Ryan Ghiles Goudjil

Mills Staylor, Amirreza Dolatpour Fathkouhi, Md Khairul Islam et al.

Large-scale astronomical image data processing and prediction are essential for astronomers, providing crucial insights into celestial objects, the universe's history, and its evolution. While modern deep learning models offer high predictive accuracy, they often demand substantial computational resources, making them resource-intensive and limiting accessibility. We introduce the Cloud-based Astronomy Inference (CAI) framework to address these challenges. This scalable solution integrates pre-trained foundation models with serverless cloud infrastructure through a Function-as-a-Service (FaaS). CAI enables efficient and scalable inference on astronomical images without extensive hardware. Using a foundation model for redshift prediction as a case study, our extensive experiments cover user devices, HPC (High-Performance Computing) servers, and Cloud. Using redshift prediction with the AstroMAE model demonstrated CAI's scalability and efficiency, achieving inference on a 12.6 GB dataset in only 28 seconds compared to 140.8 seconds on HPC GPUs and 1793 seconds on HPC CPUs. CAI also achieved significantly higher throughput, reaching 18.04 billion bits per second (bps), and maintained near-constant inference times as data sizes increased, all at minimal computational cost (under $5 per experiment). We also process large-scale data up to 1 TB to show CAI's effectiveness at scale. CAI thus provides a highly scalable, accessible, and cost-effective inference solution for the astronomy community. The code is accessible at https://github.com/UVA-MLSys/AI-for-Astronomy.

Md. Khairul Islam, Zeyu Xia, Ryan Goudjil et al.

Reconstructing the early Universe from the evolved present-day Universe is a challenging and computationally demanding problem in modern astrophysics. We devise a novel generative framework, Cosmo3DFlow, designed to address dimensionality and sparsity, the critical bottlenecks inherent in current state-of-the-art methods for cosmological inference. By integrating 3D Discrete Wavelet Transform (DWT) with flow matching, we effectively represent high-dimensional cosmological structures. The Wavelet Transform addresses the ``void problem'' by translating spatial emptiness into spectral sparsity. It decouples high-frequency details from low-frequency structures through spatial compression, and wavelet-space velocity fields facilitate stable ordinary differential equation (ODE) solvers with large step sizes. Using large-scale cosmological $N$-body simulations, at $128^3$ resolution, we achieve up to $50\times$ faster sampling than diffusion models, combining a $10\times$ reduction in integration steps with lower per-step computational cost from wavelet compression. Our results enable initial conditions to be sampled in seconds, compared to minutes for previous methods.