# 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 a BNS, NSBH, BBH merger, or 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