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تسجيل مستخدم جديدThe PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies
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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.
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We present a mock catalogue for the Physics of the Accelerating Universe Survey (PAUS) and use it to quantify the competitiveness of the narrow band imaging for measuring spectral features and galaxy clustering. The mock agrees with observed number c
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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 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.
The PAU Survey (PAUS) is an innovative photometric survey with 40 narrow bands at the William Herschel Telescope (WHT). The narrow bands are spaced at 100AA intervals covering the range 4500AA to 8500AA and, in combination with standard broad bands,
enable excellent redshift precision. This paper describes the technique, galaxy templates and additional photometric calibration used to determine early photometric redshifts from PAUS. Using BCNz2, a new photometric redshift code developed for this purpose, we characterise the photometric redshift performance using PAUS data on the COSMOS field. Comparison to secure spectra from zCOSMOS DR3 shows that PAUS achieves $sigma_{68} /(1+z) = 0.0037$ to $i_{mathrm{AB}} < 22.5$ when selecting the best 50% of the sources based on a photometric redshift quality cut. Furthermore, a higher photo-z precision ($sigma_{68}/(1+z) sim 0.001$) is obtained for a bright and high quality selection, which is driven by the identification of emission lines. We conclude that PAUS meets its design goals, opening up a hitherto uncharted regime of deep, wide, and dense galaxy survey with precise redshifts that will provide unique insights into the formation, evolution and clustering of galaxies, as well as their intrinsic alignments.
We present the 3-{it dimensional} intrinsic alignment power spectra between the projected 2d galaxy shape/spin and the 3d tidal field across $0.1<k/h{rm Mpc}^{-1}<60$ using cosmological hydrodynamical simulation, Illustris-TNG300, at redshifts rangin
g from $0.3$ to $2$. The shape-tidal field alignment increases with galaxy mass and the linear alignment coefficient $A_{rm IA}$, defined with respect to the primordial tidal field, is found to have weak redshift dependence. We also show a promising detection of the shape/spin-tidal field alignments for stellar mass limited samples and a weak or almost null signal for star-forming galaxies for the TNG300 volume, $sim 0.01~(h^{-1}{rm Gpc})^3$. We further study the morphology and environmental dependence of the intrinsic alignment power spectra. The shape of massive disk- and spheroid-galaxies tend to align with the tidal field. The spin of low mass disks (and spheroids at low redshifts) tend to be parallel with the tidal field, while the spin of massive spheroids and disks tend to be perpendicular to tidal field. The shape and spin of massive centrals align with the tidal field at both small and large scales. Satellites show a radial alignment within the one-halo term region, and low mass satellites have an intriguing alignment signal in the two-halo term region. We also forecast a feasibility to measure the intrinsic alignment power spectrum for spectroscopic and imaging surveys such as Subaru HSC/PFS and DESI. Our results thus suggest that galaxy intrinsic alignment can be used as a promising tool for constraining the galaxy formation models.
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