Identifying the Host Galaxy of Gravitational Wave Signals

One of the goals of the current LIGO-GEO-Virgo science run is to identify transient gravitational wave (GW) signals in near real time to allow follow-up electromagnetic (EM) observations. An EM counterpart could increase the confidence of the GW detection and provide insight into the nature of the source. Current GW-EM campaigns target potential host galaxies based on overlap with the GW sky error box. We propose a new statistic to identify the most likely host galaxy, ranking galaxies based on their position, distance, and luminosity. We test our statistic with Monte Carlo simulations of GWs produced by coalescing binaries of neutron stars (NS) and black holes (BH), one of the most promising sources for ground-based GW detectors. Considering signals accessible to current detectors, we find that when imaging a single galaxy, our statistic correctly identifies the true host ~20% to ~50% of the time, depending on the masses of the binary components. With five narrow-field images the probability of imaging the true host increases to ~50% to ~80%. When collectively imaging groups of galaxies using large field-of-view telescopes, the probability improves to ~30% to ~60% for a single image and to ~70% to ~90% for five images. For the advanced generation of detectors (c. 2015+), and considering binaries within 100 Mpc (the reach of the galaxy catalogue used), the probability is ~40% for one narrow-field image, ~75% for five narrow-field images, ~65% for one wide-field image, and ~95% for five wide-field images, irrespective of binary type.Comment: 5 pages, 2 figure

Improving the Sensitivity of Advanced LIGO Using Noise Subtraction

This paper presents an adaptable, parallelizable method for subtracting linearly coupled noise from Advanced LIGO data. We explain the features developed to ensure that the process is robust enough to handle the variability present in Advanced LIGO data. In this work, we target subtraction of noise due to beam jitter, detector calibration lines, and mains power lines. We demonstrate noise subtraction over the entirety of the second observing run, resulting in increases in sensitivity comparable to those reported in previous targeted efforts. Over the course of the second observing run, we see a 30% increase in Advanced LIGO sensitivity to gravitational waves from a broad range of compact binary systems. We expect the use of this method to result in a higher rate of detected gravitational-wave signals in Advanced LIGO data.Comment: 15 pages, 6 figure

The Issues of Mismodelling Gravitational-Wave Data for Parameter Estimation

Bayesian inference is used to extract unknown parameters from gravitational wave signals. Detector noise is typically modelled 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 mismodelling, and found the effect of any form of non-stationarity 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, non-stationarity in the data does not have a large effect on the parameter estimation, but the errors from non-stationary 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, including an estimation of the Hubble parameter. This methodology for handling the non-stationarity will also be invaluable for analysis of waveforms that last minutes or longer, such as those we expect to see with the Einstein Telescope.Comment: 15 pages, 5 figures. Comments welcom

The c-terminal extension of a hybrid immunoglobulin A/G heavy chain is responsible for its Golgi-mediated sorting to the vacuole

We have assessed the ability of the plant secretory pathway to handle the expression of complex heterologous proteins by investigating the fate of a hybrid immunoglobulin A/G in tobacco cells. Although plant cells can express large amounts of the antibody, a relevant proportion is normally lost to vacuolar sorting and degradation. Here we show that the synthesis of high amounts of IgA/G does not impose stress on the plant secretory pathway. Plant cells can assemble antibody chains with high efficiency and vacuolar transport occurs only after the assembled immunoglobulins have traveled through the Golgi complex. We prove that vacuolar delivery of IgA/G depends on the presence of a cryptic sorting signal in the tailpiece of the IgA/G heavy chain. We also show that unassembled light chains are efficiently secreted as monomers by the plant secretory pathway

Large-Scale Image Processing with the ROTSE Pipeline for Follow-Up of Gravitational Wave Events

Electromagnetic (EM) observations of gravitational-wave (GW) sources would bring unique insights into a source which are not available from either channel alone. However EM follow-up of GW events presents new challenges. GW events will have large sky error regions, on the order of 10-100 square degrees, which can be made up of many disjoint patches. When searching such large areas there is potential contamination by EM transients unrelated to the GW event. Furthermore, the characteristics of possible EM counterparts to GW events are also uncertain. It is therefore desirable to be able to assess the statistical significance of a candidate EM counterpart, which can only be done by performing background studies of large data sets. Current image processing pipelines such as that used by ROTSE are not usually optimised for large-scale processing. We have automated the ROTSE image analysis, and supplemented it with a post-processing unit for candidate validation and classification. We also propose a simple ad hoc statistic for ranking candidates as more likely to be associated with the GW trigger. We demonstrate the performance of the automated pipeline and ranking statistic using archival ROTSE data. EM candidates from a randomly selected set of images are compared to a background estimated from the analysis of 102 additional sets of archival images. The pipeline's detection efficiency is computed empirically by re-analysis of the images after adding simulated optical transients that follow typical light curves for gamma-ray burst afterglows and kilonovae. We show that the automated pipeline rejects most background events and is sensitive to simulated transients to limiting magnitudes consistent with the limiting magnitude of the images