LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning
  Approaches and Exploring its Applications
Rahul Shah Soumadeep Saha Purba Mukherjee Utpal Garain Supratik Pal
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. Our analysis
advocates for interesting yet cautious consideration of machine learning
applications in these contexts.