A. Rani | Searcharxiv

arXiv2025

Backscattering Study of Electrons from 0.1 to 3.4 MeV

Benchmarking simulation codes for electron transport and scattering in matter is a crucial step for estimating uncertainties in many applications. However, experimental data for electron energies of a few MeV is scarce to make such comparisons. We report here the measurement and the quantitative analysis of backscattering probabilities of electrons in the energy range 0.1 to 3.4~MeV impinging on YAP:Ce scintillator. The setup consists of a $2\times 2\pi$ calorimeter which enables, in particular, the inclusion of large incidence angles. The results are used to benchmark various scattering models incorporated in Geant4, showing relative deviations smaller than 5% between experiment and simulations. They demonstrate the current rather high reliability of the simulations when employing appropriate electromagnetic Physics Lists.

nucl-ex↗

arXiv2025

Synthetic Fungi Datasets: A Time-Aligned Approach

Fungi undergo dynamic morphological transformations throughout their lifecycle, forming intricate networks as they transition from spores to mature mycelium structures. To support the study of these time-dependent processes, we present a synthetic, time-aligned image dataset that models key stages of fungal growth. This dataset systematically captures phenomena such as spore size reduction, branching dynamics, and the emergence of complex mycelium networks. The controlled generation process ensures temporal consistency, scalability, and structural alignment, addressing the limitations of real-world fungal datasets. Optimized for deep learning (DL) applications, this dataset facilitates the development of models for classifying growth stages, predicting fungal development, and analyzing morphological patterns over time. With applications spanning agriculture, medicine, and industrial mycology, this resource provides a robust foundation for automating fungal analysis, enhancing disease monitoring, and advancing fungal biology research through artificial intelligence.

cs.CV↗