Outputs#

Below, we describe the contents of the post-processed files in detail. Note that that the size of each output is only for one SED. If multiple SEDs were in the input catalogue, all outputs will have an additional final dimension the length of the number of SEDs.

Note

We define the array size variables here for convenience.

  • Nfilters : the number of filters included in the input.

  • Nmodels: the number of models. If using the MPFIT algorithm, this value will be 1. If using an MCMC algorithm, this value is equal to FINAL_CHAIN_LENGTH in the configuration.

  • Nhighres_models: the number of high resolution models. If using the MPFIT algorithm, this value will be 1. If using an MCMC algorithm, this value is determined by HIGH_RES_MODEL_FRACTION in the configuration.

  • Nparam: the number of parameters used in the SED fit (including fixed parameters).

Basic Outputs#

By default, the post-processed outputs include:

SED_IDstring

The unique identifier for each SED. If not specified in the input, this value will be automatically generated as integer values.

REDSHIFTdouble

The redshift of each SED. If not specified in the input, this value will be 0.

LUMIN_DISTdouble

The luminosity distance of each SED. If not specified in the input, this value will be determined from the redshift using the cosmology chosen during configuration \([{\rm Mpc}]\).

FILTER_LABELSstring array(Nfilters)

The list of filter labels specified in the input.

WAVE_FILTERSdouble array(Nfilters)

The mean wavelength of each filter \([{\mu \rm m}]\):

\[\bar\lambda = \frac{\int \lambda T(\lambda) d\lambda}{\int T(\lambda) d\lambda},\]

where \(T(\lambda)\) is the filter transmission function.

LNU_OBSdouble array(Nfilters)

The observed luminosities converted from input flux data:

\[L_\nu = 4 \pi C (D_L)^2 F_{\nu},\]

where \(L_{\nu}\) is the observed luminosities \([L_\odot\ {\rm Hz}^{-1}]\), \(C\) is the unit conversion constant (\(C = 2.4778 \times 10^{-8}\)), \(D_L\) is the luminosity distance given by LUMIN_DIST \([{\rm Mpc}]\), and \(F_{\nu}\) is the input flux data \([{\rm Jy}]\).

LNU_UNCdouble array(Nfilters)

The uncertainties on the observed luminosities converted from input flux uncertainties \([L_\odot\ {\rm Hz}^{-1}]\).

LNU_MODdouble array(Nfilters, Nmodels)

The mean \(L_\nu\) produced by the model in each filter \([L_\odot\ {\rm Hz}^{-1}]\):

\[\bar L_\nu = \frac{\int T(\lambda) L_\nu d\lambda}{\int T(\lambda) d\lambda}.\]

In the case of the MPFIT algorithm, this is the best-fitting mean \(L_\nu\) in each filter. In the case of the MCMC algorithms, this is the posterior distribution on the mean \(L_\nu\) in each filter.

MODEL_UNCdouble

The fractional uncertainties on the model chosen during configuration.

WAVE_HIRESdouble array(1000)

The wavelength grid for the high resolution UV-to-FIR model \([\mu \rm m]\).

LNU_MOD_HIRESdouble array(1000, Nhighres_models)

The total high resolution UV-to-FIR luminosities produced by the model \([L_\odot\ {\rm Hz}^{-1}]\).

LNPROBdouble array(Nmodels)

The natural log probability of each model. In the case of the MPFIT algorithm, this is the best-fitting log probability. In the case of an MCMC algorithm, this is the sampled posterior log probability.

LNPROB_BESTFITdouble

The best-fitting log probability value.

Note

Only appears in the output if using an MCMC algorithm.

CHI2double array(Nmodels)

The \(\chi^2\) of each model calculated as

\[\chi^2 = \sum_i \frac{(L_{\nu,\ i}^{\rm obs} - L_{\nu,\ i}^{\rm mod})^2}{\sigma_{{\rm total},\ i}^2},\]

where \(L_{\nu,\ i}^{\rm obs}\) is LNU_OBS in filter \(i\), \(L_{\nu,\ i}^{\rm mod}\) is LNU_MOD in filter \(i\), and \(\sigma_{{\rm total},\ i}\) is the total uncertainty in filter \(i\) from the combined observational and model uncertainty (see MODEL_UNC in the configuration for details). In the case of the MPFIT algorithm, this is the best-fitting \(\chi^2\). In the case of an MCMC algorithm, this is the sampled posterior \(\chi^2\).

CHI2_BESTFITdouble

The best-fitting \(\chi^2\) value.

Note

Only appears in the output if using an MCMC algorithm.

PARAMETER_NAMESstring array(Nparam)

The names of the parameters used in the SED fitting (including fixed parameters).

Note

If multiple SEDs were input, it is possible that they may have a different number of parameters. This can happen, for example, with the parameters PSI_1, PSI_2, PSI_3, etc. (the SFH coefficients for each age bin) if a given age bin is older than the estimated age of the universe. In this case, the PSI_* parameters associated with these age bins will not be included in PARAMETER_NAMES. If this happens, the last entries in PARAMETER_NAMES will be left blank.

COVARIANCEdouble array(Nparam, Nparam)

The covariance matrix for the model parameters. The square root of the diagonal elements gives the estimated \(1\sigma\) uncertainty for each parameter.

Note

Only appears in the output if using the MPFIT algorithm.

Parameter Outputs#

The post-processed files also include the model parameters for each component of the model as follows, where <PARAM-NAME> is a proxy for any of the parameter names given in PARAMETER_NAMES:

Note

While PARAMETER_NAMES has each parameter as individual entries, <PARAM-NAME> will compress like parameters into a single entry. This will occur, for example, with the PSI parameters. Rather than having PSI_1, PSI_2, PSI_3, etc., they will all be compressed into a single PSI parameter that has an additional leading dimension with a length of the number of like parameters. If an entry in PARAMETER_NAMES is blank, the corresponding <PARAM-NAME> value will be NaN.

<PARAM-NAME>double array(Nmodels)

In the case of the MPFIT algorithm, this is the best-fitting value for each parameter. In the case of an MCMC algorithm, this is the sampled posterior for each parameter.

Note

If the parameter was fixed, regardless of algorithm, this will be the fixed value. In the case of an MCMC algorithm, Nmodels will then be set to 1 for the fixed parameter to conserve memory.

<PARAM-NAME>_PERCENTILESdouble array(3, Nmodels)

The 16th, 50th, and 84th percentiles of the sampled posterior distribution for each parameter.

Note

Only appears in the output if using an MCMC algorithm.

<PARAM-NAME>_BESTFITdouble

The best-fitting value for each parameter.

Note

Only appears in the output if using an MCMC algorithm.

<PARAM-NAME>_UNCdouble

The estimated \(1\sigma\) uncertainty for each parameter.

Note

Only appears in the output if using the MPFIT algorithm.

Other Outputs#

The post-processed output will always contain the basic and parameter outputs. It will additionally contain other outputs depending on the model and fitting algorithm chosen during configuration: