CzSL: Learning from citizen science, experts and unlabelled data in astronomical image classification

Abstract

Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surveys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. However, existing approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innovative learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional autoencoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multi-class, and imbalanced classification scenarios. The results demonstrate that our solution is able to improve classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling.

References in corpus (29)

• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Observational evidence for AGN feedback in early-type galaxies
• Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 galaxies
• Galaxy Zoo: the dependence of morphology and colour on environment
• Data Mining and Machine Learning in Astronomy
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Galaxy Zoo: the fraction of merging galaxies in the SDSS and their morphologies
• Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
• Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning
• Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
• Using transfer learning to detect galaxy mergers
• Planet Hunters: The First Two Planet Candidates Identified by the Public using the Kepler Public Archive Data
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Detection of a Dipole in the Handedness of Spiral Galaxies with Redshifts z ~ 0.04
• Galaxies, Human Eyes and Artificial Neural Networks
• Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
• Integrating human and machine intelligence in galaxy morphology classification tasks
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• The Milky Way Project: Leveraging Citizen Science and Machine Learning to Detect Interstellar Bubbles
• Mining for Strong Gravitational Lenses with Self-supervised Learning
• The FIRST Classifier: Compact and Extended Radio Galaxy Classification using Deep Convolutional Neural Networks
• AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised Morphological Classification of Galaxies and Synthetic Image Generation
• Computer-generated visual morphology catalog of ~3,000,000 SDSS galaxies
• Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques
• Twelve Years of Galaxy Zoo
• Classifying galaxy spectra at 0.5<z<1 with self-organizing maps
• Towards Galaxy Foundation Models with Hybrid Contrastive Learning
• Deep Transfer Learning for Blended Source Identification in Galaxy Survey Data

Cited by in corpus (1)

• Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training