Modeling the local velocity field with SNIa

DOI

We apply statistically rigorous methods of nonparametric risk estimation to the problem of inferring the local peculiar velocity field from nearby Type Ia supernovae (SNIa). We use two nonparametric methods -weighted least squares (WLS) and coefficient unbiased (CU)- both of which employ spherical harmonics to model the field and use the estimated risk to determine at which multipole to truncate the series. We show that if the data are not drawn from a uniform distribution or if there is power beyond the maximum multipole in the regression, a bias is introduced on the coefficients using WLS. CU estimates the coefficients without this bias by including the sampling density making the coefficients more accurate but not necessarily modeling the velocity field more accurately. After applying nonparametric risk estimation to SNIa data, we find that there are not enough data at this time to measure power beyond the dipole. The WLS Local Group bulk flow is moving at 538+/-86km/s toward (l,b)=(258+/-10{deg},36+/-11{deg}) and the CU bulk flow is moving at 446+/-101km/s toward (l,b)=(273+/-11{deg},46+/-8{deg}). We find that the magnitude and direction of these measurements are in agreement with each other and previous results in the literature.

Cone search capability for table J/ApJ/732/65/table1 (SNIa data)

Identifier
DOI http://doi.org/10.26093/cds/vizier.17320065
Source https://dc.g-vo.org/rr/q/lp/custom/CDS.VizieR/J/ApJ/732/65
Related Identifier https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/732/65
Related Identifier http://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/732/65
Metadata Access http://dc.g-vo.org/rr/q/pmh/pubreg.xml?verb=GetRecord&metadataPrefix=oai_b2find&identifier=ivo://CDS.VizieR/J/ApJ/732/65
Provenance
Creator Weyant A.; Wood-Vasey M.; Wasserman L.; Freeman P.
Publisher CDS
Publication Year 2012
Rights https://cds.unistra.fr/vizier-org/licences_vizier.html
OpenAccess true
Contact CDS support team <cds-question(at)unistra.fr>
Representation
Resource Type Dataset; AstroObjects
Discipline Astrophysics and Astronomy; Cosmology; Natural Sciences; Observational Astronomy; Physics; Stellar Astronomy