We have performed a WISE (Wide-Field Infrared Survey Explorer) based study to identify and characterize young stellar objects (YSOs) in 12x12 degree Perseus OB2 association. Spectral energy distribution (SED) slope in range of 3.4-12 micron and a 5si
gma selection criteria were used to select our initial sample. Further manual inspection reduced our final catalog to 156 known and 119 YSO candidate. The spatial distribution of newly found YSOs all over the field shows an older generation of star formation which most of its massive members have evolved into main sequence stars. In contrast, the majority of younger members lie within the Perseus molecular cloud and currently active star forming clusters such as NGC1333 and IC348. We also identified additional 66 point sources which passed YSO selection criteria but are likely AGB stars. However their spatial distribution suggests that they may contain a fraction of the YSOs. Comparing our results with the commonly used color-color selections, we found that while color selection method fails in picking up bright but evolved weak disks, our SED fitting method can identify such sources, including transitional disks. In addition we have less contamination with background sources such as galaxies, but in a price of loosing fainter (Jmag > 12) YSOs. Finally we employed a Bayesian Monte Carlo SED fitting method to determine the characteristics of each YSO candidate. Distribution of SED slopes and model driven age and mass confirms separated YSO populations with suggested three age groups of younger than 1 Myr old, 1-5 Myr old, and older than 5 Myrs which agrees with the age of Per OB2 association and currently star forming sites within the cloud.
The main contribution of this paper is to design an Information Retrieval (IR) technique based on Algorithmic Information Theory (using the Normalized Compression Distance- NCD), statistical techniques (outliers), and novel organization of data base
structure. The paper shows how they can be integrated to retrieve information from generic databases using long (text-based) queries. Two important problems are analyzed in the paper. On the one hand, how to detect false positives when the distance among the documents is very low and there is actual similarity. On the other hand, we propose a way to structure a document database which similarities distance estimation depends on the length of the selected text. Finally, the experimental evaluations that have been carried out to study previous problems are shown.
