A neural marking scheme trained with contrastive learning tightens constraints on σ8 by 2.9× and Ωm by 1.8× over classical marks at k_max=0.2 h/Mpc while breaking their degeneracy at the Fisher level.
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Coverage tests for simulation-based inference of f_NL can pass while the posteriors are underconfident in the tails and sometimes yield weaker constraints than using power spectrum or bispectrum alone.
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Interpretable Neural Marked Statistics for Cosmological Inference
A neural marking scheme trained with contrastive learning tightens constraints on σ8 by 2.9× and Ωm by 1.8× over classical marks at k_max=0.2 h/Mpc while breaking their degeneracy at the Fisher level.
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Coverage is not enough: Frequentist tests of simulation-based inference for primordial non-Gaussianity
Coverage tests for simulation-based inference of f_NL can pass while the posteriors are underconfident in the tails and sometimes yield weaker constraints than using power spectrum or bispectrum alone.