Rubin-LSST

Abstract:

We investigate the relation between the radio (Fr) and optical (Fo) flux densities of a variety of classes of radio transients and variables, with the aim of analysing whether this information can be used, in the future, to classify such events. Using flux density values between 1 and 10 GHz and the optical bands V and R, we build a sample with a total of 12 441 Fr and Fo measurements. The sample contains both Galactic objects, such as stellar sources and X-ray binaries, and extragalactic objects, such as gamma-ray bursts and quasars. By directly comparing the two parameters, it is already possible to distinguish between the Galactic and extragalactic populations. Although individual classes are harder to separate from the Fr − Fo parameter space to a high accuracy, and can only provide approximations, the basic approach provides an already useful foundation to develop a more accurate classification technique. In addition, we illustrate how example objects from different classes move in the parameter space as they evolve over time, offering a feature that could be used to reduce the confusion between classes. A small, blind test of the classification performance is also undertaken using a catalogue of VLA FIRST transient and variable sources, to demonstrate the advantages and current limitations of the approach. With more multiwavelength data becoming available in the future, we discuss other classification techniques which the Fr − Fo method could be combined with and potentially become an important part of an automatic radio transient classification system.

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.