Rotation-invariant convolutional neural networks for galaxy morphology prediction (1503.07077v1)

Abstract: Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by trained experts, which is time-consuming and does not scale to large ($\gtrsim10^4$) numbers of images. Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images. We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project. For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy ($> 99\%$) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts' workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the LSST.

• The paper introduces a novel approach using rotation-invariant CNNs that leverage multiple rotated views to enhance galaxy morphology classification.
• The model utilizes deep CNN architectures with data augmentation, dropout, and averaging techniques to achieve over 99% accuracy on high-agreement images.
• The method paves the way for automated analysis of large-scale galaxy surveys, reducing manual effort and enabling scalable astronomical data processing.

Rotation-Invariant Convolutional Neural Networks for Galaxy Morphology Prediction

The paper by Dieleman et al. explores a deep learning approach using convolutional neural networks (CNNs) to automate the classification of galaxy morphologies. This work addresses the limitations associated with manual and crowdsourced galaxy classification, especially given the increasing volume of data from astronomical surveys like the Sloan Digital Sky Survey (SDSS).

Background and Challenges

Traditionally, the analysis of galaxy morphology has relied on visual inspection, which is labor-intensive and impractical for large datasets. Automatic methods have existed but often lack the desired accuracy. The Galaxy Zoo project brought a crowdsourced approach, enlisting volunteers to classify images. While effective, this method cannot scale sustainably with data from upcoming projects such as the Large Synoptic Survey Telescope (LSST).

Methodology

The authors propose a model based on convolutional neural networks, specifically designed to exploit translational and rotational symmetries of galaxy images. Unlike prior systems which depended heavily on handcrafted features, this model learns directly from the raw pixel data, capturing hierarchies of features that reflect complex patterns inherent in galaxy images.

A significant novelty introduced is the handling of rotational symmetry by generating multiple rotated viewpoints of each image. This allows CNNs to learn invariant features crucial for accurate morphology prediction without extensive interpolation, which is typically computationally expensive.

The CNN architecture used includes four convolutional layers followed by dense layers. Techniques like dropout regularization, data augmentation, and averaging over multiple model predictions were critical in minimizing overfitting given the limited training data size.

Results

The proposed method achieved high classification accuracy, outperforming other present methodologies, evidenced by their result in the Galaxy Challenge where they ranked first among 326 participants. The model demonstrated over 99% accuracy on high-agreement galaxy images from the Galaxy Zoo dataset. Additionally, the model showed robustness in confidence-based predictions, suggesting it could reliably filter large datasets to reduce expert workload.

Implications and Future Work

The model holds practical implications in scaling up the morphological analysis of massive galaxy surveys. Moreover, due to its generic design, the approach is adaptable to other radial symmetry data tasks beyond astronomy. Upcoming improvements could involve training on even larger datasets, such as newer Galaxy Zoo phases, to enhance model robustness and applicability to high redshift galaxies.

Diving deeper into architectures–potentially those with greater than 20 layers–and integrating with existing astronomical workflows can further refine and leverage model capabilities. This aligns with the broader goal of transitioning towards fully automated, scalable, and accurate astronomical data processing, laying groundwork for exponential growth in galactic research.