The weird and the wonderful in our Solar System: Searching for serendipity in the Legacy Survey of Space and Time (2401.08763v1)

Abstract: We present a novel method for anomaly detection in Solar System object data, in preparation for the Legacy Survey of Space and Time. We train a deep autoencoder for anomaly detection and use the learned latent space to search for other interesting objects. We demonstrate the efficacy of the autoencoder approach by finding interesting examples, such as interstellar objects, and show that using the autoencoder, further examples of interesting classes can be found. We also investigate the limits of classic unsupervised approaches to anomaly detection through the generation of synthetic anomalies and evaluate the feasibility of using a supervised learning approach. Future work should consider expanding the feature space to increase the variety of anomalies that can be uncovered during the survey using an autoencoder.

• The paper's main contribution is demonstrating how deep autoencoders effectively identify anomalies in high-dimensional Solar System data for LSST.
• It outlines a comparative analysis with unsupervised and supervised methods, highlighting trade-offs and significant performance variations.
• The approach enables dimensionality reduction and similarity searching, paving the way for real-time detection of unusual celestial phenomena.

Anomaly Detection in Solar System Data Using Deep Learning Approaches

The paper discusses advanced methodologies for anomaly detection in the field of Solar System data, particularly in preparation for the upcoming Legacy Survey of Space and Time (LSST). The central theme of the research is the application of deep autoencoders for anomaly detection, aimed at not only identifying unusual Solar System objects but also providing a robust framework for such detections within an expansive feature space.

Anomaly Detection in LSST

The Legacy Survey of Space and Time, executed by the Vera C. Rubin Observatory, will yield the most extensive survey of Solar System objects to date. It is anticipated to catalog over five million new objects, radically enhancing current records. This massive influx of data necessitates efficient anomaly detection systems to discover novel or peculiar celestial objects. The unexplored nature of this data creates the potential for serendipitous discoveries, necessitating tools that can operate without preconceived notions.

Machine Learning Approaches

The paper explores different machine learning methodologies for anomaly detection:

1. Unsupervised Anomaly Detection: Traditional unsupervised learning methods were assessed, using synthetic data to evaluate their efficacy in identifying various forms of anomalies—global, cluster, and local. Gaussian Mixture Models (GMM) were also employed as part of this investigation. The conclusion was that while these methods provide a baseline, their performance varies significantly based on anomaly type.
2. Supervised Learning: A comparison is drawn with supervised methods requiring labeled data. The paper highlights the trade-off in labor for higher efficacy; a minimal amount of labeled data can markedly boost the performance over unsupervised methods. It is posited as a complementary approach, potentially suitable for refining search processes once initial anomalies have been flagged.
3. Deep Autoencoders: The paper's focal innovation is utilizing deep autoencoders. These neural networks compress input features into a latent space and reconstruct them, with the reconstruction loss serving as a metric for anomalousness. Objects with high reconstruction losses are flagged for being unusual.

Application and Implications

The deep autoencoder approach encapsulates multiple advantages:

• Dimensionality Reduction: By encoding essential features into fewer latent dimensions, it enables efficient processing and interpretation of high-dimensional data inherent in LSST datasets.
• Reconstruction Loss for Anomaly Detection: The paper argues for using reconstruction loss as a primary anomaly indicator. This allows for identifying objects that diverge significantly from the norm without requiring predefined anomaly categories.
• Similarity Searching: Utilizing the latent space representation, similar objects can be identified, offering insights into the context of anomalies. This provides an exploratory tool for astronomers to investigate peculiar objects further.

Future Directions

The results imply several future trajectories:

• Extended Feature Spaces: Incorporating time series, light curves, and dynamical changes of objects into the feature set could provide richer insights and enable the detection of transient phenomena.
• Combining Methods: There's potential in blending unsupervised and supervised methods, employing human-in-the-loop approaches to refine anomaly searches.
• Real-Time Applications: As LSST operates, real-time anomaly detection can drive follow-up observations, maximizing the scientific yield of the survey.

Conclusion

This research lays a foundation for anomaly detection in an era of unprecedented data volume from LSST. The employed deep learning techniques, particularly autoencoders, hold significant promise in navigating the vast parameter space of Solar System objects, unlocking opportunities for novel discoveries. As the methodologies mature, they offer the potential for ongoing adaptation and application across astrophysical domains. Such tools will be pivotal as the astronomical community seeks to leverage the forthcoming data influx for transformative scientific understanding.