Electromagnetic radiation's spectral energy distribution (SED) is a function, i.e., plot of brightness or flux density (i.e., power) versus the frequency or wavelength (the choice of which produce different results: see below) from a source. The shape of the continuum and any spectral signatures reveal information about the source.
A single instrument (e.g., a single spectrograph) always has bandwidth limitations, and overcoming that with multiple instruments leads to data-calibration challenges. If the SED of an astronomical object is measured from Earth, the effects of Earth atmosphere must be compensated for. Reddening requires similar compensation, even from space.
The alternate term, spectral power distribution (SPD) is used in some other (non-astronomy) fields.
Plotting by wavelength versus plotting by frequency has consequences: the peak wavelength differs in the two cases if each is plotted linearly, a consequence of plotting density functions over values that have a reciprocal relationship. Taking black-body spectrum as an example, plotting wavelengths by millimeter (0 mm, 1 mm, 2 mm, 3 mm, ...) crowds all the infrared, visible light, ultraviolet, etc., between 0 and 1 and spreads radio over a wide range. Plotting frequency by 100 GHz (0 GHz, 100 GHz, 200 GHz, 300 GHz, ...) crowds the radio into 0-300 GHz and spreads the others over the rest. The CMB (black-body) SED's peak is cited at various valid wavelengths and frequencies, each representing a temperature of 2.72548 K, differing according to how it is plotted:
|plotted by||peak frequency||peak wavelength||peak photon energy|
|linear wavelength||282 GHz||1.063 mm||1.168 × 10-3 eV|
|linear frequency||160.23 GHz||1.871 mm||6.26 × 10-4 eV|
|log of either||222.6 GHz||1.347 mm||9.2 × 10-4 eV|
Plots against the log of either produce an equivalent plot: 1 mm≡300 GHz, 10 mm≡30 GHz, 100 mm≡3 GHz, etc.
SED fitting (aka spectral energy distribution fitting) matches an observed SED with SEDs implying certain characteristics of the source. A common use is classifying distant galaxies, to help determine their age and degree of star formation. A challenge is that a young galaxy with dust has a SED across ultraviolet, visible light, and near infrared as an old galaxy. A fitting method is Markov chain Monte Carlo (MCMC), using random walks around a parameter-space to determine the degree of unique fit between an SED and a known type of object. GalMC is an example of software that takes this approach.