Planetary transit candidates in the CoRoT LRa01 field

Context. CoRoT is a pioneering space mission whose primary goals are stellar seismology and extrasolar planets search. Its surveys of large stellar fields generate numerous planetary candidates whose lightcurves have transit-like features. An extensive analytical and observational follow-up effort is undertaken to classify these candidates. Aims. We present the list of planetary transit candidates from the CoRoT LRa01 star field in the Monoceros constellation toward the Galactic anti-center direction. The CoRoT observations of LRa01 lasted from 24 October 2007 to 3 March 2008. Methods. We acquired and analyzed 7470 chromatic and 3938 monochromatic lightcurves. Instrumental noise and stellar variability were treated with several filtering tools by different teams from the CoRoT community. Different transit search algorithms were applied to the lightcurves. Results. Fifty-one stars were classified as planetary transit candidates in LRa01. Thirty-seven (i.e., 73% of all candidates) are "good" planetary candidates based on photometric analysis only. Thirty-two (i.e., 87% of the "good" candidates) have been followed-up. At the time of writing twenty-two cases were solved and five planets were discovered: three transiting hot-Jupiters (CoRoT-5b, CoRoT-12b, and CoRoT-21b), the first terrestrial transiting planet (CoRoT-7b), and another planet in the same system (CoRoT-7c, detected by radial velocity survey only). Evidence of another non-transiting planet in the CoRoT-7 system, namely CoRoT-7d, was recently found as well.

PERFORMANCE EVALUATION OF RESOURCE-AWARE BUSINESS PROCESSES USING STOCHASTIC AUTOMATA NETWORKS

In this work, we study the performance evaluation of resource-aware business process models. We define a new framework that allows the generation of analytical models for performance evaluation from business process models annotated with resource management information. This framework is composed of a new notation that allows the specification of resource management constraints and a method to convert a business process specification and its resource constraints into Stochastic Automata Networks (SANs). We show that the analysis of the generated SAN model provides several performance indices, such as average throughput of the system, average waiting time, average queues size, and utilization rate of resources. Using the BP2SAN tool - our implementation of the proposed framework - and a SAN solver (such as the PEPS tool) we show through a simple use-case how a business specialist with no skills in stochastic modeling can easily obtain performance indices that, in turn, can help to identify bottlenecks on the model, to perform workload characterization, to define the provisioning of resources, and to study other performance related aspects of the business process.