LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications. (arXiv:2401.17029v1 [astro-ph.CO])

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 arxiv astro astro-ph.co data deep learning deep learning framework framework novel supernovae type universe