A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.
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Cross-correlation of CLAMATO Lyman-alpha forest with COSMOS galaxies yields stellar-mass-dependent biases of approximately 2.1, 3.2, and 3.8, corresponding to halo masses of log M_h ~ 10.5, 11.7, and 12.1 from Bolshoi-Planck mocks, with hints of enhanced low-mass star formation.
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A multi-eigenbasis approach to covariance matrix denoising for cosmological inference
A multi-eigenbasis denoising technique using mock reference and classifier eigenbases is introduced and shown on held-out mocks to outperform smoothing for covariance estimation in Lyα forest analyses.
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Cross-correlations between the CLAMATO Lyman-alpha forest and galaxies within the COSMOS field
Cross-correlation of CLAMATO Lyman-alpha forest with COSMOS galaxies yields stellar-mass-dependent biases of approximately 2.1, 3.2, and 3.8, corresponding to halo masses of log M_h ~ 10.5, 11.7, and 12.1 from Bolshoi-Planck mocks, with hints of enhanced low-mass star formation.