Optimizing NILC Extractions of the Thermal Sunyaev-Zeldovich Effect with Deep Learning
The Astrophysical Journal(2024)
摘要
All-sky maps of the thermal Sunyaev-Zel'dovich effect (SZ) tend to suffer
from systematic features arising from the component separation techniques used
to extract the signal. In this work, we investigate one of these methods known
as needlet internal linear combination (NILC) and test its performance on
simulated data. We show that NILC estimates are strongly affected by the choice
of the spatial localization parameter (Γ), which controls a
bias-variance trade-off. Typically, NILC extractions assume a fixed value of
Γ over the entire sky, but we show there exists an optimal Γ that
depends on the SZ signal strength and local contamination properties. Then we
calculate the NILC solutions for multiple values of Γ and feed the
results into a neural network to predict the SZ signal. This extraction method,
which we call Deep-NILC, is tested against a set of validation data, including
recovered radial profiles of resolved systems. Our main result is that
Deep-NILC offers significant improvements over choosing fixed values of
Γ.
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关键词
Sunyaev-Zeldovich effect,Astronomy data analysis,Neural networks
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