Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

Abstract:

Five archived X-ray observations of NGC 4051, taken using the NuSTAR observatory, have been analysed, revealing lags between flux variations in bands covering a wide range of X-ray photon energy. In all pairs of bands compared, the harder band consistently lags the softer band by at least 1000s, at temporal frequencies ~5E-5 Hz. In addition, soft-band lags up to 400s are measured at frequencies ~2E-4 Hz. Light echos from an excess of soft band emission in the inner accretion disk cannot explain the lags in these data, as they are seen in cross-correlations with energy bands where the softer band is expected to have no contribution from reflection. The basic properties of the time delays have been parameterised by fitting a top hat response function that varies with photon energy, taking fully into account the covariance between measured time lag values. The low-frequency hard-band lags and the transition to soft-band lags are consistent with time lags arising as reverberation delays from circumnuclear scattering of X-rays, although greater model complexity is required to explain the entire spectrum of lags. The scattered fraction increases with increasing photon energy as expected, and the scattered fraction is high, indicating the reprocessor to have a global covering fraction ~50% around the continuum source. Circumnuclear material, possibly associated with a disk wind at a few hundred gravitational radii from the primary X-ray source, may provide suitable reprocessing.