A Probabilistic Autoencoder for Type Ia Supernova Spectral Time Series
A Probabilistic Autoencoder for Type Ia Supernova Spectral Time Series
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DOI：
10.3847/1538-4357/ac7c08
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发表时间：
2022-07
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通讯作者：
G. Stein;U. Seljak;V. Boehm;G. Aldering;P. Antilogus;C. Aragon;S. Bailey;C. Baltay;S. Bongard-S.-B
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作者：
G. Stein;U. Seljak;V. Boehm;G. Aldering;P. Antilogus;C. Aragon;S. Bailey;C. Baltay;S. Bongard-S.-B
我们构建了一个物理参数化概率自动编码器 (PAE)，从一组稀疏的光谱时间序列中学习 Ia 型超新星 (SNe Ia) 的内在多样性。 PAE 是一个两阶段生成模型，由自动编码器组成，在使用归一化流进行训练后，自动编码器会进行概率解释。我们证明 PAE 学习了一个低维潜在空间，可以捕获群体中存在的非线性特征范围，并可以直接从数据中准确地模拟 SNe Ia 在整个波长范围和观察时间范围内的光谱演化。通过在物理参数化网络中引入相关惩罚项和多阶段训练设置，我们表明可以在训练过程中分离内在和外在的变异模式，从而无需使用额外的模型来执行幅度标准化。然后，我们在 SNe Ia 上的许多下游任务中使用我们的 PAE 来进行越来越精确的宇宙学分析，包括自动检测 SN 异常值、生成与数据分布一致的样本，以及在存在噪声和不完整数据的情况下解决逆问题以约束宇宙学距离测量。我们发现内在模型参数的最佳数量似乎是三个，与之前的研究一致，并且表明我们可以将 SNe Ia 的测试样本标准化为 0.091 ± 0.010 mag 的均方根，如果去除特殊的速度贡献，则对应于 0.074 ± 0.010 mag。经过训练的模型和代码发布在 https://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 autoencoder that is interpreted probabilistically after training using a normalizing flow. 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 multistage 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 the 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 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova.