Tootfinder

Sorcha: A Solar System Survey Simulator for the Legacy Survey of Space and Time
Stephanie R. Merritt, Grigori Fedorets, Megan E. Schwamb, Samuel Cornwall, Pedro H. Bernardinelli, Mario Juric, Matthew J. Holman, Jacob A. Kurlander, Siegfried Eggl, Drew Oldag, Maxine West, Jeremy Kubica, Joseph Murtagh, R. Lynne Jones, Peter Yoachim, Ryan R. Lyttle, Michael S. P. Kelley, Joachim Moeyens, Kathleen Kiker, Shantanu P. Naidu, Colin Snodgrass, Shannon M. Matthews, Colin Orion Chandler

Sorcha: A Solar System Survey Simulator for the Legacy Survey of Space and Time
The upcoming Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory is expected to revolutionize solar system astronomy. Unprecedented in scale, this ten-year wide-field survey will collect billions of observations and discover a predicted $\sim$5 million new solar system objects. Like all astronomical surveys, its results will be affected by a complex system of intertwined detection biases. Survey simulators have long been used to forward-model the effects of these biases on a…

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Rafael Ferreira da Silva, Milad Abolhasani, Dionysios A. Antonopoulos, Laura Biven, Ryan Coffee, Ian T. Foster, Leslie Hamilton, Shantenu Jha, Theresa Mayer, Benjamin Mintz, Robert G. Moore, Salahudin Nimer, Noah Paulson, Woong Shin, Frederic Suter, Mitra Taheri, Michela Taufer, Newell R. Washburn

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institu…

Secure API-Driven Research Automation to Accelerate Scientific Discovery
Tyler J. Skluzacek, Paul Bryant, A. J. Ruckman, Daniel Rosendo, Suzanne Prentice, Michael J. Brim, Ryan Adamson, Sarp Oral, Mallikarjun Shankar, Rafael Ferreira da Silva
https://arxiv.org/abs/2506.11950

Secure API-Driven Research Automation to Accelerate Scientific Discovery
The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M revolutionizes programmatic access to high performance computing (HPC) while maintaining uncompromising security. This framework allows intelligent agents and experimental facilities to dyn…