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