University of California, Santa Cruz

About: University of California, Santa Cruz is a education organization based out in Santa Cruz, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 15541 authors who have published 44120 publications receiving 2759983 citations. The organization is also known as: UCSC & UC, Santa Cruz.

The Great Observatories Origins Deep Survey: Initial results from optical and near-infrared imaging

Abstract: This special issue of the Astrophysical Journal Letters is dedicated to presenting initial results from the Great Observatories Origins Deep Survey (GOODS) that are primarily, but not exclusively, based on multiband imaging data obtained with the Hubble Space Telescope and the Advanced Camera for Surveys (ACS). The survey covers roughly 320 arcmin2 in the ACS F435W, F606W, F814W, and F850LP bands, divided into two well-studied fields. Existing deep observations from the Chandra X-Ray Observatory and ground-based facilities are supplemented with new, deep imaging in the optical and near-infrared from the European Southern Observatory and from the Kitt Peak National Observatory. Deep observations with the Space Infrared Telescope Facility are scheduled. Reduced data from all facilities are being released worldwide within 3-6 months of acquisition. Together, this data set provides two deep reference fields for studies of distant normal and active galaxies, supernovae, and faint stars in our own Galaxy. This Letter serves to outline the survey strategy and describe the specific data that have been used in the accompanying letters, summarizing the reduction procedures and sensitivity limits.

Measuring Distance and Properties of the Milky Way’s Central Supermassive Black Hole with Stellar Orbits

Abstract: We report new precision measurements of the properties of our Galaxy's supermassive black hole. Based on astrometric (1995-2007) and radial velocity (RV; 2000-2007) measurements from the W. M. Keck 10m telescopes, a fully unconstrained Keplerian orbit for the short-period star S0-2 provides values for the distance (R_0) of 8.0±0.6 kpc, the enclosed mass (M_(bh)) of 4.1±0.6x10^6 M☉ and the black hole's RV, which is consistent with zero with 30 km/s uncertainty. If the black hole is assumed to be at rest with respect to the Galaxy (e. g., has no massive companion to induce motion), we can further constrain the fit, obtaining R_0 = 8.4±0.4kpc and M_(bh) 4.5±0.4x10^6 M☉. More complex models constrain the extended dark mass distribution to be less than 3-4x10^5 M☉ within 0.01 pc, ~100 times higher than predictions from stellar and stellar remnant models. For all models, we identify transient astrometric shifts from source confusion (up to 5 times the astrometric error) and the assumptions regarding the black hole's radial motion as previously unrecognized limitations on orbital accuracy and the usefulness of fainter stars. Future astrometric and RV observations will remedy these effects. Our estimates of R_0 and the Galaxy's local rotation speed, which it is derived from combining R_0 with the apparent proper motion of Sgr A*, (θ_0 = 229±18 km/s), are compatible with measurements made using other methods. The increased black hole mass found in this study, compared to that determined using projected mass estimators, implies a longer period for the innermost stable orbit, longer resonant relaxation timescales for stars in the vicinity of the black hole and a better agreement with the M_(bh)-σ relation.

Subtle and blatant prejudice in Western Europe

Abstract: This paper develops, measures, and tests two types of intergroup prejudice—blatant and subtle. Blatant prejudice is the traditional, often studied form; it is hot, close and direct. Subtle prejudice is the modern form; it is cool, distant and indirect. Using data from seven independent national samples from western Europe, we constructed 10-item scales in four languages to measure each of these varieties of prejudice. We report the properties, structure and correlates of both scales across the seven samples, and make initial checks on their validity. The cross-nationally consistent results support the value of the blatant-subtle distinction as two varieties of prejudice. While they share many correlates, their distinctive differences suggest better specification of these correlates of prejudice. And the blatant-subtle distinction also aids in more precise specification of the effects of prejudice on attitudes toward immigrants. The paper closes with a normative interpretation of Subtle Prejudice.

The K2 Mission: Characterization and Early Results

Abstract: The K2 mission will make use of the Kepler spacecraft and its assets to expand upon Kepler's groundbreaking discoveries in the fields of exoplanets and astrophysics through new and exciting observations. K2 will use an innovative way of operating the spacecraft to observe target fields along the ecliptic for the next 2-3 years. Early science commissioning observations have shown an estimated photometric precision near 400 ppm in a single 30 minute observation, and a 6-hr photometric precision of 80 ppm (both at V = 12). The K2 mission offers long-term, simultaneous optical observation of thousands of objects at a precision far better than is achievable from ground-based telescopes. Ecliptic fields will be observed for approximately 75 days enabling a unique exoplanet survey which fills the gaps in duration and sensitivity between the Kepler and TESS missions, and offers pre-launch exoplanet target identification for JWST transit spectroscopy. Astrophysics observations with K2 will include studies of young open clusters, bright stars, galaxies, supernovae, and asteroseismology.

Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

Abstract: Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. For suitable classes of functions, learning algorithms are available that make a bounded number of mistakes, with the bound independent of the number of examples seen by the learner. We present one such algorithm that learns disjunctive Boolean functions, along with variants for learning other classes of Boolean functions. The basic method can be expressed as a linear-threshold algorithm. A primary advantage of this algorithm is that the number of mistakes grows only logarithmically with the number of irrelevant attributes in the examples. At the same time, the algorithm is computationally efficient in both time and space.