Karen Nowogrodzki
Microlensing with LSST: simulation, classification, and follow-up observations from CASLEO
Among the transient events that LSST will observe is microlensing, a phenomenon where a massive object passes along the line of sight between an observer and a light source, bending the light and causing the image to brighten. The main objective of this work
is to assess the detection and characterization of microlensing events and constrain the properties of the lensing objects, using data from the LSST at the Vera C. Rubin Observatory. The ongoing research encompasses three interconnected projects that will
help us prepare for LSST data: 1) a microlensing event classifier using MicroLIA to assess the detection of such events with the ELAsTiCC dataset (simulated LSST-like light curves), 2) a pipeline for inserting and extracting light curves in LSST simulations (DP0), enabling the evaluation of light curve reconstruction and the constraint of parameters, and 3) follow-up observations with the HSH telescope at CASLEO to contribute to the global characterization of microlensing events, in collaboration with the OMEGA international network. This project aims to both develop the end-to-end microlensing process, from target selection to light curve assembly and fitting, and to prepare for real-time follow-up of microlensing events detected by the LSST. The extracted light curves from DP0 will be used to train and refine MicroLIA’s classifier while also providing hands-on experience with the Rubin pipeline.
Meanwhile, ongoing HSH observations of microlensing events will help optimize the workflow for following up on LSST-detected events
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Microlensing with LSST: simulation, classification, and follow-up observations from CASLEO
