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Computational models of vision have traditionally been developed in a bottom-up fashion, by hierarchically composing a series of straightforward operations - i.e. convolution and pooling - with the aim of emulating simple and complex cells…

The upcoming galactic core-collapse supernova is expected to produce a considerable number of neutrino events within terrestrial detectors. By using Bayesian inference techniques, we address the feasibility of distinguishing among various…

Results are presented of a search for cosmic sources of high energy neutrinos with the ANTARES neutrino telescope. The data were collected during 2007 and 2008 using detector configurations containing between 5 and 12 detection lines. The…

We present maximum-likelihood search methods for time-dependent fluxes from point sources, such as flares or periodic emissions. We describe a method for the case when the time dependence of the flux can be assumed a priori from other…

A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of the toy detector are generated in the…

A study on the use of a machine learning algorithm for the level 1 trigger decision in the JUNO experiment ispresented. JUNO is a medium baseline neutrino experiment in construction in China, with the main goal of determining the neutrino…

In order to allow machine learning algorithms to extract knowledge from raw data, these data must first be cleaned, transformed, and put into machine-appropriate form. These often very time-consuming phase is referred to as preprocessing.…

When data is generated by multiple sources, conventional training methods update models assuming equal reliability for each source and do not consider their individual data quality. However, in many applications, sources have varied levels…

Deep models are designed to operate on huge volumes of high dimensional data such as images. In order to reduce the volume of data these models must process, we propose a set-based two-stage end-to-end neural subsampling model that is…

Point source searches with the IceCube neutrino telescope have been restricted to one hemisphere, due to the exclusive selection of upward going events as a way of rejecting the atmospheric muon background. We show that the region above the…

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…

The amount of available Earth observation data has increased dramatically in the recent years. Efficiently making use of the entire body information is a current challenge in remote sensing and demands for light-weight problem-agnostic…

Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within…

Kilometer scale neutrino telescopes are now being constructed (IceCube) and designed (KM3NeT). While no neutrino flux of cosmic origin has been discovered so far, the first weak signals are expected to be discerned in the next few years.…

Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from…

Reconstructed tracks of muons produced in neutrino interactions provide the precise probe for the neutrino direction. Therefore, track-like events are a powerful tool to search for neutrino point sources. Recently, Baikal-GVD has…

Event cameras offer high temporal resolution and power efficiency, making them well-suited for edge AI applications. However, their high event rates present challenges for data transmission and processing. Subsampling methods provide a…

Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of…

Neutrino observations are crucial for multi-messenger astronomy, but currently limited by effective area and atmospheric background. However, while other telescopes must point their limited field of view, IceCube can perform full-sky…

Recent measurements of the small-$x$ deep-inelastic regime at HERA translate to new expectations for the neutrino-nucleon cross section at ultrahigh energies. We present event rates for large underground neutrino telescopes based on the new…