How to Accurately Map the Milky Way with a Billion Sources: A Journey into the Gaia-verse

Accurately modelling the phase-space distribution of Milky Way sources relies on two vital components: unbiased distance estimators and survey selection functions. Without either, models are susceptible to significant systematic uncertainties. My case study of the tilt of the local velocity ellipsoid demonstrates this. Well-constructed distances for the Gaia DR2 RVS sample return a velocity ellipsoid broadly consistent with spherical alignment. Using the reciprocal parallax distance estimator significantly alters the conclusions. I produce selection functions for catalogues needed to model the phase-space structure of the Galaxy. My spectrograph selection function method is generalisable to many multi- fibre observatories. I supplement this with tools to combine selection functions for unions of samples and transform from observable to intrinsic coordinates. I produce selection functions for Gaia catalogues including astrometry and RVS samples. My model fits the complex behaviour of the Gaia spacecraft impressively well. To enhance our understanding of the published Gaia astrometry, I introduce the Astrometric Spread Function, the expected covariance for a simple point source in Gaia. This reproduces the mean behaviour of published observations to degree level resolution. This is brought together to model the vertical distribution of Milky Way sources. Systematics are minimized by marginalising over parallax uncertainties and regulating the likelihood with Gaia EDR3 selection functions. The veracity of the method is demonstrated on a Gaia-like mock population. Applying to Gaia EDR3, I infer a north-south asymmetry weaker than previously reported and provide updated parameter values for the vertical scale heights of the thin and thick disks, the halo power-law exponent, local stellar mass density and surface density of the Milky Way. My thesis demonstrates the potential of Gaia when distances are well modelled and incompleteness is accounted for. My tools will be invaluable for answering further questions about the Milky Way using future Gaia data.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2021

Author dc:creator

• Everall, Andrew

Advisors dc:contributor.advisor

• Evans, Neil Wyn
• Belokurov, Vasily

• Data analysis
• Statistics
• Galaxy
• Stellar content
• Milky Way
• Instrumentation and Methods for Astrophysics

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a8e248f5-ca22-4f32-9abd-d21c6b74e5ce/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

Author Identifier

0000-0002-0038-9584

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/330642