How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

Abstract

We present the results of applying new object classification techniques to difference images in the context of the Nearby Supernova Factory supernova search. Most current supernova searches subtract reference images from new images, identify objects in these difference images, and apply simple threshold cuts on parameters such as statistical significance, shape, and motion to reject objects such as cosmic rays, asteroids, and subtraction artifacts. Although most static objects subtract cleanly, even a very low false positive detection rate can lead to hundreds of non-supernova candidates which must be vetted by human inspection before triggering additional followup. In comparison to simple threshold cuts, more sophisticated methods such as Boosted Decision Trees, Random Forests, and Support Vector Machines provide dramatically better object discrimination. At the Nearby Supernova Factory, we reduced the number of non-supernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming projects such as PanSTARRS and LSST.

Sunfall: A Collaborative Visual Analytics System for Astrophysics

“…Palomar Oschin 1.2-m telescope [8] Supernova candidates are identified from among the over 600,000 objects processed per night by a set of image features including object shape (roundness and contour irregularity computed from Fourier contour descriptors) [11,12], position, distance from nearest object, and motion. These features are used as input to machine learning algorithms that select candidates to be sent to humans for scanning and vetting [9,13]. Promising SN candidates that pass the human scanning and vetting procedure are sent for confirmation and spectrophotometric follow-up by SNIFS (the SuperNova Integral Field Spectrograph) [2] on the University of Hawaii 2.2m telescope on Mauna Kea, Hawaii.…”

“…Sunfall Search has increased efficiency of supernova detection and enabled more effective human intervention by reducing the number of false positive candidates by nearly an order of magnitude [6,13] and, through the use of intelligent interfaces, by increasing human efficiency in evaluating candidates by a factor of four.…”

“…Thus of the objects originally imaged, somewhere near .0002% prove to be Type Ia supernovae. This process is described in more detail in [9,13].…”

Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet stars

“…These methods use the statistical information contained in the infrared colours for a set of known objects, including non-WR stellar populations which are frequently confused as WR candidates due to similar colours, to providing an automated classification of the unknown objects. The use of supervised machinelearning methods in astronomy has rapidly increased over the last decade, e.g., for automated classification of celestial objects in large catalogues and all-sky surveys (Malek et al 2013;Kurcz et al 2016;Lochner et al 2016), photometric redshift estimation of galaxies (Tagliaferri et al 2003;Lima et al 2008;Sheldon et al 2012;Heinis et al 2016), morphological galaxy classification (Banerji et al 2010;Shamir et al 2013;Kuminski et al 2014;Pasquato & Chung 2016) and candidate type of object selection (Bailey et al 2007;Yèche et al 2010;Hsieh & Lai 2013;Marton et al 2016). To our knowledge, this is the first time that machine-learning methods are used to classify objects in this colour space defined by J, H, K s , [3.6], [4.5], [5.8] and [8.0] photometric bands.…”

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

“…ML techniques are by now ubiquitous in Astronomy, where they have been successfully applied to photometric redshift estimation in large surveys such as the Sloan Digital Sky Survey (Tagliaferri et al 2003;Li et al 2007;Ball et al 2007;Gerdes et al 2010;Singal et al 2011;Geach 2012;Carrasco Kind & Brunner 2013;Cavuoti et al 2014;Hoyle et al 2015a,b), automatic identification of quasi stellar objects (Yèche et al 2010), galaxy morphology classification (Banerji et al 2010;Shamir et al 2013;Kuminski et al 2014), detection of HI bubbles in the interstellar medium (Thilker et al 1998;Daigle et al 2003), classification of diffuse interstellar bands in the Milky Way (Baron et al 2015), prediction of solar flares (Colak & Qahwaji 2009;Yu et al 2009), automated classification of astronomical transients and detection of variability (Mahabal et al 2008;Djorgovski et al 2012;Brink et al 2013;du Buisson et al 2015;Wright et al 2015), cataloguing of impact craters on Mars (Stepinski et al 2009), prediction of galaxy halo occupancy in cosmological simulations (Xu et al 2013), dynamical mass measurement of galaxy clusters (Ntampaka et al 2015), and supernova identification in supernova searches (Bailey et al 2007). Software tools developed specifically for astronomy are also becoming available to the community, still mainly with large observational datasets in mind (VanderPlas et al 2012;Vander Plas et al 2014;VanderPlas et al 2014;Ball & Gray 2014).…”