About this Research Topic
The use of machine learning (ML) tools for enhancing the capabilities of astronomical instrumentation is proliferating rapidly. In this Editor's Challenge in Astronomical Instrumentation, we seek to showcase how ML techniques for the control and data analysis of instrumentation are improving the power and accessibility of astronomical instrumentation.
Topics can include but are not limited to:
* Using ML to enhance useful outputs from surveys and directed observations
* How ML can make large astronomical datasets more scientifically productive
* Use of ML techniques to improve the dynamic range and detection thresholds in astronomical data
* The development of general ML algorithms
* Examples of how ML has simplified the control of complex instrumentation
* Examples of how ML can simplify the design and development of complex instrumentation
With the launch of this Editor’s Challenge research topic we aim to provide a space for articles addressing advances in this rapidly growing discipline and the potential impact for the field of astronomy, while demonstrating your role in this growth. We are keen to receive critical, ambitious, and courageous contributions to these and related topics with the common goal of addressing the latest ML advances in the field of astronomical instrumentation. We welcome a range of article types including Original Research, Review and Perspective articles to address the topic.
The Specialty Chief Editors of Frontiers in Astronomy and Space Sciences launch a new series of Research Topics to highlight current challenges across the fields of Astronomy and Space Sciences. Other titles in the series are:
Editor's Challenge in Astrostatistics: Deep Learning in Astrophysics - What are the Lessons?
Editor's Challenge in Exoplanets: Next Generation of Exoplanet Research
Topics can include but are not limited to:
* Using ML to enhance useful outputs from surveys and directed observations
* How ML can make large astronomical datasets more scientifically productive
* Use of ML techniques to improve the dynamic range and detection thresholds in astronomical data
* The development of general ML algorithms
* Examples of how ML has simplified the control of complex instrumentation
* Examples of how ML can simplify the design and development of complex instrumentation
With the launch of this Editor’s Challenge research topic we aim to provide a space for articles addressing advances in this rapidly growing discipline and the potential impact for the field of astronomy, while demonstrating your role in this growth. We are keen to receive critical, ambitious, and courageous contributions to these and related topics with the common goal of addressing the latest ML advances in the field of astronomical instrumentation. We welcome a range of article types including Original Research, Review and Perspective articles to address the topic.
The Specialty Chief Editors of Frontiers in Astronomy and Space Sciences launch a new series of Research Topics to highlight current challenges across the fields of Astronomy and Space Sciences. Other titles in the series are:
Editor's Challenge in Astrostatistics: Deep Learning in Astrophysics - What are the Lessons?
Editor's Challenge in Exoplanets: Next Generation of Exoplanet Research
Keywords: astronomy, machine learning, neural network, astronomical instrumentation
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.
