Pipeline for debiasing and improving error bar estimates for photometry on top of structured/filamentary background. The procedure first estimates the covariance matrix of the residuals from a previous photometric model and then computes corrections to the estimated flux and flux uncertainties.
Discontinued. Please see CloudCovFix.jl
CloudCovFix.jl combines CloudCovErr.jl and CloudClean.jl. The CloudCovErr.jl repo is now mostly a documentation repo. The main issue is that the python dependencies we built to interface with crowdsource cannot be made stable due to Conda package management issues. The new implementation at CloudCovFix.jl is "python-free" and any interfacing will have to be done as a wrapper.
CloudCovErr is a registered package so a stable version can be installed using Pkg.add.
import Pkg
Pkg.add("CloudCovErr")For the most recent development version, install directly from the GitHub
import Pkg
Pkg.add(url="https://github.com/andrew-saydjari/CloudCovErr.jl")Currently, we only support compatibility with linux and macOS in order to easily interface with dependencies of crowdsource. Due to older versions of Julia bundling outdated libstcd++, we only support Julia 1.6+ again to make interfacing with python-based photometric pipelines easier (see issue). However, workarounds exist for both problems. Please open an issue if there is some compatibility you would like supported.
Detailed documentation can be found here.
Users may also find it helpful to consult the manuscript accompanying this release.
A key ingredient to our flux debiasing and uncertainty estimation algorithm is a good estimate of the distribution of possible backgrounds behind a star. An example is shown below.
This is a new piece of software. Filing an issue to report a bug or request a feature is extremely valuable in helping us prioritize what to work on, so don't hesitate.