Training the SALT3 Model¶
usage:
trainsalt -c <configfile> <options>
Although there are a number of training configuration files
in the examples/ directory, the simplest way to train the
SALT3.K21 model with all data and spectra and with the latest calibrations
is to use the configuration files and data in the
examples/SALT3TRAIN_K21_PUBLIC directory.
To train the SALT3.K21 model, run:
trainsalt -c Train_SALT3_public.conf
This directory contains all the lightcurves, spectra, and
filter definition files needed to train the model, with outputs
in the output directory.
The training is slow given the large data volume and takes approximately
1 to 1.5 days, but can be sped up with a couple reasonable choices.
The first is changing the steps_between_errorfit argument
to estimate model uncertainties less frequently, as uncertainty estimation
(~4.5 hours) is the slowest component of the code:
trainsalt -c Train_SALT3_public.conf --steps_between_errorfit 15
Another option is to bin the spectra, which will reduce the amount of spectroscopic data points by an order of magnitude:
trainsalt -c Train_SALT3_public.conf --binspec True
This should not result in any noticeable difference to the model surfaces but hasn’t yet been tested fully. Additional speed and memory usage improvements are currently in progress.
SALT3 Training Configuration Options¶
See the examples/SALT3TRAIN_K21_PUBLIC/Train_SALT3_public.conf file
and the examples/SALT3TRAIN_K21_PUBLIC/training.conf files for
the full list of training options. Two configuration files are used with the
goal that users should rarely have to modify the default training.conf options.
Descriptions of each option are given below.
| Name | Default | Description |
|---|---|---|
| main config file | ||
| [iodata] | ||
| snlists | ASCII file or comma-separated list of files. Each file contains a list of SN files (see Photometry and Spectroscopy Format for input file format) | |
| tmaxlist | Time of maximum light for each SN in training. See examples/SALT3TRAIN_K21_PUBLIC/SALT3_PKMJD_INIT.LIST. |
|
| snparlist | initial list x0,x1,c and FITPROB (prob. that the data matches model, from SALT2). See examples/SALT3TRAIN_K21_PUBLIC/SALT3_PARS_INIT.LIST |
|
| specrecallist | Option to provide an initial set of spectral recalibration parameters. No longer recommended. | |
| dospec | True | If set, use spectra in training |
| maxsn | None | Debug option to limit the training to a given number of SNe |
| outputdir | Directory for trained model outputs | |
| keeponlyspec | False | Debug option - keep only those SNe with spectroscopic data |
| initm0modelfile | Hsiao07.dat | Initial SN SED model. Initial parameter guesses are derived from this file. Default is the Hsiao model. |
| initm1modelfile | Initial SN SED model. Will guess M1 from a time-dilated Hsiao model if no file is given. | |
| initsalt2model | True | If True, use SALT2 as the initial guess. Otherwise use initm0modelfile. |
| initsalt2var | False | If set, initialize model uncertainties using SALT2 values. No longer recommended as SALT3 error prescription is different. |
| initbfilt | Bessell90_B.dat | Nominal B-filter for putting priors on the normalization |
| resume_from_outputdir | Resume the training from an existing output directory | |
| resume_from_gnhistory | If resume_from_outputdir is set, set to same directory name to resume training from a gnhistory.pickle file. This is useful if training crashes. | |
| loggingconfig | logging.yaml | Gives configuration options for the training logs |
| trainingconfig | training.conf | Additional configuration file. Will look in the package directory if it’s not found in the current directory |
| calibrationshiftfile | A file that can adjust the calibration of the input files, e.g. for estimating systematics | |
| filter_mass_tolerance | 0.01 | Amount of filter “mass” allowed to be outside the SALT wavelength range |
| fix_salt2modelpars | False | Debug option - if True, does not fit for M0 and M1. |
| validate_modelonly | False | If True, only produces model validation plots but not plots spectra or lightcurves (slow, and occasionally crashes). |
[survey_<SURVEY>] |
The parameters file requires a category for every SURVEY key in SN data files |
|
| kcorfile | Kcorfile (includes filter ZPT offsets and filter definitions) for each SURVEY key in SN data files |
|
| subsurveylist | Comma-separated list of sub-surveys for every survey, e.g. CFA4 is the subsurvey for survey name PS1_LOWZ_COMBINED(CFA4) |
|
| [trainparams] | ||
| gaussnewton_maxiter | 30 | Maximum number of Gauss-Newton iterations allowed if convergence (delta chi^2 < 1) is not reached |
| regularize | True | Include regularization if True |
| fitsalt2 | False | Try to fit SN parameters with SALT2 model in the validation stage if True |
| n_repeat | 1 | deprecated, leave alone |
| fit_model_err | True | If True, fits model errors every steps_between_errorfit iterations |
| fit_cdisp_only | False | If True and fit_model_err is True, fits for the color scatter but no other model errors |
| steps_between_errorfit | 5 | Estimate model errors every x iterations |
| model_err_max_chisq | 4 | Begin estimating model errors when the reduced chi^2 of the training is below this |
| condition_number | 1e-80 | Conditioning matrices for the Gauss-Newton process. Leave this alone. |
| fit_tpkoff | False | if true, fit for time of maximum light along with other parameters (not well tested yet) |
| fitting_sequence | all | optionally, can fit for different model components in sequence. Can make it hard for training to converge |
| training.conf file | In most cases, leave these alone | |
| [trainingparams] | ||
| specrecal | 1 | if 1 (or True), do the spectral recalibration |
| n_processes | 1 | deprecated |
| estimate_tpk | False | not recommended estimate time of maximum light for each SN before beginning the training. Not robust. |
| fix_t0 | False | deprecated |
| n_min_specrecal | 3 | minimum number of parameters for the spectral recalibration polynomial |
| n_max_specrecal | 10 | maximum number of parameters for the spectral recalibration polynomial |
| regulargradientphase | 1e4 | amplitude of gradient regularization chi^2 penalty for phase (semi-arbitrary) |
| regulargradientwave | 1e5 | amplitude of gradient regularization chi^2 penalty for wavelength (semi-arbitrary) |
| regulardyad | 1e4 | amplitude of dyadic regularization chi^2 penalty (semi-arbitrary) |
| m1regularization | 100 | multiply regularization amplitude for the M1 component by this amount (semi-arbitrary) |
| specrange_wavescale_specrecal | 2500 | normalizes the spectra for recalibration |
| n_specrecal_per_lightcurve | 0.5 | add one spectral recal parameter for every two photometric bands in a given SN |
| regularizationScaleMethod | fixed | options for adjusting regularization scale in training/saltresids.py |
| wavesmoothingneff | 1 | Gaussian smoothing scale for the amount of training data at each wavelength for smoothly varying Neff |
| phasesmoothingneff | 3 | Gaussian smoothing scale for the amount of training data at each phase for smoothly varying Neff |
| nefffloor | 1e-4 | below nefffloor, regularization does not continue to increase in strength |
| neffmax | 0.01 | above neffmax, regularization is turned off |
| binspec | False | use spectral binning if True |
| binspecres | 29 | resolution of the spectral binning |
| spec_chi2_scaling | 0.5 | tuned so that spectra and photometry contribute ~equally to total chi^2 in training |
| [modelparams] | ||
| waverange | 2000,11000 | wavelength range over which the model is defined |
| colorwaverange | 2800,8000 | wavelength range over which the color law polynomial is fit |
| interpfunc | bspline | function for interpolating the model between control points (b-spline is default) |
| errinterporder | 0 | order of the spline interpolation for the errors |
| interporder | 3 | order of the spline interpolation for the model |
| wavesplineres | 69.3 | number of Angstroms between wavelength control points |
| waveinterpres | 10 | wavelength resolution of the model used during training (Angstroms) |
| waveoutres | 10 | wavelength resolution of the trained model written to output directory (Angstroms) |
| phaserange | -20,50 | phase range over which the model is defined (rest-frame days) |
| phasesplineres | 3.0 | phase resolution of the trained output model (days) |
| phaseinterpres | 0.2 | phase resolution of the model used during training (days) |
| phaseoutres | 1 | phase resolution of the trained model written to output directory (days) |
| n_colorpars | 5 | number of parameters used to define the color law polynomial |
| n_colorscatpars | 5 | number of parameters used to define the color scatter |
| n_components | 2 | number of model components (M0, M1) - additional components not yet allowed |
| error_snake_phase_binsize | 6 | spacing in days for the SALT error model B-spline interpolation |
| error_snake_wave_binsize | 1200 | spacing in Angstroms for the SALT error model B-spline interpolation |
| use_snpca_knots | False | if true, use the knot locations from the SALT2 training |
| [priors] | key is the name of a decorator in training/priors.py; value determines the (semi-arbitrary) width of each prior |
|
| x1mean | 0.1 | mean x1 = 0 |
| x1std | 0.1 | standard deviation of x1 values = 1 |
| m0endalllam | 1e-5 | at -20 days, M0 must go to zero flux |
| m1endalllam | 1e-4 | at -20 days, M1 must go to zero flux |
| colorstretchcorr | 1e-4 | color and stretch should not be correlated |
| colormean | 1e-3 | mean sample color is zero |
| m0positiveprior | 1e-2 | M0 is not allowed to be negative |
| recalprior | 50 | don’t allow spectral recalibration to go crazy |
| [bounds] | ||
| x1 | -5,5,0.01 | min,max,prior width on x1 |