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Machine Learning Methods for Automated Interstellar Object Classification with LSST

Richard Cloete, Peter Vereš, Abraham Loeb

TL;DR

This work tackles the challenge of identifying interstellar objects (ISOs) in the LSST era by evaluating four machine learning approaches (GBM, RF, SGD, NN) on simulated LSST tracklets. It leverages LSST DP0.3 data, augments ISO samples to balance training, and augments features with Digest2 outputs, finding that gradient boosting (GBM) and random forests (RF) deliver the best ISO discrimination with near-perfect metrics. Digest2-derived features dominate the predictive power, often surpassing direct LSST observables. The results support developing an automated ISO discovery pipeline in the LSST data processing flow, enabling timely follow-up and deeper insights into materials and processes from planetary systems beyond our own.

Abstract

The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. We demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.

Machine Learning Methods for Automated Interstellar Object Classification with LSST

TL;DR

This work tackles the challenge of identifying interstellar objects (ISOs) in the LSST era by evaluating four machine learning approaches (GBM, RF, SGD, NN) on simulated LSST tracklets. It leverages LSST DP0.3 data, augments ISO samples to balance training, and augments features with Digest2 outputs, finding that gradient boosting (GBM) and random forests (RF) deliver the best ISO discrimination with near-perfect metrics. Digest2-derived features dominate the predictive power, often surpassing direct LSST observables. The results support developing an automated ISO discovery pipeline in the LSST data processing flow, enabling timely follow-up and deeper insights into materials and processes from planetary systems beyond our own.

Abstract

The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. We demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.

Paper Structure

This paper contains 14 sections, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. The search for interstellar objects
  3. Methodology
  4. LSST input data
  5. Boosting the sample count of synthetic ISO tracklets
  6. Digest2
  7. Data preprocessing
  8. Model training and evaluation
  9. Model selection
  10. Evaluation results
  11. Validation on nightly datasets
  12. 1I/'Oumuamua and 2I/Borisov
  13. Discussion
  14. Conclusion

Figures (4)

  • Figure 1: Histogram of absolute magnitudes of synthetic ISOs used in this work.
  • Figure 2: Hammer-Aitoff sky-plane projection of synthetic positions of Solar System objects in downloaded LSST data.
  • Figure 3: Digest2 output values ranked in the top nine our of ten features identified as important for ISO classification.
  • Figure 4: Confusion matrices for GBM (top left), SGD (top right), RF (bottom left), and NN (bottom right).