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Feb, 2023
自监督学习模拟类星体伽马射线变异性
Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars
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Aryeh Brill
TL;DR
使用自我监督的Transformer编码器架构构建高能伽玛射线可变性的有效表示,为描述由随机过程产生的数据非常适合的模型预测了每个时间步长处的通量概率分布的一组分位数,并进行了一项初步搜索周时间尺度的伽玛射线飞行时反演不对称性的科学意义信息的提取,但未发现异常情况。
Abstract
blazars
are active galactic nuclei with relativistic jets pointed almost directly at Earth.
blazars
are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and ti
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