Tootfinder

Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick C\^ot\'e
https://arxiv.org/abs/2510.04957

Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Galaxy model subtraction removes the smooth light of nearby galaxies so that fainter sources (e.g., stars, star clusters, background galaxies) can be identified and measured. Traditional approaches (isophotal or parametric fitting) are semi-automated and can be challenging for large data sets. We build a convolutional denoising autoencoder (DAE) for galaxy model subtraction: images are compressed to a latent representation and reconstructed to yield the smooth galaxy, suppressing other objects.…

Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment
Mateusz Slysz, Piotr Rydlichowski, Krzysztof Kurowski, Omar Bacarezza, Esperanza Cuenca Gomez, Zohim Chandani, Bettina Heim, Pradnya Khalate, William R. Clements, James Fletcher
https://arxiv.org/abs/2508.16297

Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment
Achieving a practical quantum advantage for near-term applications is widely expected to rely on hybrid classical-quantum algorithms. To deliver this practical advantage to users, high performance computing (HPC) centers need to provide a suitable software and hardware stack that supports algorithms of this type. In this paper, we describe the world's first implementation of a classical-quantum environment in an HPC center that allows multiple users to execute hybrid algorithms on multiple quan…