Probabilistic cosmological inference on HI tomographic data

• Published in: Astrophysics and space science Vol. 370; no. 8; p. 76
• Main Author: Andrianomena, Sambatra
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2025; Springer Nature B.V
• Subjects: Approximation method; Astrobiology; Astronomy; Astrophysics and Astroparticles; Coders; Cosmology; Dark energy; Datasets; Deep learning; Density; Information retrieval; Investigations; Magnetic fields; Neural networks; Observations and Techniques; Physics; Physics and Astronomy; Probabilistic inference; Probabilistic models; Probability theory; Red shift; Representations; Simulation; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Uncertainty; Cosmology; Statistical methods; Simulation; Machine learning

We explore the possibility of retrieving cosmological information along with its inherent uncertainty from 21-cm tomographic data at intermediate redshift. The first step in our approach consists of training an encoder, composed of several three dimensional convolutional layers, to cast the neutral...