Prof Chris Lintott

Abstract:

We consider active galactic nucleus (AGN) photoionization of gas in companion galaxies (cross-ionization) as a way to sample the intensity of AGN radiation in both direction and time, independent of the gas properties of the AGN host galaxies. From an initial set of 212 AGN+companion systems, identified with the help of Galaxy Zoo participants, we obtained long-slit optical spectra of 32 pairs that were a priori likely to show cross-ionization based on projected separation or angular extent of the companion. From emission-line ratios, 10 of these systems are candidates for cross-ionization, roughly the fraction expected if most AGNs have ionization cones with 70° opening angles. Among these, Was 49 remains the strongest nearby candidate. NGC 5278/9 and UGC 6081 are dual-AGN systems with tidal debris, complicating identification of cross-ionization. The two weak AGNs in the NGC 5278/9 system ionize gas filaments to a projected radius 14 kpc from each galaxy. In UGC 6081, an irregular high-ionization emission region encompasses both AGNs, extending more than 15 kpc from each. The observed AGN companion galaxies with and without signs of external AGN photoionization have similar distributions in estimated incident AGN flux, suggesting that geometry of escaping radiation or long-term variability controls this facet of the AGN environment. This parallels conclusions for luminous QSOs based on the proximity effect among Lyman α absorbers. In some galaxies, mismatch between spectroscopic classifications in the common BPT diagram and the intensity of weaker He II and [Ne V] emission lines highlights the limits of common classifications in low-metallicity environments.

Abstract:

Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp.) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch, and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machine learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development.

Abstract:

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold, classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of nonparametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine, and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computation cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large scale surveys.