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Continuous quantum measurement is the backbone of various methods in quantum control, quantum metrology, and quantum information. Here, we present a generalized formulation of dispersive measurement of a complex quantum systems. We describe the complex system as an open quantum system that is strongly coupled to a non-Markovian environment, enabling the treatment of a broad variety of natural or engineered complex systems. The system is monitored via a probe resonator coupled to a broadband (Markovian) reservoir. Based on this model, we derive a formalism of Stochastic Hierarchy Equations of Motion (SHEM) describing the decoherence dynamics of the system conditioned on the measurement record. Furthermore, we demonstrate a spectroscopy method based on weak quantum measurement to reveal the non-Markovian nature of the environment, which we term weak spectroscopy., Comment: Published version, the section on continuous state tomography will be published in a separate manuscript

Energy transfer in the photosynthetic complex of the Green Sulfur Bacteria known as the Fenna-Matthews-Olson (FMO) complex is studied theoretically taking all three subunits (monomers) of the FMO trimer and the recently found eighth bacteriochlorophyll (BChl) molecule into account. We find that in all considered cases there is very little transfer between the monomers. Since it is believed that the eighth BChl is located near the main light harvesting antenna we look at the differences in transfer between the situation when BChl 8 is initially excited and the usually considered case when BChl 1 or 6 is initially excited. We find strong differences in the transfer dynamics, both qualitatively and quantitatively. When the excited state dynamics is initialized at site eight of the FMO complex, we see a slow exponential-like decay of the excitation. This is in contrast to the oscillations and a relatively fast transfer that occurs when only seven sites or initialization at sites 1 and 6 is considered. Additionally we show that differences in the values of the electronic transition energies found in the literature lead to a large difference in the transfer dynamics.

A master equation, derived from the non-Markovian quantum state diffusion (NMQSD), is used to calculate excitation energy transfer in the photosynthetic Fenna-Matthews-Olson (FMO) pigment-protein complex at various temperatures. This approach allows us to treat spectral densities that contain explicitly the coupling to internal vibrational modes of the chromophores. Moreover, the method is very efficient, with the result that the transfer dynamics can be calculated within about one minute on a standard PC, making systematic investigations w.r.t. parameter variations tractable. After demonstrating that our approach is able to reproduce the results of the numerically exact hierarchical equations of motion (HEOM) approach, we show how the inclusion of vibrational modes influences the transfer.

We investigate theoretically the phenomenon of exchange narrowing in the absorption spectrum of a chain of monomers, which are coupled via resonant dipole-dipole interaction. The individual (uncoupled) monomers exhibit a broad absorption line shape due to the coupling to an environment consisting of a continuum of vibrational modes. Upon increasing the interaction between the monomers, the absorption spectrum of the chain narrows. For a non-Markovian environment with a Lorentzian spectral density, we find a narrowing of the peak width (full width at half maximum (FWHM)) by a factor 1/N, where N is the number of monomers. This is much stronger than the usual 1/sqrt{N} narrowing. Furthermore it turns out that for a Markovian environment no exchange narrowing at all occurs. The relation of different measures of the width (FWHM, standard deviation) is discussed.

The analysis and an efficient scientific exploration of the Digital Palomar Observatory Sky Survey (DPOSS) represents a major technical challenge. The input data set consists of 3 Terabytes of pixel information, and contains a few billion sources. We describe some of the specific scientific problems posed by the data, including searches for distant quasars and clusters of galaxies, and the data-mining techniques we are exploring in addressing them. Machine-assisted discovery methods may become essential for the analysis of such multi-Terabyte data sets. New and future approaches involve unsupervised classification and clustering analysis in the Giga-object data space, including various Bayesian techniques. In addition to the searches for known types of objects in this data base, these techniques may also offer the possibility of discovering previously unknown, rare types of astronomical objects., Comment: Invited paper, to appear in Applications of Digital Image Processing XX, ed. A. Tescher, Proc. S.P.I.E. vol. 3164, in press; 10 pages, a self-contained TeX file, and 3 separate postscript figures

The search for surface and near-surface liquid water on Mars is a central part of current and planned future exploration, which include radar sounders on Mars Express and MRO and proposed synthetic aperture radar (SAR) imagers. In order to penetrate sand and dust cover, these systems are proposed for longer wavelengths (e.g, from [2]: 24 cm / L-band and 74 cm / P-band) than those considered optimal for the detection of soil moisture (6 cm / C-band). However, there has been some success in detecting soil moisture at longer wavelengths. Given the size and mass constraints for Mars missions, the optimization of radar instrument parameters for meeting science objectives, such as searching for liquid water, is essential. In this on-going study, we are using repeat coverage of Mars analog sites with multifrequency (C, L and P band) airborne radar and ground truth soil sample data to assess the detectability of soil moisture.

In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent.

The Autonomous Sciencecraft Constellation-Techsat 21 mission is a NASA New Millennium Program mission selected for flight in 2004. ASC will autonomously perform science analyses of X-band radar data, while in Earth orbit. Additional information is contained in the original extended abstract.

This paper talks about the techsat-21 autonomous sciencecraft constellation.

The paper talks about the planning, scheduling, and execution framework used in ASC (Autonomous Sciencecraft Constellation).

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys.