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<p>In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) with the machine learning (ML) algorithms from the Pantheon+ sample of type Ia supernovae in a cosmology-independent way. By using K-Nearest Neighbors (KNN) and Random Forest (RF) selected with the best performance in the ML algorithms, we calibrate the Amati relation (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10509_2025_4401_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msub> <mi>E</mi> <mi mathvariant="normal">p</mi> </msub> </math></EquationSource> <EquationSource Format="TEX">$E_{\mathrm{p}}$</EquationSource> </InlineEquation>-<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10509_2025_4401_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="28" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <msub> <mi>E</mi> <mi mathvariant="normal">iso</mi> </msub> </math></EquationSource> <EquationSource Format="TEX">$E_{\mathrm{iso}}$</EquationSource> </InlineEquation>) relation with the A219 sample to construct the Hubble diagram of GRBs. Via the Markov Chain Monte Carlo numerical method with GRBs at high redshift and latest observational Hubble data, we find the results of constraints on cosmological models by using KNN and RF algorithms are consistent with those obtained from GRBs calibrated by using the Gaussian Process.</p>

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Model-independent gamma-ray bursts constraints on cosmological models using machine learning

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Bin Zhang,
  • Huifeng Wang,
  • Xiaodong Nong,
  • GuangZhen Wang,
  • Puxun Wu,
  • Nan Liang

摘要

In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) with the machine learning (ML) algorithms from the Pantheon+ sample of type Ia supernovae in a cosmology-independent way. By using K-Nearest Neighbors (KNN) and Random Forest (RF) selected with the best performance in the ML algorithms, we calibrate the Amati relation ( E p $E_{\mathrm{p}}$ - E iso $E_{\mathrm{iso}}$ ) relation with the A219 sample to construct the Hubble diagram of GRBs. Via the Markov Chain Monte Carlo numerical method with GRBs at high redshift and latest observational Hubble data, we find the results of constraints on cosmological models by using KNN and RF algorithms are consistent with those obtained from GRBs calibrated by using the Gaussian Process.