Machine-learning nonstationary noise out of gravitational-wave detectors

Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation

Modified Bloch equations in presence of a nonstationary bath

Based on the system-reservoir description we propose a simple solvable microscopic model for a nonequilibrium bath. This captures the essential features of a nonstationary quantum Markov process. We establish an appropriate generalization of the fluctuation-dissipation relation pertaining to this process and explore the essential modifications of the Bloch equations to reveal the nonexponential decay of the Bloch vector components and transient spectral broadening in resonance fluorescence. We discuss a simple experimental scheme to verify the theoretical results.Comment: Revtex, 27 pages, 2 ps figures. To appear in European Physical Journal

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

Fringe Visibility Estimators for the Palomar Testbed Interferometer

Visibility estimators and their performance are presented for use with the Palomar Testbed Interferometer (PTI). One operational mode of PTI is single-baseline visibility measurement using pathlength modulation with synchronous readout by a NICMOS-3 infrared array. Visibility is estimated from the fringe quadratures, either incoherently, or using source phase referencing to provide a longer coherent integration time. The visibility estimators differ those used with photon-counting detectors in order to account for biases attributable to detector offsets and read noise. The performance of these estimators is affected not only by photon noise, but also by the detector read noise and errors in estimating the bias corrections, which affect the incoherent and coherent estimators differently. Corrections for visibility loss in the coherent estimators using the measured tracking jitter are also presented.Comment: PASP in press (Jan 99). 13 Pages, no figure

Detecting Long-Duration Narrow-Band Gravitational Wave Transients Associated with Soft Gamma Repeater Quasi-Periodic Oscillations

We have performed an in-depth concept study of a gravitational wave data analysis method which targets repeated long quasi-monochromatic transients (triggers) from cosmic sources. The algorithm concept can be applied to multi-trigger data sets in which the detector-source orientation and the statistical properties of the data stream change with time, and does not require the assumption that the data is Gaussian. Reconstructing or limiting the energetics of potential gravitational wave emissions associated with quasi-periodic oscillations (QPOs) observed in the X-ray lightcurve tails of soft gamma repeater flares might be an interesting endeavour of the future. Therefore we chose this in a simplified form to illustrate the flow, capabilities, and performance of the method. We investigate performance aspects of a multi-trigger based data analysis approach by using O(100 s) long stretches of mock data in coincidence with the times of observed QPOs, and by using the known sky location of the source. We analytically derive the PDF of the background distribution and compare to the results obtained by applying the concept to simulated Gaussian noise, as well as off-source playground data collected by the 4-km Hanford detector (H1) during LIGO's fifth science run (S5). We show that the transient glitch rejection and adaptive differential energy comparison methods we apply succeed in rejecting outliers in the S5 background data. Finally, we discuss how to extend the method to a network containing multiple detectors, and as an example, tune the method to maximize sensitivity to SGR 1806-20 flare times.Comment: 11 pages, 8 figure

A new regularized TVAR-based algorithm for recursive detection of nonstationarity and its application to speech signals

This paper develops a new recursive nonstationarity detection method based on time-varying autoregressive (TVAR) modeling. A local likelihood estimation approach is introduced which gives more weights to observations near the current time instant but less to those distance apart. It thus allows the Wald test to be computed based on RLS-type algorithms with low computational cost. A reliable and efficient state regularized variable forgetting factor (VFF) QR decomposition (QRD)-based RLS (SR-VFF-QRRLS) algorithm is adopted for estimation for its asymptotically unbiased property and immunity to lacking of excitation. Advantages of the proposed approach over conventional approaches are 1) it provides continuous parameter estimates and the corresponding stationary intervals with low complexity, 2) it mitigates low excitation problems using state regularization, and 3) stationarity at different scales can be detected by appropriately choosing a certain window size. The effectiveness of the proposed algorithm is evaluated by testing vocal tract changes in real speech signals. © 2012 IEEE.published_or_final_versio

Entropic derivation of F=ma for circular motion

We examine the entropic picture of Newton's second law for the case of circular motion. It is shown that one must make modifications to the derivation of F=ma due to a change in the effective Unruh temperature for circular motion. These modifications present a challenge to the entropic derivation of Newton's second law, but also open up the possibility to experimentally test and constrain this model for large centripetal accelerations.Comment: 8 pages and no figures, added discussion and references. To be published in PL