We present a method for mapping variations between probability distribution functions and apply this method within the context of measuring galaxy redshift distributions from imaging survey data. This method, which we name PITPZ for the probability integral transformations it relies on, uses a difference in curves between distribution functions in an ensemble as a transformation to apply to another distribution function, thus transferring the variation in the ensemble to the latter distribution function. This procedure is broadly applicable to the problem of uncertainty propagation. In the context of redshift distributions, for example, the uncertainty contribution due to certain effects can be studied effectively only in simulations, thus necessitating a transfer of variation measured in simulations to the redshift distributions measured from data. We illustrate the use of PITPZ by using the method to propagate photometric calibration uncertainty to redshift distributions of the Dark Energy Survey Year 3 weak lensing source galaxies. For this test case, we find that PITPZ yields a lensing amplitude uncertainty estimate due to photometric calibration error within 1 per cent of the truth, compared to as much as a 30 per cent underestimate when using traditional methods.
 Authors:
 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
 Award ID(s):
 2009210
 Publication Date:
 NSFPAR ID:
 10388483
 Journal Name:
 Monthly Notices of the Royal Astronomical Society
 Volume:
 519
 Issue:
 2
 Page Range or eLocationID:
 p. 17921808
 ISSN:
 00358711
 Publisher:
 Oxford University Press
 Sponsoring Org:
 National Science Foundation
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