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Register a new userThe PAU Survey: Operation and orchestration of multi-band survey data
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Pau Tallada Cresp\\'i
Publication date
2018
fields
Physics
and research's language is
English
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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. PAUs 18-CCD camera (PAUCam), installed at the prime focus of the William Herschel Telescope at the Roque de los Muchachos Observatory (La Palma, Canary Islands), scans part of the northern sky, to collect low resolution spectral information of millions of galaxies with its 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. 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 dInformacio Cientifica (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 support the operations, to the software tools and web services for the data process orchestration and exploration. In particular we will focus on the PAU database, developed for the coordination of the different PAUdm tasks, and to preserve and guarantee the consistency of data and metadata.
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Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample.
We present the first measurements of the projected clustering and intrinsic alignments (IA) of galaxies observed by the Physics of the Accelerating Universe Survey (PAUS). With photometry in 40 narrow optical passbands ($450rm{nm}-850rm{nm}$), the quality of photometric redshift estimation is $sigma_{z} sim 0.01(1 + z)$ for galaxies in the $19,rm{deg}^{2}$ Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) W3 field, allowing us to measure the projected 3D clustering and IA for flux-limited, faint galaxies ($i < 22.5$) out to $zsim0.8$. To measure two-point statistics, we developed, and tested with mock photometric redshift samples, `cloned random galaxy catalogues which can reproduce data selection functions in 3D and account for photometric redshift errors. In our fiducial colour-split analysis, we made robust null detections of IA for blue galaxies and tentative detections of radial alignments for red galaxies ($sim1-3sigma$), over scales of $0.1-18,h^{-1}rm{Mpc}$. The galaxy clustering correlation functions in the PAUS samples are comparable to their counterparts in a spectroscopic population from the Galaxy and Mass Assembly survey, modulo the impact of photometric redshift uncertainty which tends to flatten the blue galaxy correlation function, whilst steepening that of red galaxies. We investigate the sensitivity of our correlation function measurements to choices in the random catalogue creation and the galaxy pair-binning along the line of sight, in preparation for an optimised analysis over the full PAUS area.
This paper defines the UK Infra-Red Telescope (UKIRT) Hemisphere Survey (UHS) and release of the remaining ~12,700 sq.deg of J-band survey data products. The UHS will provide continuous J and K-band coverage in the northern hemisphere from a declination of 0 deg to 60 deg by combining the existing Large Area Survey, Galactic Plane Survey and Galactic Clusters Survey conducted under the UKIRT Infra-red Deep Sky Survey (UKIDSS) programme with this new additional area not covered by UKIDSS. The released data includes J-band imaging and source catalogues over the new area, which, together with UKIDSS, completes the J-band UHS coverage over the full ~17,900 sq.deg area. 98 per cent of the data in this release have passed quality control criteria, the remaining 2 per cent being scheduled for re-observation. The median 5-sigma point source sensitivity of the released data is 19.6 mag (Vega). The median full width at half-maximum of the point spread function across the dataset is 0.75 arcsec. In this paper, we outline the survey management, data acquisition, processing and calibration, quality control and archiving as well as summarising the characteristics of the released data products. The data are initially available to a limited consortium with a world-wide release scheduled for August 2018.
In this paper we introduce the textsc{Deepz} deep learning photometric redshift (photo-$z$) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS field. textsc{Deepz} reduces the $sigma_{68}$ scatter statistic by 50% at $i_{rm AB}=22.5$ compared to existing algorithms. This improvement is achieved through various methods, including transfer learning from simulations where the training set consists of simulations as well as observations, which reduces the need for training data. The redshift probability distribution is estimated with a mixture density network (MDN), which produces accurate redshift distributions. Our code includes an autoencoder to reduce noise and extract features from the galaxy SEDs. It also benefits from combining multiple networks, which lowers the photo-$z$ scatter by 10 percent. Furthermore, training with randomly constructed coadded fluxes adds information about individual exposures, reducing the impact of photometric outliers. In addition to opening up the route for higher redshift precision with narrow bands, these machine learning techniques can also be valuable for broad-band surveys.
The C-Band All-Sky Survey (C-BASS) is an all-sky full-polarization survey at a frequency of 5 GHz, designed to provide data complementary to the all-sky surveys of WMAP and Planck and future CMB B-mode polarization imaging surveys. We describe the design and performance of the digital backend used for the northern part of the survey. In particular we describe the features that efficiently implement the demodulation and filtering required to suppress contaminating signals in the time-ordered data, and the capability for real-time correction of detector non-linearity and receiver balance.
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