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

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. …

algorithm applications astro-ph.co astro-ph.im compilation cs.lg data deep learning deep learning framework framework novel supernovae type universe