Imaging a boson star at the Galactic center

Millimeter very long baseline interferometry will soon produce accurate images of the closest surroundings of the supermassive compact object at the center of the Galaxy, Sgr A*. These images may reveal the existence of a central faint region, the so-called shadow, which is often interpreted as the observable consequence of the event horizon of a black hole. In this paper, we compute images of an accretion torus around Sgr A* assuming this compact object is a boson star, i.e. an alternative to black holes within general relativity, with no event horizon and no hard surface. We show that very relativistic rotating boson stars produce images extremely similar to Kerr black holes, showing in particular shadow-like and photon-ring-like structures. This result highlights the extreme difficulty of unambiguously telling the existence of an event horizon from strong-field images.Comment: 21 pages, 9 figures, accepted in CQG; main difference wrt previous version is the last paragraph of the conclusio

A magnetized torus for modeling Sgr A* millimeter images and spectra

Context. The supermassive black hole, Sagittarius (Sgr) A*, in the centre of our Galaxy has the largest angular size in the sky among all astrophysical black holes. Its shadow, assuming no rotation, spans ~ 50 microarcsec. Resolving such dimensions has long been out of reach for astronomical instruments until a new generation of interferometers being operational during this decade. Of particular interest is the Event Horizon Telescope (EHT) with resolution ~ 20 microarcsec in the millimeter-wavelength range 0.87 mm - 1.3 mm. Aims. We investigate the ability of the fully general relativistic Komissarov (2006) analytical magnetized torus model to account for observable constraints at Sgr A* in the centimeter and millimeter domains. The impact of the magnetic field geometry on the observables is also studied. Methods. We calculate ray-traced centimeter- and millimeter-wavelength synchrotron spectra and images of a magnetized accretion torus surrounding the central black hole in Sgr A*. We assume stationarity, axial symmetry, constant specific angular momentum and polytropic equation of state. A hybrid population of thermal and non-thermal electrons is considered. Results. We show that the torus model is capable of reproducing spectral constraints in the millimeter domain, and in particular in the observable domain of the EHT. However, the torus model is not yet able to fit the centimeter spectrum. 1.3 mm images at high inclinations are in agreement with observable constraints. Conclusions. The ability of the torus model to account for observations of Sgr A* in the millimeter domain is interesting in the perspective of the future EHT. Such an analytical model allows very fast computations. It will thus be a suitable test bed for investigating large domains of physical parameters, as well as non-black-hole compact object candidates and alternative theories of gravity.Comment: Major changes wrt the June 2014 version. Accepted by A&

Circular geodesics and thick tori around rotating boson stars

Accretion disks play an important role in the evolution of their relativistic inner compact objects. The emergence of a new generation of interferometers will allow to resolve these accretion disks and provide more information about the properties of the central gravitating object. Due to this instrumental leap forward it is crucial to investigate the accretion disk physics near various types of inner compact objects now to deduce later constraints on the central objects from observations. A possible candidate for the inner object is the boson star. Here, we will try to analyze the differences between accretion structures surrounding boson stars and black holes. We aim at analysing the physics of circular geodesics around boson stars and study simple thick accretion tori (so-called Polish doughnuts) in the vicinity of these stars. We realize a detailed study of the properties of circular geodesics around boson stars. We then perform a parameter study of thick tori with constant angular momentum surrounding boson stars. This is done using the boson star models computed by a code constructed with the spectral solver library KADATH. We demonstrate that all the circular stable orbits are bound. In the case of a constant angular momentum torus, a cusp in the torus surface exists only for boson stars with a strong gravitational scalar field. Moreover, for each inner radius of the disk, the allowed specific angular momentum values lie within a constrained range which depends on the boson star considered. We show that the accretion tori around boson stars have different characteristics than in the vicinity of a black hole. With future instruments it could be possible to use these differences to constrain the nature of compact objects.Comment: Accepted for publication in CQ

The cost of coordination can exceed the benefit of collaboration in performing complex tasks

Humans and other intelligent agents often rely on collective decision making based on an intuition that groups outperform individuals. However, at present, we lack a complete theoretical understanding of when groups perform better. Here, we examine performance in collective decision making in the context of a real-world citizen science task environment in which individuals with manipulated differences in task-relevant training collaborated. We find 1) dyads gradually improve in performance but do not experience a collective benefit compared to individuals in most situations; 2) the cost of coordination to efficiency and speed that results when switching to a dyadic context after training individually is consistently larger than the leverage of having a partner, even if they are expertly trained in that task; and 3) on the most complex tasks having an additional expert in the dyad who is adequately trained improves accuracy. These findings highlight that the extent of training received by an individual, the complexity of the task at hand, and the desired performance indicator are all critical factors that need to be accounted for when weighing up the benefits of collective decision making

Artificial intelligence in government: Concepts, standards, and a unified framework

Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI.Comment: 35 pages with references and appendix, 3 tables, 2 figure

Approaches to the Algorithmic Allocation of Public Resources: A Cross-disciplinary Review

Allocation of scarce resources is a recurring challenge for the public sector: something that emerges in areas as diverse as healthcare, disaster recovery, and social welfare. The complexity of these policy domains and the need for meeting multiple and sometimes conflicting criteria has led to increased focus on the use of algorithms in this type of decision. However, little engagement between researchers across these domains has happened, meaning a lack of understanding of common problems and techniques for approaching them. Here, we performed a cross disciplinary literature review to understand approaches taken for different areas of algorithmic allocation including healthcare, organ transplantation, homelessness, disaster relief, and welfare. We initially identified 1070 papers by searching the literature, then six researchers went through them in two phases of screening resulting in 176 and 75 relevant papers respectively. We then analyzed the 75 papers from the lenses of optimization goals, techniques, interpretability, flexibility, bias, ethical considerations, and performance. We categorized approaches into human-oriented versus resource-oriented perspective, and individual versus aggregate and identified that 76% of the papers approached the problem from a human perspective and 60% from an aggregate level using optimization techniques. We found considerable potential for performance gains, with optimization techniques often decreasing waiting times and increasing success rate by as much as 50%. However, there was a lack of attention to responsible innovation: only around one third of the papers considered ethical issues in choosing the optimization goals while just a very few of them paid attention to the bias issues. Our work can serve as a guide for policy makers and researchers wanting to use an algorithm for addressing a resource allocation problem

Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur

A multidomain relational framework to guide institutional AI research and adoption

Calls for new metrics, technical standards and governance mechanisms to guide the adoption of Artificial Intelligence (AI) in institutions and public administration are now commonplace. Yet, most research and policy efforts aimed at understanding the implications of adopting AI tend to prioritize only a handful of ideas; they do not fully connect all the different perspectives and topics that are potentially relevant. In this position paper, we contend that this omission stems, in part, from what we call the ‘relational problem’ in socio-technical discourse: fundamental ontological issues have not yet been settled—including semantic ambiguity, a lack of clear relations between concepts and differing standard terminologies. This contributes to the persistence of disparate modes of reasoning to assess institutional AI systems, and the prevalence of conceptual isolation in the fields that study them including ML, human factors, social science and policy. After developing this critique, we offer a way forward by proposing a simple policy and research design tool in the form of a conceptual framework to organize terms across fields—consisting of three horizontal domains for grouping relevant concepts and related methods: Operational, Epistemic, and Normative. We first situate this framework against the backdrop of recent socio-technical discourse at two premier academic venues, AIES and FAccT, before illustrating how developing suitable metrics, standards, and mechanisms can be aided by operationalizing relevant concepts in each of these domains. Finally, we outline outstanding questions for developing this relational approach to institutional AI research and adoption