Supernova Models

Getting Started

Create a model using the built-in “source” named 'hsiao':

>>> import sncosmo
>>> model = sncosmo.Model(source='hsiao')

Set the redshift, time-of-zero-phase and the amplitude:

>>> model.set(z=0.5, t0=55000., amplitude=1.e-10)

Generate synthetic photometry through an observer-frame bandpass:

>>> model.bandmag('desr', 'ab', [54990., 55000., 55020.])
array([ 24.82381795,  24.41496701,  25.2950865 ])

Equivalent values in photons / s / cm^2:

>>> model.bandflux('desr', [54990., 55000., 55020.])
array([  7.22413301e-05,   1.05275209e-04,   4.68034980e-05])

Equivalent values scaled so that 1 is equivalent to an AB magnitude of 25:

>>> model.bandflux('desr', [54990., 55000., 55020.], zp=25., zpsys='ab')
array([ 1.17617737,  1.71400939,  0.7620183 ])

Generate an observer-frame spectrum at a given time and wavelengths (in ergs/s/cm^2/Angstrom):

>>> model.flux(54990., [4000., 4100., 4200.])
array([  4.31210900e-20,   7.46619962e-20,   1.42182787e-19])

Creating a model using a built-in source

A Model in sncosmo consists of

  • One “source” A model of the spectral evolution of the source (e.g., a supernova).

  • Zero or more “propagation effects” Models of how intervening structures (e.g., host galaxy dust, milky way dust) affect the spectrum.

In the above example, we created a model with no propagation effects, using one of the built-in Source instances that sncosmo knows about: 'hsiao'. See the full List of Built-in Sources that sncosmo knows about.


In fact, the data for “built-in” sources are hosted remotely, downloaded as needed, and cached locally. So the first time you load a given model, you need to be connected to the internet. You will see a progress bar as the data are downloaded. By default, SNCosmo will use a subdirectory of the AstroPy cache directory for this purpose, e.g., $HOME/.astropy/cache/sncosmo, but this can be changed by setting the data_dir configuration parameter in $HOME/.astropy/config/sncosmo.cfg. See Directory Configuration for more information.

Some built-in source models have multiple versions, which can be explicitly retrieved using the get_source function:

>>> source = sncosmo.get_source('hsiao', version='2.0')
>>> model = sncosmo.Model(source=source)

Model parameters

Each model has a set of parameter names and values:

>>> model.param_names
['z', 't0', 'amplitude']
>>> model.parameters
array([ 0.,  0.,  1.])

These can also be retrieved as:

>>> model.get('z')
>>> model['z']

Parameter values can be set by any of the following methods:

>>> model.parameters[0] = 0.5
>>> model.parameters = [0.5, 0., 1.]  # set the entire array
>>> model['z'] = 0.5
>>> model.set(z=0.5)
>>> model.set(z=0.5, amplitude=2.0)  # Can specify multiple parameters
>>> model.update({'z': 0.5, 'amplitude': 2.0})

What do these parameters mean? The first two, z and t0 are common to all Model instances:

  • z is the redshift of the source.

  • t0 is the observer-frame time corresponding to the source’s phase=0.

Note that in some sources phase=0 might be at explosion while others might be at max: the definition of phase is arbitrary. However, observed time is always related to phase via time = t0 + phase * (1 + z)

The next, amplitude, is specific to the particular type of source. In this case, the source is a simple spectral timeseries that can only be scaled up and down. Other sources could have other parameters that affect the shape of the spectrum at each phase.

For a given model, you can set the amplitude (or x0 in case you are using a SALT model) according to a desired absolute magnitude in a specific band by using the method set_source_peakabsmag(). Note that the redshift z affects your result. Therefore, you could specify:

>>> model.set(z=1.6)
>>> model.set_source_peakabsmag(-19.0, 'bessellb', 'ab')

Specifically, for SALT models, it is recommended to call set_source_peakabsmag() after setting the other model parameters, such as x1 and c. It probably won’t make a difference if you are using the 'bessellb' bandpass, but if you were setting the absolute magnitude in another band, it would make a small difference.

The reason for this peculiarity is that “absolute magnitude” is not a parameter in the SALT2 model, per se. The parameters are x0, x1, c, t0, and z. x0 is a simple multiplicative scaling factor on the whole spectral timeseries. The set_source_peakabsmag() method is a convenience for setting x0 such that the integrated flux through a given bandpass is as desired. Since the integrated flux depends on the spectral shape, it will depend on x1 and c.

Creating a model with a source and effect(s)

Let’s create a slightly more complex model. Again we will use the Hsiao spectral time series as a source, but this time we will add host galaxy dust.

>>> dust = sncosmo.CCM89Dust()
>>> model = sncosmo.Model(source='hsiao',
...                       effects=[dust],
...                       effect_names=['host'],
...                       effect_frames=['rest'])

The model now has additional parameters that describe the dust, hostebv and hostr_v:

>>> model.param_names
['z', 't0', 'amplitude', 'hostebv', 'hostr_v']
>>> model.parameters
array([ 0. ,  0. ,  1. ,  0. ,  3.1])

These are the parameters of the CCM89Dust instance we created:

>>> dust.param_names
['ebv', 'r_v']

In the model, the parameter names are prefixed with the name of the effect (host).

At any time you can print the model to get a nicely formatted string representation of its components and current parameter values:

>>> print(model)
<Model at 0x...>
  class      : TimeSeriesSource
  name       : hsiao
  version    : 3.0
  phases     : [-20, .., 85] days (22 points)
  wavelengths: [1000, .., 25000] Angstroms (481 points)
effect (name='host' frame='rest'):
  class           : CCM89Dust
  wavelength range: [1250, 33333] Angstroms
  z         = 0.0
  t0        = 0.0
  amplitude = 1.0
  hostebv   = 0.0
  hostr_v   = 3.1000000000000001

Also, str(model) will return this string rather than printing it.

Adding Milky Way dust

Dust in the Milky Way will affect the shape of an observed supernova spectrum. It is important to take this into account in our model when fitting the model to observed data. As with host galaxy dust treated above, we can model Milky Way dust as a “propagation effect”. The only difference is that Milky Way dust is in the observer frame rather than the supernova rest frame. Here, we create a model with dust in both the SN rest frame and the observer frame:

>>> dust = sncosmo.CCM89Dust()
>>> model = sncosmo.Model(source='hsiao',
...                       effects=[dust, dust],
...                       effect_names=['host', 'mw'],
...                       effect_frames=['rest', 'obs'])

We can see that the model includes four extra parameters (two describing the host galaxy dust and two describing the milky way dust):

>>> model.param_names
['z', 't0', 'amplitude', 'hostebv', 'hostr_v', 'mwebv', 'mwr_v']
>>> model.parameters  # default values
array([ 0. ,  0. ,  1. ,  0. ,  3.1,  0. ,  3.1])

The host galaxy dust parameters are prefixed with 'host' and the Milky Way dust parameters are prefixed with 'mw'. These are just the names we supplied when constructing the model. The effect names have no significance beyond this. The effect frames, on the other hand, are significant. The only allowed values are 'rest' (rest frame) and 'obs' (observer frame).

A typical use pattern is to get an estimate of the amount of Milky Way dust at the location of the supernova from a dust map, and then to fix that amount of dust in the model. The following example illustrates how to do this using the Schlegel, Finkbeiner and Davis (1998) dust map with the sfdmap package. First, load the dust map (do this only once):

>>> import sfdmap

>>> dustmap = sfdmap.SFDMap("/path/to/dust/maps")

Now, for each SN you wish to fit, get the amount of dust at the SN location and set the mwebv model parameter appropriately. For example, if the SN is located at RA=42.8 degrees, Dec=0 degrees:

>>> ebv = dustmap.ebv(42.8, 0.0)

>>> model.set(mwebv=ebv)

>>> # proceed with fitting the other model parameters to the data.

Note that we wish to fix the mwebv model parameter rather than fitting it to the data like the other parameters: We’re supposing that this value is perfectly known from the dust map. Therefore, when using a function such as fit_lc to fit the parameters, be sure not to include 'mwebv' in the list of parameters to vary.

Phase Dependant effects

Primarily to simulate lensed transients, phase dependant effects can also be applied. Derived classes of PropagationEffect can add the `self._minphase and self._maxphase parameters. Once your phase dependant effect is defined, it can be added to a model in the same way as chromatic effects, by specifying the appropriate effects, effect_frames, and effect_names when defining your Model.

Model spectrum

To retrieve a spectrum (in ergs / s / cm^2 / Angstrom) at a given observer-frame time and set of wavelengths:

>>> wave = np.array([3000., 3500., 4000., 4500., 5000., 5500.])
>>> model.flux(-5., wave)
array([  5.29779465e-09,   7.77702880e-09,   7.13309678e-09,
         5.68369041e-09,   3.06860759e-09,   2.59024291e-09])

We can supply a list or array of times and get a 2-d array back, representing the spectrum at each time:

>>> model.flux([-5., 2.], wave)
array([[  5.29779465e-09,   7.77702880e-09,   7.13309678e-09,
          5.68369041e-09,   3.06860759e-09,   2.59024291e-09],
       [  2.88166481e-09,   6.15186858e-09,   7.87880448e-09,
          6.93919846e-09,   3.59077596e-09,   3.27623932e-09]])

Changing the model parameters changes the results:

>>> model.parameters
array([0., 0., 1., 0., 3.1])
>>> model.flux(-5., [4000., 4500.])
array([  7.13309678e-09,   5.68369041e-09])
>>> model.set(amplitude=2., hostebv=0.1)
>>> model.flux(-5., [4000., 4500.])
array([  9.39081327e-09,   7.86972003e-09])

Synthetic photometry

To integrate the spectrum through a bandpass, use the bandflux method:

>>> model.bandflux('sdssi', -5.)

Here we are using the SDSS I band, at time -5. days. The return value is in photons / s / cm^2. It is also possible to supply multiple times or bands:

>>> model.bandflux('sdssi', [-5., 2.])
array([ 180213.72886169,  176662.68287381])
>>> model.bandflux(['sdssi', 'sdssz'], [-5., -5.])
array([ 180213.72886169,   27697.76705621])

Instead of returning flux in photons / s / cm^2, the flux can be normalized to a desired zeropoint by specifying the zp and zpsys keywords, which can also be scalars, lists, or arrays.

>>> model.bandflux(['sdssi', 'sdssz'], [-5., -5.], zp=25., zpsys='ab')
array([  5.01036850e+09,   4.74414435e+09])

Instead of flux, magnitude can be returned. It works very similarly to flux:

>>> model.bandmag('sdssi', 'ab', [0., 1.])
array([ 22.6255077 ,  22.62566363])
>>> model.bandmag('sdssi', 'vega', [0., 1.])
array([ 22.26843273,  22.26858865])

We have been specifying the bandpasses as strings ('sdssi' and 'sdssz'). This works because these bandpasses are in the sncosmo “registry”. However, this is merely a convenience. In place of strings, we could have specified the actual Bandpass objects to which the strings correspond. See Bandpasses for more on how to directly create Bandpass objects.

The magnitude systems work similarly to bandpasses: 'ab' and 'vega' refer to built-in MagSystem objects, but you can also directly supply custom MagSystem objects. See Magnitude Systems for details.

Initializing Sources directly

You can initialize a source directly from your own template rather than using the built-in source templates.

Initializing a TimeSeriesSource

These sources are created directly from numpy arrays. Below, we build a very simple model, of a source with a flat spectrum at all times, rising from phase -50 to 0, then declining from phase 0 to +50.

>>> import numpy as np
>>> phase = np.linspace(-50., 50., 11)
>>> disp = np.linspace(3000., 8000., 6)
>>> flux = np.repeat(np.array([[0.], [1.], [2.], [3.], [4.], [5.],
...                            [4.], [3.], [2.], [1.], [0.]]),
...                  6, axis=1)
>>> source = sncosmo.TimeSeriesSource(phase, disp, flux)

Typically, you would then include this source in a Model:

>>> model = sncosmo.Model(source)

Initializing a SALT2Source

The SALT2 model is initialized directly from data files representing the model. You can initialize it by giving it a path to a directory containing the files.

>>> source = sncosmo.SALT2Source(modeldir='/path/to/dir')

By default, the initializer looks for files with names like 'salt2_template_0.dat', but this behavior can be altered with keyword parameters:

>>> source = sncosmo.SALT2Source(modeldir='/path/to/dir',
...                              m0file='mytemplate0file.dat')

See SALT2Source for more details.