Related papers: 2D Convolutional Neural Network for Event Reconstr…

In-ice radio detectors are a promising tool for the discovery of EeV neutrinos. For astrophysics, the implications of such a discovery will rely on the reconstruction of the neutrino arrival direction. This paper describes a first complete…

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…

IceCube collaboration has published two papers on ultrahigh energy neutrinos observation, recently. They have used the data collected in two years in their first publication, which reveals observation of two PeV energy neutrino events. The…

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D…

We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level…

Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass…

We explore why deep convolutional neural networks (CNNs) with small two-dimensional kernels, primarily used for modeling spatial relations in images, are also effective in speech recognition. We analyze the representations learned by deep…

NOvA is a long-baseline neutrino oscillation experiment. It is optimized to measure $\nu_e$ appearance and $\nu_{\mu}$ disappearance at the Far Detector in the $\nu_{\mu}$ beam produced by the NuMI facility at Fermilab. NOvA uses a…

The observation of an astrophysical neutrino flux in IceCube and its detection capability to separate between the different neutrino flavors has led IceCube to constraint the flavor content of this flux. IceCube-Gen2 is the planned…

We present preliminary results for a neutrino oscillation analysis in progress on data collected with the IceCube 22-string detector during 2007 and 2008. The goal of this analysis is to measure muon neutrino disappearance as a function of…

Generic model predictions for the early neutrino signal of a core-collapse supernova (SN) imply that IceCube can reconstruct the bounce to within about +/- 3.5 ms at 95% CL (assumed SN distance 10 kpc), relevant for coincidence with…

Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object…

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods.…

The IceCube Neutrino Observatory at the South Pole detects neutrinos from the entire sky, both of astrophysical and atmospheric origin, via the Cherenkov light emitted when these neutrinos interact in the ice, giving rise to rapidly moving…

The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN architecture for variable flow conditions and object geometry,…

Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of…

Neutrino oscillations continue to provide one of the most promising avenues for uncovering physics beyond the Standard Model. In particular, beyond-standard-model neutrino matter interactions may perturb neutrino oscillations in matter,…

Deep learning-based speech enhancement methods have significantly improved speech quality and intelligibility. Convolutional neural networks (CNNs) have been proven to be essential components of many high-performance models. In this paper,…

The IceCube neutrino observatory in operation at the South Pole, Antarctica, comprises three distinct components: a large buried array for ultrahigh energy neutrino detection, a surface air shower array, and a new buried component called…

Existing techniques to encode spatial invariance within deep convolutional neural networks only model 2D transformation fields. This does not account for the fact that objects in a 2D space are a projection of 3D ones, and thus they have…