REVIEW 1 cited by
Learning Reionization History from Quasars with Simulation-Based Inference
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Understanding the entire history of the ionization state of the intergalactic medium (IGM) is at the frontier of astrophysics and cosmology. A promising method to achieve this is by extracting the damping wing signal from the neutral IGM. As hundreds of redshift $z>6$ quasars are observed, we anticipate determining the detailed time evolution of the ionization fraction with unprecedented fidelity. However, traditional approaches to parameter inference are not sufficiently accurate. We assess the performance of a simulation-based inference (SBI) method to infer the neutral fraction of the universe from quasar spectra. The SBI method adeptly exploits the shape information of the damping wing, enabling precise estimations of the neutral fraction $\left<x_{\rm HI}\right>_{\rm v}$ and the wing position $w_p$. Importantly, the SBI framework successfully breaks the degeneracy between these two parameters, offering unbiased estimates of both. This makes the SBI superior to the traditional method using a pseudo-likelihood function. We anticipate that SBI will be essential to determine robustly the ionization history of the Universe through joint inference from the hundreds of high-$z$ spectra we will observe.
Forward citations
Cited by 1 Pith paper
-
Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks
A graph neural network plus simulation-based inference infers subhalo mass and velocity from simulated GD-1 streams with 3 to 11 times tighter mass constraints than the 1D power spectrum, with better-calibrated posteriors.
Discussion (0). Continue with ORCID to comment.