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International audience; HARMONI is the first light, adaptive optics assisted, integral field spectrograph for the European Southern Observatory’s Extremely Large Telescope (ELT). A work-horse instrument, it provides the ELT’s diffraction limited spectroscopic capability across the near-infrared wavelength range. HARMONI will exploit the ELT’s unique combination of exquisite spatial resolution and enormous collecting area, enabling transformational science. The design of the instrument is being finalized, and the plans for assembly, integration and testing are being detailed. We present an overview of the instrument’s capabilities from a user perspective, and provide a summary of the instrument’s design. We also include recent changes to the project, both technical and programmatic, that have resulted from red-flag actions. Finally, we outline some of the simulated HARMONI observations currently being analyzed.

HARMONI is the adaptive optics assisted, near-infrared and visible light integral field spectrograph for the Extremely Large Telescope (ELT). A first light instrument, it provides the work-horse spectroscopic capability for the ELT. As the project approaches its Final Design Review milestone, the design of the instrument is being finalized, and the plans for assembly, integration and testing are being detailed. We present an overview of the instrument's capabilities from a user perspective, provide a summary of the instrument's design, including plans for operations and calibrations, and provide a brief glimpse of the predicted performance for a specific observing scenario. The paper also provides some details of the consortium composition and its evolution since the project commenced in 2015.

A significant challenge in the production of Earth observation satellites is the precise alignment of the telescope optical components. We have developed a strategy to perform automated alignment of two-mirror telescopes for use in a realistic factory-based setting. A Ritchey–Chrétien telescope was used as an example. The secondary mirror was mounted on a high precision hexapod and its misalignment inferred from the Zernike coefficients for tilt, defocus, and coma, as measured by a phase-shifting interferometer. The required corrections to the position of the secondary mirror were implemented using an integral controller and alignment was achieved within minutes, compared to within days when using a manual alignment process. The Zernike coefficient for each aberration was reduced to within one standard deviation of the fluctuations due to residual instability (48 nm).

The multi-billion dollar Earth observation applications market continues to demand better spatial and temporal resolution; simply put, this means bigger apertures and more satellites. This paper describes a novel deployable telescope that addresses the market needs for a Surrey Satellite Technology Ltd. (SSTL) has already demonstrated low-cost 1m GSD imagery from the Carbonite-2 platform, but the deployable telescope solution presented here provides the opportunity to build on this capability by significantly improving revisit time, without the typical increase in cost. SSTL is developing, alongside the Surrey Space Centre (SSC) and the Dynamic Optics and Photonics Group at the University of Oxford, a telescopic deployable structure and a fine alignment system to align the Cassegrain-type telescope in-orbit. The three-concentric barrel deployable structure and mechanisms are discussed including the associated requirements and trade-off study that led to this design. The dynamic nature of this system exacerbates traditional optical challenges such as alignment and stray light control; solutions to these are proposed, the optical design rationale is explained and predicted imaging performance shown. The novel autonomous fine alignment system, both algorithms and mechanisms, is presented. The last section deals with the spacecraft level implications and accommodation. Then finally the constellation level system design is shown with regards to the launch vehicle options and orbit configuration for coverage optimization; both global and target-specific.

A new approach for mitigating atmospheric turbulence effects on free-space laser communication performance is presented. The method is based on evaluating Maximum Likelihood Thresholds using Kalman filter estimates in on-off keyed laser communications in atmospheric turbulence. Experimental results presented in this chapter clearly demonstrate the usefulness of the method. Although the results are shown for low data rate of 6.3 Kbps, the concept is valid for much higher data rate taking advantage of the higher bandwidth capability of laser communications. When looking at the table of the comparison of various threshold approaches, it is shown clearly that the anticipatory a posteriori processing of 50 data points provides a very good bit error performance, if the extra computational burden of processing 50 prior data points for every likelihood ratio test at every sample point is acceptable. The next best approach is the use of the dual Kalman filters which yields a BER that is within a factor of two of a posteriori approach, but without having to reprocess 50 prior data points before each threshold decision. Clearly, this is an acceptable alternative to the a posteriori approach with its more extensive processing and its unavoidable delays. Because the required update rates of h is determined by the slowly varying atmospheric turbulence, high data rate links using the dual Kalman approach will allow many more times the number of data samples and bits to be tested against and validated by the most up-to-date h. The dual Kalman approach is a new approach that is expected to give better BER performance in low and high data rate atmospheric optical links and to simplify the necessary processing in the receiver. The central issue addressed in this chapter is whether Kalman Filtering is helpful in dealing with atmospheric turbulence, and this chapter shows that it is within the bounds of statistical approximations.

The Payload Technology Validation section in the Future Missions office of ESA's Science directorate at ESTEC provides testing support to present and future missions at different stages in their lifetime, from early technology developments to mission operation validation. In this framework, a test setup to characterize near-infrared (NIR) detectors has been created. In the context of the Astronomy Large Format Array for the near-infrared ("ALFA-N") technology development program, detectors from different suppliers are tested. We report on the characterization progress of the ALFA-N detectors, for which a series of rigorous tests have been performed on two different detectors; one provided by CEA/Leti-CEA/IRFU-SOFRADIR, France and the other by SELEX- UK/ATC, UK. Experimental techniques, the test bench and methods are presented. The conversion gain of two different detectors is measured using the photon transfer curve method. For a Leti LPE detector the persistence effect has been probed across a range of illumination levels to reveal a sharp linear increase of persistence below full-well and a plateauing beyond saturation. The same detector has been proton irradiated which has resulted in no significant dark current increase.

International audience; Euclid is an ESA mission to map the geometry of the dark Universe with a planned launch date in 2020. Euclid is optimised for two primary cosmological probes, weak gravitational lensing and galaxy clustering. They are implemented through two science instruments on-board Euclid, a visible imager (VIS) and a near-infrared spectro-photometer (NISP), which are being developed and built by the Euclid Consortium instrument development teams. The NISP instrument contains a large focal plane assembly of 16 Teledyne HgCdTe HAWAII-2RG detectors with 2.3μm cut-off wavelength and SIDECAR readout electronics. While most Euclid NISP detector system on-ground tests involve flat-field illumination, some performance tests require point-like sources to be projected onto the detector. For this purpose a dedicated test bench has been developed by ESA at ESTEC including a spot projector capable of generating a Euclid-like PSF. This paper describes the test setup and results from two characterisation tests involving the spot projector. One performance parameter to be addressed by Euclid is image (charge) persistence resulting from previous exposures in the science acquisition sequence. To correlate results from standard on-ground persistence tests from flat-field illumination to realistic scenes, the persistence effect from spot illumination has been evaluated and compared to the flat-field. Another important aspect is the photometric impact of intra-pixel response variations. Preliminary results of this measurement on a single pixel are presented.

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled.

We argue from the Church-Turing thesis (Kleene Mathematical logic. New York: Wiley 1967) that a program can be considered as equivalent to a formal language similar to predicate calculus where predicates can be taken as functions. We can relate such a calculus to Wittgenstein’s first major work, the Tractatus, and use the Tractatus and its theses as a model of the formal classical definition of a computer program. However, Wittgenstein found flaws in his initial great work and he explored these flaws in a new thesis described in his second great work; the Philosophical Investigations. The question we address is “can computer science make the same leap?” We are proposing, because of the flaws identified by Wittgenstein, that computers will never have the possibility of natural communication with people unless they become active participants of human society. The essential difference between formal models used in computing and human communication is that formal models are based upon rational sets whereas people are not so restricted. We introduce irrational sets as a concept that requires the use of an abductive inference system. However, formal models are still considered central to our means of using hypotheses through deduction to make predictions about the world. These formal models are required to continually be updated in response to peoples’ changes in their way of seeing the world. We propose that one mechanism used to keep track of these changes is the Peircian abductive loop.

We argue that abduction does not work in isolation from other inference mechanisms and illustrate this through an inference scheme designed to evaluate multiple hypotheses. We use game theory to relate the abductive system to actions that produce new information. To enable evaluation of the implications of this approach we have implemented the procedures used to calculate the impact of new information in a computer model. Experiments with this model display a number of features of collective belief-revision leading to consensus-formation, such as the influence of bias and prejudice. The scheme of inferential calculations invokes a Peircian concept of ‘belief’ as the propensity to choose a particular course of action.

Case studies of diverse scientific fields show how scientists use a range of resources to generate new interpretative models and to establish their plausibility as explanations of a domain. They accomplish this by manipulating imagistic representations in particular ways. I show that scientists in different domains use the same basic transformations. Common features of these transformations indicate that general cognitive strategies of interpretation, simplification, elaboration, and argumentation are at work. Social and historical studies of science emphasize the diversity of local contexts of practice. However, the existence of common strategies shows that this diversity masks an important repertoire of cognitive strategies. Scientists use this repertoire to adapt their representations to meet the cognitive demands of different contexts of practice. This paper considers the implications of this finding for the notion of scientists as cognitive agents in distributed knowledge-producing systems.

Faraday is often described as an experimentalist, but his work is a dialectical interplay of concrete objects, visual images, abstract, theoretically-informed visual models and metaphysical precepts. From phenomena described in terms of patterns formed by lines of force he created a general explanation of space-filling systems of force which obey both empirical laws and principles of conservation and economy. I argue that Faraday's articulation of situated experience via visual models into a theory capable of verbal expression owed much to his strategy of moving—via conjectural visual models—between the phenomenology of particulars (often displayed as patterns) and the general features of dynamical phenomena which he depicted as structures.

Visualisation involves making and manipulating images that convey novel phenomena, ideas and meanings. It is central to the intellectual objectives of almost every area of science. It is therefore surprising that we still lack an understanding of how visual thinking works. Examples from different sciences illustrate how scientists interpret and explain phenomena by actively manipulating those patternlike features of the world that lend themselves to structural representation, and vice versa. The ability to move between two, three and four dimensions structures both imagination and analysis, enabling scientists to vary the complexity of each visualisation as its function changes during the process of discovery and communication. These findings suggest that there is a widely used visual method that relates human cognitive abilities to imaging practices in the sciences and the arts.

This paper presents a study of the generation, manipulation and use of visual representations in different episodes of scientific discovery. The study identifies a common set of transformations of visual representations underlying the distinctive methods and imagery of different scientific fields. The existence of common features behind the diversity of visual representations suggests a common dynamical structure for visual thinking, showing how visual representations facilitate cognitive processes such as pattern-matching and visual inference through the use of tools, technologies and other cultural resources. This dynamical model suggests a way of theorizing the interaction of cognitive, socio-cultural and technological aspects of science without losing sight of the essential contribution each makes to the processes of discovery. Whereas scientific work is often construed epistemically, as having the aim of improving the fit between theories and phenomena or culturally given notions of what counts as reality, this study shows that scientists use transformations to modify visual representations in ways that achieve other kinds of match: between a representation and the cognitive demands of a task (such as pattern matching or mental rotation) or between an emerging representation and the social need to communicate and negotiate new meanings in a context of culturally embedded conventions. By showing how images connect each of the overlapping contexts of scientific work the proposed model negotiates the sometimes contested boundary between cognitive and social aspects of science.

The sciences use a wide range of visual devices, practices, and imaging technologies. This diversity points to an important repertoire of visual methods that scientists use to adapt representations to meet the varied demands that their work places on cognitive processes. This paper identifies key features of the use of visualization in a range of scientific domains and considers the implications of this repertoire for understanding scientists as cognitive agents.

This article argues that we can understand the work of scientists who created new ways of seeing nature in much the same way as we understand the work of artists and composers who changed the sensibilities of their own or succeeding generations. I describe Faraday's invention of the electric motor in September 1821 to illustrate the general thesis about similarities between artistic production and the creative use of experiment in science.

Philosophical discussions of experiment usually focus exclusively on testing predictions. In this paper I compare G. Morpurgo's experimental test of the Gell‐Mann/ Zweig quark hypothesis with two neglected uses of experiment: constructing representations of new phenomena and inventing the instruments that produce such phenomena. These roles are illustrated by J. B. Biot's 1821 observations of electromagnetism and by Michael Faraday's invention of the first electromagnetic motor, also in 1821. The comparison identifies similarities between observation and experiment, showing how both observation and experiment actively engage the natural world and how each engagement shapes representation and subsequent empirical work. This challenges the post‐empiricist assumption of the sufficiency of knowing only the outcomes of experiments. I conclude that traditional views of observational access have looked in the wrong place for empirical constraints on theorizing. The active character of observation implie...

It is well known that atmospheric turbulence diminishes the performance of laser communications systems. Among the multiple degradations caused by turbulence is fading and surging of the received signal, usually referred to as scintillation. If a minimum probability of error receiver is employed for on-off keying (OOK), it is necessary to understand the two conditional probability densities (pdfs) corresponding to the transmission of ones and zeros. These probability densities are the distributions of signals received when the laser is on when sending binary ones and when the laser is off sending binary zeros. Many theoretical studies have determined the expected forms of the pdfs. An ongoing experimental study operating a low-power, low data rate link over a range of 9.3 Km has been started at Colorado State University-Pueblo to carefully examine the effects of atmospheric turbulence on laser communications. Experimental models of actual, true and typical pdfs have been obtained. The results do not always match theoretical predictions. The non-stationary nature of these pdfs is also a problem that must be addressed. This paper summarizes the experimental testing and shares a number of its conclusions.

This chapter draws attention to case studies of scientific discovery as an important source of information about visualisation as a real-time cognitive process. These show that visual modes of representation are essential to the generation, communication and dissemination of new knowledge. I survey a number of strategies of visualisation and develop a process model based on the ways that scientists in a wide range of fields manipulate the dimensionality of images in order to move between representations that are local, situated and often personal and images that are widely understood and have an objective status as depictions of facts or laws. This shows that the cognitive status of visual images changes as scientists integrate novel representations into their arguments.

Traditional dualist assumptions about how humans acquire and represent knowledge of the world support theories that deal in second- or third-order representations of their subject matter, such as images, diagrams, equations and theories. These accounts ignore the processes whereby such representations are achieved. I provide examples from the work of Michael Faraday which show that abstraction, or discerning patterns and structure in phenomenological chaos depends on the development observational techniques, which I characterize as cognitive technologies. The examples also illustrate the importance of human agency to the process of making representations of the world which enable scientists to understand and think about it.

This session looks at some uses of diagrams in scientific discovery, particularly their role as intermediate representations which mediate between phenomena, descriptions which can be communicated and descriptions which are general. A range of examples will illustrate a variety of uses, including: the abstractive, generative role; diagrams as encoded knowledge; reasoning with diagrammatic representations in discovery; and communication (exposition and argumentation). The tutorial will encourage consideration of two issues: (a) whether, from a cognitive standpoint, diagrams are essential to reasoning about natural phenomena and processes, and (b) the relationship of diagrammatic reasoning to other types of visualisation and visual thinking in the sciences, including cognitive and computational modeling of discovery.

Every physicist knows that Faraday was a master experimentalist who discovered fundamental physical phenomena which laid the foundations for a field-theoretical approach to electricity and magnetism. He also introduced the imagery of lines of force that was to provide a framework for the field theory developed by Thomson and Maxwell. Faraday's search for new phenomena was motivated by his vision of a unified theory of all natural powers. His theory inverted the traditional priority of matter over force, attributing the familiar properties of matter to forces filling the space between bodies.

This paper argues that an adequate theory of discovery must bring the formal approach of computer science and cognitive psychology closer to the empirical approach of history and the social studies of science. We argue that a computer simulation is analogous to a theory of scientific investigation in that it is comprised of a number of models of activities and entities. We describe a model of experimentation to illustrate an approach to discovery simulation that shows how formal and empirical approaches may be integrated.

Visual functional programming has been developed as a knowledge acquisition tool. Design and evaluation of this method are motivated by a particular application, the representation of the experimental strategies of the 19thC physicist Michael Faraday as recorded in his laboratory diaries. However, the methods have wider application. We argue that a functional database language has the same morphology as a role taxonomy for knowledge and that this similarity of form provides a clear descriptive language. It is further argued that a graphical representation exploits one of the fundamental capacities for creative human insight. The combination of the two approaches, as realised through the CLARITY functional programming environment, provides a powerful knowledge acquisition tool.

In previous chapters we described the contexts in which Faraday lived and worked. We now turn to examine his experimental work and the world-views that informed it.

Faraday’s next major discovery strengthened his doubts about traditional ways of conceptualising active agents, like matter and electricity. In 1834 he learnt of a new electro-magnetic effect. A strong aftershock was felt both on breaking a circuit containing a coil with a soft iron core, and on breaking a circuit consisting of a very long wire. In the former case the aftershock was due to an induced current, but at first Faraday thought it was produced by a change in the magnetisation of the soft iron core when the current was broken. As for the second case, it appeared that the current acquired a greater intensity by passing through an increased length of the circuit. An obvious explanation was to suppose that the current is a fluid which, like water in a pipe, acquires more momentum as it passes through a greater length of wire. This explained, for example, the fact that a spark jumps when a circuit is broken. It also implied a mechanical explanation of electrical phenomena which Faraday did not want to accept. If, as he now believed, an electric current releases tensions between chemical affinities, then a tension should exist prior to the passage of a current. He found that he could draw sparks on closing a circuit (before a current is flowing in it) as well as on breaking a circuit. This showed that such tensions exist. It cast doubt on the idea that the sparks drawn on breaking a circuit are caused by the electric fluid’s momentum forcing it across the gap. The momentum-explanation takes the materiality of electricity for granted, and it flew in the face of the transfer of magnetic effect from one circuit to another. Electric sparks were at least visible, but how could a substance move imperceptibly from one wire to another?

Faraday’s work has had an impact on many different fields of activity. From electrical engineering, through electrotherapy screening, to the upper reaches of theoretical physics, Faraday’s name is to be found. In this final chapter we examine three of the many areas affected by him and his work. First, we look at the effect of his scientific research on certain areas of science and engineering; second, the public image of Faraday; and, finally, we point to some of the ways in which this study of Faraday should influence our understanding of science.

Faraday first encountered science in the books he bound as an apprentice. They introduced him to the fascinating world of natural phenomena and to the idea that these phenomena obey divinelyordained laws that we can comprehend. He therefore resolved to study science. What is so impressive is the strength of his resolve for during the next few years he thoroughly prepared himself for a career in science at a time when science offered few openings, particularly for somebody without inherited wealth or contacts. He commenced his studies by reading any scientific works he could find and by attending lectures at the City Philosophical Society. He also attended four of Sir Humphry Davy’s lectures at the Royal Institution, with tickets donated by a customer at the bindery. Moreover, at the City Philosophical Society (see section 3.2) he met another young man, Benjamin Abbott, and they started a regular correspondence with the aim of self-improvement. We see from these letters how Faraday set about improving his mind, his letter-writing skills and his knowledge of chemistry. In the course of this correspondence he commented extensively on the lecturing styles of the lecturers he heard; this critical evaluation of others doubtless paved the way for his own polished performance in the lecture theatre.

In this chapter I show how experimental skill enables the convergence of practices and the experience they produce. In chapter 8 I apply this idea to theory-testing and in chapters 9 and 10 to the process of theorizing new phenomena. Convergence in turn explains the belief that representations correspond to what they are representations of. I argue that the correspondence of representations to their objects is the end product of a process of making convergences which are subsequently reconstructed as correspondences. Reconstruction has a material and a verbal aspect. The material embodiment of skills into apparatus and techniques that we saw in section 6.9 establishes the objective distance of phenomena from representations of it, while narrative reconstruction imparts the logical structure found in published reports.

Arguments meant to justify a discovery by ‘grounding’ it in theory rely on the operations and experiments that enabled it to be represented. This generative aspect of justification has been unfashionable since it was displaced in the early nineteenth century by the deductive style of a new, unified mathematical physics.1 As a result, an important problem for a theory of representation, has been neglected: how do observers represent experience that is new to all of them in a communicable form? This question is inseparable from another: how do observers tell when they have successfully communicated what, in their own experience, they intend others to see? Satisfactory answers to these questions would show how observational assent reflects similarity of semantic ascent, and vice versa. A further question is how novel, anomalous information retains enough of its anomalous character to promote changes in a theoretical system that has apparently assimilated it. These questions remain unanswered.

In chapter two I showed how aspects of new phenomena are made accessible to experience and how their given-ness in experience is also constructed through a process of representing, or construing. I argued that observers’ agency is essential both to eliciting experiential possibilities and to rendering them as observerable features of nature. Rendering includes the invention of frameworks of activity. These and the outcomes of activity understood in terms of them, were represented by images such as Biot’s ‘circular contour’ and Davy’s ‘chords’. Making images is just as creative an activity when practiced by scientists as when practiced by artists. Faraday’s mnemonics may lack the aesthetic appeal of Leonardo’s sketches, yet they did a very similar job: they conveyed, through an image, aspects of experience that had been (or was being) made sense of.1

It is easy to dismiss this as a chicken-and-egg question. As construed by received philosophies of science, as requiring a definitive and general answer, it seems irrelevant. Theorizing is often the continuation of experiment by other means (pace Popper and van Frassen). Hacking has shown that different episodes in the history of science show different answers to the question of the priority of theory and experiment.1 In earlier chapters I showed how phenomena are created: first construed, then interpreted and integrated into arguments. The bias towards the literary or theoretician’s view of experiment means there are few philosophical studies of how phenomena are made into centrepieces of theory. In this chapter I show how a significant innovation in the physical understanding of force — Faraday’s concept of a field of action defined in terms of properties of systems of lines of force — emerged from the attempt to integrate electrostatics and new discoveries in magnetism made during the 1840s.

It is inevitable that language has, as Ian Hacking put it, mattered to philosophy.1 It is not inevitable that practices — especially extra-linguistic practices — have mattered so little. Philosophy has not yet addressed an issue that is central to any theory of the language of observation and therefore, to any theory of science: how do observers ascend from the world to talk, thought and argument about that world? The importance of practice is sometimes noticed by philosophers and historians of science.2 The more historically minded have persued it into the experimental coal-face where new experience is mined.3 One reason for neglect of observational practice is that scientists’ own accounts of experimental work support the disembodied view that philosophers tacitly endorse. Human agency is written out of these accounts; at best it appears in an appropriately deliberative and methodical role. This would seem to justify the philosophical practice of rendering observation in the passive form ‘X was observed in order to …’, as if observers have eyes, minds and theories but never hands in the matter. A second reason is that concepts and theories are found in texts: they are easy to write and talk about. The literary view of scientific concepts suits the linguistic turn in philosophy and conforms to wider cultural assumptions about the priority of head over hands. Yet many scientists spend most of their time solving problems of a different order, in a manner that is procedural and involves manipulations in the material world as well as the world of concepts.

Scientific theories are supposed to have empirical credentials as well as logical ones. The idea that observation and experiment provide an empirical base for theories is central to all theories of scientific method. Hypo-thetico deductivist methodology, and fallibilism in particular, hold that theory comes first: phenomena cannot raise problems of their own because no item of information in the so-called empirical base has significance apart from the theory that picks it out. When experimentation is readily identified with measuring (as we saw in chapter 7) it is easy to treat it as just the “continuation of theory by other means”, as van Fraasen does.1 In The Logic of Scientific Discovery Popper grudgingly recognized the empirical base, likening it to the muddy bottom of a swamp into which theoreticians drive piles down through a platform, until “they are satisfied that they are firm enough to carry the structure, at least for the time being”.2