MaNGA Firefly value-added catalog

FIREFLY is a full spectral fitting code to derive galaxy properties as described in this page. There are two Value Added catalogs related to the FIREFLY code: a MANGA VAC and an eBOSS VAC). This page describes the MANGA VAC.


The MaNGA FIREFLY VAC provides measurements of spatially resolved stellar population properties in MaNGA galaxies. It is built on and complements the products of the MaNGA data analysis pipeline (DAP, Westfall et al. 2019) by providing higher-order and model-based data products. These are measurements of the full star formation history, as well as average age, metallicity, stellar mass and its partition into remnant masses, and dust attenuation. The parameters are derived from full spectral fitting with the code FIREFLY using the supercomputer Sciama2 at Portsmouth University. This VAC is provided in two variants: the first variant employs the stellar population models of ​Maraston & Stromback (2011) based on the ​​MILES stellar library (Sánchez-Blázquez et al. 2006). The second variant uses the MaStar models​ described in ​​Maraston et al. (2020). The VAC is a single fits file for each version (6.1 GB) containing measurements for 10,735 datacubes including 10,010 unique MaNGA galaxies in DR17. The catalogue contains basic galaxy information (e.g., galaxy ID, galaxy mass), global parameters (e.g., central age), gradient parameters (e.g., age gradient) and spatially resolved quantities (e.g., 2-D age maps). These various categories are described in detail below. The DR17 catalogue should be referenced through Neumann et al. 2022 and Goddard et al. 2017.

Compared to the FIREFLY VAC in DR15, the radius in HDU4 is now given in elliptical coordinates and the azimuth is added, too. Masses in HDU11 and HDU12 are given per spaxel and as total mass per Voronoi cell. We do not provide absorption index measurements anymore.

Data Access

Information regarding the catalogue can be found from the following links:

Basic Galaxy Properties (HDU1)

Each object in the catalogue is characterised by the MaNGA identifier, MaNGA plate and IFU numbers, and coordinates (right ascension, declination). The catalogue further provides redshift, stellar mass (from the NSA catalogue), and version numbers of the DRP, DAP and FIREFLY runs used to produce and analyse the data. This information is found in the header HDU1.

Other basic galaxy information used in the MaNGA VAC, e.g. the effective radii (Re) values, the axis ratios and position angles used to assign Re fractions to x- and y-positions on the sky are adopted from the NSA catalogue and are available through the MaNGA DRPall file. The kinematics (stellar velocities and velocity dispersions) used to derive the properties in this VAC are available from the MaNGA Data Analysis Pipeline (DAP).

Global Properties (HDU2)

Light-weighted and mass-weighted stellar population ages (log(age/Gyr)) and metallicities ([Z/H]) are provided with 1-sigma errors for a central 3 arcsec aperture and for an elliptical shell at 1 effective radius Re (obtained from Voronoi binning, see below). Stellar population age and metallicity are obtained from full spectral fitting using the code FIREFLY as described below. This information is found in the header HDU2.

Gradient Properties (HDU3)

In addition to the global parameters, gradients in light-weighted and mass-weighted age and metallicity are provided. The gradients are characterised through zero point and slope and their errors. The gradient is measured linear in radius within 1.5 Re with units dex/Re via Maximum Likelihood Estimation by fitting a linear model to the stellar population properties derived on each Voronoi bin of the 2-dimensional map (see below). Errors are obtained using an Monte-Carlo Markov Chain sampler. This information is found in the header HDU3.

Spatially Resolved Quantities (HDU4-15)

Finally, the catalogue provides the spatially resolved measurements of a number of stellar population indicators in 2-dimensional maps. These stellar population parameters are age, metallicity, stellar mass and dust attenuation E(B-V) derived from full spectral fitting with the code FIREFLY. This information is in the headers HDU4-15. The 2-dimensional maps are created by the MaNGA DAP (Westfall et al. 2019) through Voronoi binning using the algorithm of Cappellari & Copin 2003. Individual MaNGA spectra for each galaxy were binned to create cells with a minimum signal-to-noise ratio of 10 per pixel. This threshold was chosen as the optimum compromise between quality of the spectra and spatial resolution (Goddard et al. 2017).
Spatial information (HDU4, HDU5, HDU15):
The bin number, x position (arcsec), y position (arcsec), radius (in Re) and azimuth of each cell of the 2-dimensional maps are provided in HDU4. The geometry of the IFU is given in HDU5. The corresponding S/N ratios (per pixel within r-band) are in HDU15.
Stellar population parameters (HDU6-14):
The light-weighted and mass-weighted stellar ages (log age/Gyr) and metallicities (dex), E(B-V) (mag), mass (log M/Msun), remnant masses (log M/Msun), and surface mass density (log M/Msun/kpc2) with associated errors for each spatial bin are provided in HDU6-13. The full star formation history per spatial bin reflected by the mass weight per SSP is provided in HDU14.

FIREFLY spectral fitter

The stellar population parameters age, metallicity, mass, and dust attenuation are determined through the full spectral fitting code FIREFLY (Wilkinson et al. 2015; Wilkinson et al. 2017). FIREFLY is a chi-squared minimisation fitting code that for a given input spectral energy distribution compares combinations of single-burst stellar population models, following an iterative best-fitting process until convergence is achieved. The weight of each component can be arbitrary and no regularisation or additional prior than the adopted model grid is applied. Dust attenuation is added in a novel way, using a High-Pass Filter 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 derives full star formation histories and 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. Light-weighted properties involve weighting each stellar population component by their geometrically averaged total luminosity across the wavelength range, whereas mass-weighted properties involve weighting by the stellar mass contribution over the wavelength range. Errors on these properties are obtained by the likelihood of solutions within the statistical cut (of order 100-1000).

Rest-framed, emission-line free spectra and velocity dispersions are needed as input to the FIREFLY code. Both the kinematics (velocities and velocity dispersions) and the emission-line spectra are determined by the DAP (Westfall et al. 2019) using an adapted version of the pPXF code by Cappellari & Emsellem 2004.

In the 'mastar' variant of the DR17 VAC, the MaStar models​ described in ​​Maraston et al. (2020) are used, based on the MaStar stellar library (Yan et al. in prep.), with Kroupa IMF (Kroupa 2001). In this VAC, version 1.1 of the models is used. The models use a combination of the stellar parameters derived by Hill et al. (2021) and Chen et al. (2020). These models cover the full wavelength range of MaNGA spectra, i.e. 3600-10,300A, with a grid of metallicities spanning -2.25 < [Z/H] < 0.35 and ages spanning 0.003 < Age(Gyr) < 15.0.

In the 'miles' variant of the DR17 VAC, as well as earlier DR15 version of this VAC, the input stellar population models of Maraston & Stromback 2011 are used, based on the MILES stellar library, with Kroupa IMF (Kroupa 2001). Each spectrum was fitted with FIREFLY over the wavelength range 3600-7500A, with a grid of metallicities spanning -2.3 < [Z/H] < 0.3 and ages spanning 0.0065 < Age(Gyr) < 15.0.

Example fits to binned spectra and stellar populations maps are shown below:

Note: For technical details regarding the code, see the aforementioned papers. The latest version of the FIREFLY source code is available on github. The version used for the DR17 VAC is tagged as v1.0.1 on github. The version of FIREFLY used for earlier versions of this VAC is still publicly available on the SDSS SVN as Firefly fitter (v1_1_0) code, which depends on a set of publicly available stellar population (v1_0_2) models. We recommend that you always use the latest version of the FIREFLY code.

Python plotting script

We have written two small python scripts (available on github here) as an example of how to plot 2D maps and 1D profiles from the MaNGA FIREFLY VAC. The fits file of the VAC should be downloaded and by default placed in the same directory as this script. The script should then be run directly by typing:

python [plate] [ifu]
python [plate] [ifu]

on the commandline. Replace [plate] and [ifu] with the numbers of the datacube that you want to plot.

By default, the scripts use the 'miles' version of the VAC, this can be changed in the scripts.

DR15 MaNGA Firefly Value Catalog

We always recommend to use the most recent version of our value added catalogs, which is DR17 (v3_1_1). The DR15 version of the MaNGA Firefly Value Catalog is still available, and documented on the DR15 website.

We have written a small python script (available on github here) as an example of how to plot maps from the MaNGA DR15 FIREFLY VAC. The fits file of the VAC should be downloaded and by default placed in the same directory as this script. The script should then run directly by typing python on the commandline. The script plots the map of one property of a single galaxy, and can be adapted to plot other properties and galaxies.

Please note: there is an minor issue in the SNR compiled in the DR15 MaNGA FIREFLY VAC which is artificially shifted from measurements by -9999 by mistake. To use the SNR information, you need to shift it back by adding 9999 to the value in the VAC.



  • Daniel Thomas
  • Justus Neumann
  • Jianhui Lian
  • Claudia Maraston
  • Taniya Parikh
  • Daniel Goddard
  • Kyle Westfall
  • Johan Comparat
  • Sofia Meneses-Goytia
  • Violeta Gonzalez-Perez