The following examples are adapted from Photometry.jl to show the same examples combined with AstroImages.jl. To learn how to measure background levels, perform aperture photometry, etc see the Photometry.jl documentation.

Background Estimation

From Photometry.jl:

Estimating backgrounds is an important step in performing photometry. Ideally, we could perfectly describe the background with a scalar value or with some distribution. Unfortunately, it's impossible for us to precisely separate the background and foreground signals. Here, we use mixture of robust statistical estimators and meshing to let us get the spatially varying background from an astronomical photo. Let's show an example Now let's try and estimate the background using estimate_background. First, we'll si gma-clip to try and remove the signals from the stars. Then, the background is broken down into boxes, in this case of size (50, 50). Within each box, the given statistical estimators get the background value and RMS. By default, we use SourceExtractorBackground and StdRMS. This creates a low-resolution image, which we then need to resize. We can accomplish this using an interpolator, by default a cubic-spline interpolator via ZoomInterpolator. The end result is a smooth estimate of the spatially varying background and background RMS.

using Photometry
using AstroImages
using Plots # optional, for implot functionality

# Download our image, courtesy of astropy
image = AstroImage(download(""))

# sigma-clip
clipped = sigma_clip(image, 1, fill=NaN)

# get background and background rms with box-size (50, 50)
bkg, bkg_rms = estimate_background(clipped, 50)


Or, if you have Plots loaded:

using Plots


    implot(image, title="Original"),
    implot(clipped, title="Sigma-Clipped"),
    implot(bkg, title="Background"),
    implot(bkg_rms, title="Background RMS"),
    layout=(2, 2)