gwcelery.tasks.em_bright module

This module computes the probabilities that there is a neutron star in the binary, and that the coalescence event resulted in creation of tidally disrupted matter.

The result is returned in the form of a JSON file:

‘{“HasNS”: 1.0, “HasRemnant”: 1.0}’

• HasNS: The probability that at least one of the component masses

  in the binary is a neutron star. The definition of a neutron star in this context simply means an object with mass less than 3.0 solar mass.

• HasRemnant: The probability that the binary system can produce

  tidally disrupted matter during coalescence. This is computed using the fitting formula in arXiv:1807.00011 We are currently using an extremely stiff equation of state (2H) to compute the compactness of the neutron star. This results in a higher chance of labelling a systems with non-zero HasRemnant value.

Qualitative source properties for CBC events.

(task)gwcelery.tasks.em_bright.handle(alert)

IGWN alert handler to plot and upload a visualization of every em_bright.json.

(task)gwcelery.tasks.em_bright.plot(contents)

Make a visualization of the source properties.

Examples

>>> from gwcelery.tasks import em_bright
>>> contents = '{"HasNS": 0.9137, "HasRemnant": 0.0, "HasMassGap": 0.0}'  # noqa E501
>>> em_bright.plot(contents)

(Source code)

(task)gwcelery.tasks.em_bright.em_bright_posterior_samples(posterior_file_content)

Returns the probability of having a NS component and remnant using Bilby posterior samples.

Parameters:

posterior_file_content (hdf5 posterior file content) –

Returns:

JSON formatted string storing HasNS, HasRemnant, and HasMassGap probabilities

Return type:

str

Examples

>>> em_bright_posterior_samples(GraceDb().files('S190930s',
... 'Bilby.posterior_samples.hdf5').read())
{"HasNS": 0.014904901243599122, "HasRemnant": 0.0, "HasMassGap": 0.0}

(task)gwcelery.tasks.em_bright.source_properties(mass1, mass2, spin1z, spin2z, snr, pipeline='gstlal', search='allsky')

Returns the probability of having a NS component, the probability of having non-zero disk mass, and the probability of any component being the lower mass gap for the detected event.

Parameters:

• mass1 (float) – Primary mass in solar masses

• mass2 (float) – Secondary mass in solar masses

• spin1z (float) – Dimensionless primary aligned spin component

• spin2z (float) – Dimensionless secondary aligned spin component

• snr (float) – Signal to noise ratio

• pipeline_search (tuple) – The pipeline and the search as a tuple. This is used to select the appropriate classifiers in ligo.em-bright for SSM search only. This is unused for AllSky searches.

Returns:

JSON formatted string storing HasNS, HasRemnant, HasMassGap probabilities for AllSky searches, and HasSSM, HasNS, HasMassGap probabilities for SSM searches.

Return type:

str

Examples

>>> em_bright.source_properties(2.0, 1.0, 0.0, 0.0, 10.)
'{"HasNS": 1.0, "HasRemnant": 1.0, "HasMassGap": 0.0}'
>>> em_bright.source_properties(2.0, 1.0, 0.0, 0.0, 10.,
... pipeline='gstlal', search='ssm')
'{"HasSSM": 0.52, "HasNS": 0.9199999999999999, "HasMassGap": 0.08}'