Zobrazeno 1 - 10
of 127
pro vyhledávání: '"GRACZYK, KRZYSZTOF M."'
Autor:
Prasad, Hemant, Sobczyk, Jan T., Ankowski, Artur M., Bonilla, J. Luis, Banerjee, Rwik Dharmapal, Graczyk, Krzysztof M., Kowal, Beata E.
We present the implementation and results of a new model for the n-particle n-hole ($\it{np-nh}$) contribution in the NuWro event generator, grounded in the theoretical framework established by the Valencia group in 2020. For the $\it{2p2h}$ componen
Externí odkaz:
http://arxiv.org/abs/2411.11523
Autor:
Graczyk, Krzysztof M., Kowal, Beata E., Ankowski, Artur M., Banerjee, Rwik Dharmapal, Bonilla, Jose Luis, Prasad, Hemant, Sobczyk, Jan T.
Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the physics of one process, and after fine-tu
Externí odkaz:
http://arxiv.org/abs/2408.09936
Autor:
Kowal, Beata E., Graczyk, Krzysztof M., Ankowski, Artur M., Banerjee, Rwik Dharmapal, Prasad, Hemant, Sobczyk, Jan T.
Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of deep-inelastic scat
Externí odkaz:
http://arxiv.org/abs/2312.17298
Autor:
Banerjee, Rwik Dharmapal, Ankowski, Artur M., Graczyk, Krzysztof M., Kowal, Beata E., Prasad, Hemant, Sobczyk, Jan T.
Publikováno v:
Phys. Rev. D 109, 073004 (2024)
The Short-Baseline Neutrino program in Fermilab aims to resolve the nature of the low-energy excess events observed in LSND and MiniBooNE, and analyze with unprecedented precision neutrino interactions with argon. These studies require reliable estim
Externí odkaz:
http://arxiv.org/abs/2312.13369
We present the application of the physics-informed neural network (PINN) approach in Bayesian formulation. We have adopted the Bayesian neural network framework to obtain posterior densities from Laplace approximation. For each model or fit, the evid
Externí odkaz:
http://arxiv.org/abs/2308.13222
Autor:
Graczyk, Krzysztof M., Kowal, Beata E.
The elastic and inelastic neutral current $\nu$ ($\overline{\nu}$) scattering off the polarized nucleon is discussed. The inelastic scattering concerns the single-pion production process. We show that the spin asymmetries' measurement can help to dis
Externí odkaz:
http://arxiv.org/abs/2307.00661
Publikováno v:
Sci Rep 13, 9769 (2023)
We adopt convolutional neural networks (CNN) to predict the basic properties of the porous media. Two different media types are considered: one mimics the sand packings, and the other mimics the systems derived from the extracellular space of biologi
Externí odkaz:
http://arxiv.org/abs/2304.02104
Applying an efficient pattern-based computational method of generating the so-called 'resonating' algebraic structures results in a broad class of the new Lie (super)algebras. Those structures inherit the AdS base (anti)commutation pattern and can be
Externí odkaz:
http://arxiv.org/abs/2212.03950
We present new superalgebra for $\mathcal{N}=2$ $D=3,4$ supergravity theory endowed with the $U(1)$ generator. The superalgebra is rooted in the so-called Soroka-Soroka algebra and spanned by the Lorentz $J_{ab}$ and Lorentz-like $Z_{ab}$, translatio
Externí odkaz:
http://arxiv.org/abs/2205.05921
Publikováno v:
Sci Rep 12, 10583 (2022)
The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U$^2$-Net. Two statistical methods for deep neural networks are utilized: the bootstrap and the Monte
Externí odkaz:
http://arxiv.org/abs/2203.09474