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Linking Large-Scale Structure and Peculiar Velocities in the Low-Redshift Universe

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Linking Large-Scale Structure and Peculiar Velocities in the Low-Redshift Universe Linking Large-Scale Structure and Peculiar Velocities in the Low-Redshift Universe Caitlin Adams Presented in fulfillment of the requirements of the degree of Doctor of Philosophy January 2019 Faculty of Science, Engineering and Technology Swinburne University i Abstract The standard cosmological model has been thoroughly tested over the past two decades, but we still remain in the dark about the underlying cause of accelerating expansion. Interestingly, the two primary classes of viable models that explain this behaviour, dark energy and modified gravity, can be distinguished by measurements of the growth rate of structure in the low-redshift universe. Given the smaller cosmological volume probed by low-redshift surveys in contrast to high-redshift surveys, it is critical that methods are de- veloped to extract as much information about the growth rate as possible from low-redshift data. In this thesis, we present a significant contribution to low-redshift cosmology through the development and application of a method that utilises the information provided by redshift-space distortions, peculiar velocities and their cross-correlation. This method is the first of its kind to self-consistently model all three sources of information and use them to simultaneously constrain the growth rate of structure. Throughout the thesis, we con- sistently show that the inclusion of the cross-correlation allows higher-precision constraints from low-redshift surveys than using redshift-space distortions or peculiar velocities alone, which is due to their highly complementary nature. To begin, we develop the theoretical models that underpin all of the analysis presented in this thesis. Chapter 2 focusses on the construction of fully self-consistent models for the galaxy overdensity auto-correlation function, peculiar velocity auto-correlation function, and the cross-correlation function. These can be used to construct a covariance matrix that is a function of the growth rate fσ8, allowing this parameter to be constrained through a maximum likelihood approach. Throughout this chapter, we take care to model relevant observational effects, making the model highly physically accurate. In Chapter 3, we present the first application of our method to a subsample of the 6-degree Field Galaxy Survey (6dFGS). This survey is one of the best low-redshift sam- ples, especially since it provides the largest single sample of peculiar velocities to date. In this first analysis, we focus on constraining the growth rate from the peculiar velocity information, which helps to highlight the benefits of including cross-correlation informa- +0:087 tion. In the absence of the cross-covariance, we find fσ8 = 0:461 0:079. We find that − the statistical uncertainty is reduced by 20% when including the cross-covariance, giv- +0:067 ing fσ8 = 0:424 0:064. Our constraint is consistent with other results from 6dFGS, and − makes a significant addition to the literature on the use of multiple cosmological probes to improve cosmological parameter constraints. In addition to our constraints, we also find 1 evidence of the cross-correlation signal in the 6dFGS data on scales up to 50 h− Mpc. ii In Chapter 4, we extend the analysis performed in Chapter 3 by implementing a complete model for redshift-space distortions and by using a larger galaxy redshift sample from 6dFGS. The inclusion of redshift-space distortions adds additional information on the growth rate of structure from the galaxy distribution, which is complementary to the information from peculiar velocities. We also conduct thorough tests of the underlying model assumptions by applying our method to sophisticated mock catalogues of 6dFGS. By identifying the key systematic effects of our model, we provide a systematic uncertainty for our growth rate constraint in addition to the statistical uncertainty. Using our complete covariance model, we find fσ = 0:384 0:052(stat) 0:061(sys), which has a 64% reduction 8 in statistical uncertainty compared to only using the galaxy overdensity information, and a 50% reduction in statistical uncertainty compared to only using the peculiar velocity information. We find that the improved modelling and larger galaxy redshift sample leads to an 18% reduction in the statistical uncertainty of our constraint from Chapter 3, demonstrating the power of our improved model. Finally, in Chapter 5, we consider our method in the context of the Taipan Galaxy Survey, which represents the next generation in low-redshift large-scale structure surveys. Taipan is set to significantly extend 6dFGS, measuring up to 2 million galaxy redshifts and 50,000 peculiar velocities over the next 4 years. We apply our method to mock catalogues for the survey and run an equivalent Fisher matrix forecast, allowing us to make a direct comparison between these two methods. We find that the Fisher matrix typically underestimates the uncertainties obtained with our method: the uncertainty in the growth rate constraint from our covariance matrix method is 1.55 times that of the forecast. We use this difference to estimate the percentage constraints that could be obtained by applying our method to Taipan, based on existing forecasts for the survey. We find that our method could yield a 4.2% constraint on the growth rate of structure by the end of the survey, which is in line with one of Taipan's key cosmological goals of a growth rate constraint of less than 5%. This demonstrates the success of our method in harnessing cosmological information about the growth rate of structure at low redshifts and highlights the value of our approach for the next generation of low-redshift observations. iii iv Acknowledgements I count myself incredibly fortunate to have had the unconditional support of so many people while completing my PhD. Firstly, I'd like to extend a huge thank you to Chris Blake, my primary supervisor. I am grateful to you for always being excited about the science we were working on; for providing such a supportive environment, which helped me flourish as a researcher; for always being kind when I was hard on myself; and for always encouraging me, no matter how ambitious I got (and, of course, for helping me handle things when it turned out to be a little too ambitious). I feel so privileged to have worked with you. I'd also like to thank David Parkinson for his supporting supervision. Thank you for all of your help, feedback and advice over the years { I treasured all of it. You are largely responsible for my love of cosmology and Bayesian statistics, for which I am incredibly grateful. Finally, thank you for always being one of my strongest advocates; knowing how highly you think of me has always provided me great comfort and confidence. During my PhD, I was lucky to be mentored and supported by Emma Ryan-Weber and Cath Trott. Thank you to both of you { your advice and encouragement at various critical junctures made so much difference. I am also extremely fortunate to have had the support of an entire research centre throughout my PhD. CAS has been such a wonderful place to work, and I'm incredibly grateful to all its staff and students for making my experience such a positive one. I would particularly like to thank Andrew Johnson, Adam Stevens, Emily Petroff, Rebecca Allen, Steph Pointon, Geoff Bryan, Michelle Cluver, Ned Taylor, Rossana Ruggeri, Sara Webb, Simon Goode, Jennifer Piscionere and Manodeep Sinha. A special thanks goes to my two closest friends, Sabine Bellstedt and Leonie Chevalier. Both of you have been wonderful friends to me and I am so lucky to have met you. Thank you for all the cups of tea, board games and chats, without which I surely would have gone mildly mad. Throughout my PhD, I have been incredibly grateful for the financial support I received from both the Australian Government Research Training Program and the ARC Centre of Excellence for All-sky Astrophysics (CAASTRO). I would also like to thank Athol Whitten, Simone Stuckey and Michael Smith for taking me on as an intern, and then employee, at Mezo Research. It was such a joy to work with all of you. Thank you for always being so respectful of me, especially when I needed some time away from work to finish writing my thesis. v I'd like to express a huge amount of gratitude to my parents, Liz and Lewis Adams, for all of their love and support. Your encouragement and advice has helped me so much throughout the ups and downs of my PhD. Finally, my thanks go to my wonderful partner, Jacob Shearer. Thank you for all that you have done for me during the past few years: for making me laugh whenever I despaired; for always celebrating my milestones and achievements; and for encouraging me to keep at it, even when it all felt insurmountable. Your constant companionship has meant (and continues to mean) the world to me. vi vii Declaration The work presented in this thesis has been carried out in the Centre for Astrophysics & Supercomputing at Swinburne University of Technology between 2015 and 2019. This thesis contains no material that has been accepted for the award of any other degree or diploma. To the best of my knowledge, this thesis contains no material previously published or written by another author, except where due reference is made in the text of the thesis. The content of the chapters listed below has appeared in refereed journals. Minor alterations have been made to the published papers in order to maintain argument continuity and consistency of spelling and style. The majority of Chapter 2 (all aside from Section 2.3.4) and all of Chapter 3 has • been published as Improving constraints on the growth rate of structure by modelling the density-velocity cross-correlation in the 6dF Galaxy Survey Adams, C., & Blake, C.
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