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, including 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, 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.

13 pages, 6 sets of figures, 5 tables. To appear in the Astrophys. J. Suppl. Ser. Code available at https://github.com/rahulshah1397/LADDER

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• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
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• ${\Lambda }_{\mathrm{s}}$CDM cosmology: Alleviating major cosmological tensions by predicting standard neutrino properties
• Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression
• A possible late-time transition of $M_B$ inferred via neural networks
• Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning
• Gamma-Ray Bursts Calibrated from the Observational $H(z)$ Data in Artificial Neural Network Framework
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• Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample
• Investigating the Redshift Evolution of Lensing Galaxy Density Slopes via Model-Independent Distance Ratios
• What can we learn about Reionization astrophysical parameters using Gaussian Process Regression?
• Testing the Distance Duality Relation with Cosmological Observations at high Redshift using Artificial Neural Network
• Testing the cosmic distance duality relation using model-independent approach