CloudCovErr.jl

Pipeline for debiasing and improving error bar estimates for photometry on top of structured/filamentary background.
Author andrew-saydjari
Popularity
2 Stars
Updated Last
11 Months Ago
Started In
October 2021

CloudCovErr.jl CloudCovErr Logo

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.

Installation

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 Due to older versions of Julia bundling outdated libstcd++, we only support Julia 1.6+ again to make interfacing with python-based photometric pipelines easier (see issue).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.

Documentation

Detailed documentation can be found here.

Users may also find it helpful to consult the manuscript accompanying this release.

Example

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.

Contributing and Questions

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.