Advanced LIGO and Advanced Virgo will be all-sky monitors for merging compact objects within a few hundred Mpc. Finding the electromagnetic counterparts to these events will require an understanding of the transient sky at low red-shift (z<0.1). We performed a systematic search for extragalactic, low red-shift, transient events in the XMM-Newton Slew Survey. In a flux limited sample, we found that highly-variable objects comprised 10% of the sample, and that of these, 10% were spatially coincident with cataloged optical galaxies. This led to 4x10^-4 transients per square degree above a flux threshold of 3x10^-12 erg cm-2 s-1 [0.2-2 keV] which might be confused with LIGO/Virgo counterparts. This represents the first extragalactic measurement of the soft X-ray transient rate within the Advanced LIGO/Virgo horizon. Our search revealed six objects that were spatially coincident with previously cataloged galaxies, lacked evidence for optical AGNs, displayed high luminosities around 10^43 erg s-1, and varied in flux by more than a factor of ten when compared with the ROSAT All-Sky Survey. At least four of these displayed properties consistent with previously observed tidal disruption events.
The sensitivity of searches for astrophysical transients in data from the LIGO is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These glitches can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGOs fourth science run and one week of LIGOs sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used.
Gravitational wave astronomy relies on the use of multiple detectors, so that coincident detections may distinguish real signals from instrumental artifacts, and also so that relative timing of signals can provide the sky position of sources. We show that the comparison of instantaneous time-frequency and time- amplitude maps provided by the Hilbert-Huang Transform (HHT) can be used effectively for relative signal timing of common signals, to discriminate between the case of identical coincident signals and random noise coincidences, and to provide a classification of signals based on their time-frequency trajectories. The comparison is done with a chi-square goodness-of-fit method which includes contributions from both the instantaneous amplitude and frequency components of the HHT to match two signals in the time domain. This approach naturally allows the analysis of waveforms with strong frequency modulation.
