SNRStacker¶
- class rubin_sim.maf.stackers.SNRStacker(app_mag_col='appMag', m5_col='fiveSigmaDepth', gamma=0.038, sigma=0.12, random_seed=None)¶
Bases:
BaseMoStacker
Add SNR and visibility for a particular object, given the five sigma depth of the image and the apparent magnitude (whether from AppMagStacker or CometAppMagStacker, etc).
The SNR simply calculates the SNR based on the five sigma depth and the apparent magnitude. The ‘vis’ column is a probabilistic flag (0/1) indicating whether the object was detected, assuming a 5-sigma SNR threshold and then applying a probabilistic cut on whether it was detected or not (i.e. there is a gentle roll-over in ‘vis’ from 1 to 0 depending on the SNR of the object). This is based on the Fermi-Dirac completeness formula as described in equation 24 of the Stripe 82 SDSS analysis here: http://iopscience.iop.org/0004-637X/794/2/120/pdf/apj_794_2_120.pdf.
- Parameters:
- app_mag_col
str
, optional Name of the column describing the apparent magnitude of the object. Default ‘appMag’.
- m5_col
str
, optional Name of the column describing the 5 sigma depth of each visit. Default fiveSigmaDepth.
- gamma
float
, optional The ‘gamma’ value for calculating SNR. Default 0.038. LSST range under normal conditions is about 0.037 to 0.039.
- sigma
float
, optional The ‘sigma’ value for probabilistic prediction of whether or not an object is visible at 5sigma. Default 0.12. The probabilistic prediction of visibility is based on Fermi-Dirac completeness formula (see SDSS, eqn 24, Stripe82 analysis: http://iopscience.iop.org/0004-637X/794/2/120/pdf/apj_794_2_120.pdf).
- random_seed: `int` or None, optional
If set, then used as the random seed for the numpy random number generation for the probability of detection. Default: None.
- app_mag_col
Attributes Summary
Attributes Documentation
- cols_added = ['SNR', 'vis']¶