cosmologyinformed

Cosmology-informed refers to approaches that deliberately integrate cosmological knowledge, theory, and physical constraints into data analysis, modeling, and interpretation. It emphasizes aligning methods with established cosmological physics rather than relying solely on data-driven patterns.

In practice, cosmology-informed methods employ forward models based on cosmological theories, use informative priors grounded in cosmology, and enforce physical constraints such as conservation laws, scaling relations, and symmetry properties. They often involve combining simulations, analytical models, and observational data to produce inferences that are consistent with our understanding of the universe. The approach can be applied across a range of modalities, including imaging, spectroscopy, time-domain astronomy, and gravitational-wave astronomy.

Common applications include Bayesian parameter estimation in cosmology, where priors reflect measurements of the Hubble parameter, matter density, and other Lambda-CDM parameters; survey design and analysis that account for cosmic variance and selection effects; and the development of physics-informed machine learning models that respect cosmological symmetries and forward-model physics. Examples include cosmology-informed emulators of large-scale structure, forward modelling of galaxy surveys, and the use of cosmological priors to constrain transient event analyses.

Benefits of cosmology-informed methods include improved robustness with limited or noisy data, better generalization by enforcing physical plausibility, and enhanced consistency with the broader theoretical framework. Challenges involve potential bias if priors or models are inaccurate, substantial computational demands, and the need for close collaboration between theorists and data scientists to ensure correct implementation and interpretation. The term is widely used in research contexts to denote a principled integration of cosmology into analytical workflows.