The goal of this module is to implement the probability of getting a given set
of extrinsics parameters for each detector (snr, horizon distance, end time and
phase) assuming that the event is a gravitational wave signal, *s*, coming from
an isotropic distribution uniform in location, orientation and the volume of
space. The implementation of this in the calling code can be found in
of extrinsic parameters for a set of detectors parameterized by n-tuples of
trigger parameters: (snr, horizon distance, end time and phase) assuming that
the event is a gravitational wave signal, *s*, coming from an isotropic
distribution in location, orientation and the volume of space. The
implementation of this in the calling code can be found in
:py:mod:`stats.inspiral_lr`.
The probabilities are factored in the following way:
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@@ -98,7 +99,7 @@ where:
* :math:`\\vec{\\rho}` denotes the vector of SNRs with one component from each detector
* :math:`\\vec{t}` denotes the vector of end time with one component from each detector
* :math:`\\vec{\phi}` denotes the vector of measured phases with one component from each detector
* :math:`\\vec{O}` denotes the vector of observing IFOs with one component from each detector
* :math:`\\vec{O}` denotes the vector of observing IFOs with one component from each detector. Strictly speaking this is just a label that desribes what detectors the components of the other vectors correspond to
* :math:`\\vec{D_H}` denotes the vector of horizon distances with one component from each detector
* :math:`s` denotes the signal hypothesis
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@@ -117,7 +118,8 @@ Sanity Checks
The code here is new for O3. We compared the result to the O2 code on 100,000
seconds of data searching for binary neutron stars in H and L. The injection
set was identical.
set was identical. Although an improvement for an HL search was not expected,
in fact it appears that the reimplementation is a bit more sensitive.
