使用深循环造型改进引力波观测台的宇宙范围
Jonas Buchli1, Brendan Tracey1, Tomislav Andric2,3
1Google DeepMind, London, UK.
概括
我们开发了一种新的人工智能控制方法, 这一突破提升了黑洞和中子星合并观测的灵敏度,
科学领域:
- 引力波天文学
- 天体物理学
- 科学仪器中的机器学习
背景情况:
- 在引力波观测中提高低频感度对于研究中等质量黑洞合并,二进制黑洞异常度以及对二进制中子星合并进行多传媒观测的早期预警至关重要.
- 像LIGO这样的天文台目前的镜像稳定控制系统带来有害的噪音, 阻碍了探测器灵敏度的显著提高.
研究的目的:
- 消除镜像稳定控制系统所产生的噪声,这是提高引力波观测器灵敏度的主要障碍.
- 展示一种新的强化学习方法来提高引力波探测器的性能.
主要方法:
- 实施深度循环塑造,一种利用频域奖励的强化学习技术.
- 在LIGO利文斯顿天文台 (LLO) 验证深环造型方法.
主要成果:
- 开发的控制器在10到30赫兹频段的控制噪声显著降低了30倍以上.
- 低频段噪声降低达到了100倍,超过了由量子极限驱动的灵敏度提高目标.
- 在现实世界引力波观测台设置中成功消除噪音.
结论:
- 深循环造型为引力波观测站提供了一种强有力的解决方案.
- 这种人工智能驱动的控制策略有可能增强当前和未来的引力波探测系统.
- 该方法的广泛适用性延伸到超越引力波天文学的各种仪器和控制系统的改进.
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