Position.rst

.. File Position.rst

.. _position_iso:

Position and shape parameters derived from the isophotal profile
================================================================

The following parameters are derived from the spatial distribution
:math:`\cal S` of pixels detected above the analysis threshold (see :ref:`description<isophotal_measurements>`). *Unless otherwise noted,
pixel values* :math:`I_i` *are taken from the (filtered) detection image*.

.. note::
  Unless otherwise noted, all parameter names given below are only prefixes. They must be followed by _IMAGE if the results shall be expressed in pixel coordinates or _WORLD, _SKY, _J2000 or _B1950 for |WCS|_ coordinates (see :ref:`coord_suffix`).

Limits: XMIN, YMIN, XMAX, YMAX
------------------------------

These coordinates define two corners of a rectangle which encloses the
detected object:

.. math::
  :label: xminymax

   \begin{aligned}
   {\tt XMIN} & = & \min_{i \in {\cal S}} x_i,\\
   {\tt YMIN} & = & \min_{i \in {\cal S}} y_i,\\
   {\tt XMAX} & = & \max_{i \in {\cal S}} x_i,\\
   {\tt YMAX} & = & \max_{i \in {\cal S}} y_i,
   \end{aligned}

where :math:`x_i` and :math:`y_i` are respectively the x-coordinate and
y-coordinate of pixel :math:`i`.

Barycenter: X, Y
----------------

Barycenter coordinates generally define the position of the “center” of
a source, although this definition can be inadequate or inaccurate if
its spatial profile shows a strong skewness or very large wings. X and Y
are simply computed as the first order moments of the profile:

.. math::
  :label: xy

   \begin{aligned}
   {\tt X} & = & \overline{x} = \frac{\displaystyle \sum_{i \in {\cal S}}
   I_i x_i}{\displaystyle \sum_{i \in {\cal S}} I_i},\\
   {\tt Y} & = & \overline{y} = \frac{\displaystyle \sum_{i \in {\cal S}}
   I_i y_i}{\displaystyle \sum_{i \in {\cal S}} I_i}.
   \end{aligned}

In practice, :math:`x_i` and :math:`y_i` are summed relative to XMIN
and YMIN in order to reduce roundoff errors in the summing.

Position of the peak: XPEAK, YPEAK
----------------------------------

It is sometimes useful to have the position XPEAK,YPEAK of the pixel
with maximum intensity in a detected object, for instance when working
with likelihood maps, or when searching for artifacts. For better
robustness, PEAK coordinates are computed on *filtered* profiles if
available. On symmetrical profiles, PEAK positions and barycenters
coincide within a fraction of pixel (XPEAK and YPEAK coordinates are
quantized by steps of 1 pixel, thus XPEAK_IMAGE and YPEAK_IMAGE are
integers). This is no longer true for skewed profiles, therefore a
simple comparison between PEAK and barycenter coordinates can be used to
identify asymmetrical objects on well-sampled images.

2nd order moments: X2, Y2, XY
-----------------------------

(Centered) second-order moments are convenient for measuring the spatial
spread of a source profile. In |SExtractor| they are computed with:

.. math::
  :label: x2y2

   \begin{aligned}
   {\tt X2} & = \overline{x^2} = & \frac{\displaystyle \sum_{i \in {\cal
   S}} I_i x_i^2}{\displaystyle \sum_{i \in {\cal S}} I_i} -
   \overline{x}^2,\\ {\tt Y2} & = \overline{y^2} = & \frac{\displaystyle
   \sum_{i \in {\cal S}} I_i y_i^2}{\displaystyle \sum_{i \in {\cal S}}
   I_i} - \overline{y}^2,\\ {\tt XY} & = \overline{xy} = &
   \frac{\displaystyle \sum_{i \in {\cal S}} I_i x_i y_i}{\displaystyle
   \sum_{i \in {\cal S}} I_i} - \overline{x}\,\overline{y},
   \end{aligned}

These expressions are more subject to roundoff errors than if the
1st-order moments were subtracted before summing, but allow both 1st and
2nd order moments to be computed in one pass. Roundoff errors are
however kept to a negligible value by measuring all positions relative
here again to XMIN and YMIN.

.. _shape_iso:

Basic shape parameters: A, B, THETA
-----------------------------------

These parameters are intended to describe the detected object as an
elliptical shape. A and B are its semi-major and semi-minor axis
lengths, respectively. More precisely, they represent the maximum and
minimum spatial dispersion of the object profile along any direction.
THETA is the position-angle of the A axis relative to the first image
axis. It is counted positive in the direction of the second axis. Here
is how they are computed:

2nd-order moments can easily be expressed in a referential rotated from
the :math:`x,y` image coordinate system by an angle +\ :math:`\theta`:

.. math::
  :label: varproj

   \begin{array}{lcrrr}
   \overline{x_{\theta}^2} & = & \cos^2\theta\:\overline{x^2} & +\,\sin^2\theta\:\overline{y^2}
               & -\,2 \cos\theta \sin\theta\:\overline{xy},\\
   \overline{y_{\theta}^2} & = & \sin^2\theta\:\overline{x^2} & +\,\cos^2\theta\:\overline{y^2}
               & +\,2 \cos\theta \sin\theta\:\overline{xy},\\
   \overline{xy_{\theta}} & = & \cos\theta \sin\theta\:\overline{x^2} &
   -\,\cos\theta \sin\theta\:\overline{y^2} & +\,(\cos^2\theta -
   \sin^2\theta)\:\overline{xy}.
   \end{array}

One can find interesting angles :math:`\theta_0` for which the variance
is minimized (or maximized) along :math:`x_{\theta}`:

.. math::
  :label: theta0

  {\left.\frac{\partial \overline{x_{\theta}^2}}{\partial \theta} \right|}_{\theta_0} = 0,

which leads to

.. math::
  :label: theta0_2

   2 \cos\theta \sin\theta_0\:(\overline{y^2} - \overline{x^2})
       + 2 (\cos^2\theta_0 - \sin^2\theta_0)\:\overline{xy} = 0.

If :math:`\overline{y^2} \neq \overline{x^2}`, this implies:

.. math::
   :label: theta0_3

   \tan 2\theta_0 = 2 \frac{\overline{xy}}{\overline{x^2} - \overline{y^2}},

a result which can also be obtained by requiring the covariance
:math:`\overline{xy_{\theta_0}}` to be null. Over the domain
:math:`[-\pi/2, +\pi/2[`, two different angles — with opposite signs —
satisfy :eq:`theta0_3`. By definition, THETA is the position angle for
which :math:`\overline{x_{\theta}^2}` is *max*\ imized. THETA is
therefore the solution to :eq:`theta0_3`. that has the same sign as the
covariance :math:`\overline{xy}`. A and B can now simply be expressed
as:

.. math::
  :label: aimage

   \begin{aligned}
   {\tt A}^2 & = & \overline{x^2}_{\tt THETA},\ \ \ {\rm and}\\
   {\tt B}^2 & = & \overline{y^2}_{\tt THETA}.\end{aligned}

A and B can be computed directly from the 2nd-order moments, using the
following equations derived from :eq:`varproj` after some algebra:

.. math::
  :label: aimage_2

   \begin{aligned}
   {\tt A}^2 & = & \frac{\overline{x^2}+\overline{y^2}}{2}
       + \sqrt{\left(\frac{\overline{x^2}-\overline{y^2}}{2}\right)^2 + \overline{xy}^2},\\
   {\tt B}^2 & = & \frac{\overline{x^2}+\overline{y^2}}{2},
       - \sqrt{\left(\frac{\overline{x^2}-\overline{y^2}}{2}\right)^2 + \overline{xy}^2}.\end{aligned}

Note that A and B are exactly halves the :math:`a` and :math:`b`
parameters computed by the COSMOS image analyser :cite:`1980SPIE_264_208S`.
Actually, :math:`a` and :math:`b` are defined in :cite:`1980SPIE_264_208S`
as the semi-major and semi-minor axes of an elliptical shape with constant
surface brightness, which would have the same 2nd-order moments as the
analyzed object.

.. _ellipse_iso:

Ellipse parameters: CXX, CYY, CXY
---------------------------------

A, B and THETA are not very convenient to use when, for instance, one
wants to know if a particular |SExtractor| detection extends over some
position. For this kind of application, three other ellipse parameters
are provided; CXX, CYY and CXY. They do nothing more than describing the
same ellipse, but in a different way: the elliptical shape associated to
a detection is now parameterized as

.. math::
  :label: ellipse

   {\tt CXX} (x-\overline{x})^2 + {\tt CYY} (y-\overline{y})^2
       + {\tt CXY} (x-\overline{x})(y-\overline{y}) = R^2,

where :math:`R` is a parameter which scales the ellipse, in units of A
(or B). Generally, the isophotal limit of a detected object is well
represented by :math:`R\approx 3` (:numref:`fig_ellipse`). Ellipse
parameters can be derived from the 2nd order moments:

.. math::
  :label: ellipse_2

   \begin{aligned}
   {\tt CXX} & = & \frac{\cos^2 {\tt THETA}}{{\tt A}^2} + \frac{\sin^2
   {\tt THETA}}{{\tt B}^2} =
   \frac{\overline{y^2}}{\overline{x^2} \overline{y^2} - \overline{xy}^2}\\
   {\tt CYY} & = & \frac{\sin^2 {\tt THETA}}{{\tt
   A}^2} + \frac{\cos^2 {\tt THETA}}{{\tt B}^2} =
   \frac{\overline{x^2}}{\overline{x^2} \overline{y^2} - \overline{xy}^2}\\
   {\tt CXY} & = & 2 \,\cos {\tt THETA}\,\sin {\tt
   THETA} \left( \frac{1}{{\tt A}^2} - \frac{1}{{\tt B}^2}\right) = -2\,
   \frac{\overline{xy}}{\overline{x^2} \overline{y^2} - \overline{xy}^2}\end{aligned}

.. _fig_ellipse:

.. figure:: figures/ellipse.*
   :figwidth: 100%
   :align: center

   Meaning of shape parameters.

By-products of shape parameters: ELONGATION and ELLIPTICITY
-----------------------------------------------------------

These parameters [#elongation]_ are directly derived from A and B:

.. math::
  :label: elongation

   \begin{aligned}
   {\tt ELONGATION} & = & \frac{\tt A}{\tt B}\ \ \ \ \ \mbox{and}\\
   {\tt ELLIPTICITY} & = & 1 - \frac{\tt B}{\tt A}.\end{aligned}

.. _poserr:

Position uncertainties: ERRX2, ERRY2, ERRXY, ERRA, ERRB, ERRTHETA, ERRCXX, ERRCYY, ERRCXY
-----------------------------------------------------------------------------------------

Uncertainties on the position of the barycenter can be estimated using
photon statistics. In practice, such estimates are a lower-value of the full
uncertainties because they do not include, for instance, the contribution of
detection biases or contamination by neighbors. Furthermore, |SExtractor| does
not currently take into account possible correlations of the noise between adjacent
pixels. Hence variances simply write:

.. math::
  :label: errxy

   \begin{aligned}
   {\tt ERRX2} & = {\rm var}(\overline{x}) = & \frac{\displaystyle
   \sum_{i \in {\cal S}} \sigma^2_i (x_i-\overline{x})^2} {\displaystyle
   \left(\sum_{i \in {\cal S}} I_i\right)^2},\\ {\tt ERRY2} & = {\rm
   var}(\overline{y}) = & \frac{\displaystyle \sum_{i \in {\cal S}}
   \sigma^2_i (y_i-\overline{y})^2} {\displaystyle \left(\sum_{i \in
   {\cal S}} I_i\right)^2},\\ {\tt ERRXY} & = {\rm
   cov}(\overline{x},\overline{y}) = & \frac{\displaystyle \sum_{i \in
   {\cal S}} \sigma^2_i (x_i-\overline{x})(y_i-\overline{y})}
   {\displaystyle \left(\sum_{i \in {\cal S}} I_i\right)^2}.\end{aligned}

:math:`\sigma_i` is the flux uncertainty estimated for pixel :math:`i`:

.. math:: \sigma^2_i = {\sigma_B}^2_i + \frac{I_i}{g_i},

where :math:`{\sigma_B}_i` is the local background noise and
:math:`g_i` the local gain — conversion factor — for pixel :math:`i`
(see :ref:`effect_of_weighting` for more details). Semi-major axis ERRA, semi-minor
axis ERRB, and position angle ERRTHETA of the :math:`1\sigma` position
error ellipse are computed from the covariance matrix exactly like in
[chap:abtheta] for shape parameters:

.. math::
  :label: errabtheta

   \begin{aligned}
   {\tt ERRA}^2 & = & \frac{{\rm var}(\overline{x})+{\rm var}(\overline{y})}{2}
       + \sqrt{\left(\frac{{\rm var}(\overline{x})-{\rm var}(\overline{y})}{2}\right)^2
       + {\rm cov}^2(\overline{x},\overline{y})},\\
   {\tt ERRB}^2 & = & \frac{{\rm var}(\overline{x})+{\rm var}(\overline{y})}{2}
       - \sqrt{\left(\frac{{\rm var}(\overline{x})-{\rm var}(\overline{y})}{2}\right)^2
       + {\rm cov}^2(\overline{x},\overline{y})},\\
   \tan (2{\tt ERRTHETA}) & = & 2 \,\frac{{\rm cov}(\overline{x},\overline{y})}
                       {{\rm var}(\overline{x}) - {\rm var}(\overline{y})}.\end{aligned}

And the ellipse parameters are:

.. math::
  :label: errellipse

   \begin{aligned}
   {\tt ERRCXX} & = & \frac{\cos^2 {\tt ERRTHETA}}{{\tt ERRA}^2} +
   \frac{\sin^2 {\tt ERRTHETA}}{{\tt ERRB}^2} = \frac{{\rm
   var}(\overline{y})}{{\rm var}(\overline{x}) {\rm var}(\overline{y}) -
   {\rm cov}^2(\overline{x},\overline{y})},\\
   {\tt ERRCYY} & = & \frac{\sin^2 {\tt ERRTHETA}}{{\tt ERRA}^2} +
   \frac{\cos^2 {\tt ERRTHETA}}{{\tt ERRB}^2} =
   \frac{{\rm var}(\overline{x})}{{\rm var}(\overline{x}) {\rm var}(\overline{y}) -
   {\rm cov}^2(\overline{x},\overline{y})},\\
   {\tt ERRCXY} & = & 2 \cos {\tt
   ERRTHETA}\sin {\tt ERRTHETA} \left( \frac{1}{{\tt ERRA}^2} -
   \frac{1}{{\tt ERRB}^2}\right)\\ & = & -2 \frac{{\rm
   cov}(\overline{x},\overline{y})}{{\rm var}(\overline{x}) {\rm var}(\overline{y}) -
   {\rm cov}^2(\overline{x},\overline{y})}.\end{aligned}

Handling of “infinitely thin” detections
----------------------------------------

Apart from the mathematical singularities that can be found in some of
the above equations describing shape parameters (and which |SExtractor|
handles, of course), some detections with very specific shapes may yield
quite unphysical parameters, namely null values for B, ERRB, or even A
and ERRA. Such detections include single-pixel objects and horizontal,
vertical or diagonal lines which are 1-pixel wide. They will generally
originate from glitches; but very undersampled and/or low S/N genuine
sources may also produce such shapes.

For basic shape parameters, the following convention was adopted: if the
light distribution of the object falls on one single pixel, or lies on a
sufficiently thin line of pixels, which we translate mathematically by

.. math::
  :label: singutest

   \overline{x^2}\,\overline{y^2} - \overline{xy}^2 < \rho^2,

then :math:`\overline{x^2}` and :math:`\overline{y^2}` are incremented
by :math:`\rho`. |SExtractor| sets :math:`\rho=1/12`, which is the
variance of a 1-dimensional top-hat distribution with unit width.
Therefore :math:`1/\sqrt{12}` represents the typical minor-axis values
assigned (in pixels units) to undersampled sources in |SExtractor|.

Positional errors are more difficult to handle, as objects with very
high signal-to-noise can yield extremely small position uncertainties,
just like singular profiles do. Therefore |SExtractor| first checks that
:eq:`singutest` is true. If this is the case, a new test is conducted:

.. math::
  :label: singutest2

   {\rm var}(\overline{x})\,{\rm var}(\overline{y}) - {\rm
   covar}^2(\overline{x}, \overline{y}) < \rho^2_e,

where :math:`\rho_e` is arbitrarily set to :math:`\left( \sum_{i \in {\cal S}}
\sigma^2_i \right) / \left(\sum_{i \in {\cal S}} I_i\right)^2`. If
:eq:`singutest2` is true, then :math:`\overline{x^2}` and
:math:`\overline{y^2}` are incremented by :math:`\rho_e`.

.. [#elongation] These parameters are dimensionless, and for historical reasons do not accept suffixes such as _IMAGE or _WORLD.

.. include:: keys.rst