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 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, that include 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, use as a model-independent mock catalog generator for future probes, etc. INTRODUCTION Knowledge of accurate distances to astronomical entities at various redshifts is essential for deducing the expansion history of the Universe. Observationally, however, this task is not simple since there does not exist one single standardizable measure of distances at all scales of cosmological interest. Hence one has to resort to a progressive method of calibrating distances, called the "cosmic distance ladder" method, using overlapping regions of potentially different standardizable objects as "rungs of the ladder". The conventional distance ladder method (Riess & Breuval 2023) starts with direct measures of geometric distance measures and progresses to calibrating Cepheid variables (Freedman & Madore 2023) or Tip of the Red Giant Branch (TRGB) stars (Freedman et al. 2020), and finally Type Ia supernovae (SNIa).