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Physics 164/238: Observational Astronomy Research Lab. Lecture 3 Image Reduction Methods and Photometry 15 January 2014. Announcements. First assignment will be due at beginning of lecture hour Friday 17 January 2014. Observing this week! Check Lecture 2 slides for schedule

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Physics 164/238:Observational Astronomy Research Lab

Lecture 3

Image Reduction Methods and Photometry

15 January 2014

  • First assignment will be due at beginning of lecture hour
  • Friday 17 January 2014.
  • Observing this week! Check Lecture 2 slides for schedule
  • and meeting time/place (for those who have not yet
  • observed).

Outline for Today

  • How to reduce CCD imaging data.
  • Color indices and photometric measurements.

Reducing Imaging Data




Check at each step along reduction that the data is changing

in the way you want and expect it to!


Removing Bias Signal

  • Median-combine all bias frames into a single bias
  • master frame. This helps remove cosmic rays and
  • improves the accuracy of your calibration frame!
  • The bias master frame should be subtracted from all
  • frames. In practice, other master calibration frames
  • (e.g., darks and flats) can be combined into their
  • own respective master frames before bias subtraction.
  • Overscan region can be used to correct changes in the
  • bias signal per frame.

Removing Dark Signal

  • Median-combine all dark frames into respective dark
  • master frames for each integration time used.
  • The dark master frames should be subtracted from
  • frames with the same integration time used.
  • Usually most important to subtract dark frames from
  • flat-field frames!

Preparing and Applying the Flat-field

  • Collect each dark-subtracted flat-field frame for each filter
  • used. These frames then need to be normalized such that
  • the pixel values fluctuate about unity. This can be done by
  • dividing the entire frame by the median or average pixel
  • value.
  • The normalized flat-field frames are then median-combined
  • to produce the master flat-field.
  • All science and background frames then need to be divided
  • by this master flat-field to flatten the pixel response across
  • the detector.

Removing Background

  • Background frames associated with a given source
  • observation should be median-combined to produce
  • master background frames. This is true for both
  • on-source and off-source background measurements!
  • The master background frame is then subtracted from
  • all relevant source frames.




Select Science Region of Detector

  • Select subset of detector pixels that contain science data
  • only.
  • Remove overscan pixels and other unused pixels in the
  • array.
  • Usually a good idea to trim 5-10 pixels from the detector
  • edges.

Identify and Remove or Mask Bad Pixels

  • Mask off bad columns and detector edges (if not already
  • trimmed away).
  • Mask off any otherwise unused pixels that are not already
  • trimmed.
  • Identify bad pixels using flat field and dark images, mask
  • or interpolate over using nearest neighbors.


Rotate Images

  • Sometimes images are not aligned with one another or
  • with your desired sky alignment.
  • Need to rotate, transpose, or both to get desired image
  • alignment.
  • Astronomical images typically presented with North
  • up and East to the left.

Melis et al. (2010)


Register Images

  • Want to align the scenes on individual images so that
  • when overlaid the same sources match up pixel to
  • pixel.
  • Can be done manually by identifying shift between each
  • image and correcting for it. Or automated routines can be
  • employed that determine this shift using features in the
  • images.

Stack Frames

  • Once all reduction steps are performed for all images,
  • these images are median- or average-combined to
  • generate the final reduced frame.

Carolyn Brinkworth/Spitzer Science Center


CCD Gain

  • CCD pixel values are reported in Data Numbers (DN),
  • counts, or Analog to Digital Units (ADU). These are
  • the amplified and digitized voltage levels induced by
  • the stream of electrons released by the incident photons
  • per pixel.
  • Conversion between the digital signals recorded by the
  • CCD system and the electrons that produced them
  • uses the photon transfer function, or more simply the
  • CCD gain g in electrons/DN.

Color Indices

  • Can define a color index for an astronomical object
  • as the difference between magnitudes at two separate
  • wavelengths.
  • For example, a star may have a B magnitude of 12.1
  • and a V magnitude of 11.1. Its B-V color would be
  • 1.0 and hence indicates a red source since there is more
  • light being emitted by the object in the V-band (550 nm)
  • compared to the B-band (440 nm).
  • Apparent magnitudes change with distance for a given
  • object, but colors do not!


  • Method by which a numerical value for the brightness
  • of an astronomical object is retrieved.
  • With care, one can achieve photometry accurate to the
  • 1% level or better with CCD measurements.
  •  Exoplanet transit observations routinely obtain
  • millimagnitude time-series precision!
  • Absolute photometry, which delivers a true physical
  • flux for an astronomical object, is typically limited
  • by systematics to >1%.

Aperture Photometry

  • Collect stellar signal within a
  • software aperture. The total signal
  • within the aperture is the sum of
  • all pixel values that fall within the
  • aperture.


Aperture Photometry: Sky Signal

  • Background, or “sky”,
  • signal is measured using
  • larger apertures that form
  • an annulus that does not
  • include the stellar signal.
  • Pixel values in this
  • annulus are median
  • combined or averaged to
  • obtain the background
  • signal per pixel, which is
  • then subtracted from all
  • pixels that fall within the
  • aperture.


Aperture Photometry: Optimal Aperture Size

The best S/N is with an aperture radius of 3/4FWHM, but this only encompasses 65% of the signal!



Aperture Photometry: Curve of Growth

When comparing photometric measurements, the aperture selected for each flux extraction should collect the same flux ratio. If not, then an aperture correction derived from the plot of flux ratio versus aperture size (or the “curve of growth”) must be applied to the measured flux.

Curve of Growth



Aperture Photometry: Noise Estimates

  • An estimate of the noise
  • per (square) pixel can be
  • made from the rms scatter
  • in the background annulus
  • and be used to calculate
  • the S/N ratio at the aperture.


  • S/N ≈ (signal in aperture)____

  (aperture radius)2  noise


Profile or PSF Fitting

  • One can also extract flux
  • by fitting a model to the
  • stellar profile or point
  • spread function. Stellar
  • images in the seeing
  • limited regime often are
  • modeled with a Gaussian
  • intensity profile:
  • I(r)= I(0) e-(r/)
  • The 1-sigma width of the
  • distribution is related to
  • the image full-width at
  • half maximum by:
  • FWHM = 1.665

Krishnakumargif and Venkatakrishnan (1997)



Automatic Source Extraction

  • Software packages exist that
  • detect sources in astronomical
  • images and extract their
  • photometry. They are best used
  • for identifying sources in large
  • fields.
  • SExtractor is a popular tool for
  • galaxy fields while DAOPHOT
  • is better suited for point source
  • extraction and can model PSFs
  • for identification and
  • extraction. This is very useful
  • in crowded fields.

Absolute Flux Conversions

  • Once count rates are extracted for a target source and for a
  • standard star of known magnitude (observed at a similar
  • airmass in photometric conditions!), the true flux arriving at
  • Earth can be derived using the delta-magnitude equation:
  • Be sure to be comparing count rates (electrons per second) for
  • each source!

Lecture 1