Related papers: Improving Neutrino Point Source Sensitivity with S…
Low background searches for astrophysical neutrino sources anywhere in the sky can be performed using cascade events induced by neutrinos of all flavors interacting in IceCube with energies as low as ~1 TeV. Previously, we showed that even…
Constraints on the number and luminosity of the sources of the cosmic neutrinos detected by IceCube have been set by targeted searches for point sources. We set complementary constraints by using the 2MASS Redshift Survey (2MRS) catalogue,…
Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously. Recovering brightness from events is appealing since the reconstructed images inherit the high dynamic range (HDR) and high-speed…
We present a series of new open source deep learning algorithms to accelerate Bayesian full waveform point source inversion of microseismic events. Inferring the joint posterior probability distribution of moment tensor components and…
Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodologies in the field primarily concentrate on constructing…
The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with…
Model-based algorithms are deeply rooted in modern control and systems theory. However, they usually come with a critical assumption - access to an accurate model of the system. In practice, models are far from perfect. Even precisely tuned…
Document-level neural machine translation has yielded attractive improvements. However, majority of existing methods roughly use all context sentences in a fixed scope. They neglect the fact that different source sentences need different…
The observation of high-energy neutrinos from astrophysical sources would substantially improve our knowledge and understanding of the non-thermal processes in these sources, and would in particular pinpoint the accelerators of cosmic rays.…
We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment,…
The inverse problem of recovering point sources represents an important class of applied inverse problems. However, there is still a lack of neural network-based methods for point source identification, mainly due to the inherent solution…
Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators. Parametrized neural network reweighting can be used to extend this fitting procedure to many dimensions and…
Coherent Elastic Neutrino-Nucleus Scattering (CEvNS) provides a sensitive probe of neutrino interactions at low momentum transfer, but its experimental observation is strongly constrained by detector-related effects such as energy…
With the design and development of next-generation high-energy neutrino detectors, it is important to compare different detector designs to optimize detection probability and science reach. These comparisons are nevertheless difficult due…
The ANTARES Collaboration is building a high-energy neutrino telescope at 2500 m depth in the Mediterranean Sea. The experiment aims to search for high-energy cosmic neutrinos through the detection of Cerenkov light induced by muons and…
Event cameras are novel sensors that report brightness changes in the form of asynchronous "events" instead of intensity frames. They have significant advantages over conventional cameras: high temporal resolution, high dynamic range, and…
We evaluate neutrino-nucleon cross section for energies up to $10^{21} eV$ in light of new information on the small-$x$ behavior of parton distributions. We give predictions for large underground neutrino telescope event rates for…
Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using…
With extremely high temporal resolution, event cameras have a large potential for robotics and computer vision. However, their asynchronous imaging mechanism often aggravates the measurement sensitivity to noises and brings a physical…
The IceCube Neutrino Observatory is a cubic-kilometer high-energy neutrino detector deployed in the Antarctic ice. Two major event classes are charged-current electron and muon neutrino interactions. In this contribution, we discuss the…