ProFound: Beginner’s Guide

What is ProFound?

ProFound is an R package designed to automatically detect and extract astronomical objects from images. If you have an image (like a telescope observation), ProFound can:

• Find all the stars, galaxies, or other objects in the image
• Determine their locations and sizes
• Measure their brightness
• Separate overlapping objects
• Create a catalogue you can analyse further

Think of it as a “find all the things in this picture” tool, with sophisticated algorithms to handle real astronomical data.

When Would You Use ProFound?

Typical use cases:

• Photometry: Measuring the brightness of stars or galaxies
• Source catalogues: Creating a list of all objects detected in a survey
• Image analysis: Understanding what’s in your astronomical images
• Pre-processing: Preparing data before further analysis (like spectroscopy)

The Main Function: profoundProFound()

The core of ProFound is the profoundProFound() function. Most of the time, this is the only function you need to know about.

library(ProFound)

# Run ProFound on an image
result <- profoundProFound(image = my_image)

library(Rfits)

# Read a FITS file
fits_data <- Rfits_read_image("my_observation.fits")

# Run ProFound
result <- profoundProFound(image = image)

# View the catalog
head(result$segstats)

# Access specific objects
bright_objects = result$cat[result$cat$segstats$flux > 1000, ]

Common Parameters Explained

ProFound has many parameters, but most have sensible defaults. Here are the ones you might adjust:

# Detect fainter objects
result_faint <- profoundProFound(image, skycut = 1, pixcut = 2)

# Detect only bright objects
result_bright <- profoundProFound(image, skycut = 3, pixcut = 5)

# Tell ProFound the AB magnitude of a reference zero point
result <- profoundProFound(image, magzero = 25)  # Example: 25 mag zero point
# Now result$cat$segstats will be in AB magnitudes

# Create a mask: TRUE for good pixels, FALSE for bad pixels
bad_pixels <- image$imDat < -100  # Example: very negative pixels are bad

result <- profoundProFound(image, mask = bad_pixels)
# ProFound will ignore bad pixels

# Tell ProFound the AB magnitude of a reference zero point
# gain: electrons per ADU (from your detector specs)
# pixscale: arcseconds per pixel (from your telescope/camera)
result <- profoundProFound(image, magzero = 25, gain = 2.5, pixscale = 0.5)

A Complete Example

library(ProFound)
#> Loading required package: Rfits
#> Loading required package: magicaxis
#> Loading required package: Rcpp
#> 
#> Attaching package: 'ProFound'
#> The following object is masked _by_ '.GlobalEnv':
#> 
#>     %nin%
library(Rfits)

# 1. Read your image
image = Rfits_read_image(system.file("extdata", 'VIKING/mystery_VIKING_Z.fits',
  package="ProFound"))

# 2. Create a simple mask (remove obvious bad pixels)
mask <- image$imDat < -10  # Ignore very negative pixels

# 3. Run ProFound with sensible settings for detecting galaxies
result <- profoundProFound(
  image = image,
  mask = mask,
  skycut = 1.5,      # Moderate sensitivity
  pixcut = 3,        # Require at least 3 connected pixels
  magzero = 26,      # Convert to magnitudes
  pixscale = 0.3,    # Pixel scale in arcsec
  plot = TRUE        # Show diagnostic plots
)

# 4. Examine the results
cat_objects <- result$segstats

# How many objects did we detect?
nrow(cat_objects)
#> [1] 58

# What's the brightest object? (Usually the first object with segID=1, but not always)
brightest <- cat_objects[which.max(cat_objects$flux), ]

# Plot a histogram of magnitudes
hist(cat_objects$mag, breaks = 50, 
     main = "Magnitude Distribution",
     xlab = "Magnitude")

Often the only bit people want to save is the segstats catalogue:

# Export the catalog
write.csv(cat_objects, "/path/to/use/detected_objects.csv", row.names = FALSE)

Understanding the Segmentation Image

ProFound also returns result$segim, a “segmentation image”:

• Each pixel is labelled with the ID of the object it belongs to
• Value 0 = background (no object)
• Value 1, 2, 3, … = different detected objects

# How many unique objects were detected?
length(unique(as.integer(result$segim))) - 1L
#> [1] 58

# Which will be the same as
dim(result$segstats)
#> [1] 58 62

# Visualize it
magimage(result$segim, col = rainbow(max(result$segim)))

Troubleshooting

profoundProFound(image, skycut = 2.5, pixcut = 5, plot = TRUE)

profoundProFound(image, skycut = 0.5, pixcut = 2, plot = TRUE)

profoundProFound(image, tolerance = 1, plot = TRUE)

profoundProFound(image, skytype = "mean", plot = TRUE)

Next Steps

• Check ?profoundProFound for the full parameter list
• Look at other vignettes for advanced topics (de-blending, photometry, etc.)
• Run profoundProFound(..., plot = TRUE) to see diagnostic plots
• Get advanced plots with plot method run on profoundProFound output, i.e. plot(result)

Good luck with your analysis!