Engineering topics

Djorgovski, S. George

Publications and source records attributed to Djorgovski, S. George.

4 records

DOE OSTI2013

A plausible (overlooked) super-luminous supernova in the Sloan digital sky survey stripe 82 data

We present the discovery of a plausible super-luminous supernova (SLSN), found in the archival data of Sloan Digital Sky Survey (SDSS) Stripe 82, called PSN 000123+000504. The supernova (SN) peaked at m {sub g} < 19.4 mag in the second half of 2005 September, but was missed by the real-time SN hunt. The observed part of the light curve (17 epochs) showed that the rise to the maximum took over 30 days, while the decline time lasted at least 70 days (observed frame), closely resembling other SLSNe of SN 2007bi type. The spectrum of the host galaxy reveals a redshift of z = 0.281 and the distance modulus of μ = 40.77 mag. Combining this information with the SDSS photometry, we found the host galaxy to be an LMC-like irregular dwarf galaxy with an absolute magnitude of M{sub B} = –18.2 ± 0.2 mag and an oxygen abundance of 12+log [O/H]=8.3±0.2; hence, the SN peaked at M {sub g} < –21.3 mag. Our SLSN follows the relation for the most energetic/super-luminous SNe exploding in low-metallicity environments, but we found no clear evidence for SLSNe to explode in low-luminosity (dwarf) galaxies only. The available information on the PSN 000123+000504 light curve suggests the magnetar-powered model as a likely scenario of this event. This SLSN is a new addition to a quickly growing family of super-luminous SNe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

FIRST-2MASS RED QUASARS: TRANSITIONAL OBJECTS EMERGING FROM THE DUST

We present a sample of 120 dust-reddened quasars identified by matching radio sources detected at 1.4 GHz in the Faint Images of the Radio Sky at Twenty Centimeters survey with the near-infrared Two Micron All Sky Survey catalog and color-selecting red sources. Optical and/or near-infrared spectroscopy provide broad wavelength sampling of their spectral energy distributions that we use to determine their reddening, characterized by E(B - V). We demonstrate that the reddening in these quasars is best described by Small-Magellanic-Cloud-like dust. This sample spans a wide range in redshift and reddening (0.1 {approx}< z {approx}< 3, 0.1 {approx}< E(B - V) {approx}< 1.5), which we use to investigate the possible correlation of luminosity with reddening. At every redshift, dust-reddened quasars are intrinsically the most luminous quasars. We interpret this result in the context of merger-driven quasar/galaxy co-evolution where these reddened quasars are revealing an emergent phase during which the heavily obscured quasar is shedding its cocoon of dust prior to becoming a 'normal' blue quasar. When correcting for extinction, we find that, depending on how the parent population is defined, these red quasars make up {approx}< 15%-20% of the luminous quasar population. We estimate, based on the fraction of objects in this phase, that its duration is 15%-20% as long as the unobscured, blue quasar phase.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2004

Grist : grid-based data mining for astronomy

The Grist project is developing a grid-technology based system as a research environment for astronomy with massive and complex datasets. This knowledge extraction system will consist of a library of distributed grid services controlled by a workflow system, compliant with standards emerging from the grid computing, web services, and virtual observatory communities. This new technology is being used to find high redshift quasars, study peculiar variable objects, search for transients in real time, and fit SDSS QSO spectra to measure black hole masses. Grist services are also a component of the 'hyperatlas' project to serve high-resolution multi-wavelength imagery over the Internet. In support of these science and outreach objectives, the Grist framework will provide the enabling fabric to tie together distributed grid services in the areas of data access, federation, mining, subsetting, source extraction, image mosaicking, statistics, and visualization.

grid computing↗

NASA NTRS1994

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Djorgovski, S. George↗