Statistics and Data Science in Imaging

Statistics for Astronomical Imaging Data

Under the support of the American Statistical Association (ASA) Astrostatistics Interest Group, we invite submissions to a peer-reviewed special issue on “Statistics for Astronomical Imaging Data.” This special issue aims to showcase recent advances in innovative astrostatistical methodologies and computation which have been developed for processing and analyzing imaging data in astronomy, and looks ahead into the future of astrostatistics. Current and emerging telescopes and instruments produce huge image-based datasets from diverse astrophysical sources, ranging from the Sun and planets in our solar system, to distant stars and galaxies, and the surface of last scattering from the Big Bang. There is also great diversity in imaging modalities, with data spanning the electromagnetic spectrum from radio waves to high-energy gamma rays, and “multi-messenger” data from observations of neutrinos, cosmic rays, and gravitational waves. These data hold the promise of revolutionizing our understanding of the Universe. To realize this promise requires advances in statistical and machine learning methods for problems such as image segmentation, source deblending, deconvolution, morphological classification, uncertainty quantification, and detection and characterization of sources in 2D images and image cubes that combine spatial image data with data in spectral or temporal dimensions. We welcome contributions that cover these and related topics.

There will be no publication charges for papers accepted for publication in this special issue.

In addition to the types of articles published by the journal, as described below, short papers on high-quality research can also be submitted for consideration in this special issue. Manuscripts submitted will need to meet the same requirements and standards as regular submissions. The SDSI Editor-in-Chief will conduct initial screenings of all submissions and make assignments to the guest Editors, who will oversee the peer-review process. To ensure efficiency in the review and decision-making process while maintaining a high standard of academic rigor, we aim for a review time not exceeding 10 weeks from submission to first decision.

Statistics and Data Science in Imaging is published on behalf of the ASA, under the support of its Section on Statistics in Imaging. It is an international open access journal and only publishes open access articles. Publishing open access means that your article will be free to access online immediately on publication, increasing the visibility, readership, and impact of your research.

The journal publishes original research and reviews on Statistical Analysis of Imaging Data. The primary aim of the journal is to serve as a forum for discussing methodological challenges encountered in the analysis of imaging data and for presenting statistically sound solutions to those challenges. The journal covers a broad spectrum of statistical methods and data science techniques applicable to various imaging domains, including, but not limited to, neuroimaging, medical imaging, satellite imaging, physics, forensic imaging, astronomy, remote sensing, and materials science. The target audience comprises quantitative researchers, including statisticians, engineers, computer scientists, and data scientists, along with imaging researchers in fields such as brain science, radiology, satellite, forensic imaging, environmental studies, who are involved in developing and investigating methods for analyzing imaging data.

Along with methodological research papers, the journal publishes discussion papers, soliciting concise feedback from the statistical imaging community, in-depth reviews of specific topics by leading statisticians and data scientists, case-study papers that highlight applications of statistical methods in imaging data analysis to address real world questions, short communications highlight emerging issues of interest to the statistical community, and best practices papers for pipelines of data pre-processing and analysis to facilitate access to public data repositories and use of statistical software.

For further inquiries about the SDSI special issue, contact the SDSI Editor-in-Chief or any of the guest Editors.