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 four numbers, summing to unity, that give the probability that the source is either a BNS, NSBH, BBH merger, or is of Terrestrial (i.e. a background fluctuation or a glitch) origin. This classification is present only for events from stellar mass CBC searches.

Our search pipelines use independent methods to assign those source probabilities to their own triggers. These methods all start from the common assumptions that terrestrial and astrophysical events occur as independent Poisson processes, and that NSs and BHs only exist below and above $3M_{\odot }$ respectively (see the figure in the alert contents section for the boundaries of the source classification categories in the $(m_1,m_2)$ plane). The search pipelines then account for the rates of background and astrophysical events in ways that differ in detail, but are all based on the population of events observed during the O1, O2 and O3 runs.

For details of the GstLAL method, see [1] (especially Equation 20). For MBTA, see [2]. For PyCBC Live, see [3]. For SPIIR, see [4] and [3].

RapidPE-RIFT [10] [11] provides source classification by estimating source probabilities assuming astrophysical origin and then combining them with the Terrestrial probability estimated by the search pipelines for the preferred event. The RapidPE-RIFT source classification, if available, will be used in the second preliminary alert and/or initial notice. If RapidPE-RIFT is available after the initial notice, then the RapidPE-RIFT source classification results may be included in the Update GCN Circular notice.

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 differ depending upon the type of analysis.

The masses mentioned in this section are source-frame mass values. The value reported in the preliminary alert is calculated using a supervised machine learning classifier on a feature space consisting of the masses, spins, and SNR of the best-matching template, described in [7] [8]. This is to account for the uncertainty in the reported template parameters compared to the true parameters.

Preliminary Alerts¶

For stellar mass and SSM CBC searches, these conditions are:

HasNS: At least one of the compact objects in the binary has a mass that is consistent with a neutron star. Specifically, for stellar mass searches, we define this as the probability that the secondary mass satisfies $m_2\le M_{\max }$, where $M_{\max }$ is the maximum mass allowed by the neutron star equation of state (EOS). Several NS EOSs are considered and the value is marginalized by weighting using the Bayes factors reported in [5]. On the other hand, for SSM searches, we define this as the probability that either of the masses satisfy $1M_{\odot }\le m\le 3M_{\odot }$. This definition for SSM search alerts do not account for any EOS but is purely based on the (source frame) mass boundaries.

HasRemnant: The source formed a nonzero mass outside the final compact object. Specifically for stellar mass searches, the probability is calculated using the disk mass fitting formula from [6] (Equation 4). Several neutron star EOSs are considered for computing the remnant mass. The value is marginalized by weighting based on Bayes factors in reference mentioned above. The SSM search alerts will not use EOS information to determine the possible neutron star or remnant mass. Hence the SSM search alerts will not provide this property.

HasMassGap: At least one of the compact objects in the binary has a mass in the hypothetical “mass gap” between neutron stars and black holes, defined here as $3M_{\odot }\le m\le 5M_{\odot }$.

HasSSM: At least one of the compact objects in the binary has a mass $m<1M_{\odot }$. For SSM search alerts, this defines the probability that secondary mass $m_2<1M_{\odot }$. The stellar mass CBC searches will not provide this property.

The performance of these quantities across online stellar mass CBC search pipelines is shown below. The ROC curves shown above were constructed using pipeline recovered injections from the O3 Mock Data Challenge (MDC). For details see [9].

HasNSHasRemnantHasMassGap

Similarly, the performance of these quantities across the select online SSM CBC search pipelines is shown below.

HasSSMHasNSHasMassGap

(Source code)

Alert Updates¶

The value reported in the update alerts uses the online parameter estimation to compute the value.

HasNS: At least one of the compact objects in the binary has a mass that is consistent with a neutron star. Specifically, we define this as the probability that one of the masses satisfies $1M_{\odot }\le m\le M_{\max }$, where $M_{\max }$ is the maximum mass allowed by the neutron star equation of state (EOS). Several NS EOSs are considered and the value is marginalized by weighting using the Bayes factors reported in [5].

HasRemnant: The source formed a nonzero mass outside the final compact object. Specifically, the probability is calculated using the disk mass fitting formula from [6] (Equation 4). Several neutron star EOSs are considered for computing the remnant mass. The value is marginalized by weighting based on Bayes factors in the reference mentioned above. The primary mass less than $1M_{\odot }$ is not used in computing HasRemnant probability.

HasMassGap: At least one of the compact objects in the binary has a mass in the hypothetical “mass gap” between neutron stars and black holes, defined here as $3M_{\odot }\le m\le 5M_{\odot }$.

HasSSM: See the preliminary alerts section for the definition of this property.