The Gaia-ESO Survey: Homogenisation of stellar parameters and elemental abundances

The Gaia-ESO Survey is a public spectroscopic survey that targeted ≳105 stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its final public release in May 2022. Unlike other spectroscopic surveys, Gaia-ESO is the only survey that observed stars across all spectral types with dedicated, specialised analyses: from O (Teff ~ 30 000–52 000 K) all the way to K-M (≳3500 K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different types. In the final data release (internal data release 6) of the Gaia-ESO Survey, we provide the final database containing a large number of products, such as radial velocities, stellar parameters and elemental abundances, rotational velocity, and also, for example, activity and accretion indicators in young stars and membership probability in star clusters for more than 114 000 stars. The spectral analysis is coordinated by a number of working groups (WGs) within the survey, each specialised in one or more of the various stellar samples. Common targets are analysed across WGs to allow for comparisons (and calibrations) amongst instrumental setups and spectral types. Here we describe the procedures employed to ensure all survey results are placed on a common scale in order to arrive at a single set of recommended results for use by all survey collaborators. We also present some general quality and consistency checks performed on the entirety of the survey results.This work was partly supported by the European Union FP7 programme through ERC grant number 320360 and by the Leverhulme Trust through grant RPG-2012-541. We acknowledge the support from INAF and Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) in the form of the grant “Premiale VLT 2012”. L. Magrini and M. Van der Swaelmen acknowledge support by the WEAVE Italian consortium, and by the INAF Grant “Checs”. A.J. Korn acknowledges support by the Swedish National Space Agency (SNSA). A. Lobel acknowledges support in part by the Belgian Federal Science Policy Office under contract no. BR/143/A2/BRASS and by the European Union Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie grant Agreement No. 823734. D.K. Feuillet was partly supported by grant no. 2016-03412 from the Swedish Research Council. D. Montes acknowledges financial support from the Agencia Estatal de Investigacion of the Ministerio de Ciencia, Innovation through project PID2019-109522GB-C54 /AEI/10.13039/501100011033. E. Marfil acknowledges financial support from the European Regional Development Fund (ERDF) and the Gobierno de Canarias through project ProID2021010128. J.I. Gonzalez Hernandez acknowledges financial support from the Spanish Ministry of Science and Innovation (MICINN) project PID2020-117493GB-I00. M. Bergemann is supported through the Lise Meitner grant from the Max Planck Society and acknowledges support by the Collaborative Research centre SFB 881 (projects A5, A10), Heidelberg University, of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). This project has received funding from the European Research Council (ERC) under the European Union, Horizon 2020 research and innovation programme (Grant agreement No. 949173). P. Jofré acknowledges financial support of FONDECYT Regular 1200703 as well as Nucleo Mile-nio ERIS NCN2021_017. R. Smiljanic acknowledges support from the National Science Centre, Poland (2014/15/B/ST/03981). S.R. Berlanas acknowledges support by MCIN/AEI/10.13039/501100011033 (contract FJC 2020-045785-I) and NextGeneration EU/PRTR and MIU (UNI/551/2021) through grant Margarita Salas-ULL. T. Bensby acknowledges financial support by grant No. 2018-04857 from the Swedish Research Council. T. Merle is supported by a grant from the Foundation ULB. T. Morel are grateful to Belgian F.R.S.-FNRS for support, and are also indebted for an ESA/PRODEX Belspo contract related to the Gaia Data Processing and Analysis Consortium and for support through an ARC grant for Concerted Research Actions financed by the Federation Wallonie-Brussels. W. Santos acknowledges FAPERJ for a Ph.D. fellowship. H.M. Tabernero acknowledges financial support from the Agencia Estatal de Investigation of the Ministerio de Ciencia, Innovation through project PID2019-109522GB-C51/AEI/10.13039/501100011033

The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products

Context. The Gaia-ESO Public Spectroscopic Survey is an ambitious project designed to obtain astrophysical parameters and elemental abundances for 100 000 stars, including large representative samples of the stellar populations in the Galaxy, and a well-defined sample of 60 (plus 20 archive) open clusters. We provide internally consistent results calibrated on benchmark stars and star clusters, extending across a very wide range of abundances and ages. This provides a legacy data set of intrinsic value, and equally a large wide-ranging dataset that is of value for the homogenisation of other and future stellar surveys and Gaia's astrophysical parameters. Aims. This article provides an overview of the survey methodology, the scientific aims, and the implementation, including a description of the data processing for the GIRAFFE spectra. A companion paper introduces the survey results. Methods. Gaia-ESO aspires to quantify both random and systematic contributions to measurement uncertainties. Thus, all available spectroscopic analysis techniques are utilised, each spectrum being analysed by up to several different analysis pipelines, with considerable effort being made to homogenise and calibrate the resulting parameters. We describe here the sequence of activities up to delivery of processed data products to the ESO Science Archive Facility for open use. Results. The Gaia-ESO Survey obtained 202 000 spectra of 115 000 stars using 340 allocated VLT nights between December 2011 and January 2018 from GIRAFFE and UVES. Conclusions. The full consistently reduced final data set of spectra was released through the ESO Science Archive Facility in late 2020, with the full astrophysical parameters sets following in 2022. A companion article reviews the survey implementation, scientific highlights, the open cluster survey, and data products

The Identification of Z-dropouts in Pan-STARRS1: Three Quasars at 6.5< z< 6.7

Luminous distant quasars are unique probes of the high-redshift intergalactic medium (IGM) and of the growth of massive galaxies and black holes in the early universe. Absorption due to neutral hydrogen in the IGM makes quasars beyond a redshift of $z\simeq 6.5$ very faint in the optical z band, thus locating quasars at higher redshifts requires large surveys that are sensitive above 1 micron. We report the discovery of three new $z\gt 6.5$ quasars, corresponding to an age of the universe of $\lt 850$ Myr, selected as z-band dropouts in the Pan-STARRS1 survey. This increases the number of known $z\gt 6.5$ quasars from four to seven. The quasars have redshifts of z = 6.50, 6.52, and 6.66, and include the brightest z-dropout quasar reported to date, PSO J036.5078 + 03.0498 with ${{M}_{1450}}=-27.4$. We obtained near-infrared spectroscopy for the quasars, and from the Mg ii line, we estimate that the central black holes have masses between 5 × 108 and 4 × 109 ${{M}_{\odot }}$ and are accreting close to the Eddington limit (${{L}_{{\rm Bol}}}/{{L}_{{\rm Edd}}}=0.13-1.2$). We investigate the ionized regions around the quasars and find near-zone radii of ${{R}_{{\rm NZ}}}=1.5-5.2$ proper Mpc, confirming the trend of decreasing near-zone sizes with increasing redshift found for quasars at $5.7\lt z\lt 6.4$. By combining RNZ of the PS1 quasars with those of $5.7\lt z\lt 7.1$ quasars in the literature, we derive a luminosity-corrected redshift evolution of ${{R}_{{\rm NZ},{\rm corrected}}}=(7.2\pm 0.2)-(6.1\pm 0.7)\times (z-6)$ Mpc. However, the large spread in RNZ in the new quasars implies a wide range in quasar ages and/or a large variation in the neutral hydrogen fraction along different lines of sight

Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation

Cold dust and young starbursts : spectral energy distributions of Herschel SPIRE sources from the HerMES survey

not available in arXivWe present spectral energy distributions (SEDs) for 68 Herschel sources detected at 5σ at 250, 350 and 500 μm in the HerMES SWIRE-Lockman field. We explore whether existing models for starbursts, quiescent star-forming galaxies and active galactic nucleus dust tori are able to model the full range of SEDs measured with Herschel. We find that while many galaxies (∼56 per cent) are well fitted with the templates used to fit IRAS, Infrared Space Observatory (ISO) and Spitzer sources, for about half the galaxies two new templates are required: quiescent (‘cirrus’) models with colder (10–20 K) dust and a young starburst model with higher optical depth than Arp 220. Predictions of submillimetre fluxes based on model fits to 4.5–24 μm data agree rather poorly with the observed fluxes, but the agreement is better for fits to 4.5–70 μm data. Herschel galaxies detected at 500 μm tend to be those with the highest dust masses.Peer reviewe