Tackling LSST's sparse multiband light curves with Neural Processes
Year: 2025
Type: article
Abstract: The sparse multiband light curves from LSST are crucial for informing follow-up observations and enabling the discovery of novel astronomical events. However, a challenge is the analysis of these light curves, with their long observational gaps and differing cadences over filters. While Gaussian Process (GP) regression is a common method for interpolation, it requires specifying a kernel a priori, making a strong assumption about the object's temporal nature that may not hold for diverse transients. However, the sheer volume of LSST light curves enables a data-driven approach. Neural Processes (NPs) are a family of models that learn distributions over functions, combining the probabilistic framework of GPs with the scalability and flexibility of neural networks. In this talk, I will discuss my ongoing work on the neural process family of models for light curve interpolation and its effect on downstream tasks like feature extraction, classification and anomaly detection.
Author: Chaini, Siddharth
Institution: University of Delaware
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7085517945
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