Genetic algorithm reconstructs single-field inflationary models with features in the scalar power spectrum that fit Planck 2018 CMB data better by Δχ² ≲ -10 and suggest alternative background parameters.
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Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
A model-independent method fits blended supernova light curves as superpositions of two time-delayed components and finds only one candidate above a 12-day delay threshold in 445 ZTF Type Ia supernovae, for a 0.22% false positive rate.
Reconstruction of EFT background functions from cosmic chronometer Hubble data allows model-independent tests of dark energy evolution in scalar-tensor theories.
citing papers explorer
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Reconstructing inflationary features on large scales using genetic algorithm
Genetic algorithm reconstructs single-field inflationary models with features in the scalar power spectrum that fit Planck 2018 CMB data better by Δχ² ≲ -10 and suggest alternative background parameters.
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Model-independent calibration of Gamma-Ray Bursts with neural networks
Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
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Finding Strongly Lensed Supernovae from Blended Light Curves
A model-independent method fits blended supernova light curves as superpositions of two time-delayed components and finds only one candidate above a 12-day delay threshold in 445 ZTF Type Ia supernovae, for a 0.22% false positive rate.
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EFT of Dark Energy with Cosmic Chronometers: Reconstructing Background EFT Functions
Reconstruction of EFT background functions from cosmic chronometer Hubble data allows model-independent tests of dark energy evolution in scalar-tensor theories.