A possible late-time transition of MB inferred via neural networks
Not on the map. Why?
Abstract (arXiv)
The strengthening of tensions in the cosmological parameters has led to a reconsideration of fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude MB of Type Ia supernova. In this work, we reconsider the possibility of a variation of this parameter in a model-independent way. We employ neural networks to agnostically constrain the value of the absolute magnitude as well as assess the impact and statistical significance of a variation in MB with redshift from the Pantheon+ compilation, together with a thorough analysis of the neural network architecture. We find an indication for a possible transition redshift at the z≈ 1 region.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 164 references that are in the map. First posted on arXiv 16 Feb 2024.
Builds on
6 of 164 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
- 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
- 2023Neural network reconstruction of cosmology using the Pantheon compilationcited by 4
- 2024LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applicationscited by 4
- 2023Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learningcited by 3
Built on by
Citing papers per year: 2024: 2.
2 papers.