Analytic compression of EFT parameters for Lyα forest P1D via Fisher matrix and linearization allows efficient marginalization, saturating constraints with linear bias plus five effective terms and forecasting 10% and 2% precision on Δ²_p and n_p at k_p=0.7 Mpc^{-1}.
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Varying AGN feedback parameters shows that jet feedback from the most massive black holes suppresses the Lyman-alpha forest P1D unless it reduces the number of such black holes.
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Analytic compression of the effective field theory of the Lyman-alpha forest
Analytic compression of EFT parameters for Lyα forest P1D via Fisher matrix and linearization allows efficient marginalization, saturating constraints with linear bias plus five effective terms and forecasting 10% and 2% precision on Δ²_p and n_p at k_p=0.7 Mpc^{-1}.
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Exploring the impact of AGN feedback model variations on the Lyman-$\alpha$ Forest Flux Power Spectrum
Varying AGN feedback parameters shows that jet feedback from the most massive black holes suppresses the Lyman-alpha forest P1D unless it reduces the number of such black holes.