dataesr/scientific-paragraphs-categorization huggingface dataset:
Data reduction The VANDELS data was reduced using the fully-automated pipeline Easy-Life which starts from the raw data and produces wavelength-and flux-calibrated spectra. The pipeline is an updated version of the algorithms and dataflow from the original VIPGI system, fully described in  Scodeggio et al. (2005). In Figure  3  we show a flow diagram illustrating the key features of the data reduction pipeline. The first step in the reduction of VIMOS science data is the canonical preliminary reduction of the CCD frames, which includes pre-scan level and average bias frame subtraction, trimming of the frame to eliminate pre-scan and over-scan areas, interpolation to remove bad CCD pixels and flat fielding. After the preliminary reduction step, subsequent data reduction steps are carried out on all MOS slits individually.
For each individual spectroscopic exposure the wavelength calibration derived from the arc exposures is checked against the positions of bright sky lines and the local inverse dispersion solution modified to account for any discrepancies. The next steps in the data reduction procedure are the object detection and sky subtraction for each MOS slit spectrum within each individual 1200s science exposure. Initially, the slit spectrum is collapsed along the wavelength axis, following the geometrical shape defined by the local curvature model, to produce a slit cross-dispersion profile. A robust determination of the average signal level and r. m. s in this profile is then obtained using an iterative σ-clipping procedure, and objects are detected as groups of contiguous pixels above a given detection threshold.
At the time of the data release, we believe that the spectra including the correction for blue flux loss represent our best calibration of the VANDELS spectra. However, for completeness, we are also making available the spectra without the blue flux correction. To show the effect of this blue correction, in Figure  4  we present two exam-Article number, page 5 of 17 Fig. 3. A flow diagram illustrating the various steps of the data reduction pipeline ples of spectra belonging to the SFG and PASS samples, respectively, and we compare the original and corrected flux. The effect is only noticable in the first few hundreds Å of the spectral range, and it is most important for the star forming galaxies.
The quality flags for AGN spectra are preceded by a leading 1 (e. g 12, 14 etc), the quality flags for spectra which were not primary targets (i. e. were serendipitously observed in a certain slit) are preceded by an additional 2; finally the quality flags for spectra deemed to be problematic are preceded by an additional -1. In the DR1 only the AGN flags are present, since the secondary targets will be released only at the end of the survey. Following their independent redshift determinations, the two measurers were required to compare their redshifts and flags and to reconcile any differences. As a final step, all spectra were also independently re-checked by the two PIs and any remaining discrepancies in the redshifts and quality flags were again reconciled. This final pass was especially necessary to homogenize There are 42 objects in the Data Release 1 with a previously published spectroscopic redshift.
VANDELS DR1-data The data release 1 (DR1) is available in the ESO archive and consists of all spectra obtained during the first VANDELS observing season, which ran from August 2015 until February 2016. The data were acquired during runs 194. A-2003(E-K). The data release includes the spectra for all galaxies for which the final scheduled integration time was completed during season one (356 objects including 6 that actually received more than the scheduled time). In addition, the data release also includes the spectra for 523 galaxies for which the scheduled integration time was 50% complete at the end of season one (i. e. 20 out of 40 scheduled hours and 40 out of 80 scheduled hours). The total number of spectra released is 879 (415 in CDFS and 464 in UDS). For each target the following data files are being released: 1) the one-dimensional spectrum in FITS format, containing the following arrays WAVE: wavelength in Angstroms (in air) FLUX: 1D spectrum blue-corrected flux in erg cm -2 s -1 angstrom -1 ERR: noise estimate in erg cm -2 s -1 angstrom -1 The scheduled integration time int s z phot The photometric redshift float z spec Spectroscopic redshift float z f lg The quality flag for the redshift float FILENAME The fits filename of the spectrum string t exptime The current integration time float s UNCORR-FLUX: 1D spectrum flux uncorrected for blue flux loss (see details in subsection 4.
Acknowledgement Based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under programme ID 194. A-2003(E-K). We thank the  ESO  staff for their continuous support for the VANDELS survey, particularly the Paranal staff, who helped us to conduct the observations, and the  ESO  user support group in Garching. RJM, AM, EMQ and DJM acknowledge funding from the  European Research Council , via the award Consolidator Grant (P. I. R. McLure) AC aknowledges the grant  PRIN-MIUR 2015  and  ASI n. I/023/12/0. PC acknowledges support from  CONICYT  through the project FONDECYT regular 1150216. RA acknowledges support by the  ERC Advanced  Grant  695671  "QUENCH". FB acknowledges the support by  Fundação para a Ciência e a Tecnologia (FCT)  via the postdoctoral fellowship  SFRH/BPD/103958/2014  and through the research grant  UID/FIS/04434/2013.
Context. Thanks to its spatial resolution, the ESA/Gaia space mission offers a unique opportunity to discover new multiply imaged quasars and to study the already known lensed systems at sub-milliarcsecond astrometric precisions. Aims. In this paper, we address the detection of the known multiply imaged quasars from the Gaia Data Release 2 (DR2) and determine the astrometric and photometric properties of the individually detected images found in the Gaia DR2 catalogue. Methods. We have compiled an exhaustive list of quasar gravitational lenses from the literature to search for counterparts in the Gaia DR2. We then analysed the astrometric and photometric properties of these Gaia's detections. To highlight the tremendous potential of Gaia at the sub-milliarcsecond level we finally performed a simple Bayesian modelling of the well-known gravitational lens system HE0435-1223, using Gaia DR2 and HST astrometry.
The first Gaia data release (DR1; Gaia Collaboration 2016a), besides providing the best available two-parameter astrometry (positions only) at the epoch of its publication, contained only one G mean magnitude, and it did not reach the effective angular resolution necessary to include most of the multiply imaged quasars. This happened due to data processing issues and final astrometric quality reasons  (Fabricius et al. 2016; Arenou et al. 2017 ). Yet, several multiply imaged quasars discovered from other large surveys such as Pan-STARRS, DES, SDSS-III BOSS, HSCS or VST-ATLAS, were subsequently identified with at least one Gaia DR1 detection (e. g. Agnello et al. 2015 Agnello et al. , 2017 Agnello et al. , 2018b;; Lemon et al. 2017 Lemon et al. , 2018;; Ostrovski et al. 2018). The Gaia DR2 (Gaia Collaboration 2018) on the other hand, starts to deliver colour information and thus to become selfsufficient in the search for new lenses.
This significantly simplifies the extraction of genuine extragalactic sources from the galactic stellar contaminants. Only the faintest or most problematic objects are characterised by just a two-parameters solution in this data release, which is unfortunately the case for several multiply imaged quasars as their magnitudes are often close to the Gaia limiting sensitivity at (G∼20. 7). As part of a larger effort to discover and study multiply imaged quasar candidates from the various Gaia data releases, our group first searched for new lensed systems around known or candidate quasars, enabling the discovery of highly probable multiply imaged quasar candidates for the first time from Gaia data alone (Gaia GraL Paper I;  Krone-Martins et al. 2018 ). In the present work, we report our findings regarding the identification of known gravitationally lensed quasars in Gaia DR2.
Compiled list of gravitationally lensed quasars We have attempted to compile an as-complete-as-possible list of known gravitationally lensed quasars that were published in the literature prior to the Gaia DR2, including some recent candidates that are not yet spectroscopically confirmed. The major source of known gravitational lenses included in our list is the CASTLES (CfA-Arizona Space Telescope LEns Survey of gravitational lenses) site  (Kochanek et al. 1999 ) 1  providing information for about 100 lenses, most of them observed with HST. Another important single source of known multiply imaged quasars is the SDSS Quasar Lens Search site (SQLS) 2  , aimed to discover lensed quasars from the large homogeneous data of the Sloan Digital Sky Survey (SDSS) providing data on 49 additional lensed systems.
We also included several quasar systems from the Master Lens Database (Moustakas 2012) 3  , a community-supported compilation of all discovered strong gravitational lenses. Finally we complemented our list with recent and more scattered discoveries from the literature. This list is being kept up-to-date, and will be maintained at least until the final Gaia data release. For the sake of completeness, we also included in this list the candidates with indication in the literature of just one image (usually spectroscopic candidates) expecting from the exceptional resolving power of Gaia that it may resolve some of them into multiple images in one of its data releases. Our resulting list of published lensed or lens-candidate quasars contains 481 systems (233 confirmed systems and 248 lensed quasar candidates).
2000; Boch et al. 2014). Of the 481 gravitational lens systems (including candidates), 206 have at least one image matched with a Gaia DR2 source. The overall detection statistics of known systems that result from our examination is given in Table  1. An all-sky chart in galactic coordinates of the known lenses is shown in Fig. 1  along with a specification of the Gaia detection. In Table  B. 1 we present a subset of our list: the quads for which at least one match was found in the Gaia DR2. The complete list of lenses and detection is available in electronic form only at the CDS. Of the 44 known systems with four images (or more), 29 have at least one image detected in Gaia DR2. Within this group, one system has just one image detected, eight have two images, eight have three images and 12 are fully detected with four images seen in the Gaia data around the target direction.
1, the effective angular resolution of the Gaia DR2 is much worse than that, attaining at most ∼0. 4 , but with completeness of only ∼2. 2. This loss of resolution restricts the detection of the ∼2900 lenses predicted by  Finet & Surdej (2016). This happens for multiple reasons, all of them at the ground processing stage, as the Gaia raw data cross-matching, the validation of the astrometric solution and the filtering of possible spurious sources. It is expected, however, that at each Gaia data release the effective angular resolution will improve and thus systems with each time smaller angular separations that are not yet separated in the Gaia DR2 will be disentangled.
For the five known lenses RXJ1131-1231, SDSS1004+4112, 2MASS J1134-2103, HE0435-1223, and WFI2033-4723, the Gaia DR2 provides quasar image position measurements with an unprecedented precision of a few tenths of a milliarcsecond. With an improvement of an order of magnitude over typical HST astrometric accuracy, these new astrometric data should help to better constrain the lens models. Considering four of the five known quadruply imaged quasars reported in Table  2  for which Gaia and HST astrometric data are available, the average of the Gaia astrometric uncertainties affecting the equatorial coordinates of the four lensed quasar images is found to be 0. 43 mas compared to 3. 29 mas using the corresponding HST data. This represents a huge gain (by more than a factor 7) in astrometric precision.
2014 McCully et al. , 2016)). In addition, we consider the position of the point-like source (β x , β y ) with respect to image A that is also free to vary during the optimization process, bringing the number of free parameters n k to nine. To draw samples from the posterior PDFs, we used emcee 4 (Foreman-Mackey et al. 2013), a python package which implements the affine invariant ensemble sampler for MCMC proposed by  Goodman & Weare (2010). Since we only used the lensed image positions θ obs as observational data to fit, the loglikelihood function simply reads ln p(θ obs |k) = - 1 2 2N j=1           θ obs, j -θ model, j (k) 2 σ 2 obs, j -ln σ 2 obs, j           , (1) where k is the vector of free parameters, N the number of lensed images (hence 2N constraints), σ obs the astrometric uncertainties, and θ model (k) the lensed image positions obtained from the free parameters k and generated with the python package pySPT 5 (Wertz & Orthen 2018).
To control the sampling, only two hyperparameters need to be tuned: an adjustable scale parameter a and the number N w of walkers. The scale parameter a has a direct impact on the acceptance rate of each walker, namely the ratio of accepted to proposed candidates, and was set to a = 2, following  Goodman & Weare (2010). A walker can be seen as a Metropolis-Hastings chain (see, e. g. , MacKay 2003) whose associated proposal distribution depends on the positions of all the other walkers (Foreman-Mackey et al. 2013). Prior to run the MCMC, we initialised N w = 350 walkers in a small n kdimensional ball of the parameter space around a highly probable solution, formerly obtained using the public lens modelling 4 https://github. com/dfm/emcee 5 https://github. com/owertz/pySPT A56, page 5 of 12 A&A 618, A56 (2018) 1.
The HE0435-1223 image positions measured with the Wide Field Camera 3 (IR/F160W, Robberto et al. 2002) mounted on the HST come from  Kochanek et al. (2006) , showing astrometric uncertainties between 3 and 5 mas. As expected, all the posterior PDFs obtained from the Gaia data show narrower widths than those obtained from HST data, while some of them are slightly shifted. Thus the use of Gaia astrometry data significantly reduces the ranges of valid model parameters around a highly probable solution, as shown in Table  3. The Einstein radius value has only been slightly improved with respect to HST observations, while the source position is constrained within a σ-error ellipse of (σ β x , σ β y ) = (0. 1, 0. 1) mas. This one order of magnitude improvement indicates that the sub-mas astrometry of Gaia clearly helps to better constrain the position of the point-like source as well as the source surface brightness reconstruction as part of a more realistic modelling scenario.
Conclusions The availability of high-precision and high-accuracy astrometric data as provided by the ESA/Gaia space mission opens a new window to detect and model gravitationally lensed quasar systems with an unprecedented refinement. This is bound to impact on fundamental applications in astronomy that are derived from this phenomena, such as the study of the lensing galaxy populations, distant quasars, dark matter and dark energy properties and consequently the determination of cosmological parameters. To exploit this new field with the Gaia data, we have set up a collaboration group, the Gaia GraL team, to systematically analyse the gravitationally lensed quasar content throughout the Gaia data releases. The topics covered include searches for new multiply imaged quasar candidates, identifications of known lenses in the Gaia data, modelling of the lenses using the outstanding Gaia astrometry and multi-colour photometry, and fostering groundbased follow-up for final confirmation.
In this paper we explain how we first generated an up-to-date list of known gravitationally lensed quasars, including lensed quasars too faint to be observed by Gaia. The Gaia GraL list of known gravitationally lensed quasars will be kept up-to-date with respect to the astronomical literature at least until the final Gaia data release. Each Gaia data release will be analysed to verify the detection of known gravitational lenses. Then we provide here the first ever sub-milliarcsecond astrometric data for hundreds of known gravitationally lensed quasars. The search is based on the aforementioned list matched to the Gaia DR2 astrometric catalogue, the largest and most precise astrometric reference available to date. Our lens results bring almost one order of magnitude improvement in astrometric precision compared to a typical HST observation.
Moreover, even if Gaia DR2 is still an early data release lacking many lensed images, it brings high-precision astrometry complemented with photometric data for most known lensed systems. Thus, it provides a glimpse of the content that will become available in the forthcoming Gaia data releases. Of the 481 presently known or candidate gravitationally lensed quasars, we have found in the Gaia DR2 at least one counterpart for 206 of them. From these objects, the quadruplyimaged quasars occupy a specially relevant place, as they provide the more stringent physical parameter inferences. There are 44 presently known quads. From these, 29 have been found with at least one entry in Gaia DR2 and 12 of them are fully detected with all four images. As the images of many of these objects have smaller angular separations than the Gaia DR2 effective angular resolution, we expect the forthcoming data releases to provide information for most images with separations down to ∼0.
18 , as the Gaia releases gradually reach the instrument spatial resolution. We also provide Gaia DR2 astrometric and photometric data for all known lenses to date. Finally, we show that the adoption of high-precision astrometry from Gaia DR2 to model the well-known lens system HE0435-1223 results in a significant improvement in constraining the lens parameters of a SIEg model around a highly probable solution, and that it also significantly reduces the model parameter correlations, in comparison to standard HST astrometry. Such constraints will certainly be further improved with the increased precision of forthcoming Gaia nominal mission data releases, expected for 2020 (DR3) and 2022 (DR4), and the still to be announced data release(s) of the Gaia mission extension. As a final conclusion this work vividly demonstrates the significant impact of high-precision astrometry from Gaia and future mission concepts as the JASMINE series  (Gouda 2011) , GaiaNIR  (Hobbs et al.
Acknowledgements. The authors wish to thank the referee for his constructive comments. AKM acknowledges the support from the  Portuguese Fundação para a Ciência e a Tecnologia (FCT)  through grants  SFRH/BPD/74697/2010 , from the  Portuguese Strategic Programme   UID/FIS/00099/2013  for CENTRA, from the  ESA  contract  AO/1-7836/14/NL/HB  and from the  Caltech Division of Physics, Mathematics and Astronomy  for hosting a research leave during 2017-2018, when this paper was prepared. LD and JS acknowledge support from the  ESA   PRODEX Programme "Gaia-DPAC QSOs " and from the  Belgian Federal Science Policy Office. OW acknowledges support from a fellowship for Postdoctoral Researchers by the  Alexander von Humboldt Foundation. SGD and MJG acknowledge a partial support from the  NSF  grants  AST-1413600  and  AST-1518308 , and the  NASA  grant  16-ADAP16-0232.
For sequencing library preparation, we found commercially available RNAsequencing kits, which are optimized for generating relatively short fragment libraries and thus short sequencing reads, to yield sequences that lack sufficient uniqueness for unambiguous taxonomic assignment of most reads at the genus or species level. We performed in silico simulations and found that sequences should be at least 300 bp in length for accurate taxonomic assignment (Fig. S1 ), which could be accommodated by the setup of a customized RNA-sequencing library preparation protocol and a partially overlapping 2 × 250 bp paired-end sequencing run (see Methods for details). Finally, for the bioinformatics pipeline, we realized that existing software for shotgun metagenomics (SMG) or amplicon-based sequencing (ABS) data analysis, such as MetaPhlAn 1 or QIIME,  2  was not suitable for MeTRS.
Specifically, while metagenomics analysis tools such as MetaPhlAn intentionally ignore ribosomal RNA (rRNA) sequences that represent the majority of MeTRS reads (Fig. S2 ), ABS pipelines such as QIIME implicitly assume that the sequences are derived from hypervariable rRNA regions and perform a pseudorandom taxonomic assignment when reads are rather derived from more conserved rRNA regions (Fig. S3 ). To specifically handle the unique aspects of MeTRS data, we therefore developed a customized analysis pipeline (Fig. S2a ) that first joins read pairs into longer pseudoreads and then maps each quality-filtered pseudoread based on stringent sequence similarity thresholds against a full-length, curated rDNA sequence database, such as SILVA,  3  which contains sequences from all domains of life.
In comparison, ~150,000 reads would be needed for MeTRS to achieve the same endpoint, which is only ~3-fold higher than 16S sequencing but ~20 times lower than SMG. This could be explained by the fact that, in spite of the absence of any enrichment, depletion or PCR amplification step, MeTRS reads preferentially map to ribosomal small subunit (SSU, i. e. , 16S or 18S) and large subunit (i. e. , 23S or 25-28S) regions (Fig. S3 ), which are well known for their high taxonomic value. Bioinformatic tools have been developed to extract SSU-rRNA reads from SMG data,  7, 8  but these represent only a small fraction of a typical SMG sequencing run and are usually ignored by other software (e. g. , MetaPhlAn).
15 Moreover, while MeTRS could potentially face problems in determining the exact relative abundance of species in a complex sample, it could be useful for the differentiation between live and dead cells or between metabolically more active and less active cells. Preliminary experiments seem to support this hypothesis (Table  S2  and unpublished observation). Finally, MeTRS allows direct comparison of organisms from all kingdoms of life, albeit at a greater sensitivity and a lower cost than SMG. 1 Performance comparison of microbiome profiling methods on Latin square spike-in data set. Relative abundances of yeast (C. albicans, S. cerevisiae, S. pombe) and bacteria (E. coli, L. rhamnosus, P. acnes) species are plotted as a function of the number of cells that were spiked into the background stool homogenate prior to DNA or RNA extraction (a, c, e and g).
16S and ITS ABS Equimolar concentrations of secondary PCR products were pooled and electrophoresed using 1% agarose gel. Pooled amplicon libraries were gel-   , d ). e Rarefaction data for 16S sequencing, MeTRS and metagenomics from panel d were re-plotted as a percentage of the maximum number of distinct genera identified at the highest sequencing depth. Dashed lines represent estimated sequencing depth required to detect 95% of those total genera purified using the Qiaquick Gel Extraction Kit (Qiagen). Concentrations of gel-purified libraries were estimated using the DNA 1000 kit (Agilent Technologies).
High-throughput sequencing All libraries were quantified using KAPA Library Quantification Kit (Kapa Biosystems) to ascertain the loading concentration and sequenced on a HiSeq 2500 System (Illumina) operated in Rapid Run Mode to generate 2 × 250 bp paired-end reads. Sequencing depths are listed in Table  S2. All sequencing results have been deposited at NCBI Sequence Read Archive (SRA) under accession number SRP103706.