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Companion detections through spectra and ML

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Academic year: 2021

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Companion detections

through spectra and ML

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Image (object) Spectra of every pixel ML blackbox Detailed characterisation Probability map

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Deep learning 92.3% Cross-correlation 66.6% Flux~1010 Flux~102 Flux~10-8

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ML data preparation

Initial spectrum Sum spaxels Ref spectrum Divide by ref Crop

Split into two sets of H_0 and H_1

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ML data preparation

Initial spectrum Sum spaxels Ref spectrum Divide by ref Crop

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Next steps

❖ ‘Force’ the network to learn absorption features.

❖ Create a metric to compare ML and cross-corr

❖ Compare the ‘sensitivity’ of ML and cross Corr

❖ Compare ‘specificity’ (fake companions in progress) of ML and cross

Corr

❖ Try this with PDS 70 and potentially explain why it may or may not

Références

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