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Sparse identification of variable star dynamics

Mario Pasquato

UdeM/University of Padua

Variable stars play a crucial role as standard candles and provide valuable insights into stellar physics. A full description of these stars typically requires expensive hydro-simulations of stellar interiors. I instead applied a data-driven technique - Sparse Identification of Dynamical Systems (SINDY; Brunton 2016) - to automatically model observed light curves from the Catalina Sky Survey as a low-dimension differential dynamical system. RRc and Delta Scuti variables are found to be well modeled by a simple non-linear oscillator, which can be solved analytically. I elaborate on the limits and the implications of this approach as a complementary way to model variables.

Date: Thursday, 24 February 2022
Time: 11:30
Where: Université de Montréal

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