论文标题
GAMPEN：用于估计星系形态参数的贝叶斯后期的机器学习框架
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
论文作者
论文摘要
我们介绍了一个新型的机器学习框架，用于估计任意大量星系的形态参数的贝叶斯后期。银河形态后估计网络（GAMPEN）估计了银河的凸起与按时光比（$ l_b/l_t $），有效半径（$ r_e $）和flux（$ f $）的值和不确定性。为了估计后期，Gampen使用蒙特卡洛辍学技术，并将输出参数之间的完整协方差矩阵纳入其损耗函数。 Gampen还使用空间变压器网络（STN）自动将输入星系框架裁剪到最佳的大小，然后再确定其形态。这将允许将其应用于新数据，而无需事先了解星系大小。 Training and testing GaMPEN on galaxies simulated to match $z < 0.25$ galaxies in Hyper Suprime-Cam Wide $g$-band images, we demonstrate that GaMPEN achieves typical errors of $0.1$ in $L_B/L_T$, $0.17$ arcsec ($\sim 7\%$) in $R_e$, and $6.3\times10^4$ nJy ($\sim $ f $ 1 \％$）。 GAMPEN的预测不确定性是良好且准确的（$ <5 \％$偏差） - 对于具有较高残差的参数空间区域，Gampen正确预测了相应的大型不确定性。我们还证明，我们可以将分类标签（即，诸如“高度凸起主导”的分类）应用于具有较高残留物的区域的预测，并验证这些标签是否为$ \ gtrsim 97 \％$准确。据我们所知，Gampen是确定多个形态参数联合后验分布的第一个机器学习框架，也是STN在天文学中的光学成像的第一个应用。
We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy's bulge-to-total light ratio ($L_B/L_T$), effective radius ($R_e$), and flux ($F$). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a Spatial Transformer Network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match $z < 0.25$ galaxies in Hyper Suprime-Cam Wide $g$-band images, we demonstrate that GaMPEN achieves typical errors of $0.1$ in $L_B/L_T$, $0.17$ arcsec ($\sim 7\%$) in $R_e$, and $6.3\times10^4$ nJy ($\sim 1\%$) in $F$. GaMPEN's predicted uncertainties are well-calibrated and accurate ($<5\%$ deviation) -- for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We also demonstrate that we can apply categorical labels (i.e., classifications such as "highly bulge-dominated") to predictions in regions with high residuals and verify that those labels are $\gtrsim 97\%$ accurate. To the best of our knowledge, GaMPEN is the first machine learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN to optical imaging in astronomy.
