Publications

2024

1. The ZTF Source Classification Project: III. A Catalog of Variable Sources

   Brian F. Healy, Michael W. Coughlin, Ashish A. Mahabal, and 22 more authors

   2024

   Abs

   The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost machine learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open-source and adaptable to future time-domain surveys.

2023

1. Deep Gaussian Processes for Air Quality Inference

   Aadesh Desai, Eshan Gujarathi, Saagar Parikh, and 3 more authors

   In Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD), 2023

   Abs

   Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm, and show that it performs comparably to the state-of-the-art models.