Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing
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DOI：
10.3847/1538-4357/acdfc7
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
S. Alexander;Sergei Gleyzer;Hanna Parul;P. Reddy;Marcos Tidball;M. Toomey
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作者：
S. Alexander;Sergei Gleyzer;Hanna Parul;P. Reddy;Marcos Tidball;M. Toomey
令人惊讶的是，暗物质的身份仍然难以捉摸。虽然地面实验也许能够确定一个模型，但另一种方法是根据天体物理学或宇宙学特征来识别暗物质。一种特别敏感的方法是基于星系-星系强透镜图像中暗物质子结构的独特特征。人们已经探索了机器学习应用程序来提取该信号。由于高质量强透镜图像的可用性有限，这些方法完全依赖于模拟。由于与真实仪器数据的差异，经过模拟训练的机器学习模型在应用于真实数据时预计会失去准确性。在这里，领域适应可以充当模拟和实际数据应用之间的重要桥梁。在这项工作中，我们展示了应用于具有暗物质子结构的强引力透镜数据的域适应技术的力量。我们通过代表欧几里得和哈勃太空望远镜观测的模拟数据集表明，当应用于新领域时，领域适应可以显着减轻模型性能的损失。最后，我们通过将在模拟数据集上训练的模型调整为由来自 Hyper Suprime-Cam 的真实透镜和非透镜星系组成的模型，利用域适应来解决透镜寻找问题，发现了类似的结果。这项技术可以帮助领域专家构建和应用更好的机器学习模型，以便从即将到来的调查预期的强引力透镜数据中提取有用的信息。
The identity of dark matter has remained surprisingly elusive. While terrestrial experiments may be able to nail down a model, an alternative method is to identify dark matter based on astrophysical or cosmological signatures. A particularly sensitive approach is based on the unique signature of dark matter substructure in galaxy–galaxy strong lensing images. Machine-learning applications have been explored for extracting this signal. Because of the limited availability of high-quality strong lensing images, these approaches have exclusively relied on simulations. Due to the differences with the real instrumental data, machine-learning models trained on simulations are expected to lose accuracy when applied to real data. Here domain adaptation can serve as a crucial bridge between simulations and real data applications. In this work, we demonstrate the power of domain adaptation techniques applied to strong gravitational lensing data with dark matter substructure. We show with simulated data sets representative of Euclid and Hubble Space Telescope observations that domain adaptation can significantly mitigate the losses in the model performance when applied to new domains. Lastly, we find similar results utilizing domain adaptation for the problem of lens finding by adapting models trained on a simulated data set to one composed of real lensed and unlensed galaxies from the Hyper Suprime-Cam. This technique can help domain experts build and apply better machine-learning models for extracting useful information from the strong gravitational lensing data expected from the upcoming surveys.