Astrophysics in Simulacrum The Epistemological Role of Computer Simulation in Dark Matter Studies Katia Stephanie Wilson ORCID iD: 0000-0002-3989-3032 Doctor of Philosophy December 2016 History and Philosophy of Science School of Historical and Philosophical Studies The University of Melbourne Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy. Abstract Computer simulation is a technique that is widely used across many scientific disciplines. In areas like astrophysics, the day-to-day practice of doing science is in fact dominated by simulation. In the face of simulation’s ubiquity and usefulness, this thesis asks the question: how can the virtual teach us about the real? The first part of the thesis introduces and historicises the main philosophical issues that surround simulation in science. The historical and philosophical literature on simulation in science is chiefly concerned with the relationships between simulation, theory, and experiment, though there is little agreement on specifics. The relationships between simulation and theory, and simulation and experiment, are complex, and result in what I describe as theory crafting. This iterative and interactive way of generating knowledge involves using simulation to draw together multiple sources of evidence. In order to more clearly tease out how simulation fits in with existing ways of knowledge making, the journal Simulation is used to provide a historicised look at the main themes of simulation epistemology from 1960 to the present. The shifts within these themes helps break down simulation’s various associations with models and experiment, and also provide a partial answer to how the simulation technique became established as a legitimate tool for producing knowledge. The second part of the thesis consists of two physical case studies drawn from modern astrophysics that were discovered with and solved using simulation. The bar instability problem, and the case of the missing satellites, provide two different perspectives of the role of simulation in solving problems, particularly in interaction with observation. In the first case study, simulation is shown to take on roles that are traditionally the purview of theory and experiment, demonstrating a highly fruitful flexibility. This flexibility, as the second case study shows, gives simulation the capacity to construct self-sufficient universes that can be the subject of observation and measurement. In such a manner simulation draws out the empirical content of theory, resulting in data that can be considered epistemologically on-par with observational or experimental data. It through the superposition of the simulated virtual worlds with our own that new knowledge emerges, the outcome of a complex negotiation process between the evidence, theory, model, and the simulation itself that is transformative of the categories involved. ii Declaration This is to certify that: (i) the thesis comprises only my original work towards the PhD (ii) due acknowledgement has been made in the text to all other material used; and (iii) the thesis is fewer than 100,000 words in length, exclusive of tables, maps, bibliographies, and appendices Signed, Katia Stephanie Wilson, 5 December 2016 iii Acknowledgments I would like to thank my principal supervisor, Dr. Kristian Camilleri, for his truly invaluable advice over the course of my graduate studies. This thesis has benefitted greatly from his critical eye, searching questions, and patient re-reading of drafts. I also extend my gratitude to my secondary supervisor, Dr. Gerhard Wiesenfeldt, whose insightful comments on later drafts helped in clarifying both my arguments and my writing. This thesis would not have been possible without the support of my family. My parents, Rita and Charles, have provided an endless well of encouragement and strength. I am deeply grateful for the time and emotional support they have given me. I also extend my sincerest thanks to my brother Richard for his optimism and sympathetic ear, and to my sister-in-law Kathryn for many commiserating conversations with a fellow PhD candidate. iv Table of Contents Introduction 1 1 The Philosophy of Simulation 1.1 Introduction 5 1.2 Materiality and Experiment 6 1.3 Models and Simulation 12 1.3.1 The Simulation Model 14 1.3.2 Semi-Analytic Models 17 1.3.3 Theory Crafting 19 1.4 Theory and Experiment, and Simulation 25 1.5 Philosophical Novelty 28 2 Man and Machine: Themes in Simulation from 1963 to 2016 2.1 Introduction 33 2.2 The Physical Computer 35 2.3 Models and the Modeller 41 2.4 Man and Machine 47 2.5 Selling Simulation 57 2.6 Supersimulations and Seduction 64 2.6.1 Simulating as Science 64 2.6.2 Seduction 70 2.7 Characterising Simulation 72 2.7.1 From Traditional to Sui Generis 73 2.7.2 The Value of a Historicised Perspective 75 3 The Simulacrum of an Experimental Science: The Bar Instability and Dark Halos 3.1 Introduction 79 3.2 Miller and the Metaphor of Experiment 80 3.3 A Local Instability 87 3.4 Toomre’s Criterion and The Narrative of Theory and Experiment 89 3.5 The Bar Instability 95 3.6 The Ostriker-Peebles Conjecture 98 3.7 The Problem with Halos 102 3.8 Resolving the Instability 108 v 3.9 Persuasive Pictures: Simulation and Observation 115 3.10 Reconstructing Astrophysics as an Experimental Science 123 4 The Case of the Missing Satellites: Technoscience and Astrophysics 4.1 Introduction: Technoscience in Astrophysics? 125 4.2 The Problem 128 4.3 Possible Solutions 132 4.4 ‘Future Observations’ 135 4.5 The ‘Solution’ 143 4.5.1 Hydrodynamic Simulations 145 4.6 Technical Negotiation 148 4.7 Simulation as an Epistemic Target 154 4.7.1 Simulation and Empirical Systems 161 4.7.2 Fear of Seduction (Again) 165 4.8 Conclusion 168 5 Generating Knowledge with Simulation 5.1 Introduction 171 5.2 The Empirical Content of Theory 172 5.3 Visualisation and Simulated Observations 177 5.4 Returning to the Issue of Philosophical Novelty 180 5.4.1 Styles of Reasoning 180 5.4.2 Cultures 182 5.5 Excursus: The (De)contextualisation of Simulation 184 5.6 Conclusion 187 vi List of Figures Figure 1: Number of publications in astronomy/astrophysics per year referencing the phrase ‘semi-analytic’ in the topic field. 18 Figure 2: “To provide a proper framework to review the credibility of a simulation, it is convenient to divide the simulation environment into three basic elements as depicted.” 45 Figure 3: “Simulation of Mechanical Systems adds an Important Dimension to Instruction in Engineering Design.” 50 Figure 4: Systematic moves of many particles in the game described by Miller and Prendergast. 81 Figure 5: Toomre’s analysis showed a threshold velocity dispersion is required. 88 Figure 6: An illustration of the computer model used by Hockney and Hohl showing the N by N array of cells used to calculate the gravitational potential. 90 Figure 7: “Break up of an initially uniformly rotating and balanced cold disc of 50,000 stars.” 91 Figure 8: Hohl’s simulation demonstrates the formation of an unstable bar-like structure with two arms. 96 Figure 9: “Effect of the halo on the evolution of the model galaxy.” 101 Figure 10: “Status of the wave theories. As yet, only the thickened portions of this schematic route seem reasonably secure.” 106 Figure 11: Evolution of a leading density wave in Zang’s simulation, showing the growth of spiral pattern through swing amplification. 108 Figure 12: “Superposition of all 21 Sc rotation curves.” 110 Figure 13: The total rotation curve for the galactic model is the top curve. The other curves show the contributions to the total curve from the four mass components. 112 Figure 14: Hohl compares the simulated image (left), and the atlas photograph (right) by placing them side by side. 119 Figure 15: Distribution of dark matter halos in the KKVP simulation. 131 Figure 16: Luminosity function as observed (red dot-dashed lower curve), as corrected for sky coverage (green dashed middle curve), and will all corrections included (blue solid upper curve). 138 Figure 17: The effect of different physical processes on the luminosity function of satellites in one of the simulated halos. 145 Figure 18: Distribution of dark matter (left) and stars (right) of the same region in a Local Group hydrodynamic simulation. 147 Figure 19: Left: Hubble Space Telescope (HST) Ultra Deep Field (UDF) image, an observation. Right: HST mock observation from Illustris, a simulation. 156 Figure 20: Sample galaxies from Illustris arranged by standard morphological classification. 159 For copyright reasons, figures 2 to 20 have been removed from the open access version of this thesis. vii viii Introduction The ubiquity of computer simulation across the entirety of modern science has in the last decade or so drawn the attention of historians and philosophers of science.1 Case studies on simulation in nuclear physics, particle physics, climate science, nanotechnology, evolutionary biology, epidemiology, economics, and social science have emerged, accompanied by a concerted effort to make philosophical sense of simulation in science. Though often mentioned in these studies as an example of a simulation-dominated discipline, computer simulations in astrophysics have received little direct attention. Yet astrophysics provides us with a unique perspective on how simulations are used to produce knowledge. Firstly, astrophysics is a discipline without experiments, as the targets of study are too far away (in time and space) or too unwieldy to submit themselves to manipulation or intervention. Secondly, direct observation of many astrophysical objects can be difficult, and the resultant data is often ambiguous or subject to uncertainties. Dark matter in particular is resistant to direct observation; it literally cannot, even in principle, be observed. Dark matter studies in astrophysics therefore provide us with a situation where experiment is not available to provide empirical evidence, and observation’s capacity for the same has been severely curtailed.