Related papers: Empirical fits to inclusive electron-carbon scatte…
We present an updated deep neural network model for inclusive electron-carbon scattering. Using the bootstrap model [Phys.Rev.C 110 (2024) 2, 025501] as a prior, we incorporate recent experimental data, as well as older measurements in the…
We present a new fitting technique based on the parametric bootstrap method, which relies on the idea to produce artificial measurements using the estimated probability distribution of the experimental data. In order to investigate the main…
Modelling the inelastic scattering of electrons in water is fundamental, given their crucial role in biological damage. In Monte Carlo track-structure codes used to assess biological damage, the energy loss function, from which cross…
Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result. In recent years, a numerous number of uncertainty quantification methods have been introduced. Monte…
We investigate whether a neural network approach can reproduce and predict the electron-nucleus cross sections in the kinematical domain of present and future accelerator-based neutrino oscillation experiments. For this purpose, we consider…
Pixel-space full spectrum fitting exploiting non-linear $\chi^2$ minimization became a \emph{de facto} standard way of deriving internal kinematics from absorption line spectra of galaxies and star clusters. However, reliable estimation of…
The predictions of parameteric property models and their uncertainties are sensitive to systematic errors such as inconsistent reference data, parametric model assumptions, or inadequate computational methods. Here, we discuss the…
We perform a combined analysis of inclusive electron scattering data from $A=3$ nuclei in the deep-inelastic and quasielastic scattering regions, using Monte Carlo analysis methods and the nuclear weak binding approximation to establish the…
This review paper emphasizes the significance of microscopic calculations with quantified theoretical error estimates in studying lepton-nucleus interactions and their implications for electron-scattering and accelerator…
Dropout is conventionally used during the training phase as regularization method and for quantifying uncertainty in deep learning. We propose to use dropout during training as well as inference steps, and average multiple predictions to…
The availability of the double-differential charged-current neutrino cross section, measured by the MiniBooNE collaboration using a carbon target, allows for a systematic comparison of nuclear effects in quasi-elastic electron and neutrino…
A simple Monte Carlo procedure is described for simulating the multiple scattering and absorption of electrons with the incident energy in the range 1-50 keV moving through a slab of uniformly distributed material of given atomic number,…
As deep learning-based computer vision algorithms continue to advance the state of the art, their robustness to real-world data continues to be an issue, making it difficult to bring an algorithm from the lab to the real world.…
The availability of the double-differential charged-current neutrino cross section, measured by the MiniBooNE collaboration using a carbon target, allows for a systematic comparison of nuclear effects in quasi-elastic electron and neutrino…
We perform a fit of the real Compton scattering (RCS) data below pion-production threshold to extract the electric ($\alpha_{E1}$) and magnetic ($\beta_{M1}$) static scalar dipole polarizabilities of the proton, using fixed-$t$ subtracted…
Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that proposed activating the dropout layers only during…
Standard gradient descent methods yield point estimates with no measure of confidence. This limitation is acute in overparameterized and low-data regimes, where models have many parameters relative to available data and can easily overfit.…
Monte Carlo dropout may effectively capture model uncertainty in deep learning, where a measure of uncertainty is obtained by using multiple instances of dropout at test time. However, Monte Carlo dropout is applied across the whole network…
We report the results of a theoretical study of quasielastic electron and neutrino interactions with carbon. Our approach takes into account the effects of final-state interactions between the struck nucleon and the residual nucleus,…
Using Monte Carlo integration techniques, we investigate running coupling effects compatible with the high energy bootstrap condition to all orders in the strong coupling in evolution equations valid at small values of Bjorken x in deep…