Studying the Peculiar Velocity Bulk Flow in a Sparse Survey of Type-Ia SNe
Ben Rathaus, Ely D. Kovetz, Nissan Itzhaki

TL;DR
This paper investigates the bulk flow of peculiar velocities using a sparse supernova dataset, introducing an iterative method to accurately estimate flow parameters and suggesting a possible cosmological origin for the observed flow.
Contribution
It presents a new iterative method to estimate bulk flow from sparse supernova data, accounting for data sparsity and reducing bias in the analysis.
Findings
Bulk flow amplitude is marginally consistent with LCDM expectations.
The direction's scatter is significantly lower than random simulations, indicating a potential cosmological signal.
The method improves robustness of bulk flow estimation in sparse datasets.
Abstract
Studies of the peculiar velocity bulk flow based on different tools and datasets have been consistent so far in their estimation of the direction of the flow, which also happens to lie in close proximity to several features identified in the cosmic microwave background, providing motivation to use new compilations of type-Ia supernovae measurements to pinpoint it with better accuracy and up to higher redshift. Unfortunately, the peculiar velocity field estimated from the most recent Union2.1 compilation suffers from large individual errors, poor sky coverage and low redshift-volume density. We show that as a result, any naive attempt to calculate the best-fit bulk flow and its significance will be severely biased. Instead, we introduce an iterative method which calculates the amplitude and the scatter of the direction of the best-fit bulk flow as deviants are successively removed and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
