Machine Learning Techniques for Stellar Light Curve Classification

Abstract

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (~ 2 - 4%) and good accuracy (~ 75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger datasets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

Accepted to The Astronomical Journal

References in corpus (7)

• Deep Learning in Neural Networks: An Overview
• A recurrent neural network for classification of unevenly sampled variable stars
• Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging
• Transit Shapes and Self Organising Maps as a Tool for Ranking Planetary Candidates: Application to Kepler and K2
• De-Trending Time Series for Astronomical Variability Surveys

Cited by in corpus (20)

• Wotan: Comprehensive time-series de-trending in Python
• RAPID: Early Classification of Explosive Transients using Deep Learning
• Detecting Exoplanet Transits through Machine Learning Techniques with Convolutional Neural Networks
• How to Find Variable Active Galactic Nuclei with Machine Learning
• Classification of Fermi-LAT sources with deep learning using energy and time spectra
• LSST Cadence Strategy Evaluations for AGN Time-series Data in Wide-Fast-Deep Field
• Detrending Exoplanetary Transit Light Curves with Long Short-Term Memory Networks
• Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection
• Astroconformer: The Prospects of Analyzing Stellar Light Curves with Transformer-Based Deep Learning Models
• Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework
• Variable Star Classification Using Multi-View Metric Learning
• A Detection Metric Designed for O'Connell Effect Eclipsing Binaries
• Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification
• Stellar Spectra Models Classification and Parameter Estimation Using Machine Learning Algorithms
• Light Curve Classification with DistClassiPy: a new distance-based classifier
• PIPS, an advanced platform for period detection in time series -- I. Fourier-likelihood periodogram and application to RR Lyrae Stars
• Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
• Sparse logistic regression for RR Lyrae vs binaries classification
• Exploring Late Stellar Evolution in the Era of Large Surveys: Machine Learning Prospects for Hot Subdwarfs and White Dwarfs
• Advanced Astroinformatics for Variable Star Classification