On Probability and Cosmology: Inference Beyond Data?

Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge. A central issue is what defines a 'good' model. When addressing global properties of the Universe or its initial state this becomes a particularly pressing issue. How to assess the probability of the Universe as a whole is empirically ambiguous, since we can examine only part of a single realisation of the system under investigation: at some point, data will run out. We review the basics of applying Bayesian statistical explanation to the Universe as a whole. We argue that a conventional Bayesian approach to model inference generally fails in such circumstances, and cannot resolve, e.g., the so-called 'measure problem' in inflationary cosmology. Implicit and non-empirical valuations inevitably enter model assessment in these cases. This undermines the possibility to perform Bayesian model comparison. One must therefore either stay silent, or pursue a more general form of systematic and rational model assessment. We outline a generalised axiological Bayesian model inference framework, based on mathematical lattices. This extends inference based on empirical data (evidence) to additionally consider the properties of model structure (elegance) and model possibility space (beneficence). We propose this as a natural and theoretically well-motivated framework for introducing an explicit, rational approach to theoretical model prejudice and inference beyond data

Environmental dependence of X-ray and optical properties of galaxy clusters

Galaxy clusters are widely used to constrain cosmological parameters through their properties, such as masses, luminosity, and temperature distributions. One should take into account all kind of biases that could affect these analyses in order to obtain reliable constraints. In this work, we study the difference in the properties of clusters residing in different large-scale environments, defined by their position within or outside of voids, and the density of their surrounding space. We use both observational and simulation cluster and void catalogues, i.e. XMM Cluster Survey (XCS) and redMaPPer clusters, Baryon Oscillation Spectroscopic Survey (BOSS) voids, and Magneticum simulations. We devise two different environmental proxies for the clusters and study their redshift, richness, mass, X-ray luminosity, and temperature distributions, as well as some properties of their galaxy populations. We use the Kolmogorov–Smirnov two-sample test to discover that richer and more massive clusters are more prevalent in overdense regions and outside of voids. We also find that clusters of matched richness and mass in overdense regions and outside voids tend to have higher X-ray luminosities and temperatures. These differences could have important implications for precision cosmology with clusters of galaxies, since cluster mass calibrations can vary with environment

The XMM Cluster Survey: Evidence for energy injection at high redshift from evolution of the X-ray luminosity-temperature relation

We measure the evolution of the X-ray luminosity-temperature (L_X-T) relation since z~1.5 using a sample of 211 serendipitously detected galaxy clusters with spectroscopic redshifts drawn from the XMM Cluster Survey first data release (XCS-DR1). This is the first study spanning this redshift range using a single, large, homogeneous cluster sample. Using an orthogonal regression technique, we find no evidence for evolution in the slope or intrinsic scatter of the relation since z~1.5, finding both to be consistent with previous measurements at z~0.1. However, the normalisation is seen to evolve negatively with respect to the self-similar expectation: we find E(z)^{-1} L_X = 10^{44.67 +/- 0.09} (T/5)^{3.04 +/- 0.16} (1+z)^{-1.5 +/- 0.5}, which is within 2 sigma of the zero evolution case. We see milder, but still negative, evolution with respect to self-similar when using a bisector regression technique. We compare our results to numerical simulations, where we fit simulated cluster samples using the same methods used on the XCS data. Our data favour models in which the majority of the excess entropy required to explain the slope of the L_X-T relation is injected at high redshift. Simulations in which AGN feedback is implemented using prescriptions from current semi-analytic galaxy formation models predict positive evolution of the normalisation, and differ from our data at more than 5 sigma. This suggests that more efficient feedback at high redshift may be needed in these models.Comment: Accepted for publication in MNRAS; 12 pages, 6 figures; added references to match published versio

The phenomenological approach to modeling the dark energy

In this mini-review we discuss first why we should investigate cosmological models beyond LCDM. We then show how to describe dark energy or modified gravity models in a fluid language with the help of one background and two perturbation quantities. We review a range of dark energy models and study how they fit into the phenomenological framework, including generalizations like phantom crossing, sound speeds different from c and non-zero anisotropic stress, and how these effective quantities are linked to the underlying physical models. We also discuss the limits of what can be measured with cosmological data, and some challenges for the framework.Comment: 44 pages, 5 figures; accepted review article to appear in a special volume of the "Comptes Rendus de l'Academie des Sciences" about Dark Energy and Dark Matte

Building Dynamic Capabilities in Web Startups: An Empirical Study of Norwegian Web Startups

Purpose: This thesis explores how web startups operating in a crowded, fast-moving and highly competitive marketplace can gain competitive advantage through building dynamic capabilities and what these capabilities consist of. Design/methodology/approach: Firstly, insight was obtained through reviewing key themes within classic resource based theory and entrepreneurship theory. Newer empirical research on entrepreneurial success factors, as well as popular science and advice given by expert web entrepreneurs was then reviewed. Combining this, a framework of dynamic capabilities in web startups was synthesized. Qualitative empirical data was collected from interviewing founders and key people in four active Norwegian web startups. From the empirical findings in these interviews, the synthesized framework was iterated to account for important factors that were found to apply specifically for web startups. Lastly, a set of propositions was derived from the combination of the synthesized framework and the empirical findings.Findings: It was found that many of the contributions both from established contributors, newer contributors and popular science build on the same principles, albeit with a different degree of practical versus theoretical approach. By bridging different literature and approaches, this thesis contributes to clarify many of the invented terms found in the literature and operationalize them in practice for what they actually mean for web startups, and much of this is probably applicable for startups in general. From the empirical data it was found that web startups have important differences from other types of companies and startups. This was e.g. planning on very short time spans (most planned on a weekly basis or shorter), the ability of employees to do work outside of their expertise areas and the ability to learn or acquire new skills fast according to continuously changing market needs.Research limitations/implications: The propositions have both empirical and theoretical backing, but the empirical backing is limited to four cases, all in Norway. It would be useful to test the propositions on larger sample sizes, and preferably also to include cases from other contexts and cultures than Norway.Practical implications: Entrepreneurs in web startups should focus on building a great team and company culture. Policy makers should consider introducing programmin