New Approaches to Object Classification in Synoptic Sky Surveys

TL;DR

This paper introduces neural network-based artifact filtering and a multi-pass star-galaxy classifier for real-time object classification in synoptic sky surveys, achieving around 90% artifact rejection without missing genuine transients.

Contribution

It presents novel neural network methods for artifact filtering and star-galaxy classification tailored for real-time sky survey data, improving accuracy and consistency.

Findings

Artifact filter achieves ~90% classification rate

No genuine transients misclassified during real-time scans

Multi-pass classifier improves star-galaxy classification consistency

Abstract

Digital synoptic sky surveys pose several new object classification challenges. In surveys where real-time detection and classification of transient events is a science driver, there is a need for an effective elimination of instrument-related artifacts which can masquerade as transient sources in the detection pipeline, e.g., unremoved large cosmic rays, saturation trails, reflections, crosstalk artifacts, etc. We have implemented such an Artifact Filter, using a supervised neural network, for the real-time processing pipeline in the Palomar-Quest (PQ) survey. After the training phase, for each object it takes as input a set of measured morphological parameters and returns the probability of it being a real object. Despite the relatively low number of training cases for many kinds of artifacts, the overall artifact classification rate is around 90%, with no genuine transients misclassified during our real-time scans. Another question is how to assign an optimal star-galaxy classification in a multi-pass survey, where seeing and other conditions change between different epochs, potentially producing inconsistent classifications for the same object. We have implemented a star/galaxy multipass classifier that makes use of external and a priori knowledge to find the optimal classification from the individually derived ones. Both these techniques can be applied to other, similar surveys and data sets.