arXiv:2510.06327v1 Announce Type: new
Abstract: We introduce a Bayesian null-stream method to constrain calibration errors in closed-geometry gravitational-wave (GW) detector networks. Unlike prior methods requiring electromagnetic counterparts or waveform models, this method uses sky-independent null streams to calibrate the detectors with any GW signals, independent of general relativity or waveform assumptions. We show a proof-of-concept study to demonstrate the feasibility of the method. We discuss prospects for next-generation detectors like Einstein Telescope, Cosmic Explorer, and LISA, where enhanced calibration accuracy will advance low-frequency GW science.
Conclusions
The Bayesian null-stream method presents a promising approach to constrain calibration errors in closed-geometry gravitational-wave detector networks. This method does not rely on electromagnetic counterparts or waveform models, making it versatile and independent of general relativity or waveform assumptions. The proof-of-concept study demonstrates the feasibility of this method, paving the way for enhanced calibration accuracy in next-generation detectors like the Einstein Telescope, Cosmic Explorer, and LISA. This advancement will significantly benefit low-frequency gravitational-wave science.
Future Roadmap
- Implementation: Researchers should focus on implementing the Bayesian null-stream method in existing gravitational-wave detector networks to assess its effectiveness in real-world scenarios.
- Validation: Conduct thorough validation tests to ensure the accuracy and reliability of the calibration constraints obtained through this method.
- Optimization: Explore ways to optimize the Bayesian null-stream method for improved efficiency and performance, especially in the context of next-generation detectors.
- Collaboration: Foster collaboration between research teams working on different aspects of gravitational-wave science to leverage collective expertise and resources.
- Evaluation: Regularly evaluate the impact of enhanced calibration accuracy on low-frequency gravitational-wave science to identify areas for further improvement.
Potential Challenges
- Integration of the Bayesian null-stream method into existing detector networks may pose technical challenges and require significant resources.
- Validation tests may uncover unforeseen limitations or constraints of the method that could necessitate adjustments or modifications.
- Optimizing the method for next-generation detectors like the Einstein Telescope, Cosmic Explorer, and LISA may require specialized expertise and advanced computational capabilities.
Potential Opportunities
- The Bayesian null-stream method opens up new possibilities for improving calibration accuracy in gravitational-wave detector networks, enhancing the overall scientific output in this field.
- Collaborative efforts to optimize and validate this method could lead to breakthroughs in low-frequency gravitational-wave science and attract additional research funding and support.
- The successful implementation of this method in next-generation detectors could establish a new standard for calibration techniques in the field of gravitational-wave astronomy.