Past exam of the frontiers of Observational Astrophysics course of the University of Cambridge / 2012 / Paper 1 / 2 / Solution

An observed catalogue is the result of a population, an instrument and a selection rule. Its astronomical selection function can depend on flux, color, size, surface brightness, sky location, cadence and follow-up success. The relevant cut is often on noisy measured quantities. A model that selects on noiseless true flux instead can predict a different sample even when it uses the same nominal threshold.

Malmquist bias occurs because brighter members of a population are visible through a larger volume in a flux-limited survey. For a uniform Euclidean population, $r_{\max }\propto \sqrt{L}$ and $V_{\max }\propto L^{3/2}$. The selected luminosity distribution is therefore weighted toward large $L$. For an intrinsic Gaussian absolute-magnitude distribution with mean $M_0$ and variance ${\sigma }_M^2$, multiplying by $10^{-0.6M}$ and completing the square gives the Gaussian absolute-magnitude Malmquist shiftThis assumes the stated simple population, a complete flux cut and no additional boundaries. Exactly identical noiseless standard candles have a distance truncation, not a luminosity spread to reweight. Scatter, extinction, cosmological volume and source evolution need their own model.

Lutz-Kelker bias concerns parallax errors, spatial population weighting and selected calibration samples. Uniform spatial density gives $p(r)\propto r^2$ locally, hence $p(\varpi )\propto {\varpi }^{-4}$ on the physically valid range. More distant objects can scatter into a measured-parallax cut. In addition, inversion $r=1/\hat{\varpi }$ is nonlinear and gives misleading estimates at low signal-to-noise; a negative measured parallax is possible even for a positive true distance. A posterior instead combines the parallax likelihood with a justified population prior. An unbounded uniform-volume prior is improper here; a finite population model or a proper distance prior for parallax inference is necessary.^[ref]

Bayes' theorem makes the assumptions explicit. If $\eta$ denotes population parameters, $x$ latent properties and $S$ the catalogue inclusion event, a selected-sample likelihood has the formThis selection-normalized astronomical likelihood accounts for the model-dependent probability of entering the catalogue. For individual inference, selection information already determined by the fully conditioned measured data must not be multiplied in a second time. The sampling design, inferential target and actual conditioned information determine the appropriate expression.

Other effects include Eddington bias, where steep number counts and measurement scatter move more faint objects upward than bright objects downward, and surface-brightness selection that misses diffuse galaxies despite substantial total flux. Dust extinction affects both detectability and inferred luminosity. Variable-source detection depends on cadence and duration; spectroscopic follow-up can favor easy-to-measure redshifts or particular colors. Blends, calibration drift and redshift errors can mimic population changes.

Examples include magnitude-limited supernova samples used in distance-redshift fits, where luminosity scatter and follow-up must be modeled, and radial-velocity planet surveys, which favor large-amplitude, short-period systems and leave inclination degeneracies. Forward modeling through the survey pipeline, injection-and-recovery estimates of completeness, calibrated noise and properly modeled censored or nondetected observations address these effects. A universal correction or an unexamined inverse-volume weight does not replace an explicit generative and selection model.