Pre-main sequence stars in the Cepheus flare region

We present results of optical spectroscopic and BVR_CI_C photometric observations of 77 pre-main sequence (PMS) stars in the Cepheus flare region. A total of 64 of these are newly confirmed PMS stars, originally selected from various published candidate lists. We estimate effective temperatures and luminosities for the PMS stars, and comparing the results with pre-main sequence evolutionary models we estimate stellar masses of 0.2-2.4M_sun and stellar ages of 0.1-15 Myr. Among the PMS stars, we identify 15 visual binaries with separations of 2-10 arcsec. From archival IRAS, 2MASS, and Spitzer data, we construct their spectral energy distributions and classify 5% of the stars as Class I, 10% as Flat SED, 60% as Class II, and 3% as Class III young stellar objects (YSOs). We identify 12 CTTS and 2 WTTS as members of NGC 7023, with mean age of 1.6 Myr. The 13 PMS stars associated with L1228 belong to three small aggregates: RNO 129, L1228A, and L1228S. The age distribution of the 17 PMS stars associated with L1251 suggests that star formation has propagated with the expansion of the Cepheus flare shell. We detect sparse aggregates of 6-7 Myr old PMS stars around the dark clouds L1177 and L1219, at a distance of 400 pc. Three T Tauri stars appear to be associated with the Herbig Ae star SV Cep at a distance of 600 pc. Our results confirm that the molecular complex in the Cepheus flare region contains clouds of various distances and star forming histories.Comment: 61 pages, 27 figures, 8 tables; accepted for publication by ApJ

Young Stellar Object Variability (YSOVAR): Long Timescale Variations in the Mid-Infrared

The YSOVAR (Young Stellar Object VARiability) Spitzer Space Telescope observing program obtained the first extensive mid-infrared (3.6 & 4.5 um) time-series photometry of the Orion Nebula Cluster plus smaller footprints in eleven other star-forming cores (AFGL490, NGC1333, MonR2, GGD 12-15, NGC2264, L1688, Serpens Main, Serpens South, IRAS 20050+2720, IC1396A, and Ceph C). There are ~29,000 unique objects with light curves in either or both IRAC channels in the YSOVAR data set. We present the data collection and reduction for the Spitzer and ancillary data, and define the "standard sample" on which we calculate statistics, consisting of fast cadence data, with epochs about twice per day for ~40d. We also define a "standard sample of members", consisting of all the IR-selected members and X-ray selected members. We characterize the standard sample in terms of other properties, such as spectral energy distribution shape. We use three mechanisms to identify variables in the fast cadence data--the Stetson index, a chi^2 fit to a flat light curve, and significant periodicity. We also identified variables on the longest timescales possible of ~6 years, by comparing measurements taken early in the Spitzer mission with the mean from our YSOVAR campaign. The fraction of members in each cluster that are variable on these longest timescales is a function of the ratio of Class I/total members in each cluster, such that clusters with a higher fraction of Class I objects also have a higher fraction of long-term variables. For objects with a YSOVAR-determined period and a [3.6]-[8] color, we find that a star with a longer period is more likely than those with shorter periods to have an IR excess. We do not find any evidence for variability that causes [3.6]-[4.5] excesses to appear or vanish within our data; out of members and field objects combined, at most 0.02% may have transient IR excesses.Comment: Accepted to AJ; 38 figures, 93 page

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound