A neural network emulator of the Advanced LIGO and Advanced Virgo selection function

Abstract

Characterization of search selection effects comprises a core element of gravitational-wave data analysis. Knowledge of selection effects is needed to predict observational prospects for future surveys and is essential in the statistical inference of astrophysical source populations from observed catalogs of compact binary mergers. Although gravitational-wave selection functions can be directly measured via injection campaigns -- the insertion and attempted recovery of simulated signals added to real instrumental data -- such efforts are computationally expensive. Moreover, the inability to interpolate between discrete injections limits the ability to which we can study narrow or discontinuous features in the compact binary population. For this reason, there is a growing need for alternative representations of gravitational-wave selection functions that are computationally cheap to evaluate and can be computed across a continuous range of compact binary parameters. In this paper, we describe one such representation. Using pipeline injections performed during Advanced LIGO & Advanced Virgo's third observing run (O3), we train a neural network emulator for $P(\det |\theta )$, the probability that given a compact binary with parameters is successfully detected, averaged over the course of O3. The emulator captures the dependence of $P(\det |\theta )$ on binary masses, spins, distance, sky position, and orbital orientation, and it is valid for compact binaries with components masses between $1$--$100M_{\odot }$. We test the emulator's ability to produce accurate distributions of detectable events, and demonstrate its use in hierarchical inference of the binary black hole population.

arXiv commentMinor update to match published version. 26 pages, 16 figures. Code is available at https://github.com/tcallister/learning-p-det/ and data at https://doi.org/10.5281/zenodo.13362691 . Neural network emulator is released at https://github.com/tcallister/pdet/

References in corpus (50)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• GW190814: Gravitational Waves from the Coalescence of a 23 M${}_{\odot }$ Black Hole with a 2.6 M${}_{\odot }$ Compact Object
• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• Gravitational Waves from Mergin Compact Binaries: How Accurately Can One Extract the Binary's Parameters from the Inspiral Waveform?
• Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo
• Observing binary inspiral in gravitational radiation: One interferometer
• The PyCBC search for gravitational waves from compact binary coalescence
• Comparison of post-Newtonian templates for compact binary inspiral signals in gravitational-wave detectors
• Inspiral-merger-ringdown waveforms for black-hole binaries with non-precessing spins
• Measuring gravitational waves from binary black hole coalescences: I. Signal to noise for inspiral, merger, and ringdown
• Matching post-Newtonian and numerical relativity waveforms: systematic errors and a new phenomenological model for non-precessing black hole binaries
• Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
• Double Compact Objects III: Gravitational Wave Detection Rates
• Extracting distribution parameters from multiple uncertain observations with selection biases
• Analysis Framework for the Prompt Discovery of Compact Binary Mergers in Gravitational-wave Data
• Towards models of gravitational waveforms from generic binaries II: Modelling precession effects with a single effective precession parameter
• The Rate of Binary Black Hole Mergers Inferred from Advanced LIGO Observations Surrounding GW150914
• A chi-squared time-frequency discriminator for gravitational wave detection
• Spin effects in the inspiral of coalescing compact binaries
• The evolutionary roads leading to low effective spins, high black hole masses, and O1/O2 rates of LIGO/Virgo binary black holes
• Black Holes: The Next Generation -- Repeated Mergers in Dense Star Clusters and their Gravitational-Wave Properties
• Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
• Analysis of spin precession in binary black hole systems including quadrupole-monopole interaction
• Does the Black Hole Merger Rate Evolve with Redshift?
• Measuring the binary black hole mass spectrum with an astrophysically motivated parameterization
• Implementing a search for aligned-spin neutron star -- black hole systems with advanced ground based gravitational wave detectors
• Low-latency analysis pipeline for compact binary coalescences in the advanced gravitational wave detector era
• The MBTA Pipeline for Detecting Compact Binary Coalescences in the Third LIGO-Virgo Observing Run
• Joint constraints on the field-cluster mixing fraction, common envelope efficiency, and globular cluster radii from a population of binary hole mergers via deep learning
• Counting And Confusion: Bayesian Rate Estimation With Multiple Populations
• Accuracy Requirements for Empirically-Measured Selection Functions
• Estimation of the Sensitive Volume for Gravitational-wave Source Populations Using Weighted Monte Carlo Integration
• Coherent WaveBurst, a pipeline for unmodeled gravitational-wave data analysis
• The Most Massive Binary Black Hole Detections and the Identification of Population Outliers
• Inferring the properties of a population of compact binaries in presence of selection effects
• Picky Partners: The Pairing of Component Masses in Binary Black Hole Mergers
• Validating gravitational-wave detections: The Advanced LIGO hardware injection system
• A generalized precession parameter ${\chi }_{\mathrm{p}}$ to interpret gravitational-wave data
• Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers
• Gravitational-wave selection effects using neural-network classifiers
• The ${\chi }_{\mathrm{eff}}-z$ correlation of field binary black hole mergers and how 3G gravitational-wave detectors can constrain it
• Demographics of Hierarchical Black Hole Mergers in Dense Star Clusters
• Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning
• Quick recipes for gravitational-wave selection effects

Cited by in corpus (5)

• Exploring the evolution of gravitational-wave emitters with efficient emulation: Constraining the origins of binary black holes using normalising flows
• Sampling the full hierarchical population posterior distribution in gravitational-wave astronomy
• Probing Spin-Orbit Resonances with the Binary Black Hole Population
• Comparing astrophysical models to gravitational-wave data in the observable space
• Reducing systematic uncertainties in gravitational-wave population analyses by increasing the detection threshold