This is an example of running a transient search: a search which allows
the signal to only last for some short duration (parameterised by the start
time and duration).
Generating the data
In order to demonstrate the method working, we will simulate a data set
containing a transient signal. This can be done be running the example script
transient_search_using_MCMC_make_simulated_data.py. This script contains
some setup of times and signal properties, then uses the Writer to generate
the simulated data:
In this example, we of course know the properties of the simulated signal. In a
real search, the prior will be based on the candidate one is following up.
Here for example, we are performing a directed follow up, one could search over
Alpha and Delta by specifying a prior distribution (rather than a fixed
The search.run() command runs the sampler (which may take a few minutes)
and will save an image data/glitch_robust_search_walkers.png:
The output walker plots gives the values in terms of the default units (i.e.
units for the transient start times and duration). However this behaviour
is not so intuitive as an output. Therefore, a rescale_dictionary attribute
(of the pyfstat.MCMCTransientSearch instance) controls how to rescale this.
For example, in the corner plot the default `rescale_dictionary' puts the
output in terms of days: