Constraining Early Dark Energy Models with Power Spectra Emulators

Understanding the evolution of large-scale structures in the Universe is key to decoding the processes that shaped their development from the Universe’s earliest moments to the present day. A possible resolution of the Hubble Tension has been suggested by invoking an Early Dark Energy(EDE) component which introduces a scalar field to the ΛCDM model around Matter Radiation Equality. This modification changes the calibration of the Cosmic Microwave Background(CMB) and Baryon Acoustic Oscillations and EDE parameters can therefore be constrained from observations. In this talk we explore the effects of EDE on large-scale structures by emulating the matter power spectrum. The power spectrum holds important information about the impact of dark energy on the growth of the Universe which also includes the effects of EDE. We have built a Gaussian Process emulator to perform machine learning tasks with a small number of data points with fast and highly accurate results to study the impact of EDE on the linear matter power spectrum without requiring massive computational resources. Using the emulator we perform sensitivity analysis to understand the impact of different parameters on the power spectrum, obtain fisher information to study the relation between different parameters and execute MCMC method for parameter inference. We also compare the emulator predictions with observational data from the CMB, Weak Lensing, Lyman-α forest, and cluster abundance measurements and derive constraints on EDE parameters.