Inference

For CBC events, we calculate a number of quantities that are inferred from the signal. In preliminary alerts, these quantities are based on the candidate significance and the matched-filter estimates of the source parameters. Once parameter estimation has been completed, updated values will be provided based on samples drawn from the posterior probability distribution.

Classification

The classification consists of five numbers, summing to unity, that give the probability that the source is either a BNS, NSBH, BBH merger, contains at least one MassGap component, or is of terrestrial (i.e. a background fluctuation or a glitch) origin. See the figure in the alert contents section for the boundaries of the source classification categories in the \((m_1, m_2)\) plane.

This assumes that terrestrial and astrophysical events occur as independent Poisson processes. A source-dependent weighting of matched-filter templates is used to compute the mean values of expected counts associated with each of these five categories. The mean values are updated weekly based on observed matched-filter count rates. They are then used to predict the category for new triggers uploaded by search pipelines.

For details, see [1], especially Section II E. and Equation 18 therein.

Properties

The source properties consist of a set of numbers, each between zero and unity, that give the probabilities that the source satisfies certain conditions. These conditions are:

HasNS: At least one of the compact objects in the binary (that is, the less massive or secondary compact object) has a mass that is consistent with a neutron star. Specifically, we define this as the probability that the secondary mass satisfies \(m_2 \leq 3 M_{\odot}\).

HasRemnant: The probability that the source ejected a nonzero mass outside the final remnant compact object. This is calculated using the disk mass fitting formula from [2] (Equation 4).

For preliminary estimates based on the matched-filter pipeline results, the source properties are calculated using a supervised learning technique; mass-dependent rates are the same as those used for the classification. For parameter estimation, updated properties are calculated from posterior samples.

[1]	Kapadia, S. J., Caudill, S., Creighton, J. D. E., et al., 2019. arXiv:1903.06881

[2]	Foucart, F., Hinderer, T. & Nissanke, S. 2018, Phys. Rev. D, 98, 081501. doi:10.1103/PhysRevD.98.081501