Aanchal Sahu | Searcharxiv

arXiv2026

Modeling and Analysis of Main-Belt Asteroidal Dust Flux and Velocity Distribution at Inner Planets

Interplanetary dust in the inner solar system originates from multiple sources, including short-period comets and main-belt asteroids. In this work, we focus specifically on the dynamical evolution of asteroid-derived dust using N-body simulations that incorporates solar gravity, planetary perturbations, radiation pressure, Poynting-Robertson drag and solar wind forces. We quantify dust fluxes for Mars, Venus and Mercury across an important mass range, which are essential inputs for ablation process on Mars/Venus and for contributing in the impact process on Mercury. We have also derived impact velocity distributions and compared with existing literature. In addition, we examine how close-encounter velocities depend on the orbital elements linking dust energetics directly to the orbital architecture of the dust population. Our results show that the calibrated asteroidal flux agrees excellently with the scaled Grün model for Mars (0.04 orders of magnitude offset) and Venus (0.09 orders), and with the Müller (2002) model for Mercury (0.04 orders). The velocity distributions reveal a decoupling between flux and impact velocity: low-eccentricity grains dominate the flux, while high-eccentricity grains control the high-velocity tail. These findings have direct implications covering: (i) For atmosphere-less bodies like Mercury, the high-velocity tail affects impact processes and exosphere generation; (ii) For Mars and Venus, the flux-dominated low-velocity population determines meteoroid ablation rates and metal layer formation; (iii) Our calibrated fluxes provide inputs for comparison with future observations from different missions and also, for modeling impact-driven surface modification across the inner solar system.

astro-ph.EP↗

arXiv2021

GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANs

While generative adversarial networks (GAN) are popular for their higher sample quality as opposed to other generative models like the variational autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty of the evaluation of generated samples. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among classes), the use of disentangled latent spaces, agreement of said evaluation metric with human perception, etc. In this paper, we propose a new score, namely, GM Score, which takes into various factors such as sample quality, disentangled representation, intra-class and inter-class diversity, and other metrics such as precision, recall, and F1 score are employed for discriminability of latent space of deep belief network (DBN) and restricted Boltzmann machine (RBM). The evaluation is done for different GANs (GAN, DCGAN, BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on the benchmark MNIST dataset.

cs.LG↗