Gravitational Wave Astronomy with the SKA
Bibliographic record
Abstract
On a time scale of years to decades, gravitational wave (GW) astronomy will become a reality.Low frequency (∼10 -9 Hz) GWs are detectable through long-term timing observations of the most stable pulsars.Radio observatories worldwide are currently carrying out observing programmes to detect GWs, with data sets being shared through the International Pulsar Timing Array project.One of the most likely sources of low frequency GWs are supermassive black hole binaries (SMBHBs), detectable as a background due to a large number of binaries, or as continuous or burst emission from individual sources.No GW signal has yet been detected, but stringent constraints are already being placed on galaxy evolution models.The SKA will bring this research to fruition.In this chapter, we describe how timing observations using SKA1 will contribute to detecting GWs, or can confirm a detection if a first signal already has been identified when SKA1 commences observations.We describe how SKA observations will identify the source(s) of a GW signal, search for anisotropies in the background, improve models of galaxy evolution, test theories of gravity, and characterise the early inspiral phase of a SMBHB system.We describe the impact of the large number of millisecond pulsars to be discovered by the SKA; and the observing cadence, observation durations, and instrumentation required to reach the necessary sensitivity.We describe the noise processes that will influence the achievable precision with the SKA.We assume a long-term timing programme using the SKA1-MID array and consider the implications of modifications to the current design.We describe the possible benefits from observations using SKA1-LOW.Finally, we describe GW detection prospects with SKA1 and SKA2, and end with a description of the expectations of GW astronomy. Advancing Astrophysics with the Square Kilometre Array
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".