ﻻ يوجد ملخص باللغة العربية
The Karhunen-Loeve (KL) transform can compactly represent the information contained in large, complex datasets, cleanly eliminating noise from the data and identifying elements of the dataset with extreme or inconsistent characteristics. We develop techniques to apply the KL transform to the 4000-5700A region of 9,800 QSO spectra with z < 0.619 from the SDSS archive. Up to 200 eigenspectra are needed to fully reconstruct the spectra in this sample to the limit of their signal/noise. We propose a simple formula for selecting the optimum number of eigenspectra to use to reconstruct any given spectrum, based on the signal/noise of the spectrum, but validated by formal cross-validation tests. We show that such reconstructions can boost the effective signal/noise of the observations by a factor of 6 as well as fill in gaps in the data. The improved signal/noise of the resulting set will allow for better measurement and analysis of these spectra. The distribution of the QSO spectra within the eigenspace identifies regions of enhanced density of interesting subclasses, such as Narrow Line Seyfert 1s (NLS1s). The weightings, as well as the inability of the eigenspectra to fit some of the objects, also identifies outliers, which may be objects that are not valid members of the sample or objects with rare or unique properties. We identify 48 spectra from the sample that show no broad emission lines, 21 objects with unusual [O III] emission line properties, and 9 objects with peculiar H-beta emission line profiles. We also use this technique to identify a binary supermassive black hole candidate. We provide the eigenspectra and the reconstructed spectra of the QSO sample.
We present an investigation of the optical spectra of 264 low-redshift (z < 0.2) Type Ia supernovae (SNe Ia) discovered by the Palomar Transient Factory, an untargeted transient survey. We focus on velocity and pseudo-equivalent width measurements of
We present an analysis of the maximum light, near ultraviolet (NUV; 2900-5500 A) spectra of 32 low redshift (0.001<z<0.08) Type Ia supernovae (SNe Ia), obtained with the Hubble Space Telescope (HST). We combine this spectroscopic sample with high-qua
Redshift-space distortions (RSD) generically affect any spatially-dependent observable that is mapped using redshift information. The effect on the observed clustering of galaxies is the primary example of this. This paper is devoted to another examp
We present a sample of $i_{775}$-dropout candidates identified in five Hubble Advanced Camera for Surveys fields centered on Sloan Digital Sky Survey QSOs at redshift $zsim 6$. Our fields are as deep as the Great Observatory Origins Deep Survey (GOOD
We present initial results from a Hubble Space Telescope snapshot imaging survey of the host galaxies of Swift-BAT active galactic nuclei (AGN) at z<0.1. The hard X-ray selection makes this sample sample relatively unbiased in terms of obscuration co