The ADASS XXXVI Abstract Submission is now open! NOTE: The link will take you to the ADASS Pretalx website.
Prospective speakers and poster presenters are invited to submit their abstracts by 01-Aug-2026 (Anywhere on Earth, AoE).
Key Dates for Abstract Submissions
15-June-2026: Abstract submission opens
01-Aug-2026: Oral and Tutorial contribution abstract submission deadline
01-Sept-2026: BoF abstract submission deadline
21-Sep-2026: Demo Booth applications deadline
01-Oct-2026: Poster abstract submission deadline
30-Oct-2026: Deadline for draft proceedings submissions
01-Nov-2026 to 05-Nov-2026: Conference Dates
20-Nov-2026: Final proceedings submissions deadline
Conference Session Formats
The conference will include a variety of themed sessions broadly introduced by keynote speakers. In addition, there will be workshops on the first day and BoF sessions.
Submission Guidelines
The conference POC encourage a broad range of submissions, loosely aligned with one of the proposed themes below. In the submission form, you will be asked about the most appropriate theme for your submission, but there is also the 'other' option (Theme 8). Whether you have extensive experience with astronomical software or are new to this community, your insights and perspectives are valuable. Abstracts and contributions are welcome in English.
The abstract submission system also allows to submit proposals for BoFs, tutorials and demo tables. There will be limited space for tutorials and demo tables, thus make sure to submit your proposal early.
Themes
The themes below were selected by the ADASS POC based on proposals from the community. Some of the themes have been created by merging multiple proposed smaller themes. You want to present something that does not seem to fit: The last theme is for you!
1. AI as a tool for data discovery and data management.
Description: Large Language Models and Agentic AI have provided new avenues to explore the literature and data archives. How do we develop and optimize these tools ensure that they accurately describe astronomical content and help scientists leverage them for rapid exploration of existing astronomical data.
2. AI as a tool for scientific discovery.
Description: Machine learning and AI have the potential to answer previously intractable questions. How do we correctly input physical knowledge in the analyses? How will AI help us uncover new scientific knowledge?
3. AI as a tool for software engineering.
Description: Artificial intelligence is increasingly embedded across the software development lifecycle, from code generation and optimisation to testing, validation, and maintenance. How do we effectively integrate AI into astronomy software engineering without compromising correctness, reproducibility, and long-term maintainability? What practices ensure that AI-assisted development produces robust, auditable, and scientifically trustworthy software, particularly in data-intensive and high-performance environments?
4. Building and operating science platforms and workflows in the petabyte era.
Description: Modern astronomy operates at scales where data volumes, computational demands, and operational complexity fundamentally reshape how science is conducted. How do we design, deploy, and sustain platforms that bring computation to the data while remaining efficient, cost-aware, and accessible to diverse user communities? What architectural patterns, workflow abstractions, and infrastructure strategies enable scalable, reproducible science in an environment of rapidly growing datasets and evolving hardware constraints?
5. Usability, accessibility and security in astronomy software.
Description: What methods and standards do we follow or newly develop to improve our astronomy software, from user-experience to security?
6. The art of collaboration in astronomy software development.
Description: How do we develop software in a collaborative way? How do we create software that can be used across instruments, across missions? How do we build collaborative teams?
7. Global data management and lifecycle in the exascale era.
Description: In the face of observatories collecting hundreds to thousands of petabytes of data during their lifetime we need to seriously consider questions about the use and usefulness of the collected data and the relative (financial) priority of the data archives compared with telescope construction, operations and upgrades. Will more telescopes work with a capped archive budget/capacity and thus need to delete data? How do we technically implement something like this, without sacrificing governance rules and scientific reproducibility?
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