Suchergebnisse

We present Magellan/IMACS spectroscopy of three recently discovered ultra-faint Milky Way satellites, Grus II, Tucana IV, and Tucana V. We measure systemic velocities of vhel=-110.0 ± 0.5 kms, vhel= 15.9 kms and vhel=-36.2 kms for the three objects, respectively. Their large relative velocities demonstrate that the satellites are unrelated despite their close physical proximity. We determine a velocity dispersion for Tuc IV of σ = 4.3 km s but we cannot resolve the velocity dispersions of the other two systems. For Gru II, we place an upper limit (90% confidence) on the dispersion of σ < 1.9 kms and for Tuc V, we do not obtain any useful limits. All three satellites have metallicities below, but none has a detectable metallicity spread. We determine proper motions for each satellite based on Gaia astrometry and compute their orbits around the Milky Way. Gru II is on a tightly bound orbit with a pericenter of 25 kpc and orbital eccentricity of 0.45 . Tuc V likely has an apocenter beyond 100 kpc and could be approaching the Milky Way for the first time. The current orbit of Tuc IV is similar to that of Gru II, with a pericenter of 25 kpc and an eccentricity of 0.36 . However, a backward integration of the position of Tuc IV demonstrates that it collided with the Large Magellanic Cloud at an impact parameter of 4 kpc ∼120 Myr ago, deflecting its trajectory and possibly altering its internal kinematics. Based on their sizes, masses, and metallicities, we classify Gru II and Tuc IV as likely dwarf galaxies, but the nature of Tuc V remains uncertain. ; This publication is based upon work supported by the National Science Foundation under grant AST-1714873. J.D.S. was also partially supported by program number HST-GO14734, provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. Some of this work was carried out during a stay at the Kavli Institute for Theoretical Physics, which is supported in part by the National Science Foundation under grant No. NSF PHY-1748958, for the program The Small-Scale Structure of Cold(?) Dark Matter. T.S.L. was supported by NASA through Hubble Fellowship grant HST-HF2-51439.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. We thank Dan Kelson for many helpful conversations regarding IMACS data reduction and Petchara Pattarakijwanich for early contributions to the IMACS reduction procedures, as well as the anonymous referee for comments that helped improve the paper. This research has made use of NASA's Astrophysics Data System Bibliographic Services. Contour plots were generated using corner.py (Foreman-Mackey 2016). Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência e Tecnologia, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2012- 39559, ESP2013-48274, FPA2013-47986, and Centro de Excelencia Severo Ochoa SEV-2012-0234, some of which include ERDF funds from the European Union. This material is based upon work supported by the National Science Foundation under grant No. 1138766. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Edinburgh, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A& M University.

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce LUMOS, a deep learning method to measure photometry from galaxy images. LUMOS builds on BKGNET, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed LUMOS for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, LUMOS increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10 to 2 per cent, comparing to aperture photometry. Furthermore, with LUMOS photometry, the photo-z scatter is reduced by ≈10 per cent with the Deepz machine-learning photo-z code and the photo-z outlier rate by 20 per cent. The photo-z improvement is lower than expected from the SNR increment, however, currently the photometric calibration and outliers in the photometry seem to be its limiting factor. ; The PAU Survey is partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509, PID2019-111317GB-C31 and Juan de la Cierva fellowship and LACEGAL and EWC Marie Sklodowska-Curie grant No 734374 and no.776247 with ERDF funds from the EU Horizon 2020 Programme, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA and Beatriu de Pinos program of the Generalitat de Catalunya. Funding for PAUS has also 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), Bochum University (via a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935)), University College London, Portsmouth support through the Royal Society Wolfson fellowship and from the European Union's Horizon 2020 research and innovation programme under the grant agreement No 776247 EWC. ; Peer reviewed

In this paper, we introduce the 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. DEEPZ reduces the σ68 scatter statistic by 50 per cent at iAB = 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 per cent. 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 PAU Survey is partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2017-89838, PGC2018-094773, SEV-2016-0588, SEV-2016-0597, and MDM2015-0509, 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. Funding for PAUS has also 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), University College London and from the European Union's Horizon 2020 research and innovation programme under the grant agreement No. 776247 EWC. HH is supported by a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935). ; Peer reviewed

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

U.S. Department of Energy ; U.S. National Science Foundation ; Ministry of Science and Education of Spain ; Science and Technology Facilities Council of the United Kingdom ; Higher Education Funding Council for England ; National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign ; Kavli Institute of Cosmological Physics at the University of Chicago ; Center for Cosmology and Astro-Particle Physics at the Ohio State University ; Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University ; Financiadora de Estudos e Projetos ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Ministerio da Ciencia, Tecnologia e Inovacao ; Deutsche Forschungsgemeinschaft ; Argonne National Laboratory ; University of California at Santa Cruz ; University of Cambridge ; Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid ; University of Chicago ; University College London ; DES-Brazil Consortium ; University of Edinburgh ; Eidgenossische Technische Hochschule (ETH) Zurich ; Fermi National Accelerator Laboratory ; University of Illinois at Urbana-Champaign ; Institut de Ciencies de l'Espai (IEEC/CSIC) ; Institut de Fisica d'Altes Energies ; Lawrence Berkeley National Laboratory ; Ludwig-Maximilians Universitat Munchen ; associated Excellence Cluster Universe, the University of Michigan ; National Optical Astronomy Observatory ; University of Nottingham ; Ohio State University ; University of Pennsylvania ; University of Portsmouth ; SLAC National Accelerator Laboratory ; Stanford University ; University of Sussex ; Texas AM University ; OzDES Membership Consortium ; National Science Foundation ; El ministerio de Economia, Industria y Competitividad (MINECO) ; Centro de Excelencia Severo Ochoa ; European Research Council under the European Union's Seventh Framework Programme (FP7) ; ERC ; Australian Research Council Centre of Excellence for All-sky Astrophysics (CAAS-TRO) ; National Science Foundation: AST-1138766 ; El ministerio de Economia, Industria y Competitividad (MINECO): AYA2012-39559 ; El ministerio de Economia, Industria y Competitividad (MINECO): ESP2013-48274 ; El ministerio de Economia, Industria y Competitividad (MINECO): FPA2013-47986 ; Centro de Excelencia Severo Ochoa: SEV-2012-0234 ; ERC: 240672 ; ERC: 291329 ; ERC: 306478 ; Australian Research Council Centre of Excellence for All-sky Astrophysics (CAAS-TRO): CE110001020 ; We present the discovery of a z = 0.65 low-ionization broad absorption line (LoBAL) quasar in a post-starburst galaxy in data from the Dark Energy Survey (DES) and spectroscopy from the Australian Dark Energy Survey (OzDES). LoBAL quasars are a minority of all BALs, and rarer still is that this object also exhibits broad Fe II (an FeLoBAL) and Balmer absorption. This is the first BAL quasar that has signatures of recently truncated star formation, which we estimate ended about 40 Myr ago. The characteristic signatures of an FeLoBAL require high column densities, which could be explained by the emergence of a young quasar from an early, dust-enshrouded phase, or by clouds compressed by a blast wave. The age of the starburst component is comparable to estimates of the lifetime of quasars, so if we assume the quasar activity is related to the truncation of the star formation, this object is better explained by the blast wave scenario.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Cambridge Commonwealth Trust ; UK Science and Technology Research Council (STFC) ; Raymond and Beverly Sackler visiting fellowship at the Institute of Astronomy ; U.S. Department of Energy ; U.S. National Science Foundation ; Ministry of Science and Education of Spain ; Science and Technology Facilities Council of the United Kingdom ; Higher Education Funding Council for England ; National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign ; Kavli Institute of Cosmological Physics at the University of Chicago ; Center for Cosmology and Astro-Particle Physics at the Ohio State University ; Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University ; Financiadora de Estudos e Projetos ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Ministerio da Ciencia, Tecnologia e Inovacao ; Deutsche Forschungsgemeinschaft ; University of California at Santa Cruz ; University of Cambridge ; Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid ; University of Chicago ; University College London ; DES-Brazil Consortium ; University of Edinburgh ; Eidgenossische Technische Hochschule (ETH) Zurich ; Fermi National Accelerator Laboratory ; University of Illinois at Urbana-Champaign ; Institut de Ciencies de l'Espai (IEEC/CSIC) ; Institut de Fisica d'Altes Energies ; Lawrence Berkeley National Laboratory ; Ludwig-Maximilians Universitar Munchen ; University of Michigan ; National Optical Astronomy Observatory ; University of Nottingham ; Ohio State University ; University of Pennsylvania ; University of Portsmouth ; SLAC National Accelerator Laboratory ; Stanford University ; University of Sussex ; Texas AM University ; OzDES Membership Consortium ; National Science Foundation ; MINECO ; Centro de Excelencia Severo Ochoa ; European Research Council under the European Union ; ESO ; Australian Astronomical Observatory ; ICREA ; Science and Technology Facilities Council ; National Science Foundation: AST-1138766 ; MINECO: AYA2012-39559 ; MINECO: ESP-201348274 ; MINECO: FPA2013-47986 ; Centro de Excelencia Severo Ochoa: SEV-2012-0234 ; European Research Council under the European Union: 240672 ; European Research Council under the European Union: 291329 ; European Research Council under the European Union: 306478 ; ESO: 179.A-2010 ; ESO: 096.A-0411 ; Australian Astronomical Observatory: A/2013A/018 ; Australian Astronomical Observatory: A/2013B/001 ; Science and Technology Facilities Council: ST/N004493/1 ; Science and Technology Facilities Council: ST/M003914/1 ; Science and Technology Facilities Council: ST/K004182/1 ; We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift z(s) = 2.74 and image separation of 2.9 arcsec lensed by a foreground z(l) = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars showthe lens system as a superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology-independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and gi multicolour photometric observations from the Dark Energy Survey (DES), near-IR JK photometry from the VISTA Hemisphere Survey (VHS) and WISE mid-IR photometry, we have identified a candidate system with two catalogue components with i(AB) = 18.61 and i(AB) = 20.44 comprising an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as a lensed quasar with an emission line redshift of z = 2.739 +/- 0.003 and a foreground early-type galaxy with z = 0.400 +/- 0.002. We model the system as a single isothermal ellipsoid and find the Einstein radius theta(E) similar to 1.47 arcsec, enclosed mass M-enc similar to 4 x 10(11) M-circle dot and a time delay of similar to 52 d. The relatively wide separation, month scale time delay duration and high redshift make this an ideal system for constraining the expansion rate beyond a redshift of 1.