Research Topic

Big Data and Machine Learning in the Exoplanet Sciences

About this Research Topic

Exoplanet science has slowly, but surely, entered an age of Big Data. The size of observational data from surveys as well as computer-generated model grids has increased in size by orders of magnitude. In addition, modern machine learning methods are entering the field at an accelerating pace. In this Research Topic, we provide the opportunity to showcase these data sets and state-of-the-art methods to work with them. After promising early results in the areas of transit detection and atmospheric retrieval, machine learning is now entering every aspect of exoplanet science from optimizing imaging observations to modeling exoplanetary interiors. This Research Topic will provide a platform for these exciting new data and software products, as well as a state of the art of machine learning algorithms applicable to exoplanetary science.

We want to give the community the opportunity to have their multidisciplinary work reviewed by experts in the domain science as well as in the computer science, which is a crucial hurdle for work on the interface of these two fields. In this Research Topic, we want to provide a venue for exoplanetary researchers to showcase their work and at the same time offer experts from computer science an opportunity to read into the topic and the various applications of Big Data and machine learning methods in exoplanet science. Additionally, machine learning experts will get the opportunity to understand in more depth how algorithms and methods can be applied to exoplanetary data, and to improve their approach to machine learning based on new insights from this field.

Besides Review and Original Research articles which address all different aspects of Big Data, Artificial Intelligence, and Machine Learning in exoplanet science, this Research Topic particularly invites special formats of articles such as:

● Mini Reviews on the state-of-the-art in your respective subfield of exoplanet science
● Data Reports on publicly shared data sets that can be useful for training and benchmarking
● Brief Research Reports on preliminary studies, unsuccessful experiments and alike.


Keywords: Exoplanets, Astrobiology, Machine learning, Artificial Intelligence, Big Data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Exoplanet science has slowly, but surely, entered an age of Big Data. The size of observational data from surveys as well as computer-generated model grids has increased in size by orders of magnitude. In addition, modern machine learning methods are entering the field at an accelerating pace. In this Research Topic, we provide the opportunity to showcase these data sets and state-of-the-art methods to work with them. After promising early results in the areas of transit detection and atmospheric retrieval, machine learning is now entering every aspect of exoplanet science from optimizing imaging observations to modeling exoplanetary interiors. This Research Topic will provide a platform for these exciting new data and software products, as well as a state of the art of machine learning algorithms applicable to exoplanetary science.

We want to give the community the opportunity to have their multidisciplinary work reviewed by experts in the domain science as well as in the computer science, which is a crucial hurdle for work on the interface of these two fields. In this Research Topic, we want to provide a venue for exoplanetary researchers to showcase their work and at the same time offer experts from computer science an opportunity to read into the topic and the various applications of Big Data and machine learning methods in exoplanet science. Additionally, machine learning experts will get the opportunity to understand in more depth how algorithms and methods can be applied to exoplanetary data, and to improve their approach to machine learning based on new insights from this field.

Besides Review and Original Research articles which address all different aspects of Big Data, Artificial Intelligence, and Machine Learning in exoplanet science, this Research Topic particularly invites special formats of articles such as:

● Mini Reviews on the state-of-the-art in your respective subfield of exoplanet science
● Data Reports on publicly shared data sets that can be useful for training and benchmarking
● Brief Research Reports on preliminary studies, unsuccessful experiments and alike.


Keywords: Exoplanets, Astrobiology, Machine learning, Artificial Intelligence, Big Data


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

05 February 2021 Manuscript
05 March 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

05 February 2021 Manuscript
05 March 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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