The spatial distribution of galaxies can be measured either in two dimensions as projected onto the plane of the sky or in three dimensions using the redshift of each galaxy. As it can be observationally expensive to obtain spectra for large samples of (particularly faint) galaxies, redshift information is not always available for a given sample. One can then measure the two-dimensional projected angular correlation function (), defined as the probability above Poisson of finding two galaxies with an angular separation :

(5) |

where *N* is the mean number of galaxies per steradian and
d
is the solid angle of a second galaxy at a separation
from a
randomly chosen galaxy.

Measurements of () are known to be low by an additive factor known as the "integral constraint", which results from using the data sample itself (which often does not cover large areas of the sky) to estimate the mean galaxy density. This correction becomes important on angular scales comparable to the survey size; on much smaller scales it is negligible. One can either restrict measurements of the angular clustering to scales where the integral constraint is not important or estimate the amplitude of the integral constraint correction by doubly integrating an assumed power law form of () over the survey area, , using

(6) |

where is the area subtended by the survey. In practice, this can be numerically estimated over the survey geometry using the random catalog itself (see Roche & Eales 1999 for details).

The projected angular two-point correlation function, (), can generally be fit with a power law,

(7) |

where A is the clustering amplitude at a given scale (often 1^{'})
and is the slope of the
correlation function.

From measurements of
() one can infer the
three-dimensional spatial two-point correlation function,
(*r*), if
the redshift distribution
of the sources is well known. The two-point correlation function,
(*r*), is
usually fit as a power law,
(*r*) =
(*r* / *r*_{0})^{-},
where *r*_{0} is the characteristic scale-length of the galaxy
clustering, defined as the scale at which
= 1. As the
two-dimensional galaxy clustering seen in the plane of the sky is a
projection of the three-dimensional clustering,
() is directly
related to its three-dimensional analog
(*r*).
For a given
(*r*),
one can predict the amplitude and slope of
() using Limber's equation,
effectively integrating
(*r*)
along the redshift direction (e.g.
Peebles 1980).
If one assumes
(*r*)
(and thus
()) to be a power
law over the redshift range of interest, such that

(8) |

then

(9) |

where Γ is the usual gamma function.
The amplitude factor, *A*, is given by

(10) |

where *dN* / *dz* is the number of
galaxies per unit redshift interval and *g*(*z*) depends on
and the
cosmological model:

(11) |

Here *F*(*r*) is the curvature factor in the Robertson-Walker
metric,

(12) |

If the redshift distribution of sources,
*dN* / *dz*, is well known, then the amplitude of
() can be predicted for a
given power law model of
(*r*),
such that measurements of
() can
be used to place constraints on the evolution of
(*r*).

Interpreting angular clustering results can be difficult, however, as
there is a degeneracy between the inherent clustering amplitude and
the redshift distribution of the sources in the sample. For example,
an observed weakly clustered signal projected on the plane of the sky
could be due either to the galaxy population being intrinsically
weakly clustered and projected over a relatively short distance along
the line of sight, or it could result from an inherently strongly
clustered distribution integrated over a long distance, which would
wash out the signal. The uncertainty on the redshift distribution is
therefore often the dominant error in analyses using angular
clustering measurements. The assumed galaxy redshift distribution
(*dN* / *dz*) has varied widely in different studies, such
that similar
observed angular clustering results have led to widely different
conclusions. A further complication is that each sample usually spans
a large range of redshifts and is magnitude-limited, such that the
mean intrinsic luminosity of the galaxies is changing with redshift
within a sample, which can hinder interpretation of the evolution of
clustering measured in () studies.

Many of the first measurements of large scale structure were studies
of angular clustering. One of the earliest determinations was the
pioneering work of
Peebles (1975)
using photographic plates from the
Lick survey (Fig. 1). They found
() to be well fit by a power
law with a slope of =
-0.8. Later studies using CCDs were able to
reach deeper magnitude limits and found that fainter galaxies had a
lower clustering amplitude. One such study was conducted by
Postman et
al. (1998),
who surveyed a contiguous 4° by 4° field
to a depth of *I*_{AB} = 24 mag, reaching to *z* ~
1. Later surveys that covered multiple fields on the sky found similar
results. The lower clustering amplitude observed for galaxies with fainter
apparent magnitudes can in principle be due either to clustering
being a function of luminosity and/or a function of redshift. To
disentangle this dependence, each author assumes a *dN*/*dz*
distribution for galaxies as a function of apparent magnitude and then
fits the observed
() with different models of
the redshift dependence of clustering. While many authors measure
similar values of the dependence of
() on apparent magnitude, due
to differences
in the assumed *dN* / *dz* distributions different conclusions are
reached regarding the amount of luminosity and redshift dependence to
galaxy clustering. Additionally, quoted error bars on the inferred
values of *r*_{0} generally include only Poisson and/or
cosmic variance
error estimates, while the dominant error is often the lack of
knowledge of *dN* / *dz* for the particular sample in question.

Because of the sensitivity of the inferred value of *r*_{0}
on the redshift distribution of sources, it is preferable to measure the
three dimensional correlation function. While it is much easier to
interpret three dimensional clustering measurements, in cases where it
is still not feasible to obtain redshifts for a large fraction of
galaxies in the sample, angular clustering measurements are still
employed. This is currently the case in particular with high redshift
and/or very dusty, optically-obscured galaxy samples, such as
sub-millimeter galaxies (e.g.,
Brodwin et al. 2008,
Maddox et al. 2010).
However, without knowledge of the redshift distribution of the sources,
these measurements are hard to interpret.