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revised exposure factor implementation for bright time #109

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py/desisurvey/ephem.py

  • sun_ra and sun_dec added to ephem table

py/desisurvey/etc.py

  • bright_exposure_factor function calculates the bright time exposure factor from airmass, moon illumination, moon separation, moon altitude, sun separation, and sun altitude as input.
  • _bright_exposure_factor_notwi function calculates the bright time exposure factor without twilight contributions from airmass and moon parameters. The function uses a polynomial regression model fit to exposure factors calculated using an improved sky brightness model (see notebook). The model is based on re-fitting Krisciunas & Schaefer (1991) to BOSS sky fiber data plus twilight contribution from Parker Fagrelius's thesis work. See notebook for a comparison of the original KS1991 sky model to refit+twilight model.
  • _bright_exposure_factor_twi function calculates the twilight contribution to the bright time exposure factor. The function uses a simple linear regression model that takes airmass, sun separation, and sun altitude as input.

py/desisurvey/scheduler.py

  • next_tile method: If use_brightsky==True, bright time exposure factor is included in next tile selection
  • update_exposure_factor method implemented. This method is also called in surveysim.nightops.simulate_night to correct the ETC on the fly.

changhoonhahn and others added 30 commits February 21, 2019 23:01
…exposure_factor; changes in etc.bright_exposure_factor to deal with moon and sun parameter inputs; setup.py adjusted to read in gp files; GP files are made using python2 and thus incompatible...
- regression models for bright sky exposure factor implemented in
`desisurvey.etc`
- `desisurvey.scheduler.Scheduler` now has a method
`update_exposure_factor` to calculate exposure factor
- if `use_brightsky == True` for `desisurvey.scheduler.Scheduler`,
bright sky exposure factor is used in `next_tile`.
@dkirkby
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dkirkby commented Mar 3, 2020

Let me know when this is ready for review. Note that @schlafly is working on #108 in parallel.

@schlafly
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schlafly commented Mar 3, 2020

I think these will be pretty orthogonal, fortunately!

@changhoonhahn
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@dkirkby I think this pull request is ready for review. I've checked that it runs expectedly in conjunction with the pull request I submitted for surveysim. This pull request should also address the issues that @sbailey pointed out in the previous pull request I submitted.

changhoonhahn added a commit to desi-bgs/feasiBGS that referenced this pull request Apr 1, 2020
- new polynomial regression model fit to exposure time factors
measured from BOSS and DESI CMX data.
- previous regression model, which is the one implemented in
[desisurvey pull request #109](desihub/desisurvey#109)
show significant discrepancies for high exposure time factors.
- new model does significantly better, especially for DESI CMX exposures.
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3 participants