Speaker
Description
The Epoch of Reionization Spectrometer (EoR-Spec) instrument on the Fred Young Submillimeter Telescope (FYST) will undertake a Line Intensity Mapping (LIM) survey targeting the [CII] line across redshifts 3.5 − 8.0. The observed frequency range for EoR-Spec, 210 to 420 GHz, is substantially influenced by atmospheric emissions that affect LIM power spectrum measurements. One of the challenges is to efficiently separate the cosmological signal from the correlated atmospheric noise. Traditional data cleaning techniques, including various filtering methods and Principal Component Analysis (PCA), are currently employed to mitigate these effects.
Machine Learning (ML) methods such as Convolutional Neural Networks (CNNs) and Gaussian Process Regression (GPR) can assist in tackling this inverse problem. In addition, incorporating outlier, glitch, and anomaly detection into the data reduction pipeline could strengthen the handling of systematics in detector timestreams.
