Focal plane wavefront sensor achromatization : The multireference self-coherent camera

High contrast imaging and spectroscopy provide unique constraints for exoplanet formation models as well as for planetary atmosphere models. But this can be challenging because of the planet-to-star small angular separation and high flux ratio. Recently, optimized instruments like SPHERE and GPI were installed on 8m-class telescopes. These will probe young gazeous exoplanets at large separations (~1au) but, because of uncalibrated aberrations that induce speckles in the coronagraphic images, they are not able to detect older and fainter planets. There are always aberrations that are slowly evolving in time. They create quasi-static speckles that cannot be calibrated a posteriori with sufficient accuracy. An active correction of these speckles is thus needed to reach very high contrast levels (>1e7). This requires a focal plane wavefront sensor. Our team proposed the SCC, the performance of which was demonstrated in the laboratory. As for all focal plane wavefront sensors, these are sensitive to chromatism and we propose an upgrade that mitigates the chromatism effects. First, we recall the principle of the SCC and we explain its limitations in polychromatic light. Then, we present and numerically study two upgrades to mitigate chromatism effects: the optical path difference method and the multireference self-coherent camera. Finally, we present laboratory tests of the latter solution. We demonstrate in the laboratory that the MRSCC camera can be used as a focal plane wavefront sensor in polychromatic light using an 80 nm bandwidth at 640 nm. We reach a performance that is close to the chromatic limitations of our bench: contrast of 4.5e-8 between 5 and 17 lambda/D. The performance of the MRSCC is promising for future high-contrast imaging instruments that aim to actively minimize the speckle intensity so as to detect and spectrally characterize faint old or light gaseous planets.Comment: 14 pages, 20 figure

A linear speed-up theorem for cellular automata

AbstractIbarra (1985) showed that, given a cellular automaton of range 1 recognizing some language in time n+1+R(n), we can obtain another CA of range 1 recognizing exactly the same language but in time n+1+R(n)/k (k⩾2 arbitrary). Their proof proceeds indirectly (through the simulation of CAs by a special kind of sequential machines, the STMs) and we think it misses that way some of the deep intuition of the problem. We, therefore, provide here a direct proof of this result (extended to the case of CAs of arbitrary range) involving the explicit construction of a CA working in time n+1+R(n)/k. This speeded-up automaton first gathers the cells of the line k by k in n+1 steps which then enables it to start computing by “leaps” of k steps, thus completing the R(n) remaining steps in time R(n)/k. The major problem arising from the obligation to pass from one phase to the other synchronously is solved using a synchronization process derived from the solutions of the well-known “firing-squad synchronization problem” (FSSP)

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai

Exploring Millions of 6-State FSSP Solutions: the Formal Notion of Local CA Simulation

In this paper, we come back on the notion of local simulation allowing to transform a cellular automaton into a closely related one with different local encoding of information. This notion is used to explore solutions of the Firing Squad Synchronization Problem that are minimal both in time (2n -- 2 for n cells) and, up to current knowledge, also in states (6 states). While only one such solution was proposed by Mazoyer since 1987, 718 new solutions have been generated by Clergue, Verel and Formenti in 2018 with a cluster of machines. We show here that, starting from existing solutions, it is possible to generate millions of such solutions using local simulations using a single common personal computer

A Simple n-Dimensional Intrinsically Universal Quantum Cellular Automaton

We describe a simple n-dimensional quantum cellular automaton (QCA) capable of simulating all others, in that the initial configuration and the forward evolution of any n-dimensional QCA can be encoded within the initial configuration of the intrinsically universal QCA. Several steps of the intrinsically universal QCA then correspond to one step of the simulated QCA. The simulation preserves the topology in the sense that each cell of the simulated QCA is encoded as a group of adjacent cells in the universal QCA.Comment: 13 pages, 7 figures. In Proceedings of the 4th International Conference on Language and Automata Theory and Applications (LATA 2010), Lecture Notes in Computer Science (LNCS). Journal version: arXiv:0907.382

Dynamics of particles and cages in an experimental 2D glass former

We investigate the dynamics of a glass forming 2D colloidal mixture and show the existence of collective motions of the particles. We introduce a mean square displacement MSD with respect to the nearest neighbors which shows remarkable deviations from the usual MSD quantifying the individual motion of our particles. Combined with the analysis of the self part of the Van Hove function this indicates a coupled motion of particles with their cage as well as intra cage hopping processes.Comment: Submitted to EP

New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems

We propose two uniform solutions to an open question: the Firing Squad Synchronization Problem (FSSP), for hyperdag and symmetric neural P systems, with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively, where e_c is the eccentricity of the commander cell of the dag or digraph underlying these P systems. The first and fast solution is based on a novel proposal, which dynamically extends P systems with mobile channels. The second solution is substantially longer, but is solely based on classical rules and static channels. In contrast to the previous solutions, which work for tree-based P systems, our solutions synchronize to any subset of the underlying digraph; and do not require membrane polarizations or conditional rules, but require states, as typically used in hyperdag and neural P systems