论文标题
IA型超新星光谱时间序列的概率自动编码器
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series
论文作者
论文摘要
我们从一组稀疏的光谱时间序列中构建了一个物理参数化的概率自动编码器（PAE），以学习IA型超新星（SNE IA）的内在多样性。 PAE是一个两阶段的生成模型，由自动编码器（AE）组成，该模型在使用归一化流（NF）训练后概率地解释。我们证明，PAE学习了一个低维的潜在空间，该空间可捕获人口内存在的非线性特征范围，并且可以直接从数据直接从数据中的全部波长和观察时间进行准确地对SNE IA的光谱演变进行建模。通过引入相关性惩罚项和多阶段训练设置以及我们的物理参数化网络，我们表明，可以在训练期间分离内在和外在的可变性模式，从而消除了对执行幅度标准化的其他模型的需求。然后，我们在SNE IA的许多下游任务中使用PAE进行越来越精确的宇宙学分析，包括自动检测SN Outiers，与数据分布一致的样本的产生以及在存在噪声和不完整数据的情况下解决逆问题以限制宇宙学距离测量。我们发现，与以前的研究相一致的固有模型参数的最佳数量似乎是三个，并表明我们可以以$ 0.091 \ pm 0.010 $ mag的RMS测试样本标准化我们的测试样本，如果$ 0.074 \ pm 0.010 $ 0.010 $ 0.010 $ MAG，则除了特殊的Velocity贡献是否取消了。训练有素的模型和代码在\ href {https://github.com/georgestein/supaernova} {github.com/georgestein/supaernova}
We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an Auto-Encoder (AE) which is interpreted probabilistically after training using a Normalizing Flow (NF). We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population, and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multi-stage training setup alongside our physically-parameterized network we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an RMS of $0.091 \pm 0.010$ mag, which corresponds to $0.074 \pm 0.010$ mag if peculiar velocity contributions are removed. Trained models and codes are released at \href{https://github.com/georgestein/suPAErnova}{github.com/georgestein/suPAErnova}
