Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning

Abstract

Gravitational wave observations of the inspiral of stellar-mass compact objects into massive black holes (MBHs), extreme mass ratio inspirals (EMRIs), enable precision measurements of parameters such as the MBH mass and spin. The Laser Interferometer Space Antenna is expected to detect sufficient EMRIs to probe the underlying source population, testing theories of the formation and evolution of MBHs and their environments. Population studies are subject to selection effects that vary across the EMRI parameter space, which bias inference results if unaccounted for. This bias can be corrected, but evaluating the detectability of many EMRI signals is computationally expensive. We mitigate this cost by (i) constructing a rapid and accurate neural network interpolator capable of predicting the signal-to-noise ratio of an EMRI from its parameters, and (ii) further accelerating detectability estimation with a neural network that learns the selection function, leveraging our first neural network for data generation. The resulting framework rapidly estimates the selection function, enabling a full treatment of EMRI detectability in population inference analyses. We apply our method to an astrophysically motivated EMRI population model, demonstrating the potential selection biases and subsequently correcting for them. Accounting for selection effects, we predict that LISA will measure the MBH mass function slope to a precision of 8.8%, the CO mass function slope to a precision of 4.6%, the width of the MBH spin magnitude distribution to a precision of 10% and the event rate to a precision of 12% with EMRIs at redshifts below z=6.

12 pages, 4 figures; changes to match published version

References in corpus (21)

• Array Programming with NumPy
• Use and Abuse of the Fisher Information Matrix in the Assessment of Gravitational-Wave Parameter-Estimation Prospects
• Intermediate and Extreme Mass-Ratio Inspirals -- Astrophysics, Science Applications and Detection using LISA
• One Channel to Rule Them All? Constraining the Origins of Binary Black Holes using Multiple Formation Pathways
• Precision of Hubble constant derived using black hole binary absolute distances and statistical redshift information
• FastEMRIWaveforms: New tools for millihertz gravitational-wave data analysis
• Nested Sampling with Normalising Flows for Gravitational-Wave Inference
• Adiabatic waveforms for extreme mass-ratio inspirals via multivoice decomposition in time and frequency
• LISA extreme-mass-ratio inspiral events as probes of the black hole mass function
• Wet Extreme Mass Ratio Inspirals May Be More Common For Spaceborne Gravitational Wave Detection
• Parameter estimation with gravitational waves
• Gravitational wave background from extreme mass ratio inspirals
• Adiabatic waveforms from extreme-mass-ratio inspirals: an analytical approach
• Gravitational wave cosmology with extreme mass-ratio inspirals
• Extreme mass ratio inspirals on the equatorial plane in the adiabatic order
• The Combined Effects of Two-Body Relaxation Processes and the Eccentric Kozai-Lidov Mechanism on the EMRI Rate
• Non-local parameter degeneracy in the intrinsic space of gravitational-wave signals from extreme-mass-ratio inspirals
• Extreme mass ratio inspirals and tidal disruption events in nuclear clusters. I. Time dependent rates
• Can supernova kicks trigger EMRIs in the Galactic Centre?
• Forward modeling of galaxy populations for cosmological redshift distribution inference
• The gravitational capture of compact objects by massive black holes

Cited by in corpus (10)

• Applications of machine learning in gravitational wave research with current interferometric detectors
• Exploring the evolution of gravitational-wave emitters with efficient emulation: Constraining the origins of binary black holes using normalising flows
• Testing gravity with Extreme-Mass-Ratio Inspirals
• One to many: comparing single gravitational-wave events to astrophysical populations
• Calibrating signal-to-noise ratio detection thresholds using gravitational-wave catalogs
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• A neural network emulator of the Advanced LIGO and Advanced Virgo selection function
• Revealing massive black hole astrophysics: The potential of hierarchical inference with extreme mass-ratio inspiral observations
• Reducing systematic uncertainties in gravitational-wave population analyses by increasing the detection threshold
• Stochastic gravitational wave background from the collisions of dark matter halos