Tootfinder

SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
Sujit Roy, Dinesha V. Hegde, Johannes Schmude, Amy Lin, Vishal Gaur, Rohit Lal, Kshitiz Mandal, Talwinder Singh, Andr\'es Mu\~noz-Jaramillo, Kang Yang, Chetraj Pandey, Jinsu Hong, Berkay Aydin, Ryan McGranaghan, Spiridon Kasapis, Vishal Upendran, Shah Bahauddin, Daniel da Silva, Marcus Freitag, Iksha Gurung, Nikolai Pogorelov, Campbell Watson, Manil Maskey, Juan Bernabe-Moreno,…

SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/9]:
- Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data
Yang, Giezendanner, Civitarese, Jakubik, Schmitt, Chandra, Vila, Hohl, Hill, Watson, Wang

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation
Piyush Raikwar (CERN), Anna Zaborowska (CERN), Peter McKeown (CERN), Renato Cardoso (CERN), Mikolaj Piorczynski (CERN, Warsaw University of Technology), Kyongmin Yeo (Thomas J. Watson Research Center)
https://arxiv.org/abs/2509.07700…

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation
Collider experiments, such as those at the Large Hadron Collider, use the Geant4 toolkit to simulate particle-detector interactions with high accuracy. However, these experiments increasingly require larger amounts of simulated data, leading to huge computing cost. Generative machine learning methods could offer much faster calorimeter shower simulations by directly emulating detector responses. In this work, we present CaloDiT-2, a diffusion model which uses transformer blocks. As is the case …