mlsanityData to 7 Oct 2026

A possible late-time transition of MB inferred via neural networks

arXiv 2402.10502 ↗First posted 16 Feb 2024astro-ph.CO cs.LG gr-qccited by 2

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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.

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6 of 164 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  4. 2023Neural network reconstruction of cosmology using the Pantheon compilationcited by 4
  5. 2024LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applicationscited by 4
  6. 2023Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learningcited by 3

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Citing papers per year: 2024: 2.

2 papers.