arXiv subjects

A. Hussain

Publications and source records attributed to A. Hussain.

26 records · Page 2Linked to original sources

arXiv2025

First study of neutrino angle reconstruction using quasielastic-like interactions in MicroBooNE

We investigate the expected precision of the reconstructed neutrino direction using a νμ-argon quasielastic-like event topology with one muon and one proton in the final state and the reconstruction capabilities of the MicroBooNE liquid argon time projection chamber. This direction is of importance in the context of DUNE sub-GeV atmospheric oscillation studies. MicroBooNE allows for a data-driven quantification of this resolution by investigating the deviation of the reconstructed muon-proton system orientation with respect to the well-known direction of neutrinos originating from the Booster Neutrino Beam with an exposure of 1.3 x 1021 protons on target. Using simulation studies, we derive the expected sub-GeV DUNE atmospheric-neutrino reconstructed simulated spectrum by developing a reweighting scheme as a function of the true neutrino energy. We further report flux-integrated single- and double-differential cross section measurements of charged-current νμ quasielastic-like scattering on argon as a function of the muon-proton system angle using the full MicroBooNE data sets. We also demonstrate the sensitivity of these results to nuclear effects and final state hadronic reinteraction modeling.

hep-ex↗

arXiv2025

First Measurement of Differential Cross Sections for Muon Neutrino Charged Current Interactions on Argon with a Two-proton Final State in the MicroBooNE Detector

We present the first measurement of differential cross sections for charged-current muon neutrino interactions on argon with one muon, two protons, and no pions in the final state. These final states are dominated by two-nucleon knockout interactions, which are complicated to model and for which there is currently limited information about the characteristics of these interactions in existing neutrino-nucleus scattering data. Detailed investigations of two-nucleon knockout are vital to support upcoming experiments exploring the nature of the neutrino. Among the different kinematic quantities measured, the opening angle between the two protons, the angle between the total proton momentum and the muon, and the total transverse momentum of the final state system are most sensitive to the underlying physics processes as embodied in various theoretical models.

hep-ex↗

arXiv2025

First Search for Dark Sector $e^+e^-$ Explanations of the MiniBooNE Anomaly at MicroBooNE

We present MicroBooNE's first search for dark sector $e^+e^-$ explanations of the long-standing MiniBooNE anomaly. The MiniBooNE anomaly has garnered significant attention over the past 20 years including previous MicroBooNE investigations into both anomalous electron and photon excesses, but its origin still remains unclear. In this letter, we provide the first direct test of dark sector models in which dark neutrinos, produced through neutrino-induced scattering, decay into missing energy and visible $e^+e^-$ pairs comprising the MiniBooNE anomaly. Many such models have recently gained traction as a viable solution to the anomaly while evading past bounds. Using an exposure of $6.87 \times 10^{20}$ protons-on-target in the Booster Neutrino Beam, we implement a selection targeting forward-going, coherently produced $e^+e^-$ events. After unblinding, we observe 95 events, which we compare with the constrained background-only prediction of $69.7 \pm 17.3$. This analysis sets the world's first direct limits on these dark sector models and, at the 95\% confidence level, excludes the majority of the parameter space viable as a solution to the MiniBooNE anomaly.

hep-ex↗

arXiv2025

First Search for Neutral Current Coherent Single-Photon Production in MicroBooNE

This article presents the first search for neutrino-induced neutral current coherent single-photon production (NC coherent 1$γ$). The search makes use of data from the MicroBooNE 85-tonne active volume liquid argon time projection chamber detector, situated in the Fermilab Booster Neutrino Beam (BNB), with an average neutrino energy of $\langle E_ν\rangle \sim 0.8$ GeV. A targeted selection of candidate neutrino interactions with a single photon-like electromagnetic shower in the final state and no visible vertex activity was developed to search for the NC coherent 1$γ$ process, along with two auxiliary selections used to constrain the dominant background from NC$π^0$ production. With an integrated exposure of $6.87 \times 10^{20}$ protons on target delivered by the BNB, we set the world's first limit for this rare process, corresponding to an upper limit on the flux-averaged cross section of $σ<1.49 \times 10^{-41}\text{cm}^2$ at 90\% C.L.

hep-ex↗

arXiv2023

Resolving the Decreased Rank Attack in RPL's IoT Networks

The Routing Protocol for Low power and Lossy networks (RPL) has been developed by the Internet Engineering Task Force (IETF) standardization body to serve as a part of the 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard, a core communication technology for the Internet of Things (IoT) networks. RPL organizes its network in the form of a tree-like structure where a node is configured as the root of the tree while others integrate themselves into that structure based on their relative distance. A value called the Rank is used to define each node's relative position and it is used by other nodes to take their routing decisions. A malicious node can illegitimately claim a closer position to the root by advertising a lower rank value trapping other nodes to forward their traffic through that malicious node. In this study, we show how this behavior can have a detrimental side effect on the network via extensive simulations and propose a new secure objective function to prevent such an attack.

cs.NI↗

arXiv2022

Perspectives on the Calibration of CNN Energy Reconstruction in Highly Granular Calorimeters

We present a study which shows encouraging stability of the response linearity for a simulated high granularity calorimeter module reconstructed by a CNN model to miscalibration, bias, and noise effects. Our results also show an intuitive, quantifiable relationship between these factors and the calibration parameters. We trained a CNN model to reconstruct energy in the calorimeter module using simulated single-pion events; we then observed the response of the model under various miscalibration, bias, and noise conditions that affected the model input. From these data, we estimated linear response models to calibrate the CNN. We also quantified the relationship between these factors and the calibration parameters by regression analysis.

physics.ins-det↗

arXiv2022

On the Use of Neural Networks for Energy Reconstruction in High-granularity Calorimeters

We contrasted the performance of deep neural networks - Convolutional Neural Network (CNN) and Graph Neural Network (GNN) - to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

physics.ins-det↗

arXiv2016

Size dependent magnetic and electrical properties of Ba-doped nanocrystalline BiFeO$_3$

Improvement in magnetic and electrical properties of multiferroic BiFeO$_3$ in conjunction with their dependence on particle size is crucial due to its potential applications in multifunctional miniaturized devices. In this investigation, we report a study on particle size dependent structural, magnetic and electrical properties of sol-gel derived Bi$_{0.9}$Ba$_{0.1}$FeO$_3$ nanoparticles of different sizes ranging from $\sim$ 12 to 49 nm. The substitution of Bi by Ba significantly suppresses oxygen vacancies, reduces leakage current density and Fe$^{2+}$ state. An improvement in both magnetic and electrical properties is observed for 10 % Ba-doped BiFeO$_3$ nanoparticles compared to its undoped counterpart. The saturation magnetization of Bi$_{0.9}$Ba$_{0.1}$FeO$_3$ nanoparticles increase with reducing particle size in contrast with a decreasing trend of ferroelectric polarization. Moreover, a first order metamagnetic transition is noticed for $\sim$ 49 nm Bi$_{0.9}$Ba$_{0.1}$FeO$_3$ nanoparticles which disappeared with decreasing particle size. The observed strong size dependent multiferroic properties are attributed to the complex interaction between vacancy induced crystallographic defects, multiple valence states of Fe, uncompensated surface spins, crystallographic distortion and suppression of spiral spin cycloid of BiFeO$_3$.

cond-mat.mes-hall↗