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PreprintCC BY 4.0arXiv

Revisiting the Hubble Tension with DESI DR2 Baryon Acoustic Oscillation Observations and Machine Learning Methods

  • Chenfa Zheng
  • Shuo Cao
  • Wuzheng Guo
  • Qiumin Wang
  • Xinyue Jiang
  • Marek Biesiada
  • Tonghua Liu
Open at sourcePDFDOICite

Abstract

One of the key unresolved questions in modern cosmology is the Hubble tension, which arises from the inconsistency between determinations of the Hubble constant (H0) based on nearby observations and those predicted by early-Universe data under the standard ΛCDM paradigm. Recent advances have identified the new Dark Energy Spectroscopic Instrument (DESI) survey as a promising observational tool for probing late-time cosmology. In this paper, we use the latest DESI DR2 baryon acoustic oscillation (BAO) measurements, combined with nonparametrically reconstructed Type Ia supernovae and cosmic chronometer data, to constrain the Hubble constant using three complementary machine learning methodologies: Gaussian process regression (GPR), artificial neural networks (ANNs), and long short-term memory (LSTM) networks. We perform individual and joint constraints on H0 at the effective redshifts of DESI DR2 tracers, yielding H0 = 61.8 ± 5.3 km s−1 Mpc−1 (GPR), 66.3 ± 5.2 km s−1 Mpc−1 (ANN), and 68.7 ± 6.0 km s−1 Mpc−1 (LSTM). Our results exhibit excellent agreement with the recent Planck measurements within 1σ, which highlight the potential of data-driven, model-independent methods to address the Hubble tension. Finally, we further test the sensitivity of our results to potential systematics in the DESI data, emphasizing the importance of understanding systematic uncertainties in BAO surveys.

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Cite

BibTeXRIS
BibTeX
@misc{zheng2026revisiting,
  title = {{Revisiting the Hubble Tension with DESI DR2 Baryon Acoustic Oscillation Observations and Machine Learning Methods}},
  author = {Chenfa Zheng and Shuo Cao and Wuzheng Guo and Qiumin Wang and Xinyue Jiang and Marek Biesiada and Tonghua Liu},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.08836},
  eprint = {2610.08836},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.08836},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Revisiting the Hubble Tension with DESI DR2 Baryon Acoustic Oscillation Observations and Machine Learning Methods
AU  - Chenfa Zheng
AU  - Shuo Cao
AU  - Wuzheng Guo
AU  - Qiumin Wang
AU  - Xinyue Jiang
AU  - Marek Biesiada
AU  - Tonghua Liu
PY  - 2026
DA  - 2026/10/01
PB  - arXiv
DO  - 10.48550/arxiv.2610.08836
UR  - https://arxiv.org/abs/2610.08836
AB  - One of the key unresolved questions in modern cosmology is the Hubble tension, which arises from the inconsistency between determinations of the Hubble constant (H_0) based on nearby observations and those predicted by early-Universe data under the standard ΛCDM paradigm. Recent advances have identified the new Dark Energy Spectroscopic Instrument (DESI) survey as a promising observational tool for probing late-time cosmology. In this paper, we use the latest DESI DR2 baryon acoustic oscillation (BAO) measurements, combined with nonparametrically reconstructed Type Ia supernovae and cosmic chronometer data, to constrain the Hubble constant using three complementary machine learning methodologies: Gaussian process regression (GPR), artificial neural networks (ANNs), and long short-term memory (LSTM) networks. We perform individual and joint constraints on H_0 at the effective redshifts of DESI DR2 tracers, yielding H_0 = 61.8 ± 5.3 km s^−1 Mpc^−1 (GPR), 66.3 ± 5.2 km s^−1 Mpc^−1 (ANN), and 68.7 ± 6.0 km s^−1 Mpc^−1 (LSTM). Our results exhibit excellent agreement with the recent Planck measurements within 1σ, which highlight the potential of data-driven, model-independent methods to address the Hubble tension. Finally, we further test the sensitivity of our results to potential systematics in the DESI data, emphasizing the importance of understanding systematic uncertainties in BAO surveys.
ER  -