A neural ODE trained only on LambdaCDM spectra predicts nonlinear matter power spectra to about 4 percent accuracy for smooth w(z) dark energy models, pending stronger validation.
Probing Dark Energy Dynamics from Current and Future Cosmological Observations
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We report the constraints on the dark energy equation-of-state w(z) using the latest 'Constitution' SNe sample combined with the WMAP5 and SDSS data. Based on the localized principal component analysis and the model selection criteria, we find that the LCDM model is generally consistent with the current data, yet there exists weak hint of the possible dynamics of dark energy. In particular, a model predicting w(z)<-1 at z\in[0.25,0.5) and w(z)>-1 at z\in[0.5,0.75), which means that w(z) crosses -1 in the range of z\in[0.25,0.75), is mildly favored at 95% confidence level. Given the best fit model for current data as a fiducial model, we make future forecast from the joint data sets of JDEM, Planck and LSST, and we find that the future surveys can reduce the error bars on the w bins by roughly a factor of 10 for a 5-w-bin model.
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Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
A neural ODE trained only on LambdaCDM spectra predicts nonlinear matter power spectra to about 4 percent accuracy for smooth w(z) dark energy models, pending stronger validation.