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Andrew Lundgren

Publications and source records attributed to Andrew Lundgren.

At least 19 recordsLinked to original sources

A parametric signal plus noise inference framework for short duration non-Gaussian noise transients

Gravitational waves are now routinely detected with ground-based observatories, and, through a process known as Bayesian inference, their source properties are inferred. However, terrestrial noise artifacts, often referred to as glitches, commonly overlap astrophysical signals. This invalidates a fundamental assumption of gravitational wave analyses: the noise is no longer stationary and Gaussian. As a result, traditional techniques can provide biased inferences in realistic data. One method for mitigating the effect of glitches is to jointly analyse both the signal and noise in a single framework. In this work, we introduce bilby-antiglitch to infer the astrophysical signal properties in non-Gaussian noise. By additionally including a quasi-physical glitch model to describe short duration non-Gaussian noise transients, we show that unlike traditional techniques, we infer the true source properties of simulated signals contaminated with loud glitches. We also show that bilby-antiglitch prevents false violation claims of General Relativity, and validates the exceptional nature of gravitational wave signals in spurious data.

gr-qc

Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model

Ground-based gravitational wave detectors are now routinely surveying the dark Universe, finding hundreds of collisions between compact objects. However, terrestrial non-Gaussian noise artefacts, commonly known as glitches, reduce the sensitivity to signals and can overlap signals, producing biased astrophysical inferences. We introduce a hierarchical Bayesian model to measure the glitch rate, which improves upon existing trigger-counting methods in its capacity to measure the rate down into the low signal-to-noise regime without contamination from the Gaussian noise background, provided the population is accurately modelled. The framework accommodates any glitch model, and measures the rate with respect to the model chosen: here we use the antiglitch model, so the rate inferred is that of short-duration glitches rather than of all glitches. The methodology builds on standard hierarchical inference, but includes several novel features: hierarchical inference with quantile compression (HIQC), a generic approximation for the recycled hyperlikelihood, and a time-domain rate estimated by fitting basis functions. We validate the methodology using simulated data with injected glitches and then apply it to data from the fourth LIGO-Virgo-KAGRA observing run, demonstrating time-resolved inferences of the glitch rate over a 24 h period. The inferred glitch rate is consistent with estimates from trigger counts, but requires no arbitrary threshold and provides a more fine-grained view of the temporal behaviour. Finally, we demonstrate how our individual-detector rate estimates can be transformed into a coincident glitch probability and utilise this to provide evidence that the retracted gravitational-wave candidate GW230630_070659 is likely a pair of coincident glitches.

gr-qc

Inference with finite time series II: the window strikes back

Smooth window functions are often applied to strain data when inferring the parameters describing the astrophysical sources of gravitational-wave transients. Within the LIGO-Virgo-KAGRA collaboration, it is conventional to include a term to account for power loss due to this window in the likelihood function. We show that the inclusion of this factor leads to biased inference. The simplest solution to this, omitting the factor, leads to unbiased posteriors and Bayes factor estimates provided the window does not suppress the signal for signal-to-noise ratios $\lesssim O(100)$, but unreliable estimates of the absolute likelihood. Instead, we propose a multi-stage method that yields consistent estimates for the absolute likelihood in addition to unbiased posterior distributions and Bayes factors for signal-to-noise ratios $\lesssim O(1000)$. Additionally, we demonstrate that the commonly held wisdom that using rectangular windows necessarily leads to biased inference is incorrect.

gr-qc

TemplateGeNN: Neural Networks used to accelerate Gravitational Wave Template Bank Generation

We introduce TemplateGeNN, a fast stochastic template bank generation algorithm which uses Graphical Processing Units (GPUs) and a LearningMatch model (Siamese neural network). TemplateGeNN generated a binary black hole template bank (chirp mass varied from $5 M_{\odot} \leq \mathcal{M}_{c} \leq 20M_{\odot}$, symmetric mass ratio varied from $0.1 \leq \eta \leq 0.24999$, and equal aligned spin varied from $-0.99 \leq \chi_{1,2}\leq 0.99$) of 31,640 templates in $\sim 1$ day on a single A100 GPU. To test the sensitivity of this template bank we injected 7746 binary black hole templates into LIGO Gaussian noise. This template bank recovered 98$\%$ of the injections with a fitting factor greater than 0.97. For lower mass regions (black hole mass region between $5 M_{\odot} \leq m_{1, 2} \leq 25 M_{\odot}$), 99$\%$ of 9469 injections were recovered with a fitting factor greater than 0.97. LearningMatch and TemplateGeNN are a machine-learning pipeline that can be used to accelerate template bank generation for future gravitational-wave data analysis.

gr-qc

LearningMatch: Siamese Neural Network Learns the Match Manifold

The match, which is defined as the the similarity between two waveform templates, is a fundamental calculation in computationally expensive gravitational-wave data-analysis pipelines, such as template bank generation. In this paper we introduce LearningMatch, a Siamese neural network that has learned the mapping between the parameters, specifically $\lambda_{0}$ (which is proportional to the chirp mass), $\eta$ (symmetric mass ratio), and equal aligned spin ($\chi_{1}$ = $\chi_{2}$), of two gravitational-wave templates and the match. The trained Siamese neural network, called LearningMatch, can predict the match to within $3.3\%$ of the actual match value. For match values greater than 0.95, a trained LearningMatch model can predict the match to within $1\%$ of the actual match value. LearningMatch can predict the match in 20 $\mu$s (mean maximum value) with Graphical Processing Units (GPUs). LearningMatch is 3 orders of magnitudes faster at determining the match than current standard mathematical calculations that involve the template being generated.

gr-qc

GWtuna: Trawling through the data to find Gravitational Waves with Optuna and Jax

GWtuna is a fast gravitational-wave search prototype built on Optuna (optimisation software library) and JAX (accelerator-orientated array computation library) [1, 2]. Using Optuna, we introduce black box optimisation algorithms and evolutionary strategy algorithms to the gravitational-wave community. Tree-structured Parzen Estimator (TPE) and Covariance Matrix Adaption Evolution Strategy (CMA-ES) have been used to create the first template bank free search and used to identify binary neutron star mergers. TPE can identify a binary neutron star merger in 1 second (median value) and less than 1000 matched-filter evaluations when 512 seconds of data is searched over. A stopping algorithm is used to curtail the TPE search if the signal-to-noise ratio (SNR) threshold has been reached, or the SNR has not improved in 500 evaluations. If the SNR threshold is surpassed, CMA-ES is used to recover the SNR and the template parameters in 9,000 matched filter iterations taking 48 seconds (median value). GWtuna showcases alternatives to the standard template bank search and therefore has the potential to revolutionise the future of gravitational-wave data analysis.

gr-qc

Possible Causes of False General Relativity Violations in Gravitational Wave Observations

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations.

gr-qc

How to Break the Mass Sheet Degeneracy with the Lightcurves of Microlensed Type Ia Supernovae

The standardizable nature of gravitationally lensed Type Ia supernovae (glSNe Ia) makes them an attractive target for time delay cosmography, since a source with known luminosity breaks the mass sheet degeneracy. It is known that microlensing by stars in the lensing galaxy can add significant stochastic uncertainty to the unlensed luminosity which is often much larger than the intrinsic scatter of the Ia population. In this work, we show how the temporal microlensing variations as the supernova disc expands can be used to improve the standardization of glSNe Ia. We find that SNe are standardizable if they do not cross caustics as they expand. We estimate that this will be the case for $\approx$6 doubly imaged systems and $\approx$0.3 quadruply imaged systems per year in LSST. At the end of the ten year LSST survey, these systems should enable us to test for systematics in $H_0$ due to the mass sheet degeneracy at the $1.00^{+0.07}_{-0.06}$\% level, or $1.8\pm0.2$\% if we can only extract time delays from the third of systems with counter images brighter than $i=24$ mag.

astro-ph.GA

Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

GW200129 is claimed to be the first-ever observation of the spin-disk orbital precession detected with gravitational waves (GWs) from an individual binary system. However, this claim warrants a cautious evaluation because the GW event coincided with a broadband noise disturbance in LIGO Livingston caused by the 45 MHz electro-optic modulator system. In this paper, we present a state-of-the-art neural network that is able to model and mitigate the broadband noise from the LIGO Livingston interferometer. We also demonstrate that our neural network mitigates the noise better than the algorithm used by the LIGO-Virgo-KAGRA collaboration. Finally, we re-analyse GW200129 with the improved data quality and show that the evidence for precession is still observed.

gr-qc

Antiglitch: a Quasi-physical Model for Removing Short Glitches from LIGO and Virgo Data

Gravitational-wave observatories become more sensitive with each observing run, increasing the number of detected gravitational-wave signals. A limiting factor in identifying these signals is the presence of transient non-Gaussian noise, which generates glitches that can mimic gravitational wave signals. Our work provides a quasi-physical model waveform for the four most common types of short transient glitches, which are particularly problematic in the search for high-mass black hole binaries. Our model has only a few, physically interpretable parameters: central frequency, bandwidth, phase, amplitude and time. We demonstrate the accuracy of this model by fitting and removing a large sample of glitches from a month of LIGO and Virgo data from the O3 observing run. We can effectively remove three of the four types of short transients. We finally map the ability of these glitches to mimic binary black hole signals.

gr-qc

A Sensitive Test of Non-Gaussianity in Gravitational-wave Detector Data

Methods for parameter estimation of gravitational-wave data assume that detector noise is stationary and Gaussian. Real data deviates from these assumptions, which causes bias in the inferred parameters and incorrect estimates of the errors. We develop a sensitive test of non-Gaussianity for real gravitational-wave data which measures meaningful parameters that can be used to characterize these effects. As a test case, we investigate the quality of data cleaning performed by the LIGO-Virgo-KAGRA collaboration around GW200129, a binary black hole signal which overlapped with the noise produced by the radio frequency modulation. We demonstrate that a significant portion of the non-Gaussian noise is removed below 50 Hz, yet some of the noise still remains after the cleaning; at frequencies above 85 Hz, there is no excess noise removed. We also show that this method can quantify the amount of non-Gaussian noise in continuous data, which is useful for general detector noise investigations. To do that, we estimate the difference in non-Gaussian noise in the presence and absence of light scattering noise.

gr-qc

SDSS-IV MaStar: Theoretical Atmospheric Parameters for the MaNGA Stellar Library

We calculate the fundamental stellar parameters effective temperature, surface gravity and iron abundance - T$_{\rm eff}$, log g, [Fe/H] - for the final release of the Mapping Nearby Galaxies at APO (MaNGA) Stellar Library (MaStar), containing 59,266 per-visit-spectra for 24,290 unique stars at intermediate resolution ($R\sim1800$) and high S/N (median = 96). We fit theoretical spectra from model atmospheres by both MARCS and BOSZ-ATLAS9 to the observed MaStar spectra, using the full spectral fitting code pPXF. We further employ a Bayesian approach, using a Markov Chain Monte Carlo (MCMC) technique to map the parameter space and obtain uncertainties. Originally in this paper, we cross match MaStar observations with Gaia photometry, which enable us to set reliable priors and identify outliers according to stellar evolution. In parallel to the parameter determination, we calculate corresponding stellar population models to test the reliability of the parameters for each stellar evolutionary phase. We further assess our procedure by determining parameters for standard stars such as the Sun and Vega and by comparing our parameters with those determined in the literature from high-resolution spectroscopy (APOGEE and SEGUE) and from lower-resolution matching template (LAMOST). The comparisons, considering the different methodologies and S/N of the literature surveys, are favourable in all cases. Our final parameter catalogue for MaStar cover the following ranges: $2592 \leq $ T$_{\rm eff} \leq 32983\;$K; $-0.7 \leq $ log g $ \leq 5.4\;$dex; $-2.9 \leq $ [Fe/H] $\leq 1.0\;$dex and will be available with the last SDSS-IV Data Release, in December 2021.

astro-ph.GA

Omicron: a tool to characterize transient noise in gravitational-wave detectors

The Omicron software is a tool developed to perform a multi-resolution time-frequency analysis of data from gravitational-wave detectors: the LIGO, Virgo, and KAGRA detectors. Omicron generates spectrograms from whitened data streams, offering a visual representation of transient detector noises and gravitational-wave events. In addition, these events can be parameterized with an optimized resolution. They can be written to disk to conduct offline noise characterization and gravitational-wave event validation studies. Omicron is optimized to process, in parallel, thousands of data streams recorded by gravitational-wave detectors. The Omicron software plays an important role in vetting gravitational-wave detection candidates and characterization of transient noise.

astro-ph.IM

SOAR/Goodman Spectroscopic Assessment of Candidate Counterparts of the LIGO-Virgo Event GW190814

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond.

astro-ph.HE

SkyPy: A package for modelling the Universe

SkyPy is an open-source Python package for simulating the astrophysical sky. It comprises a library of physical and empirical models across a range of observables and a command-line script to run end-to-end simulations. The library provides functions that sample realisations of sources and their associated properties from probability distributions. Simulation pipelines are constructed from these models using a YAML-based configuration syntax, while task scheduling and data dependencies are handled internally and the modular design allows users to interface with external software. SkyPy is developed and maintained by a diverse community of domain experts with a focus on software sustainability and interoperability. By fostering development, it provides a framework for correlated simulations of a range of cosmological probes including galaxy populations, large scale structure, the cosmic microwave background, supernovae and gravitational waves. Version 0.4 implements functions that model various properties of galaxies including luminosity functions, redshift distributions and optical photometry from spectral energy distribution templates. Future releases will provide additional modules, for example, to simulate populations of dark matter halos and model the galaxy-halo connection, making use of existing software packages from the astrophysics community where appropriate.

astro-ph.IM

The failure of the Fisher Matrix when including tidal terms: Considering construction of template banks of tidally deformed binary neutron stars

Recent gravitational-wave observations have begun to constrain the internal physics of neutron stars. However, current detection searches for neutron star systems assume that potential neutron stars are low-mass black holes, ignoring any affect on the gravitational-wave signal due to the internal neutron-star physics. We wish to create a template bank of binary neutron star waveforms including the effect of tidal deformability. However, we find that the Fisher matrix, which is commonly used to approximate match calculations when placing template banks, is unsuitable to predict the match between two binary neutron star waveforms. We find that the Fisher matrix can predict errors on the mismatch that are larger than $100\%$ when attempting to identify waveforms with a match of $0.97$. We explore the regime in which the Fisher matrix cannot be trusted and examine why it breaks down. We demonstrate that including higher-order terms in the Taylor series expansion of the match can reliably compute matches for these examples, but that it is prohibitively computationally expensive to do so. Finally, we demonstrate that stochastic placement can still be used to construct a template bank of tidally deformed neutron-star waveforms.

gr-qc

2-OGC: Open Gravitational-wave Catalog of binary mergers from analysis of public Advanced LIGO and Virgo data

We present the second Open Gravitational-wave Catalog (2-OGC) of compact-binary coalescences, obtained from the complete set of public data from Advanced LIGO's first and second observing runs. For the first time we also search public data from the Virgo observatory. The sensitivity of our search benefits from updated methods of ranking candidate events including the effects of non-stationary detector noise and varying network sensitivity; in a separate targeted binary black hole merger search we also impose a prior distribution of binary component masses. We identify a population of 14 binary black hole merger events with probability of astrophysical origin $> 0.5$ as well as the binary neutron star merger GW170817. We confirm the previously reported events GW170121, GW170304, and GW170727 and also report GW151205, a new marginal binary black hole merger with a primary mass of $67^{+28}_{-17}\,\mathrm{M}_{\odot}$ that may have formed through hierarchical merger. We find no additional significant binary neutron star merger or neutron star--black hole merger events. To enable deeper follow-up as our understanding of the underlying populations evolves, we make available our comprehensive catalog of events, including the sub-threshold population of candidates and posterior samples from parameter inference of the 30 most significant binary black hole candidates.

astro-ph.HE

Blip glitches in Advanced LIGO data

Blip glitches are short noise transients present in data from ground-based gravitational-wave observatories. These glitches resemble the gravitational-wave signature of massive binary black hole mergers. Hence, the sensitivity of transient gravitational-wave searches to such high-mass systems and other potential short duration sources is degraded by the presence of blip glitches. The origin and rate of occurrence of this type of glitch have been largely unknown. In this paper we explore the population of blip glitches in Advanced LIGO during its first and second observing runs. On average, we find that Advanced LIGO data contains approximately two blip glitches per hour of data. We identify four subsets of blip glitches correlated with detector auxiliary or environmental sensor channels, however the physical causes of the majority of blips remain unclear.

physics.ins-det