CEDAR email: Topical Collection, "Applications of Statistical Methods and Machine Learning in the Space Sciences" in Frontiers in Astronomy and Space Sciences

[Verkhoglyadova, Olga P (US 335G)]

Topical Collection, "Applications of Statistical Methods and Machine Learning in the Space Sciences" in Frontiers in Astronomy and Space Sciences

Statistical methods have been part of scientific data analysis in the space sciences for decades and machine learning (ML) is becoming an inevitable tool in the analysis of huge volumes of spacecraft data. Data science (DS) and ML are revolutionizing the way scientific problems in the space sciences are conceptualized and addressed, and have shown to be greatly successful in modeling and data analysis. In the wake of the immense volume of data acquired by the numerous spacecraft missions, methods such as time series analysis, segmentation, Bayesian methods, probabilistic inference and surrogate models, to mention a few, are critical for future scientific findings and discoveries. Though ML and deep neural networks are powerful tools for data mining and pattern recognition, and to make predictions, the interpretability and explainability of the models built on these techniques have not been explored adequately until recently.

Since statistical methods form an integral part of ML techniques, a review of these methods as applied to space sciences is timely and a virtual conference, "Applications of Statistical Methods and Machine Learning in the Space Sciences",  was held during 17-21 May 2021 (http://spacescience.org/workshops/mlconference2021.php<https://urldefense.us/v3/__http:/spacescience.org/workshops/mlconference2021.php__;!!PvBDto6Hs4WbVuu7!Z9R1lo5TVv8Lh7SAxF48g5Zq7tbEu84H0DdJAvMcTob4SkiXE6KfpT00xFhMsLCMZGNx2m-V8-i-Tg$>) that brought together experts to leverage the advancements in statistics, data science, methods of artificial intelligence (AI) such as machine learning and deep learning, and information theory to improve the analytic models and their predictive capabilities making use of the enormous data in the space sciences. The multidisciplinary conference was open to students and researchers from the space sciences (solar physics, magnetospheric studies and aeronomy, planetary sciences and exoplanets and galaxies) who implement methods of advanced statistics and AI in their research, from computer science and AI, statistics and data science, and from industry. In addition to keynote lectures and contributed talks/posters, there were discussion sessions designated to handle different topics on each each day with emphasis on the interpretability and explainability of the ML models.

The topical collection "Applications of Statistical Methods and Machine Learning in the Space Sciences" (https://www.frontiersin.org/research-topics/25408/applications-of-statistical-methods-and-machine-learning-in-the-space-sciences<https://urldefense.us/v3/__https:/www.frontiersin.org/research-topics/25408/applications-of-statistical-methods-and-machine-learning-in-the-space-sciences__;!!PvBDto6Hs4WbVuu7!Z9R1lo5TVv8Lh7SAxF48g5Zq7tbEu84H0DdJAvMcTob4SkiXE6KfpT00xFhMsLCMZGNx2m_lfswgtw$>) will consist of works presented at the virtual conference. In addition to this, we invite contributions from the broader scientific community in the form of original research articles, reviews/mini-reviews, brief reports and commentaries on the present scenario and scope of statistical methods and ML in the space sciences and relevant to the topics covered in the virtual conference. Abstracts are due by 1st October 2021 and the manuscripts by 17th December 2021.

We look forward to seeing your manuscripts,
Bala Poduval, Karly Pitman, Olga Verkhoglyadova (Associate Guest Editors)

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