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We perform a series of simulations of evolving star clusters using AMUSE (the Astrophysical Multipurpose Software Environment), a new community-based multi-physics simulation package, and compare our results to existing work. These…
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference…
Sparse regression algorithms have been proposed as the appropriate framework to model the governing equations of a system from data, without needing prior knowledge of the underlying physics. In this work, we use sparse regression to build…
Recent strides have been made developing dust evolution models for galaxy formation simulations but these approaches vary in their assumptions and degree of complexity. Here we introduce and compare two separate dust evolution models…
Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data. These approaches define a forward diffusion process for transforming data to noise and generate data by…
Diffusion models have demonstrated their effectiveness across various generative tasks. However, when applied to medical image segmentation, these models encounter several challenges, including significant resource and time requirements.…
As foundation models grow rapidly in capability and deployment, evaluating their scientific understanding becomes increasingly critical. Existing science benchmarks have made progress towards broad Range, wide Reach, and high Rigor, yet…
Molecular clouds are turbulent structures whose star formation efficiency (SFE) is strongly affected by internal stellar feedback processes. In this paper we determine how sensitive the SFE of molecular clouds is to randomised inputs in the…
Grids of stellar models are useful tools to derive the properties of stellar clusters, in particular young clusters hosting massive stars, and to provide information on the star formation process in various mass ranges. Because of their…
Massive binaries are vital sources of various transient processes, including gravitational-wave mergers. However, large uncertainties in the evolution of massive stars, both physical and numerical, present a major challenge to the…
An ab-initio numerical study of the density-dependent, evolutionary stable dispersal strategy is presented. The simulations are based on a simple discretei generation island model with four processes: reproduction, dispersal, competition…
We present a numerical study of the evolution of molecular clouds, from their formation by converging flows in the warm ISM, to their destruction by the ionizing feedback of the massive stars they form. We improve with respect to our…
We present an evolutionary stellar population synthesis model which predicts SED's for simple stellar populations, SSP's, at ~2A resolution in the visible. The input database is composed of ~550 stars, selected from the spectral library of…
We present StableMotion, a novel framework leverages knowledge (geometry and content priors) from pretrained large-scale image diffusion models to perform motion estimation, solving single-image-based image rectification tasks such as…
Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative Adversarial Networks (GANs) or the decoder of Variational Autoencoders (VAEs) which produce samples…
We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and…
1D stellar evolution calculations produce uncertain predictions for quantities like the age, core mass, core compactness, and nucleo-synthetic yields; a key source of uncertainty is the modeling of interfaces between regions that are…
Learning unknown stochastic differential equations (SDEs) from observed data is a significant and challenging task with applications in various fields. Current approaches often use neural networks to represent drift and diffusion functions,…
Using artificial dissipation to tame entanglement growth, we chart the emergence of diffusion in a generic interacting lattice model for varying U(1) charge densities. We follow the crossover from ballistic to diffusive transport above a…
This paper introduces EGG, the Empirical Galaxy Generator, a tool designed within the ASTRODEEP collaboration to generate mock galaxy catalogs for deep fields with realistic fluxes and simple morphologies. The simulation procedure is based…