Related papers: EmulART: Emulating Radiative Transfer -- A pilot s…
Dust destruction by supernovae is one of the main processes removing dust from the interstellar medium (ISM). Estimates of the efficiency of this process, both theoretical and observational, typically assume a shock propagating into a…
Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network…
This paper reviews our current understanding of interstellar dust models, what constitutes a viable dust model, what observational constraints are essential for deriving such model, and the current viable dust models. Interstellar dust…
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for…
The generation and evolution of dust in galaxies are important tracers for star formation, and can characterize the rest-frame ultraviolet to infrared emission from the galaxies. In particular understanding dust in high-redshift galaxies…
A Monte Carlo model of radiative transfer in multi-phase dusty media is applied to the situation of stars and clumpy dust in a sphere or a disk. The distribution of escaping and absorbed photons are shown for various filling factors and…
Modelling carbon monoxide (CO) line radiation is computationally expensive for traditional numerical solvers, especially when applied to complex, three-dimensional stellar atmospheres. We present COEmuNet, a 3D convolutional neural network…
Studying the UV dust attenuation, as well as its relation to other galaxy parameters such as the stellar mass, plays an important role in multi-wavelength research. This work relates the dust attenuation to the stellar mass of star forming…
Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep learning techniques, denoising diffusion probabilistic models (DDPMs), to predict the interstellar…
We constructed a simple cosmological model which approximates the Einstein-de Sitter background with periodically distributed dust inhomogeneities. By taking the metric as a power series up to the third order in some perturbative parameter…
One of the main drawback of diffusion models is the slow inference time for image generation. Among the most successful approaches to addressing this problem are distillation methods. However, these methods require considerable…
Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model's ability to fully…
Dust is an important tracer of the structure of interstellar clouds, as well as a central factor in the thermal balance and chemistry of the clouds. Our knowledge of the dust properties is nevertheless incomplete, especially regarding the…
For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to…
Physics-based models often involve large systems of parametrized partial differential equations, where design parameters control various properties. However, high-fidelity simulations of such systems on large domains or with high grid…
Cloud removal (CR) remains a challenging task in remote sensing image processing. Although diffusion models (DM) exhibit strong generative capabilities, their direct applications to CR are suboptimal, as they generate cloudless images from…
The translation from direct observables to physical properties of galaxies is a key step in reconstructing their evolutionary histories. Star-dust geometry and inhomogeneous stellar populations can induce spatial variations in the…
Light scattering at near-infrared wavelengths has been used to study the optical properties of the interstellar dust grains, but these studies are limited by the assumptions on the strength of the radiation field. On the other hand, thermal…
In this analysis we illustrate how the relatively new emission mechanism known as spinning dust can be used to characterize dust grains in the interstellar medium. We demonstrate this by using spinning dust emission observations to…
We present a detailed study of the dust energy balance in the Sombrero galaxy M104. From a full radiative transfer analysis, including scattering, absorption and thermal re-emission, we construct models that can reproduce images at…