Identifying and addressing nonstationary LISA noise

Edwards, M. C.; Maturana-Russel, Patricio; Meyer, Renate; Gair, J. R.; Korsakova, Natalia; Christensen, N.

doi:10.1103/physrevd.102.084062

Cited by 27 publications

(13 citation statements)

References 43 publications

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“…In our particular case, they function as a signature of non-Kerrness, though we expect that similar effects should occur in N > 2-body EMRIs [46,47] or when the multipole structure of the small-mass companion is taken into account [48][49][50]. Interestingly, instrumental glitches have been spotted in LISA pathfinder data [79], though such events in the acceleration noise are rather abrupt and have a distinct structure with respect to the glitches shown here. Therefore, our results serve as a phonomenological imprint of non-integrability and portray a "smoking-gun" of chaotic phenomena in EMRIs.…”

Section: Discussionsupporting

confidence: 49%

Gravitational-wave glitches: resonant islands and frequency jumps in non-integrable extreme-mass-ratio inspirals

Destounis,

Kokkotas

2021

Preprint

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The detection of gravitational waves from extreme-mass-ratio inspirals with upcoming space-borne detectors will allow for unprecedented tests of general relativity in the strong-field regime. Aside from assessing whether black holes are unequivocally described by the Kerr metric, such detections may place constraints on the degree of spacetime symmetry. In particular, depending on exactly how a hypothetical departure from the Kerr metric manifests, the Carter symmetry, which implies the integrability of the geodesic equations, may be broken. Here, we examine the gravitational waveforms associated with non-integrable extreme-mass-ratio inspirals involving a small-mass companion and a supermassive compact object of general relativity, namely the Manko-Novikov spacetime. We show that the waveforms displays sudden frequency jumps, when the companion transverses resonant islands. These findings demonstrate that such abrupt manifestations in the gravitational-wave frequencies are generic, have a genuine astrophysical origin and function as a distinctive signature of chaotic phenomena in extreme-mass-ratio binaries.

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Section: Discussionsupporting

confidence: 49%

Gravitational-wave glitches: resonant islands and frequency jumps in non-integrable extreme-mass-ratio inspirals

Destounis,

Kokkotas

2021

Preprint

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“…This arises from the length increase of the signal, which now coalesces in the center of the Tukey window, but the inspiral starts before the window, transitioning between two radically different forms of the noise. We expect to see even greater discrepancies with the stationary model when we consistently observe longer signals, a problem that has already been identified for LISA [58], for which antenna repointing will create data gaps during the observation of signals. The assumption of stationarity in these periods will lead to parameter estimation biases.…”

Section: The Effect Of Nonstationarity On the Covariance Matrix Of A ...mentioning

confidence: 66%

“…For example, Refs. [56][57][58] discussed the concept of nonstationarities in detector data evolving the power spectral density (PSD) over time, referred to as PSD drift. This causes a loss in sensitivity of the detectors, so correction methodology is offered by computing a running estimate of all matched filter overlaps.…”

Section: Characterizing the Noisementioning

confidence: 99%

Issues of mismodeling gravitational-wave data for parameter estimation

Edy

Nuttall

2021

Phys. Rev. D

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Bayesian inference is used to extract unknown parameters from gravitational-wave signals. Detector noise is typically modeled as stationary, although data from the LIGO and Virgo detectors is not stationary. We demonstrate that the posterior of estimated waveform parameters is no longer valid under the assumption of stationarity. We show that while the posterior is unbiased, the errors will be under-or overestimated compared to the true posterior. A formalism was developed to measure the effect of the mismodeling, and found the effect of any form of nonstationarity has an effect on the results, but are not significant in certain circumstances. We demonstrate the effect of short-duration Gaussian noise bursts and persistent oscillatory modulation of the noise on binary-black-hole-like signals. In the case of short signals, nonstationarity in the data does not have a large effect on the parameter estimation, but the errors from nonstationary data containing signals lasting tens of seconds or longer will be several times worse than if the noise was stationary. Accounting for this limiting factor in parameter sensitivity could be very important for achieving accurate astronomical results. This methodology for handling the nonstationarity will also be invaluable for analysis of waveforms that last minutes or longer, such as those we expect to see with the Einstein Telescope.

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“…In GW data analysis, a typical approach is to estimate the PSD offline on a large batch of data and off-source: this scheme assumes stationary data on a long timescale (longer than the data under study) and it might not reflect the structure of the noise in the analyzed time slice. Some alternatives exist for dropping this assumption and modifying the likelihood accordingly (Röver et al 2010;Röver 2011;Edwards et al 2020;Chatziioannou et al 2021). MESA can add to those an elegant way out: at each evaluation of the likelihood, a new spectral analysis is performed by computing the PSD on the residual d t − x t .…”

Section: Final Remarks and Future Prospectsmentioning

confidence: 99%

Maximum Entropy Spectral Analysis: a case study

Martini,

Schmidt,

Del Pozzo

2021

Preprint

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The Maximum Entropy Spectral Analysis (MESA) method, developed by Burg, provides a powerful tool to perform spectral estimation of a time-series. The method relies on a Jaynes' maximum entropy principle and provides the means of inferring the spectrum of a stochastic process in terms of the coefficients of some autoregressive process AR(p) of order p. A closed form recursive solution provides an estimate of the autoregressive coefficients as well as of the order p of the process. We provide a ready-to-use implementation of the algorithm in the form of a python package memspectrum. We characterize our implementation by performing a power spectral density analysis on synthetic data (with known power spectral density) and we compare different criteria for stopping the recursion. Furthermore, we compare the performance of our code with the ubiquitous Welch algorithm, using synthetic data generated from the released spectrum by the LIGO-Virgo collaboration. We find that, when compared to Welch's method, Burg's method provides a power spectral density (PSD) estimation with a systematically lower variance and bias. This is particularly manifest in the case of a little number of data points, making Burg's method most suitable to work in this regime.

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Identifying and addressing nonstationary LISA noise

Cited by 27 publications

References 43 publications

Gravitational-wave glitches: resonant islands and frequency jumps in non-integrable extreme-mass-ratio inspirals

Gravitational-wave glitches: resonant islands and frequency jumps in non-integrable extreme-mass-ratio inspirals

Issues of mismodeling gravitational-wave data for parameter estimation

Maximum Entropy Spectral Analysis: a case study

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