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Redshift Fitting

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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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