A deep X-ray view of Stripe-82: improving the data legacy in the search for new Blazars

In the era of big data, multi-messenger astrophysics and abundant computa- tional resources, strategic uses of the available resources are key to address current data analysis demands. In this work, we developed a novel techno- logical approach to a fully automated data processing pipeline for Swift-XRT observations, where all images ever observed by the satellite are downloaded and combined to provide the deepest view of the Swift x-ray sky; Sources are automatically identified and their fluxes are measured in four differ- ent bands. The pipeline runs autonomously, implementing a truly portable model, finally uploading the results to a central VO-compliant server to build a science-ready, continuously-updated photometric catalog. We applied the Swift- DeepSky pipeline to the whole Stripe-82 region of the sky to build the deep- est X-ray sources catalog to the region; down to ≈ 2 × 10−16 erg s−1cm−2 (0.2-10 keV). Such catalog was used to the identification of Blazar candidates detected only after the DeepSky pipeline.