build_photometric_error_model

lsst.validate.drp.photerrmodel.build_photometric_error_model(matchedMultiVisitDataset, selection, medianRef=100, matchRef=500)

Returns a serializable analytic photometry error model for a single visit.

This model is originally presented in http://arxiv.org/abs/0805.2366v4 (Eq 4, 5):

\[\begin{split}\sigma_1^2 &= \sigma_\mathrm{sys}^2 + \sigma_\mathrm{rand}^2 \\ x &= 10^{0.4(m-m_5)} \\ \sigma_\mathrm{rand}^2 &= (0.04 - \gamma) x + \gamma x^2~[\mathrm{mag}^2]\end{split}\]
Parameters:
matchedMultiVisitDataset : lsst.valididate.drp.matchreduce.MatchedMultiVisitDataset

A dataset containing matched statistics for stars across multiple visits.

selection : np.array of bool

The selection of sources to use to build the model.

medianRef : float or astropy.unit.Quantity, optional

Median reference astrometric scatter (millimagnitudes by default).

matchRef : int or astropy.unit.Quantity, optional

Should match at least matchRef stars.
Returns:
blob : lsst.verify.Blob

Blob with datums:

  • sigmaSys: Systematic error floor.
  • gamma: Proxy for sky brightness and read noise.
  • m5: 5-sigma photometric depth (magnitudes).
  • photRms: RMS photometric scatter for ‘good’ stars (millimagnitudes).

Notes

The scatter and match defaults are appropriate to SDSS are stored here. For SDSS, stars with mag < 19.5 should be completely well measured. This limit is a band-dependent statement most appropriate to r.