Astroinformatics and Astrostatistics

The “Astroinformatics and Astrostatistics” Section provides an interdisciplinary forum at the interface of astronomy, astrophysics, statistics, computer science, and data science. The Section focuses on the development, validation, and application of statistical, computational, and data-intensive methods that enable reliable scientific discovery from modern astronomical observations, simulations, and archives.

The rapidly increasing volume, complexity, heterogeneity, and acquisition rate of data from current and next-generation facilities—including the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (Rubin/LSST), the Square Kilometre Array telescopes, Euclid, the China Space Station Telescope (CSST), and other ground- and space-based missions—require scalable methodologies that extend beyond conventional data-analysis approaches. This Section welcomes original research articles, reviews, perspectives, technical studies, software papers, benchmark studies, and reproducible data products that advance the methodological foundations of data-driven astronomy.

Topics of interest include, but are not limited to:

• Statistical inference for astronomical and cosmological data, including Bayesian, frequentist, hierarchical, and likelihood-free approaches;
• Uncertainty quantification, probability calibration, model comparison, measurement-error modeling, and the treatment of selection effects, censored data, and survey incompleteness;
• Machine learning, deep learning, foundation models, generative models, scientific machine learning, and physics-informed or uncertainty-aware artificial intelligence;
• Interpretable, trustworthy, and reproducible AI methods for astronomical research;
• Scalable algorithms, high-performance and distributed computing, cloud-based analysis, scientific workflows, and research software;
• Astronomical databases, archives, data standards, interoperability, provenance, visualization, and open-science infrastructure;
• Time-series analysis, transient detection, variable-source characterization, anomaly detection, and real-time alert processing;
• Multimodal, multiwavelength, cross-survey, and multi-messenger data integration involving electromagnetic radiation, gravitational waves, neutrinos, and cosmic rays;
• Simulation-based inference, surrogate modeling, emulation, and the statistical comparison of observations with numerical simulations;
• Population inference, spatial statistics, image and spectral analysis, source classification, parameter estimation, and scientific discovery in large astronomical surveys;
• Benchmark datasets, validation protocols, domain adaptation, transfer learning, and methods for assessing robustness and generalization across instruments and surveys.

Submissions centered on specific astronomical objects or phenomena are particularly encouraged when they introduce, rigorously evaluate, or substantially advance statistical, computational, or information-science methodology.

• astroinformatics
• astrostatistics
• astronomical data science
• machine learning and artificial intelligence
• deep learning and foundation models
• scientific machine learning
• bayesian and statistical inference
• uncertainty quantification and calibration
• simulation-based inference
• time-domain astronomy
• transient and variable-source analysis
• multi-messenger astronomy
• multimodal and multiwavelength data fusion
• large-scale astronomical surveys
• high-performance and distributed computing
• astronomical databases and data infrastructure
• scientific workflows and research software
• data provenance and interoperability
• reproducible and open science
• explainable and trustworthy AI