Related papers: Deep-Learning-Based Kinematic Reconstruction for D…
We consider the $\nu_\mu \to \nu_{\tau}$ appearance channel in the future Deep Underground Neutrino Experiment (DUNE) which offers a good statistics of the $\nu_{\tau}$ sample. In order to measure its impact on constraining the oscillation…
We present a new approach to separate track-like and shower-like topologies in liquid argon time projection chamber (LArTPC) experiments for neutrino physics using quantum machine learning. Effective reconstruction of neutrino events in…
We review the current status of neutrino oscillation experiments, mainly focussed on T2(H)K, NO$\nu$A and DUNE. Their capability to probe high energy physics is found in the precision measurement of the CP phase and $\theta_{23}$. In…
CPT symmetry, the combination of Charge Conjugation, Parity and Time reversal, is a cornerstone of our model building strategy and therefore the repercussions of its potential violation will severely threaten the most extended tool we…
Cold electronics is a key technology in many areas of science and technology including space exploration programs and particle physics. A major experiment with a very large number of analog and digital electronics signal processing channels…
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval…
Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction…
Measurements of electrons from $\nu_e$ interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and…
Next generation neutrino oscillation experiments like DUNE and T2HK are multi-purpose observatories, with a rich physics program beyond oscillation measurements. A special role is played by their near detector facilities, which are…
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA…
Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in chunks over time. This leads to important considerations…
We investigate the effects of charged-current (CC) nonstandard neutrino interactions (NSIs) at the source and at the detector in the simulated data for the planned Deep Underground Neutrino Experiment (DUNE), while neglecting the…
The DUNE near detector will collect an unprecedented large number of neutrino interactions, allowing the precise measurement of rare processes such as neutrino trident production, i.e. the generation of a lepton-antilepton pair through the…
The current generation of short baseline neutrino experiments is approaching intrinsic source limitations in the knowledge of flux, initial neutrino energy and flavor. A dedicated facility based on conventional accelerator techniques and…
One of the primary objectives of Deep Underground Neutrino Experiment (DUNE) is to discover the leptonic CP violation and to identify it's source. In this context, we study the impact of non-standard neutrino interactions (NSIs) on…
We evaluate uncertainty quantification (UQ) methods for deep learning applied to liquid argon time projection chamber (LArTPC) physics analysis tasks. As deep learning applications enter widespread usage among physics data analysis, neural…
In this study we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex…
Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors, such as the MicroBooNE detector at Fermilab, feature large, high fidelity event images. Deep learning techniques have been extremely successful in…
We investigate the physics potential of the upcoming Deep Underground Neutrino Experiment (DUNE) in probing active-sterile mixing. We present analytic expressions for relevant oscillation probabilities for three active and one sterile…
We discuss the impact of non-standard neutrino matter interactions (NSI) in propagation on the determination of CP phase in the context of the long baseline accelerator experiments such as Deep Underground Neutrino Experiment (DUNE). DUNE…