Building Cross-Disciplinary Collaboration in Data-Intensive Astronomy: A Modular Framework for Transparent Analysis

Introduction

Modern exoplanetary research increasingly requires combining expertise from astronomy, data science, and research software engineering. The complexity and volume of data from current and future missions demand not only robust processing techniques, but also transparent and reproducible analysis frameworks. In this context, we present a modular, open-source software framework developed to support systematic comparison of different data processing and modelling methods. Its architecture allows for flexibility, extensibility, and collaboration across disciplines.

The primary aim of this tool is to improve the reliability and interpretability of data-intensive exoplanet analysis workflows. By enabling the side-by-side application of multiple methodologies, researchers can better understand how specific assumptions and preprocessing decisions affect the outcomes of scientific inference.

Method

The framework allows researchers to create interchangeable modules covering stages such as data preparation, detrending, modelling, and parameter estimation. Each module is designed to operate independently within a defined interface, allowing researchers to substitute components and evaluate their effects on final results.

This design supports collaborative development and integration of methods by contributors with varied technical backgrounds. It facilitates comparative workflows and improves reproducibility by preserving the full sequence of processing steps used in each analysis.

As a proof of concept, the framework has been applied to the analysis of the long-period exoplanet candidate TOI-4409 b. This target was observed across 21 transits using a combination of ground-based and space-based photometric instruments, including TESS, ASTEP, CHAT, OMES, LCOGT, and PEST. The tool was used to integrate the datasets and test multiple modelling approaches in order to refine the planet’s properties. Additionally, the framework is being used in the analysis of archival TESS survey data to explore the effects of different noise-processing strategies on large-scale population-level studies.

Results

The application of the framework to TOI-4409 b demonstrated its capacity to manage multi-instrument datasets and compare model outputs under controlled conditions. The combined light curve analysis yielded updated estimates of the planet’s physical and orbital parameters, building on previos findings.

In parallel, its use in TESS archive analysis highlights the framework’s scalability and utility in benchmarking data-processing techniques across large datasets, allowing users to evaluate the impact of algorithmic variation on statistical outcomes.

Conclusion

This modular framework contributes to current efforts in research software engineering by providing a reproducible and extensible platform for astronomical data analysis. Its structure supports interdisciplinary collaboration by enabling clear communication and integration of alternative methodologies.

By presenting this tool within the EPSC-DPS community, particularly in the context of cross-disciplinary planetary science collaboration, we aim to introduce it as a useful addition to researchers' analytical toolkits, and to gather practical feedback from a diverse range of users to guide its development and ensure it aligns with real scientific needs.