Stochastic gravitational wave background from supernovae in massive scalar-tensor gravity
Abstract
In massive scalar-tensor gravity, core-collapse supernovae are strong sources of scalar-polarized gravitational waves. These can be detectable out to large distances. The dispersive nature of the propagation of waves in the massive scalar field implies that the gravitational wave signals are long-lived, and many such signals can overlap to form a stochastic background. Using different models for the population of supernova events in the nearby universe, we compute predictions for the energy density in the stochastic scalar-polarized gravitational wave background from core-collapse events in massive scalar-tensor gravity for theory parameters that facilitate strong scalarization. The resulting energy density is below the current constraints on a Gaussian stochastic gravitational wave background but large enough to be detectable with the current generation of detectors when they reach design sensitivity, indicating that it will soon be possible to place new constraints on the parameter space of massive scalar-tensor gravity.
Copyright and License
© 2023 American Physical Society.
Funding
We thank Isobel Romero-Shaw and Lieke van Son for insightful discussions on astrophysical population models. R. R. M. acknowledges support by the Deutsche Forschungsgemeinschaft (DFG) under Grants No. 2176/7-1 and No. 406116891 within the Research Training Group RTG 2522/1. M. A. is supported by the Kavli Foundation. This work has been supported by STFC Research Grant No. ST/V005669/1, “Probing Fundamental Physics with Gravitational-Wave Observations,” and NSF Grant No. PHY-090003. This research project was conducted using computational resources at the Maryland Advanced Research Computing Center (MARCC) as well as the Cambridge Service for Data Driven Discovery (CSD3) system at the University of Cambridge and Cosma7 and 8 of Durham University inside the DiRAC allocation ACTP284 through STFC capital Grants No. ST/P002307/1 and No. ST/R002452/1, and STFC operations Grant No. ST/R00689X/1. We made use of presupernova models by S. Woosley and A. Heger [80]. This work made use of the following publicly available python packages: astropy (an ecosystem of tools and resources for astronomy [81]), numpy [82], scipy [83], and matplotlib [84].
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Additional details
- ISSN
- 2470-0029
- Deutsche Forschungsgemeinschaft
- 2176/7-1
- Deutsche Forschungsgemeinschaft
- 406116891
- The Kavli Foundation
- Science and Technology Facilities Council
- ST/V005669/1
- Science and Technology Facilities Council
- ST/P002307/1
- Science and Technology Facilities Council
- ST/R002452/1
- Science and Technology Facilities Council
- ST/R00689X/1
- National Science Foundation
- PHY-090003
- Accepted
-
2023-05-23Accepted paper
- Caltech groups
- TAPIR
- Publication Status
- Published