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We present a novel technique to overcome the limitations of the applicability of Principal Component Analysis to typical real-life data sets, especially astronomical spectra. Our new approach addresses the issues of outliers, missing information, large number of dimensions and the vast amount of data by combining elements of robust statistics and recursive algorithms that provide improved eigensystem estimates step-by-step. We develop a generic mechanism for deriving reliable eigenspectra without manual data censoring, while utilising all the information contained in the observations. We demonstrate the power of the methodology on the attractive collection of the VIMOS VLT Deep Survey spectra that manifest most of the challenges today, and highlight the improvements over previous workarounds, as well as the scalability of our approach to collections with sizes of the Sloan Digital Sky Survey and beyond.
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We present a comparison between two optical cluster finding methods: a matched filter algorithm using galaxy angular coordinates and magnitudes, and a percolation algorithm using also redshift information. We test the algorithms on two mock catalogues. The first mock catalogue is built by adding clusters to a Poissonian background, while the other is derived from N-body simulations. Choosing the physically most sensible parameters for each method, we carry out a detailed comparison and investigate advantages and limits of each algorithm, showing the possible biases on final results. We show that, combining the two methods, we are able to detect a large part of the structures, thus pointing out the need to search for clusters in different ways in order to build complete and unbiased samples of clusters, to be used for statistical and cosmological studies. In addition, our results show the importance of testing cluster finding algorithms on different kinds of mock catalogues to have a complete assessment of their behaviour.
Berkeley conducts 7 SETI programs at IR, visible and radio wavelengths. Here we review two of the newest efforts, Astropulse and Flys Eye. A variety of possible sources of microsecond to millisecond radio pulses have been suggested in the last several decades, among them such exotic events as evaporating primordial black holes, hyper-flares from neutron stars, emissions from cosmic strings or perhaps extraterrestrial civilizations, but to-date few searches have been conducted capable of detecting them. We are carrying out two searches in hopes of finding and characterizing these mu-s to ms time scale dispersed radio pulses. These two observing programs are orthogonal in search space; the Allen Telescope Arrays (ATA) Flys Eye experiment observes a 100 square degree field by pointing each 6m ATA antenna in a different direction; by contrast, the Astropulse sky survey at Arecibo is extremely sensitive but has 1/3,000 of the instantaneous sky coverage. Astropulses multibeam data is transferred via the internet to the computers of millions of volunteers. These computers perform a coherent de-dispersion analysis faster than the fastest available supercomputers and allow us to resolve pulses as short as 400 ns. Overall, the Astropulse survey will be 30 times more sensitive than the best previous searches. Analysis of results from Astropulse is at a very early stage. The Flys Eye was successfully installed at the ATA in December of 2007, and to-date approximately 450 hours of observation has been performed. We have detected three pulsars and six giant pulses from the Crab pulsar in our diagnostic pointing data. We have not yet detected any other convincing bursts of astronomical origin in our survey data. (Abridged)
We construct eigenspectra from the DR1 quasars in the SDSS using the Karhunen-Lo`eve (KL) transform (or Principal Component Analysis, PCA) in different redshift and luminosity bins. We find that the quasar spectra can be classified, by the first two eigenspectra, into a continuous sequence in the variation of the spectral slope. We also find a dependence on redshift and luminosity in the eigencoefficients. The dominant redshift effect is the evolution of the blended Fe~II emission (optical) and the Balmer continuum (the ``small-bump, $lambda_{rest} approx 2000-4000$AA), while the luminosity effect is related to the Baldwin effect. Correlations among several major broad emission lines are found, including the well-known ``Eigenvector-1.
The potential of the Sunyaev-Zeldovich (SZ) effect for cluster studies has long been appreciated, although not yet fully exploited. Recent technological advances and improvements in observing strategies have changed this, to the point where it is now possible to speak of this subject at a meeting devoted to {em surveys} in Cosmology. We will discuss SZ surveys by distinguishing what may be called {em pointed surveys}, dedicated to pre-selected clusters, from {em blind surveys}, those searching for clusters in blank fields. Surveys of the former type already have significant numbers of clusters with very good signal-to-noise images; surveys of the second type are currently possible, but as yet not undertaken. The discussion will focus on the kind of science that can be done in this ``new territory.
Planetary atmospheres are inherently 3D objects that can have strong gradients in latitude, longitude, and altitude. Secondary eclipse mapping is a powerful way to map the 3D distribution of the atmosphere, but the data can have large correlations and errors in the presence of photon and instrument noise. We develop a technique to mitigate the large uncertainties of eclipse maps by identifying a small number of dominant spectra to make them more tractable for individual analysis via atmospheric retrieval. We use the eigencurves method to infer a multi-wavelength map of a planet from spectroscopic secondary eclipse light curves. We then apply a clustering algorithm to the planet map to identify several regions with similar emergent spectra. We combine the similar spectra together to construct an eigenspectrum for each distinct region on the planetary map. We demonstrate how this approach could be used to isolate hot from cold regions and/or regions with different chemical compositions in observations of hot Jupiters with the James Webb Space Telescope (JWST). We find that our method struggles to identify sharp edges in maps with sudden discontinuities, but generally can be used as a first step before a more physically motivated modeling approach to determine the primary features observed on the planet.
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