Core-Collapse Supernovae

Core-Collapse Supernovae#

In skysurvey, Core-Collapse (CC) SNe, including Type II, IIn, IIb, Ib, Ic, and Ic-BL, are pre-built classes that inherit from a common base.

Class structure#

Their class structure are defined by:

VincenziModels, which provides the link to Vincenzi et al. (2019) (2019MNRAS.489.5802V) time-series models implemented in sncosmo.
MultiTemplateTSTransient, which allows combining multiple templates to model the diversity of observed CC SNe.
Each subtype inherits its own parameters: _KIND, _RATE, _MAGABS, and a set of v19-*-corr templates.

Templates#

The different CC SNe classes are associated with time-series templates from Vincenzi et al. (2019), implemented in sncosmo under the v19-*-corr name, and stored in skysurvey as a TemplateCollection object.

Let’s take the example of the SNeII class for the following sections. The SNeII Transient is already defined and ready to use:

import skysurvey

snii = skysurvey.SNeII()

The template (skysurvey object) is stored here:

snii.template

<skysurvey.template.TemplateCollection at 0x133b1ab10>

The available templates are:

snii.template.names

['v19-asassn14jb-corr',
 'v19-asassn15oz-corr',
 'v19-1987a-corr',
 'v19-1999em-corr',
 'v19-2004et-corr',
 'v19-2007od-corr',
 'v19-2008bj-corr',
 'v19-2008in-corr',
 'v19-2009n-corr',
 'v19-2009bw-corr',
 'v19-2009dd-corr',
 'v19-2009ib-corr',
 'v19-2009kr-corr',
 'v19-2012a-corr',
 'v19-2012aw-corr',
 'v19-2013ab-corr',
 'v19-2013am-corr',
 'v19-2013by-corr',
 'v19-2013ej-corr',
 'v19-2013fs-corr',
 'v19-2014g-corr',
 'v19-2016x-corr',
 'v19-2016bkv-corr']

Rates#

Each pre-computed skysurvey transient has a default volumetric rate, that provide the number of transient expected per year and per Gpc³.

The rate of the SNeII is calculated as CC_RATE * 0.649, derived from the total CC SNe rate (CC_RATE = 1.0e5) based on Perley et al. (2020) (2020ApJ…904…35P) and the relative rate from Vicenzi et al. (2019) (that combines both IIL and IIP types).

snii.rate

64900.0

The other CC SNe rates are also derived from the relative rate from Vicenzi et al. (2019) multipled by the total CC SNe rate. The SNeICL relative rate is defined joining two types, the Ic-BL and Ic-pec types.

Class	rate (target/yr/Gpc^3)
`SNeII`	64900
`SNeIIn`	4700
`SNeIIb`	10900
`SNeIb`	10800
`SNeIc`	7500
`SNeIcBL`	9700

Model#

The model is based on the modeldag package.

For the CC SNe classes, the model contains 7 entries. So the generated data will contains at least 7 columns. To display the model, you can directly print the object:

snii

{'redshift': {'kwargs': {'zmax': 0.05, 'rate': 64900.0}, 'as': 'z'},
 't0': {'func': <bound method Generator.uniform of Generator(PCG64) at 0x133B6C040>,
        'kwargs': {'low': 56000, 'high': 56200}},
 'magabs': {'func': <bound method Generator.normal of Generator(PCG64) at 0x133B6C120>,
            'kwargs': {'loc': -17.48, 'scale': 0.7}},
 'magobs': {'func': 'magabs_to_magobs',
            'kwargs': {'z': '@z', 'magabs': '@magabs'}},
 'amplitude': {'func': 'magobs_to_amplitude', 'kwargs': {'magobs': '@magobs'}},
 'radec': {'func': <function random_radec at 0x133b0fba0>,
           'as': ['ra', 'dec'],
           'kwargs': {}},
 'template': {'func': 'draw_template', 'kwargs': {'redshift': '@z'}}}

Absolute magnitude distribution#

The absolute magnitude distribution is assumed to be Gaussian, specified by (location, scatter). The chosen values are derived from the Median R-band magnitude (MR) from the BTS (Bright Transient Survey) for z<0.05.

snii._MAGABS

(-17.48, 0.7)

Class	`magabs`
`SNeII`	(-17.48, 0.7)
`SNeIIn`	(-18.0, 0.8)
`SNeIIb`	(-17.45, 0.6)
`SNeIb`	(-17.35, 0.53)
`SNeIc`	(-17.50, 0.7)
`SNeIcBL`	(-18.12, 0.9)