arXiv subjects
Publications and source records attributed to Ben Mills.
We construct an explicit isomorphism between the generalised Khovanov arc algebras of type D and the basic algebras of the anti-spherical Hecke category associated to the maximal parabolic subgroup $W (A_{n-1})$ of $W (Dn)$. This isomorphism maps generators to generators, thereby equipping the arc algebras with an Ext-quiver and relations presentation.
We provide an ${\rm Ext}$-quiver and relations presentation for the basic algebra of the anti-spherical Hecke categories of isotropic Grassmannians, $H_{(D_n, A_{n-1})}$, in terms of cup-cap meta Kazhdan--Lusztig combinatorics and Temperley--Lieb diagrammatics.
The alignment of optical systems is a critical step in their manufacture. Alignment normally requires considerable knowledge and expertise of skilled operators. The automation of such processes has several potential advantages, but requires additional resource and upfront costs. Through a case study of a simple two mirror system we identify and examine three different automation approaches. They are: artificial neural networks; practice-led, which mimics manual alignment practices; and design-led, modelling from first principles. We find that these approaches make use of three different types of knowledge 1) basic system knowledge (of controls, measurements and goals); 2) behavioural skills and expertise, and 3) fundamental system design knowledge. We demonstrate that the different automation approaches vary significantly in human resources, and measurement sampling budgets. This will have implications for practitioners and management considering the automation of such tasks.
Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of ~ 10 milliseconds, for a simulated 3-ring hexagonal close packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.
Reinforcement learning was carried out in a simulated environment to learn continuous velocity control over multiple motor axes. This was then applied to a real-world optical tweezers experiment with the objective of moving a laser-trapped microsphere to a target location whilst avoiding collisions with other free-moving microspheres. The concept of training a neural network in a virtual environment has significant potential in the application of machine learning for experimental optimization and control, as the neural network can discover optimal methods for problem solving without the risk of damage to equipment, and at a speed not limited by movement in the physical environment. As the neural network treats both virtual and physical environments equivalently, we show that the network can also be applied to an augmented environment, where a virtual environment is combined with the physical environment. This technique may have the potential to unlock capabilities associated with mixed and augmented reality, such as enforcing safety limits for machine motion or as a method of inputting observations from additional sensors.
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.
Reconfigurable photonic devices capable of routing the flow of light enable flexible integrated-optic circuits that are not hard-wired but can be externally controlled. Analogous to free-space spatial light modulators, we demonstrate all-optical wavefront shaping in integrated silicon-on-insulator photonic devices by modifying the spatial refractive index profile of the device employing ultraviolet pulsed laser excitation. Applying appropriate excitation patterns grants us full control over the optical transfer function of telecommunication-wavelength light travelling through the device, thus allowing us to redefine its functionalities. As a proof-of-concept, we experimentally demonstrate routing of light between the ports of a multimode interference power splitter with more than 97% total efficiency and negligible losses. Wavefront shaping in integrated photonic circuits provides a conceptually new approach toward achieving highly adaptable and field-programmable photonic circuits with applications in optical testing and data communication.
We demonstrate a new concept for reconfigurable nanophotonic devices exploiting ultrafast nonlinear control of shaped wavefronts in a multimode nanomaterial consisting of semiconductor nanowires. Femtosecond pulsed laser excitation of the nanowire mat is shown to provide an efficient nonlinear mechanism to control both destructive and constructive interference in a shaped wavefront. Modulations of up to 63% are induced by optical pumping, due to a combination of multimode dephasing and induced transient absorption. We show that part of the nonlinear phase dynamics can be inverted to provide a dynamical revival of the wavefront into an optimized spot with up to 18% increase of the peak to background ratio caused by pulsed laser excitation. The concepts of multimode nonlinear switching demonstrated here are generally extendable to other photonic and plasmonic systems and enable new avenues for ultrafast and reconfigurable nanophotonic devices.
Advances in silicon photonics have resulted in rapidly increasing complexity of integrated circuits. New methods are desirable that allow direct characterization of individual optical components in-situ, without the need for additional fabrication steps or test structures. Here, we present a new device-level method for characterization of photonic chips based on a highly localized modulation in the device using pulsed laser excitation. Optical pumping perturbs the refractive index of silicon, providing a spatially and temporally localized modulation in the transmitted light enabling time- and frequency-resolved imaging. We demonstrate the versatility of this all-optical modulation technique in imaging and in quantitative characterization of a variety of properties of silicon photonic devices, ranging from group indices in waveguides, quality factors of a ring resonator to the mode structure of a multimode interference device. Ultrafast photomodulation spectroscopy provides important information on devices of complex design, and is easily applicable for testing on the device-level.
The dielectric response of transparent conductive oxides near the bulk plasmon frequency is characterized by a refractive index less than vacuum. In analogy with x-ray optics, it is shown that this regime results in total external reflection and air-guiding of light. In addition, the strong reduction of the wavevector in the ITO below that of free space enables a new surface plasmon polariton mode which can be excited without requiring a prism or grating coupler. Ultrafast control of the surface plasmon polariton mode is achieved with a modulation amplitude reaching 20%.