LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications

Abstract

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 supernova 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 data sets like baryon acoustic oscillations, calibration of high-redshift data sets 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.