10 papers
A data-driven prediction for the primordial deuterium abundance
Timothy Launders, Cara Giovanetti, Hongwan Liu
We predict the primordial deuterium abundance using a novel, fully data-driven approach, where we use Gaussian process regression to fit experimental nuclear reaction data for ,…
ABCMB: A Python+JAX Package for the Cosmic Microwave Background Power Spectrum
Zilu Zhou, Cara Giovanetti, Hongwan Liu
We present ABCMB, a differentiable Einstein-Boltzmann solver for the cosmic microwave background (CMB). ABCMB is a complete code capturing important effects to linear order in $Î{…
Constraining Dark Acoustic Oscillations with the High-Redshift UV Luminosity Function
Jared Barron, David Curtin, Hongwan Liu +2
Dark acoustic oscillations (DAOs) in the matter power spectrum can arise in many different dark sector models, and can imprint on a variety of cosmological observables. In this wor…
Dynamical Heating from Dark Compact Objects and Axion Minihalos: Implications for the 21-cm Signal
Badal Bhalla, Aurora Ireland, Hongwan Liu +2
The temperature of baryons at the end of the cosmic dark ages can be inferred from observations of the 21-cm hyperfine transition in neutral hydrogen. Any energy injection from the…
LINX: A Fast, Differentiable, and Extensible Big Bang Nucleosynthesis Package
Cara Giovanetti, Mariangela Lisanti, Hongwan Liu +2
We introduce LINX (Light Isotope Nucleosynthesis with JAX), a new differentiable public Big Bang Nucleosynthesis (BBN) code designed for fast parameter estimation. By leveraging JA…
Cosmological Parameter Estimation with a Joint-Likelihood Analysis of the Cosmic Microwave Background and Big Bang Nucleosynthesis
Cara Giovanetti, Mariangela Lisanti, Hongwan Liu +2
We present the first joint-likelihood analysis of Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) data. Bayesian inference is performed on the baryon abundance…