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Nova Sco 2008 was recently shown to have resulted from the merger of the two stars in the contact binary V1309 Sco. This is the first stellar merger ever observed between two convective stars. We present archival data, new infrared photometry, and Hubble Space Telescope WFC3 imaging of V1309 Sco. Spitzer observations show that it had a large infrared excess in the 3.6 μm to 8 μm range more than a year before the merger. Standard color diagnostics of the pre-merger infrared colors place V1309 Sco in the same region where evolved stars with chemically complex mass loss are located. Since the nova outburst subsided in optical bandpasses in 2008, the merger remnant's brightness in optical bandpasses, near-IR bandpasses, and the Spitzer 3.6 μm and 4.5 μm channels has varied by several magnitudes and in complex ways. A temporary, strong increase in the reddening during 2010 suggests the occurrence of a dust formation event. We point out several peculiarities in the relative fluxes and time behavior of the optical and near-IR magnitudes, which could be explained if some of the photometric bandpasses in the 1-5 μm range are strongly affected by emission lines.

Data is now produced faster than it can be meaningfully analyzed. Many modern data sets present unprecedented analytical challenges, not merely because of their size but by their inherent complexity and information richness. Large numbers of astronomical objects now have dozens or hundreds of useful parameters describing each one. Traditional color-color plots using a limited number of symbols and some color-coding are clearly inadequate for finding all useful correlations given such large numbers of parameters. To capitalize on the opportunities provided by these data sets one needs to be able to organize, analyze and visualize them in fundamentally new ways. The identification and extraction of useful information in multiparametric, high-dimensional data sets - data mining - is greatly facilitated by finding simpler, that is, lower-dimensional abstract mathematical representations of the data sets that are more amenable to analysis. Dimensionality reduction consists of finding a lower-dimensional representation of high-dimensional data by constructing a set of basis functions that capture patterns intrinsic to a particular state space. Traditional methods of dimension reduction and pattern recognition often fail to work well when performed upon data sets as complex as those that now confront astronomy. We present here our developments of data compression, sampling, nonlinear dimensionality reduction, and clustering, which are important steps in the analysis of large-scale, complex datasets.

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other., 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy"

Spitzer IRAC and MIPS images reveal a large dust emission feature ~6' in diameter which appears to be a shell in close proximity to, and perhaps physically related to, the D-type Mira symbiotic BI Cru. Smaller optical lobes are already known to be emanating from some symbiotics including BI Cru. However, this is the first extended structure found in the IR which is associated with a symbiotic Mira system. The IR shell of BI Cru is more than 5 times larger in arc size than the star's optical lobe. Published distance estimates imply that the IR shell is ~4 to ~8 pc in diameter, which is larger than the largest optical lobe known to be associated with any Mira symbiotic system. The large disparity between its IR and optical shell sizes, along with what appear to be multiple intersecting arcs, suggest that BI Cru has undergone multiple mass-loss episodes. A trend of rapidly increasing brightness toward longer wavelengths, along with a much more diffuse structure at 70 μm than at shorter wavelengths, and suggests a greater abundance of relatively colder and older dust which may be the remnant of earlier mass outflows.