An Unbiased Method of Modeling the Local Peculiar Velocity Field with Type Ia Supernovae

Details An Unbiased Method of Modeling the Local Peculiar Velocity Field with Type Ia Supernovae An Unbiased Method of Modeling the Local Peculiar Velocity Field with Type Ia Supernovae

May 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21642811w&link_type=abstract

American Astronomical Society, AAS Meeting #216, #428.11

Mathematics

Logic

Scientific paper

Understanding the nature of dark energy is crucial to our knowledge of fundamental physics. Its discovery through Type Ia Supernovae (SNeIa) demonstrates that the universe is presently accelerating in its expansion. Dark matter structure in our universe perturb the motions of individual galaxies from the overall smooth Hubble expansion, which can create large fluctuations in redshift for z < 0.1, adding uncertainty to the Hubble Diagram. Averaging over many peculiar velocity measurements reduces scatter caused by random motions but not coherent large scale motions. By modeling the local flow field we can limit the effects of peculiar motions on derived cosmological parameters while simultaneously measuring local fluctuations in the dark matter density. Using a simulated data set and peculiar velocities of SNeIa from the recent literature, we investigate the bias introduced when modeling the flow field due to the non-uniform sampling of data caused by galactic foregrounds etc. We use risk estimation as a means of determining where to truncate any particular basis expansion of a flow field. We show that a nonparametric analysis which accounts for the sampling density can remove this bias, more accurately modeling the entire field, but find that a weighted least squares fit is more accurate where the data is well sampled. Finally, we apply these methods to SNIa data and present the latest measurement of the dipole as well constraints on the total power beyond the dipole.

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