The constant improvement of astronomical instrumentation provides the foundation for scientific discoveries. In general, these improvements have only implications forward in time, while previous observations do not profit from this trend. In solar physics, the study of long-term evolution typically exceeds the lifetime of single instruments and data driven approaches are strongly limited in terms of coherent long-term data samples.
We demonstrate that the available data sets can directly profit from the most recent instrumental improvements and provide a so far unused resource to foster novel research and accelerate data driven studies.
ITI (Instrument-to-Instrument) is a method that translates between image domains of different instruments, in order to inter-calibrate data sets, enhance physically relevant features which are otherwise beyond the diffraction limit of the telescope, mitigate atmospheric degradation effects and can estimate observables that are not covered by the instrument.
We demonstrate that our method can provide unified long-term data sets at the highest quality, by applying it to ground- and space-based solar observations.
To install the ITI tool we recommend to use the installation guide provided in the documentation.
We provide an example gallery that can be used as a starting point. A complete notebook with all the example steps (download → preprocessing → translation → visualization) can be found in the examples folder.
For more information and a detailed description about the ITI tool please visit the documentation page.
This work is the research product of the 22-MDRAIT22-0018, ITI (Instrument to Instrument) tool - unlocking collaborative sensor webs research. This has been funded and supported by NASA through a Grant and we would like to express our sincere gratitude. The research and its outputs have been designed, managed and delivered by Trillium Technologies Inc. Trillium is a research and development Trillium with a focus on intelligent systems and collaborative communities for planetary stewardship, space exploration and human health. The material is based upon work supported by NASA under Grant award No 80NSSC23K1045. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration (NASA). Trillium aspires to ensure that the latest tools and techniques in Artificial Intelligence (AI) and Machine Learning (ML) are applied to developing open science for all Humankind.