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Merge pull request #43 from mtakahiro/improvement-for-parameterizing-…
…redshift Improvement for parameterizing redshift
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@@ -19,6 +19,7 @@ gsf's documentation | |
fitting.rst | ||
output.rst | ||
prior.rst | ||
redshift_fitting.rst | ||
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auto/modules | ||
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.. _redshift_fitting: | ||
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Redshift Fitting | ||
================ | ||
While gsf is now able to search the redshift grid during the fiting process, | ||
it was not originally designed to efficiently estimate the posterior for redshift. | ||
Users can turn this redshift fitting functionality by turning ``F_ZMC`` to 1, | ||
with ``ZMCMIN`` and ``ZMCMAX`` properly specified too, in the input configuration file. | ||
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To minimize the disk space usage, gsf generates templates only at ``ZMC`` specified in the input file. | ||
How it works with redshift search is, during the fitting process, when gsf detects a large shift | ||
(specifically, larger than the limiting value specified by ``deltaz_lim`` in `function_class`) in redshift from ``ZMC``, | ||
gsf calls the filter convolution function (`function.filconv`) to recalculate the model broadband flux for the given model, | ||
to better reflect the current place of each walker in the parameter space. ``ZMC`` is also replaced to this new redshift at | ||
this point. When the shift is small, gsf interpolates the model fluxes for the given set of filters | ||
after applying the shift to wavelength. | ||
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This extra step allows more accurate parameter search for gsf with redshift as a variable, but significantly slows down the computing speed, by a factor up to 10. | ||
This overhead is primarily caused by the number of data points in the model templates and filter curves. | ||
To minimize the computation time, users may want to consder using sparse templates, by using ``--delwave`` argument (delta wave, in Aungstrome) when they run run_gsf.py | ||
script, or by directly passing the same parameter to `maketemp` function. | ||
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