论文标题
人工智能辅助反转（AIAI）：量化$^{56} $ ni的光谱特征
Artificial Intelligence Assisted Inversion (AIAI): Quantifying the Spectral Features of $^{56}$Ni of Type Ia Supernovae
论文作者
论文摘要
继我们先前对超新星分析的人工智能辅助反转（AIAI）的研究（Chen等，2020）之后，我们基于一维辐射转移代码TARDIS（Kerzendorf＆Sim 2014）训练一组深神经网络，以模拟IA IA Supernovae型光谱（sne supernovae（Sne）之间（sne IA型），以下是10天和40天。神经网络可用于在速度范围内得出56ni的质量，范围远高于光球，以153个观察到的SNE IA样本。许多SNE具有多个观测值，可以定量测试放射性56NI的衰减。发现使用观察到的光谱从AIAI衍生的56NI质量作为样品的输入，与理论56NI衰减率一致。 AIAI揭示了3890Å附近的光谱签名，可以将其识别为3950和4100Å之间的多个Ni II线产生。从AIAI推导的质量与SNE IA的光曲线形状相关，其SNE IA具有更宽的光曲线，显示了信封中的56ni质量。 AIAI使基于定义明确的物理假设在理论框架下进行定量分析SNE的光谱数据。
Following our previous study of Artificial Intelligence Assisted Inversion (AIAI) of supernova analyses (Chen et al. 2020), we train a set of deep neural networks based on the one-dimensional radiative transfer code TARDIS (Kerzendorf & Sim 2014) to simulate the optical spectra of Type Ia supernovae (SNe Ia) between 10 and 40 days after the explosion. The neural networks are applied to derive the mass of 56Ni in velocity ranges well above the photosphere for a sample of 153 well-observed SNe Ia. Many SNe have multi-epoch observations for which the decay of the radioactive 56Ni can be tested quantitatively. The 56Ni mass derived from AIAI using the observed spectra as input for the sample is found to agree with the theoretical 56Ni decay rate. The AIAI reveals a spectral signature near 3890 Åwhich can be identified as being produced by multiple Ni II lines between 3950 and 4100 Å. The mass deduced from AIAI is correlated to the light-curve shapes of SNe Ia, with the SNe Ia with broader light curves showing larger 56Ni mass in the envelope. AIAI enables spectral data of SNe to be quantitatively analyzed under theoretical frameworks based on well-defined physical assumptions.
