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The Physics of the Accelerating Universe (PAU) Survey is an international project for the study of cosmological parameters associated with Dark Energy. The PAU 18-CCD camera (PAUCam) is installed at the primary focus of the William Herschel Telescope at Observatorio del Roque de los Muchachos (La Palma, Canary Islands). The camera incorporates a unique set of 40 narrow band filters in the optical range from 450 nm to 850 nm and a set of 6 standard broad band filters. It is used to scan part of the northern sky in order to collect low resolution spectral information from millions of galaxies. The PAU data management (PAUdm) team is in charge of treating the data, including data transfer from the observatory to the PAU Survey data center, hosted at Port d’Informació Científica (PIC). PAUdm is also in charge of the storage, data reduction and, finally, of making the results available to the scientific community. We describe the technical solutions adopted to cover different aspects of the PAU Survey data management, from the computing infrastructure to the software tools and web services for the processing orchestration and data exploration. We give special attention to the PAU database, developed to preserve and guarantee the consistency of data and metadata and also used to coordinate the different PAUdm tasks.

A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-zmethods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-ztraining, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-based data interface that not only provides the framework to carry out the above steps in a systematic way, enabling the ease testing and comparison of different algorithms, but also addresses the processing requirements by parallelizing the calculation in a transparent way for the user. This framework called the Science Portal (hereafter Portal) was developed in the context the Dark Energy Survey (DES) to facilitate scientific analysis. In this paper, we show how the Portal can provide a reliable environment to access vast datasets, provide validation algorithms and metrics, even in the case of multiple photo-zs methods. It is possible to maintain the provenance between the steps of a chain of workflows while ensuring reproducibility of the results. We illustrate how the Portal can be used to provide photo-zestimates using the DES first year (Y1A1) data. While the DES collaboration is still developing techniques to obtain more precise photo-zs, having a structured framework like the one presented here is critical for the systematic vetting of DES algorithmic improvements and the consistent production of photo-zs in future DES releases.

We present the Calán-Yale Deep Extragalactic Research (CYDER) Survey. The broad goals of the survey are the study of stellar populations, the star formation history of the universe and the formation and evolution of galaxies. The fields studied include Chandra deep pointings in order to characterize the X-ray faint populations. Here we present the results on the first fields studied. We find that the redshift distribution is consistent with that found in the Chandra Deep Field North. The distribution of hardness ratios is, however, softer in our sample. We find a high redshift quasar, CXOCY J125304.0-090737 at z = 4.179, which suggests that the abundance of low luminosity high redshift quasars may be larger than what would be expected from reasonable extrapolations from the quasar optical luminosity function.

We present CosmoHub (https://cosmohub.pic.es), a web application based on Hadoop to perform interactive exploration and distribution of massive cosmological datasets. Recent Cosmology seeks to unveil the nature of both dark matter and dark energy mapping the large-scale structure of the Universe, through the analysis of massive amounts of astronomical data, progressively increasing during the last (and future) decades with the digitization and automation of the experimental techniques.