LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications
This work addresses the challenge of model-independent distance estimation in cosmology, offering tools for data validation and future probes, though it appears incremental as it applies existing deep learning methods to a specific domain.
The authors tackled the problem of reconstructing the cosmic distance ladder by developing LADDER, a deep learning framework trained on Pantheon Type Ia supernovae data, which produced predictions with errors and was validated as the best-performing model among several tested. They demonstrated its applications for consistency checks, calibration, and mock catalog generation in cosmology.
We investigate the prospect of reconstructing the ''cosmic distance ladder'' of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernovae compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other datasets like baryon acoustic oscillations, calibration of high-redshift datasets such as gamma ray bursts, and use as a model-independent mock catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts.