dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSIONS We have used the SHARDS survey to carry out a search of high-z galaxies. The narrow/medium width of the 25 SHARDS filters and their completeness in the 500-941 nm wavelength range have allowed us to develop very precise SEDs in the optical/NIR. We have therefore sampled the high-z galaxy population from z ∼ 3. 35 to z ∼ 6. 8 in a uniform way. The special characteristics of the filters have allowed us to simultaneously detect both LAEs and LBGs, using a robust strategy based on the identification of the Lyman break via colour excesses and photometric fits to the SEDs (see  Barro et al. 2018, in prep. ). The SED information has been completed using additional data from HST /ACS, HST /WFC3, Subaru/Suprime-Cam, Subaru/MOIRCS, CFHT /WIRCam, Spitzer /IRAC and GALEX. The sample we have built consists of 1558 candidates, separated in 124 pure LAEs with barely any continuum, 404 LBGs/LAEs and 1030 LBGs with no emission line.
Figure5. Comparison between our photometric redshifts with the spectroscopic ones for those candidates with spectroscopic z available in Rainbow or NED. Black dots are extracted from NED while empty circles are from Rainbow. In case the same object has spectroscopic z in both databases we plot the NED one only to avoid redundancy (after checking that the value is the same). The solid line is the equal line. Notice that none of the spectroscopically confirmed objects of our sample has a calculated photo-z over the outliers threshold ( |∆z |/(1 + z spec ) = 0. 15). The excellent correlation indicates that both our selection criteria and redshift calculations are very robust and accurate.
ACKNOWLEDGEMENTS We want to acknowledge support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grants  AYA2015-70498-C2-1-R ,  AYA2013-47742-C4-2-P  and  AYA2016-79724-C4-2-P. Based on observations made with the  Gran Telescopio Canarias (GTC) , installed in the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias , in the island of  La Palma. This work is (partly) based on data obtained with the SHARDS filter set, purchased by  Universidad Complutense de Madrid  ( UCM ). SHARDS was funded by the  Spanish Government  through grant  AYA2012-31277. PGP-G acknowledges funding by  MINECO  under grants  AYA2012-31277 ,  AYA2015-70815-ERC , and  AYA2015-63650-P. HD  acknowledges financial support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under the  2014 Ramón y Cajal program  MINECO  RYC-2014-15686.
This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. Special thanks to  Rui Marques  and  Drs. David Sobral ,  José Alfonso López Aguerri ,  Jairo Méndez Abreu  and  José Miguel Mas Hesse  for very kind and useful comments, help and discussions.
Acknowledgments: We received financial support from the  French ANR  project  ANR-13-JS01-0005-01  ( N. Rougerie ), the  US NSF  grant  PHY-1265118  ( E. H. Lieb ) and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  CORFRONMAT No 758620 ).
IV. DATA ANALYSIS Different datasets B1, B2, B3 and B4 were independently analyzed and the values of asymmetries A B (8) were computed via comparing the neutron counts N B and N -B between subsequent measurements. For each individual measurement under given applied field B we take N B as a sum of counts of both detectors, N B = N (1) B + N (2) B , while the count ratios between two detectors N  The results obtained per each measurement series are shown in Table  I  and also on Fig. 5  where the measured asymmetries are combined in bins of comparable size. The first column of Table  I  indicates the values measured in a given series, and corresponding amount N oct of the measurement octets {B} or {0|B}. (Let us remind that for canceling the effects of drift, each data unit is taken as value of A B or E B averaged within a given octet of measurements, {B} or {0|B}.
Acknowledgements We are grateful to the  Institut Laue-Langevin (ILL)  for providing the EDM beamline on PF2 facility for one reactor cycle and for excellent technical support throughout the experimental runs. The work was supported in part by  CETEMPs  at the  University of L'Aquila. Z. B. and R. B. thank the  ILL  for partial financial support. We are grateful to  Anatoly Serebrov  for help and valuable advices. Z. B. thanks  Yuri Kamyshkov ,  Valery Nesvizhevsky  and  Guido Visconti  for interest to the work and useful discussions. The results of this work were reported by Z. B. at the Workshop  INT-17-69W  " Neutron Oscillations: Appearance, Disappearance, and Baryogenesis ",  Institute of Nuclear Theory, Seattle, USA , October  23 -27 ,  2017 , and at the seminar at  Oak Ridge National Laboratory, Knoxville, USA ,  31  Oct.
The behavior of this new sensor suite could then be compared to the measurements provided by the ATV navigation sensors that have been characterized through the series of ¦ve §ights  [2]. The proposed sensor suite was composed of infrared camera that could detect the target at long-range while being entirely passive and being able to provide measurements even in challenging illumination conditions, such as eclipse or Sun within the ¦eld of view, and a three-dimensional (3D) LIDAR to provide measurements at shorter range, typically from 3. 5 km (Fig. 1 ). The LIRIS demonstration embarked two infrared cameras and one visible camera provided by SODERN and a 3D scanning LIDAR  [3, 4]  provided by Jena-Optronik on the front cone of ATV-5 as seen in Fig. 2. The sensors suite was integrated on ATV with minimum interfaces (electrical and few commands and monitoring, in addition to mechanical and thermal aspects) and dedicated recorders were used to store and retrieve data at a later stage.
3 ) located 3500 m behind ISS and 100 m above. Indeed, from that point on, it was possible to orient ATV front cone toward ISS while ensuring su©cient energy storage on ATV batteries to cope with any potential docking contingency situation. At roughly 3500 m, all the cameras but also the LIDAR started recording ATV approach to ISS up to docking which lasted about 2 h. After dismounting of the recorders by ISS crew, they have been recovered by the manufacturers following Soyuz 38 return in September 2014. More than 18 000 pictures have been recorded per camera and about 7 500 scans of the LIDAR (Table  1 ), meaning more than 72 GB have been recorded during LIRIS demonstration. Then, all the images and LIDAR data collected during the LIRIS demonstration have then been organized in a dedicated database together with all relevant information to be able to exploit the data.
LIRIS DATABASE The LIRIS database is organized in a set of ¦les in a directory tree called LIRIS-DB (Fig. 4 ) for LIRIS §ight database. It includes, in particular: LIRIS-1 Camera images for CAMIR1, CAMIR2, and CAMVIS (raw data); LIRIS-2 LIDAR data (preprocessed data); estimated ATV-ISS relative state vector vs. time for reference; and ¦xed data: sensor position/alignment, camera distortion calibration laws, etc. For the Fly-under §ight phase, the ¦le organization is the same as for the rendezvous, except it does not contain any LIDAR data. The directory tree of the database is presented in Fig. 4. The content of the ¦les in general is explained in the following subsections.
Camera Data The Camera Images folder contains all camera images acquired, stored and downloaded after the mission. One folder is dedicated to ATV-5 rendezvous (RDV) and one is dedicated to Fly-Under (FU). These sets of images are grouped in a unique subdirectory, but sorted by camera type (CAMVIS, CAMIR1, and CAMIR2) and §ight phase (RDV, FU). The image format is TIFF and their characteristics are as follows: CAMIR1/ CAMIR2: 640 × 480 pixel, ADC 14 bits per pixel. The size of one infrared image is 603 KB (uncompressed); and CAMVIS: 1360 × 1024 pixel, ADC 14 bits per pixel. The size of one visible image is 2. 66 MB (uncompressed). The images are in the camera measurement reference frames. The correspondence between the camera frames and ATV reference frame can be deduced by the de¦nition of di¨erent frames.
LIDAR Data All LIDAR data acquired, stored, and downloaded after the mission are grouped in a unique subdirectory relative to the §ight phase (RDV). The LIDAR data ¦le name contains the scan number, the scan mode identi¦er, and the date of the data postprocessing after §ight. The scienti¦c data are split in several ¦les, 1 ¦le containing 1 scan. The content of each scan ¦le is the following: 1 header containing scan description, scan number, and LIDAR mode; and a set of data for each return of the scan containing: time relative to T 0 ; counter of target returns; range; azimuth; elevation; and amplitude. Each ¦le corresponds to a scan taken by the LIDAR at a speci¦c time. The scan duration depends on the scan mode. Each return within a scan is dated separately with respect to the ATV reference time with an accuracy better than 1 ms.
Time and Synchronization Since the LIRIS demonstration was integrated with limited interfaces to ATV, it was not connected to the main data exchange bus and, hence, each experiment was running its own time during the §ight. In order to be able to correctly interpret the data collected, it was then important to correlate with a good accuracy the LIRIS time and the ATV time. To do so, the recording commanding sequence sent by ATV was recorded at experiment level with its own time. The di¨erent time delays at ATV commanding level and experiment level have been estimated prior §ight (Fig. 6 ). Furthermore, the relative drift of the experiment clock with respect to the ATV time was also evaluated and considered in the post §ight synchronization of the respective times. Finally, for each camera, a time ¦le is provided within the LIRIS database to give the ATV time associated to each set of data.
Frames and Calibration As explained above, the LIRIS sensors data are provided in their respective measurement frames. To be able to exploit these data with respect to ATV own navigation data, it is, therefore, important to know precisely the position and the orientation of the sensors as mounted on ATV. In order to obtain an accurate knowledge of each sensor orientation within ATV reference frame, a speci¦c Figure  7  The LIRIS calibration tests pre §ight calibration test was performed in the frame of acceptance tests prior to §ight with all LIRIS sensors but also ATV sensors, i. e. , the ViDeoMeter (VDM) and the TeleGonioMeter (TGM), looking at the same target. This ground target has a T-shape and is composed of laser retrore §ectors similar to the ones installed on ISS service module for ATV ¦nal approach (Fig.
To correlate LIRIS ¦ndings to external events, additional parameters are given like the Sun position or absolute position and velocity of the ATV LIRIS frame with respect to the J2000 frame (absolute GPS information). The full set of parameters included in the database are given in Table  2. These trajectories (one for §y-under and one for rendezvous) can be used as a reference for comparison with LIRIS raw data or for evaluating the performance of image processing and navigation algorithms using LIRIS data.
UTILIZATION OF DATABASE Obviously, the use of the database can be for di¨erent purposes like: §ight quality investigation of images from di¨erent technologies; distortion, contrast and §ight illumination issues; use as a real source for image processing to obtain kinematic measures; input to GNC state estimation ¦lter design, etc. Because the data of each sensor can be used to perform relative navigation without any need for speci¦c ISS data apart its geometry, LIRIS database could be used to estimate the performance of sensors in a noncooperative scenario. The following examples are provided to illustrate some of the di¨erent potential uses. the corresponding GNC state vector can then be found in the ¦le on ATV-ISS states vs. time (Fig. 9 ); and the transformation from cameras measurements frame to ATV reference frame shall ¦nally be utilized in order to perform relative state vector determination.
The next step for using images for estimation purposes and prior to that image processing will be to apply the distortion correction. The correction has to be applied at pixel level. This process needs to be repeated for each pixel in an image to build one with distortion correction. An example of a correction is shown in Fig. 9  together with a set of ATV reference data that can be associated to each image through the database. camera detector, at 70 km, the apparent ISS size is just comparable to one-pixel ¦eld of view. Even if the capability to observe the ISS at such range has been demonstrated by LIRIS, the capability to isolate and detect without ambiguity ISS from the overall ¦eld of view remains to be investigated. In particular, the impact of the camera noise and the variation in illumination and range on the ISS signal received by each camera should be further studied.
LIDAR Range Comparison to Automated Transfer Vehicle Reference Trajectory At medium range, the envisioned navigation use for such sensor suite would be to obtain range and LoS information from LIDAR measurements. Using LIRIS database, it is possible to compare the range measurements obtained by the 3D scanning LIDAR with the range derived from the ATV reference trajectory. The LIDAR range of the target is considered here as the minimum range of all target returns in a given scan corrected by the position of the LIDAR in the ATV frame. The obtained LIDAR range is then compared to the ISS range derived from ATV reference trajectory (see Fig. 9 ). This example is only an approximation to illustrate the use of the database since the geometry of the ISS docking port is complex and some structures such as the high-gain antenna are sticking out from the ISS reference plane.
CONCLUDING REMARKS The LIRIS technology demonstration successfully §ew a new suite of sensors composed of two infrared cameras, one visible camera, and a 3D scanning LIDAR during the last servicing mission of the ATV to ISS. Such sensors could be used for future exploration or servicing missions in either a cooperative or noncooperative rendezvous. Data collected during LIRIS demonstration, from a range of 70 km up to docking, has been gathered in a database that can be used to assess the in- §ight behavior of these types of sensors. The database contains also the reference relative trajectory §own during the ATV Fly-Under and rendezvous based on ATV own navigation sensors. The careful calibration and synchronization of the LIRIS sensors with the ATV reference allow using the database to also estimate the potential performance of image processing and navigation algorithms based on data coming from cameras or LIDAR.
ACKNOWLEDGMENTS TA acknowledges partial funding from a  Villum foundation  grant when part of this article was being completed. NB and GC acknowledge partial support from the  Labex-LIO (Lyon Institute of Origins)  under grant  ANR-10-LABX-66  and  FRAMA  ( FR3127 ,  Fèdèration de Recherche "André Marie Ampère") , and support from the " Institut Franco Danois ". The CP 3 -Origins centre is partially funded by the  Danish National Research Foundation , grant number  DNRF90.
We combine our data with archival data and estimate the galaxies' masses, test their scaling relations and use the high resolution of the data to investigate the morphology of the counterparts. We structure the presentation of our work as follows: in section 2 we present the HST data set of our sample of galaxies and in section 3 the new ALMA observations. We show the details of our data analysis in section 4. In section 5 we present and discuss our results, and we describe our conclusions in section 6. We present the objects that we find at the position where we had previously found H-α emission at the redshift of the absorber. North is up and east is left. A scale bar is shown in each panel and the corresponding physical scales are given on top of each column. The field of Q0302-223 was not observed with WFC3/F606W because archival WFPC2/F450W and F702W data were already available.
HST WFPC2 and WFC3 Observations To study the stellar properties such as the stellar mass of these galaxies, which requires knowledge of the underlying stellar continuum, we combined imaging data from the HST archive with new WFC3/UVIS and IR channel observations. Thereby, we obtain broad-band fluxes and the morphology of all the objects in the sample. We use the high spatial resolution of HST data to resolve the morphology of the (sub-)DLA counterparts. The new data were observed during Cycle 22 (ID: 13733; PI: Péroux) with the Wide Field Camera 3 in both the optical (UVIS) and infrared (IR) detectors, using the three broadband filters F606W, F105W and F160W. The observations took place between 13 January and 13 October 2015. We aimed at setting the roll-angle of the telescope such that the known galaxy counterpart of the quasar absorbers lies at 45 degrees from the diffraction spikes of the Point Spread Function (PSF).
We also show the derived maximum velocities and mass components. ual exposures to help with removal of cosmic rays and hot pixels. The UVIS observation was taken using the WFC3-UVIS-DITHER-BOX pattern. The two observations with the IR detector were taken using the WFC3-IR-DITHER-BOX-MIN pattern providing an optimal 4-point sampling of the PSF. A summary of the observational set-up is given in table 2. A minimum of three broad-band filters is available for each field to probe either side of their 4000 Å Balmer break. Using additional data available in the archive, we end up with 4 filters for Q0302-223 and 5 filters for Q0452-1640. Quasar Field δ ["] z DLA log(N (H i)) SFR (H-α) log(X/H) log(X/H) [N2] V max log(M dyn ) log(M halo ) log(M gas ) (Object ID) b [kpc] [cm -2 ] [M /yr] (absorption) log(X/H) [R23] [km/s] [M ] [M ] [M ] Q0302- The available F606W, F105W and F160W images are displayed in figure 1.
Since the galaxies are extended objects we need to correct these magnitudes for the missing flux of the galaxies outside the aperture. To do this we model Sersic profiles to each of the visible clumps and galaxies, using the software GALFIT  (Peng et al. 2002). Assuming that the galaxy profile looks the same in all filters outside the chosen aperture, we do this modeling in the filter with the best spatial resolution (F606W for all but Q0302-223, where we choose F160W). This model is then scaled to the resolution and pixel sampling of the other filters and used to determine the IR aperture correction. Indeed, clumps that are not detected in the optical might show in the infrared. These could be unrelated to the galaxy we are studying and therefore have an effect on the fit, but we find that including these regions has no impact on the derived magnitudes within our conservative errors.
Taking random values within these distributions we create the 1000 realisations of the galaxy model. Again, we measure the flux outside the chosen aperture and determine the standard deviation of this flux. This gives us the error on the aperture correction. Adding now the aperture correction and its error onto the flux within the aperture we obtain the magnitudes as given in table  3. For comparison we also use the GALFIT modeling on all filters to compute the magnitudes and find them to be consistent within the errors with the magnitudes that we obtain from aperture photometry. One object in our sample (Q2222-0946) overlaps with the study from  Krogager et al. (2013). Although we use their estimates for the remainder of our analysis, we perform our magnitude measurement procedure also on their original data and find that the magnitudes for this object also agree within the errors.
Spectral Energy Distribution Fit We use the derived broad-band magnitudes in table 3 which cover the 4000 Å Balmer break of the object to constrain the mass, SFR, and age of the stellar population in the galaxy with a spectral energy distribution (SED) fit to the integrated light of the galaxies. We utilised the photometricredshift code LE PHARE  (Arnouts et al. 1999; Ilbert et al. 2006 ) available as part of the GAZPAR suite of software 2. We used the code with a single burst of star formation, 2 https://gazpar. lam. fr/home Figure  4. Galaxy models with GALFIT. These models were used to determine the morphological parameters given in table 4. These models were also used to determine the aperture correction for the magnitudes. From left to right: the observed, modeled and residual from our GALFIT modeling.
ACKNOWLEDGEMENTS RA acknowledges the  ESO  and CNES studentships. CP thanks the  ESO  science visitor programme and the  DFG cluster of excellence 'Origin and Structure of the Universe'. VPK acknowledges partial support from ( NASA)  grant  NNX14AG74G  and  NASA /Space Telescope Science Institute support for Hubble Space Telescope program   GO-13733  and  13801. The data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. Support for MAST for non-HST data is provided by the  NASA Office of Space Science via  grant  NNX09AF08G  and by other grants and contracts. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2015.
5 and 20. 0 GeV/c 2. The dataset consists of pp collision data recorded by the LHCb experiment at centre-of-mass energies of √ s = 7 TeV, 8 TeV and 13 TeV between 2011 and 2017. The corresponding integrated luminosities are 1. 0 fb -1 , 2. 0 fb -1 and 3. 3 fb -1 , respectively. The Υ (1S) → µ + µ -decay is used as a normalisation channel to calculate the X production cross-section relative to that of the Υ (1S) meson.
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [26]. Simulated datasets are used to evaluate reconstruction and selection efficiencies of the Υ (1S) and X decays studied in this paper. In the simulation, pp collisions are generated using Pythia  [27, 28]  with a specific LHCb configuration  [29]. Decays of hadronic particles are described by EvtGen  [30] , in which final-state radiation is generated using Photos  [31]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit  [32]  as described in Ref.
Invariant-mass fits Unbinned extended maximum-likelihood fits are made to the m(2µ + 2µ -) and m(µ + µ -) distributions to determine X and Υ (1S) yields, respectively. Fits to three datasets collected at pp centre-of-mass energies of √ s = 7 TeV in 2011, 8 TeV in 2012 and 13 TeV in 2015-2017 are performed. In addition, a fit is made to a merged dataset that combines all 7, 8 and 13 TeV subsets. In each fit, the combinatorial background component is described by an exponential function with the slope and normalisation as free parameters. Signal components are described by Crystal Ball functions  [35]  with the tail parameters fixed to values obtained from fits to the simulated samples. In fits to the m(µ + µ -) distributions, contributions from the Υ (1S), Υ (2S) and Υ (3S) states are included.