Astrostatistics News
Issue 8, April 2026
Issue Editors: Jessi Cisewski-Kehe, David W. Hogg, Vinay L. Kashyap, Aneta Siemiginowska
Astrostatistics News (AN) is a newsletter designed to inform, promote, cultivate, and inspire the astrostatistics community.
Note from the editors: We want to include information that is relevant for our broad community. If you have ideas or recommendations for AN content, please send a message to astrostatisticsnews@gmail.com. Ideas may include relevant astrostatistics papers/data/code, visualizations, upcoming events, overviews of past events, job postings, format or commentary suggestions, etc.
By Max Bonamente (University of Alabama in Huntsville)
Workshop website: https://sites.google.com/uah.edu/sys2025/home
The sys2025: Systematic and Measurement Errors across the Sciences - AstroStatistics and Data Science workshop was held in Huntsville, AL on Nov. 14-17, 2025 on the campus of the University of Alabama in Huntsville. It attracted approximately seventy participants across a range of disciplines, mainly astronomy and statistics, with emphasis on students and early-career scientists whose participation was supported by a generous NSF grant. The focus of the workshop was on methods for systematic errors, and it was structured with lectures and review talks, and contributed presentations. This workshop was the second in a series of astrostatistics meetings in the southeast, with iid2022 being held at the neighboring Lake Guntersville State Park in 2022. The goal is to host approximately bi-yearly meetings in data science and astrostatistics that focus on student participation in a region of the US that is traditionally underrepresented in this field. Highlights of sys2025 was the participation of world-class scientists from US and foreign research institutes, and a keynote dinner lecture on the 10th anniversary of gravitational wave detections by Professor Gaby Gonzales, under the Saturn V rocket at the US Space and Rocket Center.
By Aneta Siemiginowska
Website: https://think.taylorandfrancis.com/special_issues/astronomical-imaging-data/
The open access Journal of Statistics and Data Science in Imaging (SDSI), published on behalf of the American Statistical Association, aims to address a significant gap in current outlets for researchers working on statistical methods and data science techniques for imaging data analysis. The primary aim is to serve as a forum for discussing methodological challenges encountered in the analysis of imaging data and for presenting statistically sound solutions. The issue editors are David Stenning, Yang Chen, Vinay Kashyap, Thomas Loredo, Aneta Siemiginowska, and Marina Vannucci.
The special issue on Statistics for Astronomical Imaging Data is accepting new manuscripts before December 31, 2026. All the contributions are peer-reviewed. 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.
There are no publication charges for papers accepted for publication in this special issue.
More details are available on the site.
Astrostatistics innovations of the present are highlighted in this section.
By Connor Stone (University of Toronto)
The PTED (Permutation Test using the Energy Distance) Python package offers a ready-to-use tool to statistically distinguish multi-dimensional samples, for instance, distinguishing two populations of astronomical objects (stars, galaxies, etc.) based on several measured properties. While many astronomers are familiar with the 1D Kolmogorov-Smirnov test for comparing samples, PTED provides a robust alternative for data of any dimensionality. The method uses the energy distance, a metric that compares distances within and between samples, and employs a permutation test to assess significance. This non-parametric approach makes PTED an "exact test", which maintains accurate Type I errors (the rate of falsely rejecting the null) without making any assumptions about the underlying data distribution. The package is user-friendly and applicable in a variety of astrostatistical contexts, including testing generative models. It can be used for classic two-sample comparisons, but the method also extends to a powerful function for posterior coverage testing by framing it as a two-sample-test problem (asking "was the ground truth sampled from the same distribution as the posterior samples?"). PTED can validate if posterior samples are statistically sound, providing a single p-value and diagnosing over- or under-confidence.
More details, documentation, and examples are available at the GitHub repository: https://github.com/ConnorStoneAstro/pted
ASA Astrostatistics Interest Group election results
Traditionally, the Chair-Elect and Program Chair-Elect transition into the Chair and Program Chair roles.
Yang Chen (Michigan) moved to the role of 2026 Chair
Max Autenrieth (Cambridge) moved to the role of 2026 Program Chair.
The AIG held the election of the new officers in October 2025.
Election Results for 2026:
Chair-Elect: David van Dyk (Imperial College London)
Program Chair-Elect: Amanda Cook (McGill University)
Web Director: Phil Van-Lane (University of Toronto)
Secretary: Ky Potter (Simon Fraser University and Los Alamos National Lab)
Congratulations to the election winners, and thank you to all the nominees and to those who voted!
Job Opportunities in Astrostatistics
Associate senior lecturer in mathematical statistics
Malmo University, Sweden
Details: More details on the website
Deadlines: Application deadline is May 6, 2026
Associate senior lecturer employment for four years, with the potential for promotion to a permanent position as a senior lecturer. Relevant applications should include research and proven expertise in machine learning, statistical modelling, uncertainty quantification, stochastic modelling, and/or other modern statistical methods for data analysis. The applicants do not have to formally possess a PhD in mathematics, but are required to have deep knowledge in both theory and applications of mathematical statistics. The position is closely linked to the research at the department, mainly to the fields of material sciences and/or astrophysics, and includes numeric calculations, mathematical modelling, experiments, observations, and statistical data
A list of job opportunities will be maintained at our website, astrostatisticsnews.com/job-opportunities.
Astrostatistics Events
A list of events will be maintained at our website, astrostatisticsnews.com/events.
Astrostatistics Sessions at the Joint Statistical Meeting
Aug 1-6, 2026, Boston, MA, USA
Registration and further information: https://ww2.amstat.org/meetings/jsm/2026/
Some sessions of interest at JSM 2026
Sun Aug 2, 2pm-3:50pm EDT, SPEED 1: Data Challenge, Bayesian Analysis, and Statistical Applications, Part 1
Sun Aug 2, 4pm-5:50pm EDT, Astrostatistics Interest Group: Student Paper Award
Mon Aug 3, 8:30am-10:20am EDT, Deep Space: Deep Learning in Astronomy
Tue Aug 4, 2:00pm-3:50pm EDT, Statistical Advances Motivated by Astrophysics and Space Sciences
Wed Aug 5, 10:30am-12:20pm EDT, Contributed Poster Presentations: Astrostatistics Interest Group
Wed Aug 5, 2:00pm-3:50pm EDT, Simulation-Based Inference: Robust Methods for Astronomy and the Broader Sciences
Virtual Summer School in Statistics for Astronomers
June 1-5, 2026, Online
Registration and further information: https://sites.psu.edu/astrostatistics/su26
Registration deadline: May 8, 2026
Registration fee: $120
Description: Penn State's Center for Astrostatistics and Astroinformatics is pleased to offer its 21st annual Summer School in Statistics for Astronomers Monday through Friday, June 1-5 2026. Taught by experienced faculty in statistics and astrostatistics, it provides a foundation in statistical inference, methods, and software within the context of problems arising in astronomical research. Topics include: principles of probability and inference, regression and model selection, bootstrap resampling, supervised and unsupervised learning, Bayesian data analysis, Markov chain Monte Carlo (MCMC), nested sampling, time series analysis, spatial statistics, machine learning methods, Gaussian processes regression, and machine learning methods such as deep learning neural networks and Random Forests. Extensive training in the public domain R statistical software environment is provided. Typical attendees are graduate students and young researchers, undergraduates and senior researchers are welcome.
Stimulated by the enthusiastic world-wide participation in our Summer Schools conducted online during the Covid-19 pandemic, we will provide the 2026 school in an enhanced online format. The lectures will be pre-recorded and can be viewed by participants any hour of the day. They will be supplemented by synchronous Zoom events and Slack channels where participants can communicate with instructors and teaching assistants. Participants will also learn the R statistical software language through applications to astronomical problems via recorded tutorials and by independent work using Jupyter notebooks. Teaching assistants will be available to assist. Attendees, spread across multiple time zones, are strongly encouraged to complete the tutorials before the next day’s live session begins. Asynchronous Slack channels will also be available to discuss the lectures, consult with astrostatisticians on individual participant’s research, and informally interact with other participants. Altogether, participants should expect to spend several hours per day – in their own time zones – listening and working on the Summer School curriculum during the June 1-5 week.
STAMPS Seminar Series
STAtistical Methods for the Physical Sciences Research Center (STAMPS@CMU)
https://www.cmu.edu/dietrich/statistics-datascience/stamps/index.html
launched the seminar series on September 20, 2024.
Talks are open to everyone who registers on the web site:
https://www.cmu.edu/dietrich/statistics-datascience/stamps/events/webinars/index.html
IAU - IAA Astrostatistics and Astroinformatics Seminars
Monthly Virtual online
Details: https://sites.google.com/view/iau-iaaseminar-new/home
Schedule: https://sites.google.com/view/iau-iaaseminar-new/schedule?authuser=0
This international online seminar is an initiative of the International Astrostatistics Association and the IAU Astroinformatics and Astrostatistics Commission. It focuses on statistical analysis and data mining of astronomical data. The seminar is run on Zoom monthly on second Tuesdays alternating between Europe-America and Australasia-Europe time zone instances. The standard seminar times are 8:00 UTC and 16:00 UTC. Please check the exact time and time differences with your timezone.
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