Large-scale modes decouple from enhanced small-scale perturbations in single-field inflation for PBH production, rendering one-loop back-reaction unobservable under scale separation and adiabaticity.
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One-loop corrections in a hybrid inflation model with right-handed neutrinos produce a red-tilted spectrum and suppressed tensor-to-scalar ratio consistent with current CMB data, while machine learning identifies roughly 15% of the parameter space as viable.
Derives recurrence relations for all-loop leading log quantum corrections and RG equations for the effective potential in SO(N) scalar theories in curved spacetime, applied to power-like potentials in the Jordan frame for inflation.
Hybrid PCA-neural network classifiers trained on full CMB maps, interpreted via SHAP to identify spatial signatures distinguishing LambdaCDM from primordial feature models.
citing papers explorer
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Primordial black holes from inflation: on the decoupling between large and small scales
Large-scale modes decouple from enhanced small-scale perturbations in single-field inflation for PBH production, rendering one-loop back-reaction unobservable under scale separation and adiabaticity.
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Radiatively Corrected Hybrid Inflation: Parameter Scans and Machine Learning with ACT and Future CMB Experiments
One-loop corrections in a hybrid inflation model with right-handed neutrinos produce a red-tilted spectrum and suppressed tensor-to-scalar ratio consistent with current CMB data, while machine learning identifies roughly 15% of the parameter space as viable.
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Effective potential in $SO(N)$ symmetric scalar field theories in curved spacetime
Derives recurrence relations for all-loop leading log quantum corrections and RG equations for the effective potential in SO(N) scalar theories in curved spacetime, applied to power-like potentials in the Jordan frame for inflation.
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Explaining Neural Networks on the Sky: Machine Learning Interpretability for Cosmic Microwave Background Maps
Hybrid PCA-neural network classifiers trained on full CMB maps, interpreted via SHAP to identify spatial signatures distinguishing LambdaCDM from primordial feature models.