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2026 年 6 月 27 日 星期六 · 数据截至 arXiv / ADS 最新收录日

本页经 1 次补录 ·

I.

今日头条

No Breaking · 无突发
今日无通过复核的重大进展

当日 3 篇核心与相关文献均为常规推进,核心 1 篇已按优先级列于下方。

II.

核心文献

1 篇
01
优先级 80 · 伽马暴 GRB · 由暂现源样本驱动的总体统计、宇宙学与基础物理应用 · 时域分析

Evaluating gamma-ray burst light curve reconstruction: A diverse machine learning approach

评估伽马射线暴光变曲线重建:多样化的机器学习方法

K. Gupta, K. Sil, M. G. Dainotti et al.

原文摘要Abstract

Reducing the gaps in the X-Ray afterglow light curve (LC) of Gamma-ray bursts (GRBs) proves to be of immense value as their potential to be used as cosmological probes. Accurate reconstruction tightens the two-dimensional Dainotti (<mml:math><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mspace></mml:mspace><mml:mo>−</mml:mo><mml:mspace></mml:mspace><mml:msub><mml:mi>L</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:math>) relation among the rest-frame plateau end time T<SUB>a</SUB>, its corresponding luminosity L<SUB>a</SUB>, and consequently its extension to the fundamental plane -approaching the ideal scenario of a perfect satellite that provides complete, gap-free LC coverage. This study expands on previous GRB reconstruction work on 521 GRBs by introducing five distinct models: Kernel Ridge Regression (KRR), Cubic Smoothing Spline (CSS), Gaussian Process via ANN (GP via ANN), Artificial Neural Networks (ANN), and Symbolic Regression (SR). Our research reveals that the CSS model achieves the highest uncertainty reduction for all three parameters, outperforming all previous machine learning and statistical models. Additionally, CSS, KRR, and ANN models achieve a perfect 0% outlier rate for the Good GRBs subclass. The CSS model performs best as it balances flexibility to capture intricate LC structures with stability to avoid overfitting noisy, sparse data. These progressions are crucial for utilizing GRBs as theoretical model discriminators through their LC parameters and as standard candles in cosmology, and forecasting GRB redshifts with the latest advanced machine-learning techniques.

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AI 综述 AI-generated · 以原文为准
亮点

系统比较了KRR、CSS、GP via ANN、ANN和SR五种模型在521个GRB样本上的重建效果,CSS模型在降低参数不确定性和离群率方面均表现最优,为GRB宇宙学应用提供关键改进。

脉络与展望

接续此前基于521个GRB的光变曲线重建研究,本工作引入多样化的机器学习方法进行系统评估。通过比较发现CSS模型兼具拟合复杂结构与抗过拟合的优势,实现了最佳重建精度,显著强化了GRB作为标准烛光的能力。未来可将该重建框架与前沿红移预测技术结合,进一步拓展GRB在高红移宇宙学中的探针作用。

刊出 2026-06-27 · 收录 2026-07-22