino observatories such as IceCube, Cubic Kilometre Neutrino Telescope (KM3NeT), and Super-Kamiokande cover a broad energy range that enables the study of both atmospheric neutrinos and astrophysical neutrinos. IceCube and KM3NeT focus…
While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them…