FIREFLY is a chi-squared minimisation fitting code that for a given input Spectral Energy Distribution (SED), compares combinations of single-burst stellar population models (SSP), following an iterative best-fitting process until convergence is achieved ( Wilkinson et al. 2017, MNRAS, 472, 4297
). The weight of each component can be arbitrary and no regularization or additional prior than the adopted model grid is applied. Dust attenuation is added in a novel way, using a High-Pass Filter (HPF) in order to rectify the continuum before fitting. The returned attenuation array is then matched to known analytical approximations to return an E(B-V) value. This procedure allows for removal of large scale modes of the spectrum associated with dust and/or poor flux calibration. FIREFLY provides light- and mass-weighted stellar population properties (age and metallicity), E(B-V) values and stellar mass for the most likely best fitting model. Errors on these properties are obtained by the likelihood of solutions within the statistical cut (of order 100-1000). We combined FIREFLY with the stellar population models of Maraston & Stromback, 2011, MNRAS, 418, 2785
, utilising the different stellar libraries and stellar initial mass functions to derive galaxy properties on the spectra available on the SDSS database.
There are two Value Added catalogs related to the FIREFLY code, a MANGA VAC and an eBOSS VAC.
MANGA FIREFLY VAC
All MANGA DR14 data was processed with FIREFLY, VAC is available here.
Please read the VAC webpage and the VAC paper Goddard et al. 2017.
eBOSS FIREFLY VAC
All SDSS+BOSS+eBOSS DR14 data was processed with FIREFLY, the VAC is available here.
Please read the VAC webpage and the VAC paper Comparat et al. 2017.
The official FIREFLY webpage is here, you will find the latest version of the code and a sphinx documentation describing how it works.
To guarantee a reproducible run, we tagged the version of the FIREFLY products used on the SDSS svn. Two parts are needed for the code to function. Both are available here:
- Maraston & Stromback, 2011, MNRAS, 418, 2785
- Wilkinson et al, 2015, MNRAS, 449, 328
- Goddard et al, 2017, MNRAS, 466, 4731
- Wilkinson et al. 2017, MNRAS, 472, 4297
- Comparat et al. 2017