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Maxim Gonchar

Publications and source records attributed to Maxim Gonchar.

5 recordsLinked to original sources

Exploring the electromagnetic properties of neutrinos at a short-baseline reactor neutrino experiment

Upcoming and present reactor neutrino experiments represent an appealing tool to probe fundamental properties in the neutrino sector. In this paper, we study the physics potential to determine the electromagnetic properties of neutrinos via electron--neutrino elastic scattering (E$\nu$ES) at a short-baseline neutrino experiment. We evaluate the sensitivity to the weak mixing angle, $\sin^2\theta_W$, employing antineutrinos from a nuclear reactor source. Furthermore, from the sensitivity to $\sin^2 \theta_W$, we obtain bounds on the neutrino charge radius. We also present the projected sensitivity to the effective neutrino magnetic moment, $\mu_{\nu}$. Compared with other reactor neutrino measurements, this experimental configuration may set competitive limits on the electromagnetic properties of neutrinos.

hep-ph

Vertex and Energy Reconstruction in JUNO with Machine Learning Methods

The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters $\sin^2 2θ_{12}$, $Δm^2_{21}$ and $Δm^2_{32}$ are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present a few machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: $σ_E=3\%$ at $E_\text{vis}=1~\text{MeV}$ for the energy and $σ_{x,y,z}=10~\text{cm}$ at $E_\text{vis}=1~\text{MeV}$ for the position.

physics.ins-det

GNA: new framework for statistical data analysis

We report on the status of GNA --- a new framework for fitting large-scale physical models. GNA utilizes the data flow concept within which a model is represented by a directed acyclic graph. Each node is an operation on an array (matrix multiplication, derivative or cross section calculation, etc). The framework enables the user to create flexible and efficient large-scale lazily evaluated models, handle large numbers of parameters, propagate parameters' uncertainties while taking into account possible correlations between them, fit models, and perform statistical analysis. The main goal of the paper is to give an overview of the main concepts and methods as well as reasons behind their design. Detailed technical information is to be published in further works.

cs.MS

CUDA Support in GNA Data Analysis Framework

Usage of GPUs as co-processors is a well-established approach to accelerate costly algorithms operating on matrices and vectors. We aim to further improve the performance of the Global Neutrino Analysis framework (GNA) by adding GPU support in a way that is transparent to the end user. To achieve our goal we use CUDA, a state of the art technology providing GPGPU programming methods. In this paper we describe new features of GNA related to CUDA support. Some specific framework features that influence GPGPU integration are also explained. The paper investigates the feasibility of GPU technology application and shows an example of the achieved acceleration of an algorithm implemented within framework. Benchmarks show a significant performance increase when using GPU transformations. The project is currently in the developmental phase. Our plans include implementation of the set of transformations necessary for the data analysis in the GNA framework and tests of the GPU expediency in the complete analysis chain.

cs.DC

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to ~5000 inverse-beta-decay events and ~2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the $p\to K^++\barν$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.

physics.ins-det