Prof Chris Lintott

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:

� 2017. The American Astronomical Society. All rights reserved. We consider the energy budgets and radiative history of eight fading active galactic nuclei (AGNs), identified from an energy shortfall between the requirements to ionize very extended (radius > 10 kpc) ionized clouds and the luminosity of the nucleus as we view it directly. All show evidence of significant fading on timescales of ≈50,000 yr. We explore the use of minimum ionizing luminosity Q ion derived from photoionization balance in the brightest pixels in Hα at each projected radius. Tests using presumably constant Palomar-Green QSOs, and one of our targets with detailed photoionization modeling, suggest that we can derive useful histories of individual AGNs, with the caveat that the minimum ionizing luminosity is always an underestimate and subject to uncertainties about fine structure in the ionized material. These consistency tests suggest that the degree of underestimation from the upper envelope of reconstructed Q ion values is roughly constant for a given object and therefore does not prevent such derivation. The AGNs in our sample show a range of behaviors, with rapid drops and standstills; the common feature is a rapid drop in the last ≈2 � 10 4 yr before the direct view of the nucleus. The e-folding timescales for ionizing luminosity are mostly in the thousands of years, with a few episodes as short as 400 yr. In the limit of largely obscured AGNs, we find additional evidence for fading from the shortfall between even the lower limits from recombination balance and the maximum luminosities derived from far-infrared fluxes. We compare these long-term light curves, and the occurrence of these fading objects among all optically identified AGNs, to simulations of AGN accretion; the strongest variations over these timespans are seen in models with strong and local (parsec-scale) feedback. We present Gemini integral-field optical spectroscopy, which shows a very limited role for outflows in these ionized structures. While rings and loops of emission, morphologically suggestive of outflow, are common, their kinematic structure shows some to be in regular rotation. UGC 7342 exhibits local signatures of outflows < 300 km s -1 , largely associated with very diffuse emission, and possibly entraining gas in one of the clouds seen in Hubble Space Telescope images. Only in the Teacup AGN do we see outflow signatures of the order of 1000 km s -1 . In contrast to the extended emission regions around many radio-loud AGNs, the clouds around these fading AGNs consist largely of tidal debris being externally illuminated but not displaced by AGN outflows.

Abstract:

We present quantified visual morphologies of approximately 48 000 galaxies observed in three Hubble Space Telescope legacy fields by the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and classified by participants in the Galaxy Zoo project. 90 per cent of galaxies have z ≤ 3 and are observed in rest-frame optical wavelengths by CANDELS. Each galaxy received an average of 40 independent classifications, which we combine into detailed morphological information on galaxy features such as clumpiness, bar instabilities, spiral structure, and merger and tidal signatures. We apply a consensus-based classifier weighting method that preserves classifier independence while effectively down-weighting significantly outlying classifications. After analysing the effect of varying image depth on reported classifications, we also provide depth-corrected classifications which both preserve the information in the deepest observations and also enable the use of classifications at comparable depths across the full survey. Comparing the Galaxy Zoo classifications to previous classifications of the same galaxies shows very good agreement; for some applications, the high number of independent classifications provided by Galaxy Zoo provides an advantage in selecting galaxies with a particular morphological profile, while in others the combination of Galaxy Zoo with other classifications is a more promising approach than using any one method alone. We combine the Galaxy Zoo classifications of ‘smooth’ galaxies with parametric morphologies to select a sample of featureless discs at 1 ≤ z ≤ 3, which may represent a dynamically warmer progenitor population to the settled disc galaxies seen at later epochs.