Calibrating semi-analytic VT's against reweighted injection VT's
- Document #:
- LIGO-T1800427-v2
- Document type:
- T - Technical notes
Other Versions:
- Abstract:
- The semi-analytic approach to computing VT is advantageous in its speed and its ability to compute VT anywhere in parameter space. On the other hand, re-weighted injections are much more accurate, but they only compute an average VT over an assumed population, and Monte Carlo error prevents the estimate from being accurate when considering narrow populations where few/no injections lie. We present a method and results of calibrating semi-analytic VT's against pyCBC reweighted injection VT's. The method relies on assuming a relationship between the injection and semi-analytic VT's that can be expressed via basis functions.
- Files in Document:
-
- Slides to present (2018-10-01--wysocki-oshaughnessy-vt-calibration-slides.pdf, 442.5 kB)
- Technical description of work, including example of how to use the results. (technical_notes.pdf, 193.1 kB)
- Other Files:
- Calibration results assuming a very simple model. (calibration_linear.json, 229 bytes)
- Calibration results assuming our most complicated current model, and what we currently recommend using. (calibration_quadratic.json, 359 bytes)
- Calibration results assuming the simplest possible correction -- included mainly for completeness. (calibration_scalar.json, 141 bytes)
- Example of using the results. (coeff_example.py, 1.2 kB)
- Authors:
- Referenced by:
- LIGO-T2000432-v1: Calibrating semi-analytic VT's to injections in O3a