Optimizing machine learning methods to discover strong gravitational lenses in the deep lens survey
Optimizing machine learning methods to discover strong gravitational lenses in the deep lens survey
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优化机器学习方法以发现深透镜巡天中的强引力透镜
DOI：
10.1093/mnras/stad1709
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发表时间：
2023
影响因子：
4.8
通讯作者：
Sharpnack, James
中科院分区：
文献类型：
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作者：
Keerthi Vasan, G. C.;Sheng, Stephen;Jones, Tucker;Choi, Chi Po;Sharpnack, James
机器学习模型可以通过减少所需的人工检查量，大大提高对成像调查中强引力透镜的搜索。在这项工作中，我们测试了使用ResNetV2神经网络架构训练的监督、半监督和无监督学习算法在深度透镜调查（DLS）中有效发现强引力透镜的能力。我们使用来自调查的星系图像，结合模拟透镜源，作为我们训练数据集中的标记数据。我们发现，使用半监督学习以及数据增强（在训练期间对图像进行变换，例如旋转）和生成对抗网络（GAN）生成图像的模型产生最佳性能。与监督算法相比，它们在所有召回值上提供5 - 10倍的精度。将表现最好的模型应用于完整的20度2dls调查，我们在模型的前17个图像预测中找到了3个a级镜头候选。当对模型预测的1%（~ 2500张图像）进行视觉检查时，这一数字增加到9个a级和13个b级候选对象。与目前较浅的广域巡天（如暗能量巡天）相比，这是候选透镜的天空密度的约10倍，这表明在即将到来的更深的全天空巡天中，有大量透镜等待发现。这些结果表明，寻找强透镜系统的管道可以高效地减少人力。我们还报告了通过我们的模型确定的两个a级候选者的透镜性质的光谱确认，进一步验证了我们的方法。
Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and unsupervised learning algorithms trained with the ResNetV2 neural network architecture on their ability to efficiently find strong gravitational lenses in the Deep Lens Survey (DLS). We use galaxy images from the survey, combined with simulated lensed sources, as labeled data in our training data sets. We find that models using semi-supervised learning along with data augmentations (transformations applied to an image during training, e.g. rotation) and Generative Adversarial Network (GAN) generated images yield the best performance. They offer 5 – 10 times better precision across all recall values compared to supervised algorithms. Applying the best performing models to the full 20 deg2DLS survey, we find 3 Grade-A lens candidates within the top 17 image predictions from the model. This increases to 9 Grade-A and 13 Grade-B candidates when 1 per cent (∼2500 images) of the model predictions are visually inspected. This is ≳ 10 × the sky density of lens candidates compared to current shallower wide-area surveys (such as the Dark Energy Survey), indicating a trove of lenses awaiting discovery in upcoming deeper all-sky surveys. These results suggest that pipelines tasked with finding strong lens systems can be highly efficient, minimizing human effort. We additionally report spectroscopic confirmation of the lensing nature of two Grade-A candidates identified by our model, further validating our methods.
使用 Auto ML 提高黑色素瘤诊断系统的性能
DOI：
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发表时间：
2023
期刊：
影响因子：
--
作者：
Inoue Manabu;Yoshimoto Takeshi;Tanaka Kanta;Koge Junpei;Shiozawa Masayuki;Nishii Tatsuya;Ohta Yasutoshi;Fukuda Tetsuya;Satow Tetsu;Kataoka Hiroharu;Yamagami Hiroshi;Ihara Masafumi;Koga Masatoshi;Mlynash Michael;Albers Gregory W.;Toyoda Kazunori;正木達也・北畠直人・飛塚丈輝・花崎和寿・張 維倫・永岡 隆
通讯作者：
正木達也・北畠直人・飛塚丈輝・花崎和寿・張 維倫・永岡 隆
HumVI：人类可视图像创建
DOI：
--
发表时间：
2015
期刊：
影响因子：
--
作者：
P. Marshall;C. Sandford;Anupreeta More;Hugo Buddelmeijerr
通讯作者：
Hugo Buddelmeijerr