Tootfinder

On the synergetic use of Ariel and JWST for exoplanet atmospheric science
Quentin Changeat, Pierre-Olivier Lagage, Giovanna Tinetti, Benjamin Charnay, Nicolas B. Cowan, Camilla Danielski, Elsa Ducrot, Achrene Dyrek, Billy Edwards, Theresa Lueftinger, Giuseppina Micela, Giuseppe Morello, Enzo Pascale, Severine Robert, Olivia Venot, Joanna K. Barstow, Andrea Bocchieri, James Y-K. Cho, Ryan Cloutier, Athena Coustenis, Panayotis Lavvas, Yamila Miguel, Kay Hou Yip

On the synergetic use of Ariel and JWST for exoplanet atmospheric science
This white paper explores the potential for strategic synergies between the JWST and the Ariel telescopes, two flagship observatories poised to revolutionise the study of exoplanet atmospheres. Both telescopes have the potential to address common fundamental questions about exoplanets-especially concerning their nature and origins-and serve a growing scientific community. With their operations now anticipated to overlap, starting from 2030, there is a unique opportunity to enhance the scientifi…

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…

Experiences with Model Context Protocol Servers for Science and High Performance Computing
Haochen Pan, Ryan Chard, Reid Mello, Christopher Grams, Tanjin He, Alexander Brace, Owen Price Skelly, Will Engler, Hayden Holbrook, Song Young Oh, Maxime Gonthier, Michael Papka, Ben Blaiszik, Kyle Chard, Ian Foster
https://arxiv.org/abs/2508.18489

Experiences with Model Context Protocol Servers for Science and High Performance Computing
Large language model (LLM)-powered agents are increasingly used to plan and execute scientific workflows, yet most research cyberinfrastructure (CI) exposes heterogeneous APIs and implements security models that present barriers for use by agents. We report on our experience using the Model Context Protocol (MCP) as a unifying interface that makes research capabilities discoverable, invokable, and composable. Our approach is pragmatic: we implement thin MCP servers over mature services, includi…

QISCIT: A validated concept inventory assessment for quantum information science
Kelley Durkin, Manshuo Lin, Michael H. Kolodrubetz, Ryan P. McMahan
https://arxiv.org/abs/2506.17122

QISCIT: A validated concept inventory assessment for quantum information science
Quantum information science (QIS) is a critical interdisciplinary field that requires a well-educated workforce in the near future. Numerous researchers and educators have been actively investigating how to best educate and prepare such a workforce. An open issue has been the lack of a validated tool to asses QIS understanding without requiring college-level math. In this paper, we present the systematic development and content validation of a new assessment instrument called the Quantum Inform…

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Ryan Barker
https://arxiv.org/abs/2506.09878 https://

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Virtualising the Radio Access Network (RAN) is widely touted as the corner-stone of affordable 5G and a prerequisite for AI-native 6G. Yet current discourse often isolates spectrum policy, cloud engineering and organisational readiness into silos. This paper delivers an integrated analysis that spans science, technology, business strategy and culture. I first review spectrum-auction economics and show-via a comparative study of T-Mobile US and Verizon-that mid-band contiguity leveraged through …

Few Single-Qubit Measurements Suffice to Certify Any Quantum State
Meghal Gupta, William He, Ryan O'Donnell
https://arxiv.org/abs/2506.11355 https://…

Few Single-Qubit Measurements Suffice to Certify Any Quantum State
A fundamental task in quantum information science is \emph{state certification}: testing whether a lab-prepared $n$-qubit state is close to a given hypothesis state. In this work, we show that \emph{every} pure hypothesis state can be certified using only $O(n^2)$ single-qubit measurements applied to $O(n)$ copies of the lab state. Prior to our work, it was not known whether even sub-exponentially many single-qubit measurements could suffice to certify arbitrary states. This resolves the main o…

Generative AI in Science: Applications, Challenges, and Emerging Questions
Ryan Harries, Cornelia Lawson, Philip Shapira
https://arxiv.org/abs/2507.08310 h…

Generative AI in Science: Applications, Challenges, and Emerging Questions
This paper examines the impact of Generative Artificial Intelligence (GenAI) on scientific practices, conducting a qualitative review of selected literature to explore its applications, benefits, and challenges. The review draws on the OpenAlex publication database, using a Boolean search approach to identify scientific literature related to GenAI (including large language models and ChatGPT). Thirty-nine highly cited papers and commentaries are reviewed and qualitatively coded. Results are cat…

Unveiling Unprecedented Fine Structure in Coronal Flare Loops with the DKIST
Cole A. Tamburri, Maria D. Kazachenko, Gianna Cauzzi, Adam F. Kowalski, Ryan French, Rahul Yadav, Caroline L Evans, Yuta Notsu, Marcel F. Corchado-Albelo, Kevin P. Reardon, Alexandra Tritschler
https://arxiv.org/abs/2508.05875…

Unveiling Unprecedented Fine Structure in Coronal Flare Loops with the DKIST
We present the highest-resolution H$α$ observations of a solar flare to date, collected during the decay phase of an X1.3-class flare on 8 August 2024 at 20:12 UT. Observations with the Visible Broadband Imager at the National Science Foundation's (NSF) Daniel K. Inouye Solar Telescope reveal dark coronal loop strands at unprecedented spatial resolution in the flare arcade above highly structured chromospheric flare ribbons. After surveying the 20 best-seeing images, we calculate a mean loop w…

Replaced article(s) found for physics.ed-ph. https://arxiv.org/list/physics.ed-ph/new
[1/1]:
- QISCIT: A validated concept inventory assessment for quantum information science
Kelley Durkin, Manshuo Lin, Michael H. Kolodrubetz, Ryan P. McMahan

Time-Varying Model Predictive Attitude Control for Magnetically Actuated Dual-Spin Satellites
Robert D. Halverson, Ryan J. Caverly
https://arxiv.org/abs/2506.07858

Time-Varying Model Predictive Attitude Control for Magnetically Actuated Dual-Spin Satellites
Attitude control hardware for small satellites is often limited in power and space availability given the importance of the science instruments they exist to transport. To mitigate this, a dual-spin stabilized satellite actuated via magnetic torque rods reduces the space and power required of the attitude control system, but may require advanced control policies. This paper explores the attitude control of a magnetically actuated dual-spin stabilized CubeSat with model predictive control using …

Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro
https://arxiv.org/abs/2508.10155

Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Difference image analysis (DIA) is a powerful tool for studying time-variable phenomena, and has been used by many time-domain surveys. Most DIA algorithms involve matching the spatially-varying PSF shape between science and template images, and then convolving that shape in one image to match the other. The wrong choice of which image to convolve can introduce one of the largest sources of artifacts in the final difference image. We introduce a quantitative metric to determine the optimal conv…