Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project

Achieving maximum scientific results from the overwhelming volume of astronomical data to be acquired over the next few decades will demand novel, fully automatic methods of data analysis. Artificial intelligence approaches hold great promise in contributing to this goal. Here we apply neural network learning technology to the specific domain of eclipsing binary (EB) stars, of which only some hundreds have been rigorously analyzed, but whose numbers will reach millions in a decade. Well-analyzed EBs are a prime source of astrophysical information whose growth rate is at present limited by the need for human interaction with each EB data-set, principally in determining a starting solution for subsequent rigorous analysis. We describe the artificial neural network (ANN) approach which is able to surmount this human bottleneck and permit EB-based astrophysical information to keep pace with future data rates. The ANN, following training on a sample of 33,235 model light curves, outputs a set of approximate model parameters (T2/T1, (R1+R2)/a, e sin(omega), e cos(omega), and sin i) for each input light curve data-set. The whole sample is processed in just a few seconds on a single 2GHz CPU. The obtained parameters can then be readily passed to sophisticated modeling engines. We also describe a novel method polyfit for pre-processing observational light curves before inputting their data to the ANN and present the results and analysis of testing the approach on synthetic data and on real data including fifty binaries from the Catalog and Atlas of Eclipsing Binaries (CALEB) database and 2580 light curves from OGLE survey data. [abridged]Comment: 52 pages, accepted to Ap

The LisbOn KInetics Boltzmann solver

LisbOn KInetics Boltzmann (LoKI-B) is an open-source simulation tool available at: https://github.com/IST-Lisbon/LoKIThe LisbOn KInetics Boltzmann (LoKI-B) is an open-source simulation tool (https://github.com/IST-Lisbon/LoKI) that solves a time and space independent form of the two-term electron Boltzmann equation, for non-magnetised non-equilibrium low-temperature plasmas excited by DC/HF electric fields from different gases or gas mixtures. LoKI-B was developed as a response to the need of having an electron Boltzmann solver easily addressing the simulation of the electron kinetics in any complex gas mixture (of atomic/molecular species), describing first and second-kind electron collisions with any target state (electronic, vibrational and rotational), characterized by any user-prescribed population. LoKI-B includes electron-electron collisions, it handles rotational collisions adopting either a discrete formulation or a more convenient continuous approximation, and it accounts for variations in the number of electrons due to non-conservative events by assuming growth models for the electron density. On input, LoKI-B defines the operating work conditions, the distribution of populations for the electronic, vibrational and rotational levels of the atomic/molecular gases considered, and the relevant sets of electron-scattering cross sections obtained from the open-access website LXCat (http://lxcat.net/). On output, it yields the isotropic and the anisotropic parts of the electron distribution function (the former usually termed the electron energy distribution function), the electron swarm parameters, and the electron power absorbed from the electric field and transferred to the different collisional channels. LoKI-B is developed with flexible and upgradable object-oriented programming under MATLAB (R), to benefit from its matrix-based architecture, adopting an ontology that privileges the separation between tool and data. This topical review presents LoKI-B and gives examples of results obtained for different model and real gases, verifying the tool against analytical solutions, benchmarking it against numerical calculationThis work was funded by Portuguese FCT-Fundacao para a Ciencia e a Tecnologia, under projects UID/FIS/50010/2013 and PTDC/FISPLA/1243/2014 (KIT-PLASMEBA)

Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences

Complex non-linear time series are ubiquitous in geosciences. Quantifying complexity and non-stationarity of these data is a challenging task, and advanced complexity-based exploratory tool are required for understanding and visualizing such data. This paper discusses the Fisher-Shannon method, from which one can obtain a complexity measure and detect non-stationarity, as an efficient data exploration tool. The state-of-the-art studies related to the Fisher-Shannon measures are collected, and new analytical formulas for positive unimodal skewed distributions are proposed. Case studies on both synthetic and real data illustrate the usefulness of the Fisher-Shannon method, which can find application in different domains including time series discrimination and generation of times series features for clustering, modeling and forecasting. The paper is accompanied with Python and R libraries for the non-parametric estimation of the proposed measures

Strong converse theorems using R\'enyi entropies

We use a R\'enyi entropy method to prove strong converse theorems for certain information-theoretic tasks which involve local operations and quantum or classical communication between two parties. These include state redistribution, coherent state merging, quantum state splitting, measurement compression with quantum side information, randomness extraction against quantum side information, and data compression with quantum side information. The method we employ in proving these results extends ideas developed by Sharma [arXiv:1404.5940], which he used to give a new proof of the strong converse theorem for state merging. For state redistribution, we prove the strong converse property for the boundary of the entire achievable rate region in the $(e,q)$-plane, where $e$ and $q$ denote the entanglement cost and quantum communication cost, respectively. In the case of measurement compression with quantum side information, we prove a strong converse theorem for the classical communication cost, which is a new result extending the previously known weak converse. For the remaining tasks, we provide new proofs for strong converse theorems previously established using smooth entropies. For each task, we obtain the strong converse theorem from explicit bounds on the figure of merit of the task in terms of a R\'enyi generalization of the optimal rate. Hence, we identify candidates for the strong converse exponents for each task discussed in this paper. To prove our results, we establish various new entropic inequalities, which might be of independent interest. These involve conditional entropies and mutual information derived from the sandwiched R\'enyi divergence. In particular, we obtain novel bounds relating these quantities, as well as the R\'enyi conditional mutual information, to the fidelity of two quantum states.Comment: 40 pages, 5 figures; v4: Accepted for publication in Journal of Mathematical Physic

GMCR: Graph-based Maximum Consensus Estimation for Point Cloud Registration

Point cloud registration is a fundamental and challenging problem for autonomous robots interacting in unstructured environments for applications such as object pose estimation, simultaneous localization and mapping, robot-sensor calibration, and so on. In global correspondence-based point cloud registration, data association is a highly brittle task and commonly produces high amounts of outliers. Failure to reject outliers can lead to errors propagating to downstream perception tasks. Maximum Consensus (MC) is a widely used technique for robust estimation, which is however known to be NP-hard. Exact methods struggle to scale to realistic problem instances, whereas high outlier rates are challenging for approximate methods. To this end, we propose Graph-based Maximum Consensus Registration (GMCR), which is highly robust to outliers and scales to realistic problem instances. We propose novel consensus functions to map the decoupled MC-objective to the graph domain, wherein we find a tight approximation to the maximum consensus set as the maximum clique. The final pose estimate is given in closed-form. We extensively evaluated our proposed GMCR on a synthetic registration benchmark, robotic object localization task, and additionally on a scan matching benchmark. Our proposed method shows high accuracy and time efficiency compared to other state-of-the-art MC methods and compares favorably to other robust registration methods.Comment: Accepted at icra 202