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| LALInference | |
|---|---|
| Name | LALInference |
| Title | LALInference |
| Developer | LIGO Scientific Collaboration; Virgo Collaboration |
| Released | 2015 |
| Latest release version | 2019.0.0 |
| Programming language | C, Python, Fortran |
| Operating system | Linux, macOS |
| License | GNU General Public License |
| Website | LALSuite |
LALInference is a suite of Bayesian inference tools developed for parameter estimation of compact binary coalescences and other transient signals detected by the LIGO Scientific Collaboration, Virgo Collaboration, and partner observatories. It integrates stochastic sampling algorithms with waveform models and detector calibration pipelines to estimate source parameters such as component masses, spins, sky location, and distance. The software forms part of the broader LALSuite ecosystem and has been used in landmark detections involving networks that include LIGO Hanford Observatory, LIGO Livingston Observatory, GEO600, and KAGRA.
LALInference provides Bayesian posterior inference using likelihoods constructed from matched filtering against waveform families like TaylorF2, SEOBNRv4, IMRPhenomPv2, and other models used in analyses of events such as GW150914, GW151226, GW170817, and GW190425. The package interoperates with data conditioning, calibration, and veto products generated by pipelines like PyCBC, GstLAL, MBTA (Gravitational Wave) and SPIIR. LALInference outputs posterior samples that are consumed by parameter-estimation visualization tools used in follow-up efforts coordinated with facilities including Fermi Gamma-ray Space Telescope, Swift, INTEGRAL, Zwicky Transient Facility, and electromagnetic observatories such as Keck Observatory, Very Large Telescope, and Hubble Space Telescope.
The core inference engines implement stochastic sampling methods including Nested Sampling as in MultiNest-style approaches, Markov Chain Monte Carlo techniques akin to Metropolis–Hastings, and variants of parallel tempering. LALInference integrates likelihoods that account for detector noise described by power spectral density estimates, calibration uncertainty marginalization, and marginalization over phase and time shifts similar to strategies used in analyses by NIST, Caltech, and MIT. Priors are configurable and have been chosen to match population-model studies associated with collaborations such as RIT, University of Chicago, Albert Einstein Institute, and Cardiff University.
The suite is implemented within the LALSuite framework, combining performance-sensitive routines written in C and Fortran for waveform generation and likelihood evaluation with high-level orchestration and plotting done in Python. It relies on numerical libraries and toolchains common to scientific computing such as FFTW, GSL, and NumPy, and integrates with workflow managers and job schedulers used at centers like LIGO Laboratory, CERN, and national computing facilities including XSEDE and PRACE. Data I/O follows conventions used across collaborations like LIGO Scientific Collaboration and uses formats compatible with tools from HEASoft, Astropy, and Matplotlib.
Validation has been performed through injection campaigns and mock-data challenges involving hardware and software injections used during observing runs O1, O2, and O3, coordinated with instrument teams at LIGO Livingston Observatory, LIGO Hanford Observatory, and Virgo. Comparisons of parameter recovery have been made against independent inference codes developed by groups at University of Birmingham, Caltech, Cambridge University, Monash University, and University of Glasgow, and cross-checked using simulated catalogs produced by initiatives like NINJA and follow-up studies from LSC-Virgo Working Group. Performance metrics include effective sample size, autocorrelation times, and wall-clock runtimes on HPC resources at centers such as LIGO Hanford Computing Cluster, OzSTAR, and national labs like LLNL.
LALInference has been used to produce posterior distributions informing astrophysical conclusions in high-profile results including the first binary black hole detection GW150914 and the binary neutron star event GW170817, underpinning multimessenger follow-up with observatories like Fermi Gamma-ray Space Telescope, Chandra X-ray Observatory, Very Large Array, and facilities in consortia such as EM Followup (LVC). Results from LALInference informed population studies by groups at IPTA, NANOGrav, European Pulsar Timing Array, and cosmological inferences connecting to projects like Planck (spacecraft), DESI, and LSST (Vera C. Rubin Observatory). It has also been applied to tests of general relativity in work involving collaborations with theorists at Institute for Advanced Study, Perimeter Institute, and Princeton University.
Development began as part of the LALSuite consolidation effort, with publicized releases and documentation coordinated through repositories maintained by institutions including CACR (Caltech), CIT (Caltech) computing groups, and the LIGO Lab software teams. Major versions were deployed around the first observing runs (O1/O2) and matured through the O3 cycle, with contributions from academic groups at Cardiff University, University of Birmingham, AEI, University of Glasgow, and others. Release notes and validation reports were produced during collaborations with instrument science teams at Hanford, Livingston, and Virgo and during joint workshops with partners from KAGRA and GEO600.
Known limitations include computational cost for high-dimensional models, approximations in waveform systematics relative to state-of-the-art numerical relativity codes from groups at SXS (Simulating eXtreme Spacetimes), RIT (Research Institute), and ongoing challenges in neutron-star equation-of-state parameterizations studied at IUCAA, McGill University, and Caltech. Future work discussed in community meetings at COSMIC (conference), GWPAW (workshop), and institutional collaborations with Max Planck Institute for Gravitational Physics focuses on accelerated inference via surrogate models, machine-learning emulators developed at Google Research and DeepMind, tighter integration with real-time pipelines like GstLAL and BAYESTAR, and improved calibration models informed by teams at NIST and LIGO Lab.
Category:Gravitational-wave astronomy software