卷积神经网络用于分类星系合并：微弱潮汐特征能否帮助分类合并？
Convolutional Neural Networks for classifying galaxy mergers: Can faint tidal features aid in classifying mergers?
AI总结：
本文提出利用卷积神经网络通过LSST类图像区分星系合并体，发现微弱潮汐特征可提升分类准确率5%。
摘要：
从观测数据中识别合并体一直是研究星系演化和形成过程的关键方面。潮汐特征通常比26 mag arcsec⁻²更暗，其外观范围取决于合并特征，并预计通过Rubin Observatory Large Synoptic Survey Telescope (LSST)将更详细地研究，该望远镜将以前所未有的精度揭示低表面亮度宇宙。我们的目标是评估开发一种能够根据LSST类深图像区分合并体和非合并体的卷积神经网络 (CNN) 的可行性。为此，我们使用了Illustris TNG50，这是迄今为止最高分辨率的宇宙流体动力学模拟之一，使我们能够生成具有深度约29 mag arcsec⁻²的LSST类模拟图像，用于低红移（z=0.16）星系，其标签基于其合并状态作为真实数据。我们专注于151个银河系类星系，其中包含81个非合并体和70个合并体。在应用数据增强和超参数调整后，开发出一个准确率为65-67%的CNN模型。通过额外的图像处理，模型进一步优化，当仅训练在包含只有微弱特征的图像上时，准确率提高至67-70%。这表明微弱潮汐特征可以作为区分合并体和非合并体的有效指标。基于本研究的未来改进方向也进行了讨论。
英文摘要：
Identifying mergers from observational data has been a crucial aspect of studying galaxy evolution and formation. Tidal features, typically fainter than 26 ${\rm mag\,arcsec^{-2}}$, exhibit a diverse range of appearances depending on the merger characteristics and are expected to be investigated in greater detail with the Rubin Observatory Large Synoptic Survey Telescope (LSST), which will reveal the low surface brightness universe with unprecedented precision. Our goal is to assess the feasibility of developing a convolutional neural network (CNN) that can distinguish between mergers and non-mergers based on LSST-like deep images. To this end, we used Illustris TNG50, one of the highest-resolution cosmological hydrodynamic simulations to date, allowing us to generate LSST-like mock images with a depth $\sim$ 29 ${\rm mag\,arcsec^{-2}}$ for low-redshift ($z=0.16$) galaxies, with labeling based on their merger status as ground truth. We focused on 151 Milky Way-like galaxies in field environments, comprising 81 non-mergers and 70 mergers. After applying data augmentation and hyperparameter tuning, a CNN model was developed with an accuracy of 65--67\%. Through additional image processing, the model was further optimized, achieving an accuracy of 67--70\% when trained on images containing only faint features. This represents an improvement of $\sim$ 5\% compared to training on images with bright features only. This suggests that faint tidal features can serve as effective indicators for distinguishing between mergers and non-mergers. The future direction for further improvement based on this study is also discussed.
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