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arXiv 2602.19214astro-ph.HE
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变星在类星体中的潜在周期性信号:意义、预测与深度学习

Potential periodic signals in blazars: significance, forecasting, and deep learning

Modern Academy for Engineering and Technology(现代工程与技术学院);The British University in Egypt(埃及英国大学);KEK(高能加速器研究机构);KIAS(韩国高等研究院)

M. A. Hashad, A. Hammad, Amr A. EL-Zant

AI总结:

研究利用深度学习和传统方法预测类星体中潜在周期性信号的变化,发现部分信号可能随时间增强或减弱。

摘要:

类星体表现出在多种时间尺度上的发射变化。一些光曲线显示出准周期性振荡(QPOs)的迹象,这可能包含关于发射背后物理过程的线索,或指向超大质量双黑洞。我们分析了五个之前报告出高显著性年度QPOs的类星体,应用Lomb-Scargle周期图和加权小波Z变换方法对费米-拉特数据(截至2025年初)进行处理。我们还检查了另一个源(PKS 0139-09),其中可能有新生的QPO。由于光曲线显示出更长期的趋势,我们使用STL分解对数据进行去趋势处理,这通常揭示出一个显著的季节性成分。我们发现去趋势通常导致QPO信号强度的增加。然而,除了PG 1553+113,其中存在明显持续的QPO信号外,我们检测到在约4000天的时间尺度上出现短暂性。然后,我们使用传统统计方法以及基于Transformer的深度学习模型,对光曲线进行了四年的预测。应用于测试集时,后者在预测看似从简单检查过去数据中无法预料的行为方面表现出显著的成功。分析扩展时间序列表明,在短暂行为出现在观测数据末尾的情况下,未来几年QPO信号会显著减弱。相反,在新生候选QPO源(PKS 0139-09)中,信号预计会显著增强。这些预测,可能反映QPOs的物理起源,可以通过未来数据进行检验。

英文摘要:

Blazars exhibit variable emission on diverse timescales. Some light curves show signs of quasiperiodic oscillations (QPOs), which may encode clues regarding the physical processes behind the emission or point to supermassive binary black holes. We analyzed five blazars with previously reported high significance year-long QPOs, applying the Lomb-Scargle periodogram and Weighted Wavelet Z-transform methods to Fermi-LAT data up to early 2025. We furthermore examined an additional source (PKS 0139-09), where nascent QPO may be present. As the light curves showed longer term trends, we detrended the data using an STL decomposition, which often revealed a large seasonal component. We find that detrending generally leads to an increase in the strength of the QPO signal. However, except for PG 1553+113, where a clearly persistent QPO signal is present, we detect transience on a timescale of $\lesssim$4000 days. We then forecast the light curves over the following four years, using a traditional statistical method as well as a Transformer-based deep learning model. Applied to a test set, the latter showed significant success in predicting behavior that seems unexpected from simple inspection of the past data. Analyzing the extended time series suggests a markedly weaker QPO signals over the coming years in cases where the transient behavior appears near the end of the observational data. In contrast, in the nascent candidate QPO source (PKS 0139-09) the signal is expected to strengthen significantly. These predictions, which may reflect the physical origin of the QPOs, can be tested against future data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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