Launching the VASCO citizen science project

TL;DR

The VASCO citizen science project leverages multiple approaches including machine learning and visual inspection to identify unusual astronomical transients over 70 years of observations, involving international amateur networks and analyzing thousands of candidates.

Contribution

This paper introduces the VASCO citizen science platform combining hypothesis-driven, exploratory, and machine learning methods for transient detection in large astronomical datasets.

Findings

Examined 15,593 candidate image pairs, identifying 798 vanished objects.

Demonstrated the effectiveness of citizen science in candidate selection.

Established an international amateur astronomy network for follow-up observations.

Abstract

The Vanishing & Appearing Sources during a Century of Observations (VASCO) project investigates astronomical surveys spanning a time interval of 70 years, searching for unusual and exotic transients. We present herein the VASCO Citizen Science Project, which can identify unusual candidates driven by three different approaches: hypothesis, exploratory, and machine learning, which is particularly useful for SETI searches. To address the big data challenge, VASCO combines three methods: the Virtual Observatory, user-aided machine learning, and visual inspection through citizen science. Here we demonstrate the citizen science project and its improved candidate selection process, and we give a progress report. We also present the VASCO citizen science network led by amateur astronomy associations mainly located in Algeria, Cameroon, and Nigeria. At the moment of writing, the citizen science project has carefully examined 15,593 candidate image pairs in the data (ca. 10% of the candidates), and has so far identified 798 objects classified as "vanished". The most interesting candidates will be followed up with optical and infrared imaging, together with the observations by the most potent radio telescopes.