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arXiv 2401.16624astro-ph.COastro-ph.IMphysics.data-an
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基于机器学习的真实强引力透镜系统子结构探测

Substructure Detection in Realistic Strong Lensing Systems with Machine Learning

Arthur Tsang, Atınç Çağan Şengül, Cora Dvorkin

AI总结:

本研究利用UNet图像分割架构在含真实光源、透镜体和噪声的模拟强引力透镜数据集上探测暗物质子结构,在10%假阳性率下对特定质量高浓度子晕的真阳性率达71%,但无法探测ΛCDM预期的低浓度子晕。

摘要:

预计到本十年末,将发现数万个星系-星系强引力透镜系统。这些系统将构成一个庞大的新数据集,可通过引力效应探测亚星系级暗物质结构,进而帮助我们研究小尺度下暗物质的本质。本研究展示了如何利用机器学习遍历数据,识别最可能包含暗物质子结构、值得深入研究的透镜系统。我们采用图像分割架构UNet,在模拟强引力透镜数据集上开展实验,该数据集包含真实光源(COSMOS星系)、透镜体(含多极矩和外部剪切的幂律椭圆轮廓)以及噪声。我们的机器学习算法能够在高图像分辨率和子晕高浓度条件下快速探测到大部分子结构。在10%的假阳性率下,针对质量范围为10^9-10^9.5 M⊙的子晕,我们识别含子结构系统的真阳性率可达71%。尽管近期探测结果与更高浓度的子晕特征相符,但我们发现算法无法探测低浓度子晕(这是ΛCDM模拟所预期的子晕特征)。

英文摘要:

Tens of thousands of galaxy-galaxy strong lensing systems are expected to be discovered by the end of the decade. These will form a vast new dataset that can be used to probe subgalactic dark matter structures through its gravitational effects, which will in turn allow us to study the nature of dark matter at small length scales. This work shows how we can leverage machine learning to search through the data and identify which systems are most likely to contain dark matter substructure and thus can be studied in greater depth. We use a UNet, an image segmentation architecture, on a simulated strongly-lensed dataset with realistic sources (COSMOS galaxies), lenses (power-law elliptical profiles with multipoles and external shear), and noise. Our machine learning algorithm is able to quickly detect most substructure at high image resolution and subhalo concentration. At a false positive rate of $10\%$, we are able to identify systems with substructure at a true positive rate of $71\%$ for a subhalo mass range of $10^{9}\text{-}10^{9.5}\,M_\odot$. While recent detections are consistent with higher concentrations, we find that our algorithm fails at detecting subhalos with lower concentrations (expected from $Λ$CDM simulations).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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