使用机器学习进行射电表校准
S A K Leeney1,2,3, H T J Bevins4,5,6, E de Lera Acedo4,5,6
1Astrophysics Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, UK. sakl2@cam.ac.uk.
Scientific reports
|October 2, 2025
概括
机器学习对射线计进行射电天文学校准,提高了检测微弱21厘米信号的精度. 这个新的框架模拟了复杂的工具效应,克服了传统方法的局限性.
科学领域:
- 无线电天文学 无线电天文学
- 宇宙学的宇宙学是什么?
- 仪器校准仪器仪表校准
背景情况:
- 射线计是射电天文学的关键仪器,测量电磁辐射强度.
- 接收器中的阻抗不匹配会导致信号扭曲,挑战像迪克切换这样的传统校准.
- 检测微弱的,高红移21厘米信号是一个主要的宇宙学挑战.
研究的目的:
- 引入和测试用于放射测试的新型机器学习 (ML) 校准框架.
- 达到检测具有挑战性的天空平均21厘米信号所需的精度.
- 为复杂的射电天文系统提供了传统校准方法的替代方案.
主要方法:
- 利用在已知的信号源上训练的神经网络来模拟仪器效应.
- 开发基于机器学习的辐射计校准框架.
- 测试框架在实现所需的辐射精度方面的性能.
主要成果:
- ML校准框架证明了高精度放射测量能力.
- 成功模拟和纠正复杂的仪器效应.
- 达到适合检测微弱21厘米信号的精度.
结论:
- 机器学习为校准复杂的放射测量系统提供了一种强大的方法.
- 开发的ML框架满足了21厘米信号检测实验的精度要求.
- 这项工作通过使新的检测能力成为可能,推动了观测宇宙学.
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