The smallest amount of light is known as a photon. Although photons are plentiful, controlling them one-by-one remains challenging. If we could gain more control we could make tremendous advances in many areas including imaging, sensing, computing and communications. In this project, we aim to gain more control over individual photons using a special type of atom known as a Rydberg atom. In a Rydberg atom, one electron is excited to a state where it is on average very far from the nucleus. In this Rydberg state, the atom has greatly exaggerated properties. In particular, it becomes extremely sensitive to nearby Rydberg atoms. Over the last decade in Durham, we have shown how to map this sensitivity between Rydberg atoms into a strong interaction between photons. This idea, known as Rydberg quantum optics, has resulted in the strongest interaction between photons ever demonstrated. The next steps on this Rydberg quantum optics journey is to make this system more useful. A major step change in utility that we are proposing is to combine the remarkable features of Rydberg quantum optics with the power of integrated photonics. We will use a fibre coupled chip-based architecture to project single photons on demand and control the interactions between photons. In addition, we will show how these devices can be interfaced with cold atom based quantum memories. Another important challenge to make Rydberg photonics technologically relevant is to make underlying physics and potential devices work faster. Currently the speed limit is in the range of Mbits per second. In this project, we will explore what happens when we try to extend this into the Gbits per second range. As well as increase data rates, going faster also has another advantage in that we become less sensitive to atomic motion which is currently one of the processes that degrade efficiency. The steps demonstrated in this proposal will facilities significant progress towards the dream of a quantum internet.

Noise is a problem whenever animals collect information from their environment. It can affect them in many negative ways. These include whale strandings in response to Navy sonars, hearing damage, increased stress and the avoidance of areas they would otherwise use. Communication sounds can also be affected by noise when they become less obvious in a noisy environment. While many studies have addressed the question of how animals communicate with each other, we still know relatively little about how they use other sounds they hear. Some work has reported that predators use movement sounds of their prey to locate and catch it. Since many animals can learn about sounds they may use them in even more ways to gather information about their environment. For example, a waterfall may be used as an acoustic landmark to find a foraging site or reflection of ambient noise may be used to detect an object in darkness. These possibilities suggest that there is another side to noise, a positive one that can be used by animals for orientation. The project proposed here will investigate this positive side of noise in seals. Sound travels better in water than in air, while visibility is often low. Thus, positive effects of noise are easier to study in this environment. The first part of the project will investigate whether seals can use noise that is reflected or blocked by objects to detect the objects themselves. If so, an increased noise level may make objects more detectable to seals. For this, we will train blind-folded seals to report when they detect an object that is presented to them in front of an underwater speaker. We will investigate at what distances the seal is able to detect an object in this way, how loud the noise needs to be and whether the noise needs to come from a particular directions to maximise detection. In the second experiment we want to find out whether seals will spontaneously learn to associate a novel sound source with a specific geographic location. For this, we will install such a noise source near a seal haul-out site and then test how seals from that site react to this noise when the are taken to another location. Will they approach the noise source when searching for their haul-out site, even if it has been moved to another location? Finally, we want to know whether seals in the wild learn about sounds produced by humans when looking for food. Many fish farms use acoustic devices that are supposed to keep seals away. However, many reports suggest that these sounds might attract seals just like a dinner bell. We will install an underwater speaker near a fish farm to see whether the seals are more likely to approach when we play the sounds used on the farm as compared to other control noises. Still looking at foraging, we will also provide captive seals with various sand trays with buried fish, some of which also have fish tags in them that make a sound. These tags are widely used to track fish in the wild. We want to know whether seals learn to associate the audible ping with the food in the tray, so that after a while they seek out trays with fish tags. Taken together, these studies will inform us about how seals use noise in their environment in a way that might help them rather than disturb them. While the negative effects of noise most likely outweigh any positive sides, it is still important to know both sides of the story. If seals can use ambient noise detect objects, collisions with marine turbines and engines might be less likely than we think. Similarly, the effects of noises that we introduce are important to understand. If we remove an acoustic landmark that we have provided by installing a turbine or other machinery, this might affect animals. Similarly, sounds that we use to track fish or keep seals away may have an attraction effect, which leads to undesirable results for the people using them.

Contact tracing networks carry invaluable information for researchers to understand the spread of the virus, for policy-makers to control the COVID-19 outbreak, and for the government and the media in informing the public in rich ways. However, current data science tools fall short for the exploratory and explanatory analysis of the temporal, spatial and social aspects of these networks, and little is known on how most effectively the results of such analyses can be communicated broadly. This lack of a toolbox leads to organisations wasting resources on developing partial solutions designed without broad stakeholder engagement. To this end, this project aims to follow a user-centred approach to develop visual analytics methods for the analysis of large collections of contact tracing networks along with techniques for the communication of analysis results in transparent, comprehensive, yet engaging ways. Contact networks come with noteworthy technical and ethical challenges: inherent uncertainties due to the variation in their generation mechanisms, e.g., apps, hospital records, by volunteers; and high volumes of complex and sensitive information represented as event-based interactions with spatio-temporal facets. This project responds to these challenges through two deliverables comprising visualisation methods working simultaneously at group and individual levels while communicating the general trends in the spread: 1. Visualisations aimed at experts for understanding collections of contact networks to inform public health policies and make in-depth investigations without compromising individuals' privacy. 2. Visualisations for communicating analysis results with the general public for information and evidencing policy recommendations with representations having a purely explanatory emphasis.

The edge of the Earth's atmosphere is approximately 100 km above the surface, in a region known as the mesosphere/lower thermosphere (MLT). This part of the atmosphere is subject to high energy inputs from above in the form of extreme UV radiation and energetic particle precipitation, and a roughly equal amount of energy from breaking atmospheric gravity waves which propagate up from the lower atmosphere. The MLT also acts as a filter of waves that propagate from the troposphere into the ionosphere, which has important implications for space weather. Furthermore, energetic solar protons and electrons from the radiation belts produce highly reactive species in the MLT, which can then be transported down into the stratosphere, affecting the ozone layer and impacting on tropospheric climate. The MLT is also extremely sensitive to climate change, due to the cooling effect of increasing greenhouse gases such as CO2, ozone depletion in the stratosphere, and changes to the large-scale atmospheric circulation. However, it is a difficult region in which to make direct measurements, because it is more than 40 km higher than altitudes reached by research balloons or aircraft, and is at least 100 km lower than short-lived satellite orbits. Rocket-borne measurements do provide direct access, but are unsuitable for sustained global measurements. Fortunately, the ablation of cosmic dust particles entering the atmosphere from space deposits metal atoms such as Na and Fe in layers around 90 km altitude. These layers can be observed with lasers from the ground (lidar) and by satellite-borne spectrometers, providing detailed information about the chemistry and physics (wind, temperature, gravity waves) of the region. There is increasing evidence that accurate simulations of changes to the Earth's climate require models with a well resolved and accurate stratosphere and mesosphere, and so metal species in the upper atmosphere offer a unique way of observing this region and of testing the accuracy of climate models. The purpose of this proposal is to make the first ever study of Ni and Al chemistry in the MLT. The Ni layer has recently been observed for the first time: it is much broader than the well-studied layers such as Na and Fe, and the concentration of Ni atoms is more than 10 times higher than expected based on its cosmic abundance. These very unexpected features need to be understood, since there is the clear potential to develop lidar observations of the Ni layer as a probe of the entire MLT from 70 to 115 km. Aluminium makes a very interesting contrast with Ni. The Al-O bond is so strong that it is very likely there is a substantial layer of the AlO radical in the MLT. This species has a strong optical absorption in the green part of the visible spectrum, and so there is the exciting prospect of making lidar observations of AlO and developing an accurate temperature probe over the full range of mesospheric temperatures. The project will involve first making a series of experimental studies of key neutral and ion-molecule reaction rates in the gas phase, in order to understand the unique characteristics of the Ni layer and the likely concentration of the AlO layer. At the same time, we will use a novel instrument to simulate the ablation of Ni and Al from micron-sized fragments of meteorites such as Allende and Murchison. From this a model will be developed which predicts the injection rates of these elements into the MLT as a function of location and season. The chemistry of Ni and Al, together with their meteoric ablation rates, will then be placed into a global chemistry-climate model. Of particular interest will be to see how the Ni and AlO layers are predicted to respond to perturbations caused by major solar storms, the 11-year solar cycle, and climate change in the MLT over the past 70 years and projected forward to 2100.

The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.