Suchergebnisse

In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGNET, a deep neural network to predict the background and its associated error. BKGNET has been developed for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). The images obtained with PAUCam are affected by scattered light: an optical effect consisting of light multiply reflected that deposits energy in specific detector regions affecting the science measurements. Fortunately, scattered light is not a random effect, but it can be predicted and corrected for. We have found that BKGNET background predictions are very robust to distorting effects, while still being statistically accurate. On average, the use of BKGnet improves the photometric flux measurements by 7 per cent and up to 20 per cent at the bright end. BKGNET also removes a systematic trend in the background error estimation with magnitude in the i band that is present with the current PAU data management method. With BKGNET, we reduce the photometric redshift outlier rate by 35 per cent for the best 20 per cent galaxies selected with a photometric quality parameter. ; Funding for PAUS has been provided by Durham University (via the ERC StG DEGAS-259586), ETH Zurich, Leiden University (via ERC StG ADULT-279396 and Netherlands Organisation for Scientific Research (NWO) Vici grant 639.043.512) and University College London. The PAUS participants from Spanish institutions are partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2015-88861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA program of the Generalitat de Catalunya. The PAU data center is hosted by the Port d'Informacio Cientifica (PIC), maintained through a collaboration of CIEMAT and IFAE, with additional support from Universitat Autonoma de Barcelona and ERDF. CosmoHub has been developed by PIC and was partially funded by the 'Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion program of the Spanish government. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 776247. AA is supported by a Royal Society Wolfson Fellowship. MS has been supported by the National Science Centre (grant UMO2016/23/N/ST9/02963). ; Peer reviewed

We study the modelling of the halo occupation distribution (HOD) for the eBOSS DR16 emission line galaxies (ELGs). Motivated by previous theoretical and observational studies, we consider different physical effects that can change how ELGs populate haloes. We explore the shape of the average HOD, the fraction of satellite galaxies, their probability distribution function (PDF), and their density and velocity profiles. Our baseline HOD shape was fitted to a semi-analytical model of galaxy formation and evolution, with a decaying occupation of central ELGs at high halo masses. We consider Poisson and sub/super-Poissonian PDFs for satellite assignment. We model both Navarro–Frenk–White and particle profiles for satellite positions, also allowing for decreased concentrations. We model velocities with the virial theorem and particle velocity distributions. Additionally, we introduce a velocity bias and a net infall velocity. We study how these choices impact the clustering statistics while keeping the number density and bias fixed to that from eBOSS ELGs. The projected correlation function, wp, captures most of the effects from the PDF and satellites profile. The quadrupole, ξ2, captures most of the effects coming from the velocity profile. We find that the impact of the mean HOD shape is subdominant relative to the rest of choices. We fit the clustering of the eBOSS DR16 ELG data under different combinations of the above assumptions. The catalogues presented here have been analysed in companion papers, showing that eBOSS RSD+BAO measurements are insensitive to the details of galaxy physics considered here. These catalogues are made publicly available. ; Santiago Avila is supported by the MICUES project, funded by the European Union's Horizon 2020 research programme under the H2020 Marie Skłodowska-Curie Actions grant agreement no. 713366 (InterTalentum UAM). VGP acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 769130). SA is supported by the European Research Council through the COSFORM Research Grant (#670193). EMM was funded by the ERC under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 693024). ; Peer reviewed

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2015 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved ; We present a comparison of 14 galaxy formation models: 12 different semi-analytical models and 2 halo-occupation distribution models for galaxy formation based upon the same cosmological simulation and merger tree information derived from it. The participating codes have proven to be very successful in their own right but they have all been calibrated independently using various observational data sets, stellar models, and merger trees. In this paper we apply them without recalibration and this leads to a wide variety of predictions for the stellar mass function, specific star formation rates, stellar-to- halo mass ratios, and the abundance of orphan galaxies. The scatter is much larger than seen in previous comparison studies primarily because the codes have been used outside of their native environment within which they are well tested and calibrated. The purpose of the `nIFTy comparison of galaxy formation models' is to bring together as many different galaxy formation modellers as possible and to investigate a common approach to model calibration. This paper provides a unified description for all participating models and presents the initial, uncalibrated comparison as a baseline for our future studies where we will develop a common calibration framework and address the extent to which that reduces the scatter in the model predictions seen here ; We further acknowledge the financial support of the 2014 University of Western Australia Research Council. MH acknowledges financial support from the European Research Council via Research Collaboration Award for 'Fast Approximate Synthetic Universes for the SKA', the ARC Centre of Excellence for All Sky Astrophysics (CAASTRO) grant number CE110001020, and the two ARC Discovery Projects DP130100117 and DP140100198 AK is supported by the Ministerio de Economía y Competitividad (MINECO) in Spain through grant AYA2012-31101 as well as the Consolider-Ingenio 2010 Programme of the Spanish Ministerio de Ciencia e Innovación (MICINN) under grant MultiDark CSD2009-00064. He also acknowledges support from the Australian Research Council (ARC) grants DP130100117 and DP140100198. He further thanks Nancy Sinatra for the last of the secret agents. PA ST/L000652/1). FJC acknowledges support from the Spanish Ministerio de Economía y Competitividad project AYA2012-39620. SAC acknowledges grants from CONICET (PIP-220), Argentina. DJC acknowledges receipt of a QEII Fellowship from the Australian Government. WC would like to acknowledge the UWA's Research Collaboration Award: PG12105017. PJE is supported by the SSimPL programme and the Sydney Institute for Astronomy (SIfA), DP130100117. FF acknowledges financial contribution from the grants PRIN MIUR 2009 'The intergalactic medium (IGM) as a probe of the growth of cosmic structures' and PRIN INAF 2010 'From the dawn of galaxy formation'. VGP acknowledges support from a European Research Council Starting Grant (DEGAS-259586). The work of BH was supported by Advanced Grant 246797 GALFORMOD from the European Research Council. MH acknowledges financial support from the European Research Council via an Advanced Grant under grant agreement no. 321323 NEOGAL. PM has been supported by an FRA2012 grant of the University of Trieste, PRIN2010-2011 (J91J12000450001) from MIUR, and Consorzio per la Fisica di Trieste. NDP was supported by BASAL PFB-06 CATA, and Fondecyt 1150300. CP acknowledges support of the Australian Research Council (ARC) through Future Fellowship FT130100041 and Discovery Project DP140100198. WC and CP acknowledge support of ARC DP130100117. AP was supported by beca FI and 2009-SGR-1398 from Generalitat de Catalunya and project AYA2012-39620 from MICINN. RAS acknowledges support from the NSF grant AST-1055081. RSS thanks the Downsbrough family for their generous support. SKY acknowledges support from the National Research Foundation of Korea (Doyak 2014003730)