dataesr/scientific-paragraphs-categorization huggingface dataset:
SUMMARY We have used a large (i. e. , 24264) sample of optically selected AGNs from the SDSS database to explore how the profiles of the forbidden [O III]λ5007 emission line relate to other key AGN parameters (bolometric luminosity, Eddington ratio, radio loudness and radio luminosity). We use spectral stacking analyses to determine how the average [O III]λ5007 profile changes as a function of these four parameters. We also explore the fractions of AGNs in each of these bins that have broad [O III]λ5007 emission lines. Our main results can be summarised as: • The average [O III]λ5007 profile of Type 1 AGNs displays a prominent blue wing that can be well described by a broad (i. e. , FWHM= 851 km s -1 ) gaussian component that is blueshifted by 148 km s -1 from a narrow (i. e. , FWHM1 = 335 km s -1 ) core.
ACKNOWLEDGEMENTS JRM and DMA acknowledges The  Leverhulme Trust. CMH  acknowledges funding from  STFC. This work has made use of data provided by the  SDSS. Funding  for the SDSS has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , the  U. S. Department of Energy , the  National Aeronautics and Space Administration , the  Japanese Monbukagakusho , the  Max Planck Society , and the  Higher Education Funding Council for England. The  SDSS Web Site  is.
Introduction The discovery of a new particle of mass about 125 GeV in the search for the Standard Model (SM) Higgs boson at the CERN Large Hadron Collider (LHC)  [1] , reported in July 2012 by the ATLAS  [2]  and CMS  [3]  Collaborations, is a milestone in the quest to understand the origin of electroweak symmetry breaking  [4] [5] [6] [7] [8] [9]. This paper presents measurements of several properties of the newly observed particle, including its mass, production strengths and couplings to fermions and bosons, using diboson final states: 1 H → γγ, H→ ZZ * → 4 , and H→ WW * → ν ν. Spin studies are reported elsewhere  [10]. Due to the outstanding performance of the LHC accelerator throughout 2012, the present data sample is a factor of ∼2. 5 larger than that used in Ref. [2]. With these additional data, many aspects of the ATLAS studies have been improved: several experimental uncertainties have been reduced and new exclusive analyses have been included.
In particular, event categories targeting specific production modes have been introduced, providing enhanced sensitivity to different Higgs boson couplings. The results reported here are based on the data samples recorded with the ATLAS detector  [11]  in 2011 (at √ s = 7 TeV) and 2012 (at √ s = 8 TeV), corresponding to integrated luminosities of about 4. 7 fb -1 and 20. 7 fb -1 , respectively. Similar studies, including 1 Throughout this paper, the symbol stands for electron or muon. also fermionic decays, have been reported recently by the CMS Collaboration using a smaller dataset  [12]. This paper is organised as follows. Section 2 describes the data sample and the event reconstruction. Section 3 summarises the Monte Carlo (MC) samples used to model signal and background processes. The analyses of the three decay channels are presented in Sections 4-6.
Signal and background simulation The SM Higgs boson production processes considered in these studies are gluon fusion (gg → H, denoted ggF), vector-boson fusion (qq → qq H, denoted VBF), and Higgs-strahlung (q q → WH, ZH, denoted WH/ZH or jointly V H). The small contribution from the associated production with a tt pair (gg/q q → t tH, denoted ttH) is taken into account in the H → γγ and H → ZZ * analyses. Samples of MC-simulated events   [2, [14] [15] [16]. Table  3  shows the production cross sections and the branching ratios for the final states considered in this study for a Higgs boson with mass m H = 125 GeV, while Table  1  summarises the theoretical uncertainties on the expected signal common to all channels. Backgrounds are determined using data alone or a combination of data and MC simulation, as discussed in Sections 4-6.
A Neural Network (likelihood) discriminant is used for the 8 TeV (7 TeV) data. The performance of this algorithm is studied using Z → ee decays, ignoring the tracks associated with the electrons and weighting the events so that the p T and rapidity distributions of the Z boson match those expected from the Higgs boson signal. The probability of finding a vertex within 0. 3 mm of the one computed from the electron tracks is larger than 75%. The photon energy calibration is obtained from a detailed simulation of the detector geometry and response, independently for converted and unconverted photons. The calibration is refined by applying η-dependent correction factors determined from studies of Z → ee events in data  [18] : they range from ±0. 5% to ±1. 5% depending on the pseudorapidity of the photon.
It was verified that this restriction does not cause any bias in the results. The combined mass is measured to be: m H = 125. 5 ± 0. 2 (stat) +0. 5 -0. 6 (sys) GeV (2) As discussed in Sections 4. 4 and 5. 4, the main sources of systematic uncertainty are the photon and lepton energy and momentum scales. In the combination, the consistency between the muon and electron final states in the H→ ZZ * → 4 channel causes a ∼ 0. 8σ adjustment of the overall e/γ energy scale, which translates into a ∼ 350MeV downward shift of the fitted m γγ H value with respect to the value measured from the H → γγ channel alone. To quantify the consistency between the fitted m γγ H and m 4 H masses, the data are fitted with the profile likelihood ratio Λ(∆m H ), where the parameter of interest is the mass difference ∆m H = m γγ Hm 4 H.
(  2 ) produces a ±3% variation of µ. The consistency between this measurement and the SM Higgs boson expectation (µ = 1) is about 7%; the use of a flat likelihood for the ggF QCD scale systematic uncertainty in the quoted ±1σ interval yields a similar level of consistency with the µ = 1 hypothesis. The overall compatibility between the signal strengths measured in the three final states and the SM predictions is about 14%, with the largest deviation (∼ 1. 9σ) observed in the H → γγ channel. Good consistency between the measured and expected signal strengths is also found for the various categories of the H → γγ, H→ ZZ * → 4 and H→ WW * → ν ν analyses, which are the primary experimental inputs to the fit discussed in this section. If the preliminary H → ττ  [117]  and H → b b  [118]  results, for which only part of the 8 TeV dataset is used (13 fb -1 ), were included, the combined signal strength would be µ = 1.
Acknowledgments Both researchers were supported by a  PEPS grant  from the  CNRS. I. N. acknowledges financial support from the  ANR  project  OSvsQPI   2011 BS01 008 01. C. P. acknowledges financial support from the  ANR  project  HAM-MARK, N •   ANR-09-BLAN-0098-01. We would also like to thank  Mylène Maïda  for interesting discussion around the unitary Brownian motion.
This dataset is the largest sample to date for such an analysis. Comparing the results from the H i-galaxy crosscorrelation with the H i-H i and galaxy-galaxy autocorrelations, we provide constraints on their statistical connection as a function of both H i column density and galaxy star-formation activity. Our paper is structured as follows. Sections 2 and 3 describe the IGM and galaxy data used in this work, respectively. The IGM sample is described in Section 4 while the galaxy sample is described in Section 5. Section 6 describes the formalisms used to measure the H i-galaxy cross-correlation and the H i-H i and galaxy-galaxy autocorrelations. Our observational results are presented in Section 7 and discussed in Section 8. A summary of the paper is presented in Section 9. All distances are in co-moving coordinates assuming H0 = 70 km s -1 Mpc -1 , Ωm = 0.
2005; Le Fevre et al. 2013 ) and the Gemini Deep Deep Survey (GDDS)  (Abraham et al. 2004 ). Data from programs GO 5320, GO 6100, GO 6592 and GO 11585 contain spectroscopy of three QSOs (namely Q0107-025A, Q0107-025B and Q0107-0232) whose LOSs are separated by ∼ 0. 4 -1 Mpc. This triple QSO field is ideal for measuring the characteristic sizes of the H i absorption systems but it can also be used to address the connection between H i systems and galaxies (e. g. Crighton et al. 2010). Data from program GO 11598 were originally taken to investigate the properties of the CGM by targeting QSOs whose LOS lie within 150 kpc of a known galaxy. For this paper we used one QSO observed under program GO 11598 (namely J102218. 99+013218. 8), for which we have conducted our own galaxy survey around its LOS (see Section 3).
COS data Individual exposures from COS were downloaded from the Space Telescope Science Institute (STScI) archive and reduced using calcos v2. 18. 5 in combination with Python routines developed by the authors. 4 A full description of the reduction process will be presented in  Finn et al. (2013, in prep. ) , here we present a summary. Individual files corresponding to single central wavelength setting, stripe and FP-POS (i. e. x1d files) were obtained directly from calcos. The source extraction was performed using a box of 25 pixels wide along the spatial direction for all G130M exposures, and 20 pixels for all G160M and G230L exposures. The background extraction was performed using boxes encompassing as much of the background signal as possible, whilst avoiding regions close to the detector edges.
FOS data Individual exposures from FOS were downloaded from the STScI archive and reduced using the standard calfos pipeline. Wavelength corrections given by  Petry et al. (2006)  were applied to each individual exposure. As described by  Petry et al. , these corrections were determined using a wavelength calibration exposure taken contemporaneously with the G190H grating science exposures, and were verified using Galactic Al II and Al III absorption features. The shortest wavelength region of the FOS G190H settings overlap with the longest wavelength COS settings, and we confirmed that the wavelength scales in these overlapping regions were consistent between the two instruments. Then we combined all individual exposures together, resampling to a common wavelength scale of 0. 51 Å per pixel.
GALAXY DATA Our chosen QSOs are at zQSO ∼ 0. 7 -1. 3, so we aim to target galaxies at z 1, corresponding to the last ∼ 7 Gyr of cosmic evolution. The majority of these QSOs lie in fields already surveyed for their galaxy content. We used archival galaxy data from: the VVDS (Le  Fèvre et al. 2005; Le Fevre et al. 2013) , GDDS  (Abraham et al. 2004 ) and the Canada France Hawaii Telescope (CFHT) MOS survey published by  Morris & Jannuzi (2006). Despite the existence of some galaxy data around our QSO fields we have also performed our own galaxy surveys using MOS to increase the survey completeness 6. We acquired new galaxy data from different ground-based MOS, namely: the Visible Multi-Object Spectrograph (VIMOS)  (Le Fèvre et al. 2003)  on the VLT under programs 086. A-0970 (PI: Crighton) and 087.
Data reduction for fields J1005, J1022 and J2218 The spectroscopic data were taken on 2011 and the reduction was performed using esorex v. 3. 9. 6. All three pointings of fields J1005 and J1022 were observed, while only 'pointing 3' of J2218 was observed. Due to a problem with focus, data from 'quadrant 3' of 'pointing 1' and 'pointing 3' of field J1022 were not usable. 'Pointing 2' (middle one) of fields J1005 and J1022 were observed twice to empirically asses the redshift uncertainty (see Section 3. 1. 5). We took three exposures per pointing of 1155 s followed by lamps. The reduction was performed using a peakdetection parameter (threshold for preliminary peak detection in counts) of 500 when possible, and decreasing it when needed to minimize the number of slits lost (we typically lost ∼ 1 slit per quadrant).
Further redshift calibration for field Q0107 We did not see systematic differences between quadrants, as was seen for fields J1005, J1022 and J2218. VIMOS observations of the Q0107 field were reduced differently, and the data come mainly from the blue part of the spectrum. Therefore, such an effect might not be present or, if present, might be more difficult to detect. However, we did find a systematic shift between the redshifts measured from VIMOS compared to those measured from DEIMOS. Given the much higher resolution of DEIMOS, we used its frame as reference for all our Q0107 observations. Thus, we corrected the Q0107 VIMOS redshifts to match the DEIMOS frame. This correction was ∼ 0. 0008 ( 1 VIMOS pixel) and the result is shown in the bottom panel of Figure  3  (blue lines). Figure  3.
Redshift reliability The redshift reliability for DEIMOS data was originally based on four subjective categories: (0) 'still needs work', (1) 'not good enough', (2) 'possible', (3) 'good' and (4) 'excellent'. In order to have a unified scheme we matched those DEIMOS labels with our previously defined VIMOS ones (see Section 3. 1. 5) as follows: DEIMOS label 4 is matched to label 'a' ({4}→ {'a'}); DEIMOS labels 3 and 2 are matched to label 'b' ({3,2}→ {'b'}); and DEIMOS labels 1 and 0 are matched to label 'c' ({1,0}→ {'c'}).
Data reduction The observations were taken in 2008. Three 1080 s offset science exposures were taken for each mask, dithered along the slit to cover the gaps in the CCD detectors. Arcs were taken contemporaneously to the science exposures. We used the Gemini Image Reduction and Analysis Facility (IRAF) package to reduce the spectra. A flat-field lamp exposure was divided into each bias-subtracted science exposure to remove small-scale variations across the CCDs, and the fringing pattern seen at red wavelengths. The dithered images (both arcs and science) were then combined into a single exposure. The spectrum for each mask was wavelength-calibrated by identifying known arc lines and fitting a polynomial to match pixel positions to wavelengths. Finally the wavelength-calibrated 2-d spectra were extracted to produced 1-d spectra.
CFHT MOS data We used the CFHT galaxy survey of the Q0107 field presented by  Morris & Jannuzi (2006). There are 61 galaxies in this sample, 29 of which were also observed by our GMOS survey. We use only redshift information from this sample without assigning a particular template or redshift label. We refer the reader to  Morris & Jannuzi (2006)  for details on the data reduction and construction of the galaxy sample.
Redshift reliability The redshift reliability for VVDS data was originally based on six categories: (0) 'no redshift', (1) '50% confidence'; (2) '75% confidence'; (3) '95% confidence'; (4) '100% confidence'; (8) 'single emission line'; and (9) 'single isolated emission line' (Le  Fèvre et al. 2005; Le Fevre et al. 2013). They expanded this classification system for secondary targets (objects which are present by chance in the slits) by the use of the prefix '2'. Similarly the prefix '1' means 'primary QSO target', while the prefix '21' means 'secondary QSO target'. In order to have a unified scheme we matched those VVDS labels with our previously defined VIMOS ones (see Section 3. 1. 5) as follows: VVDS label 4, 3 and their corresponding extensions are matched to label 'a'  ({4,14,24,214,3,13,23 ,213}→ {'a'}); VVDS labels 2, 9 and their corresponding extensions are matched to label  'b' ({2,12,22,212,9,19,29,219}→ {'b'}) ; and VVDS labels 1, 0 and their corresponding extensions are matched to label 'c'  ({1,11,21,211,0,10,20 ,210}→ {'c'}).
Out of those 17509 galaxies, 8293 were classified as 'SF' and 1743 as 'non-SF'; while out of those 654 H i systems, 165 were classified as 'strong' and 489 as 'weak'. Using these data, we have investigated the statistical connection between the intergalactic medium (IGM) and galaxies through a clustering analysis. This dataset is the largest sample to date for such an analysis. We presented observational results for the H i-galaxy cross-correlation and both the galaxy-galaxy and H i-H i auto-correlations at z 1. The two-point correlation functions have been measured both along and transverse to the LOS, ξ(r ⊥ , r ), on a linear grid of 0. 5 Mpc in both directions. We have measured the H i-galaxy (ξag) and galaxy-galaxy (ξgg) correlations at transverse scales r ⊥ 10 Mpc, and the H i-H i auto-correlation (ξaa) at transverse scales r ⊥ 2 Mpc.
ACKNOWLEDGMENTS We thank the anonymous referee for helpful comments and suggestions which improved the paper. We thank  Pablo Arnalte-Mur ,  Rich Bielby ,  John Lucey ,  Peder Norberg  and  Tom Shanks  for helpful discussions. We thank  Pablo Arnalte-Mur  for his help in obtaining the prediction for the dark matter clustering at z 1. We thank  Mark Swinbank  for his help in the reduction of GMOS data. We thank contributors to SciPy  26  ,  Matplotlib 27  , and the Python programming language 28 ; the free and opensource community; and the  NASA  Astrophysics Data System 29 for software and services. N. T. acknowledges grant support by  CONICYT , Chile (PFCHA/Doctorado al Extranjero 1 a Convocatoria, 72090883). S. L. M. was partially supported by  STFC   Rolling Grant   PP/C501568/1  Extragalactic Astronomy and Cosmology at Durham 20082013.
Acknowledgements We acknowledge  M. Bianchi ,  M. Cvetič ,  F. Morales ,  O. Schlotterer  and  S. Stieberger  for interesting discussions and correspondence. P. A. is supported by the  Austrian Science Fund  ( FWF) program  M  1428-N27. M. D. G. was supported by a  Marie Curie Intra European Fellowship  within the 7th European Community Framework.
After carefully cross-identifying a previously discovered GALEX-selected Lyman Break Galaxy (LBG) candidates one-to-one with their optical counterparts in the field of the CDF-S, we re-estimate their photometric redshifts using multi-wavelength data from UV, optical to NIR. With the consideration of their re-estimated photometric redshifts and SEDs, we refine a new updated sample of 383 LBGs at 0. 7 < ∼ z < ∼ 1. 4 with two confirmed AGNs being excluded. 260 and 111 LBGs are classified as starburst and irregular types. Ages of LBGs spread from several Myr to 1. 5Gyr with a median of ∼ 50Myr. Their dust-corrected star formation rates (SFRs) and stellar masses (M * ) are from 4 M ⊙ yr -1 to 220 M ⊙ yr -1 and from 2. 3 × 10 8 M ⊙ to 4 × 10 11 M ⊙ with median values of ∼ 25 M ⊙ yr -1 and ∼ 10 10 M ⊙.
Visual classifications of morphologies In the present subsection, an image gallery of LBGs in variety of shapes with classifications by eye is established firstly. We cut areas of 5 ′′ × 5 ′′ individually as counter images of LBGs centered on their COMBO-17 coordinates. Since LBGs in our sample are at z ∼ 1, images of 5 ′′ × 5 ′′ (corresponding to 40 kpc × 40 kpc at z ∼ 1) are large enough to study their morphologies. The widely used software SExtractor 6   (Bertin & Arnouts 1996 ) is performed to detect LBGs in images with the detection threshold being chosen as 3σ for F606W and F850LP respectively (see also in next section). As pointed out by  Caldwell et al. (2008) , the GEMS can reliably detect most of COMBO 17 galaxies with magnitudes R < 24 and its detection ability decreases significantly for objects with R > 24.
Morphological analysis The powerful software GALFIT with the one-Sérsic-component model, is adopted to analyze morphological properties of LBGs. GALFIT is a galaxy/point source fitting algorithm developed by  Peng et al. (2002 Peng et al. ( , 2010) )  7 , that fits 2D parameterized axiallysymmetric directly to images. We refer  Peng et al. (2002 Peng et al. ( , 2010) )  for details. In the present paper, we focus on 277 LBGs with images detected in both F606W and F850LP (see the previous section) bands. The Sérsic index and the half-light radius, the most important structure parameters of a galaxy, are obtained through performing GAL-FIT to its image. Simple prescriptions, instead of the details for the processes and other parameters chosen, are as follows. SExtractor  (Bertin & Arnouts 1996)  is applied firstly to individual counter images of LBGs to get their initial parameters and mask files required by GALFIT, with the detection threshold being chosen as 3σ for both F606W and F850LP bands, respectively.
The point spread function (PSF) adopted here is generated by the TinyTim software  (Krist 1993)  which is designed to reproduce the HST modeled PSF. Although 277 LBGs with image detected successfully by SExtractor in both F606W and F850LP can get their Sérsic indices and half-light radii through GALFIT with the one-Sérsic-component model, results of half LBGs are not physically reliable. For instance, the 7 "spiral" LBGs classified in the previous section cannot be reliably fitted, since they display at least two components in their images. Obviously, the 10 "chain" LBGs cannot be fitted using GALFIT, since they show multiple components with different shapes. There are 87 among 132 "clump" LBGs with their residual images still having several bright knots after GALFIT performance. It implies that they display complicate structures.
CONCLUSIONS Burgarella et al. (  2007 ) defined a preliminary sample of 420 LBG candidates, with the completeness ∼ 80% down to NUV ∼ 24. 8, at z ∼ 1 by FUV -NUV > 2 from the deep GALEX observation in the field of the CDF-S according to their photometric redshifts from COMBO-17. Four confirmed AGNs are included. Al-though the global accuracies of photometric redshifts in the both COMBO 17 and MUSYC catalogs are pretty high, differences of photometric redshifts for LBG candidates between these two catalogs are significant. Together with the MUSYC BVR selected catalog, we re-estimate their photometric redshifts using broader multiwavelength data from UV, optical to NIR by the code Hyperz. Comparisons between re-estimated photometric redshifts and spectroscopic redshifts if available show that our photometric redshift determinations are fairly good.
The counter images of LBGs are analyzed using SExtractor. There are 309 and 279 LBGs successfully detected in the images of F606W and F850LP respectively, with 277 being commonly detected in both bands. We establish an image gallery of these 277 LBGs by visually classifying their images individually as types of "chain", "spiral", "tadpole", "bulge" and "clump", respectively. It is concluded that Red-LBGs are more structured than Blue-LBGs. The powerful software GALFIT of the one-component model is applied to LBG images availably detected in both F606W and F850LP bands. A morphological sample of 142 LBGs with reliable GALFIT results is established to represent all LBGs. We find more than 80% LBGs in the morphological sample with their Sérsic indices smaller than 2 in both bands. It is concluded that LBGs at z ∼ 1 are dominated by disklike galaxies, consistent with  Burgarella et al.
ACKNOWLEDGMENTS We would like to thank thorough reviewing and valuable comments from the anonymous referee. ZC is very grateful to the financial support by  DB  and the  LAM  for the visit to LAM and is also grateful to DB and VB for hospitalities during the stay in Marseille. This work is partly supported by the  Chinese National Nature Science foundation Nos. 10878003 ,  10833005 & 10778725 ,  Shanghai Science Foundations and Leading Academic Discipline Project of Shanghai Normal University  ( DZL805 ). Some/all of the data presented in this paper were obtained from the  Multimission Archive at the Space Telescope Science Institute (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.
ACKNOWLEDGMENTS The present work was financially supported by  Grantsin-Aid for Young Scientists (B) ( 22740241 ) and  Scientific Research on Innovation Areas"Heavy Electrons " (20102002,  23102726 ), from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT). This work was also supported by the  EC program gTransnational Accessh (EuromagNET II) ,  ERC   starting grant  ( NewHeavyFermion)  and  French ANR  project ( COR-MAT, SINUS ).