dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the ASPECS CO galaxies tend to have a flatter distribution of molecular gas masses and some of them show lower values than the PHIBSS1/2 CO galaxies. Since only part of this can be attributed to differences in the assumed α CO factors (as the PHIBSS1/2 survey assumes a metallicity/stellar mass dependent α CO ), this might reflect differences in parameter space between these surveys, i. e. , the lower stellar masses and SFRs inherent to our survey. To quantify these differences between the ASPECS CO and the PHIBSS1/2 CO z > 1 samples, we computed the two sided Kolmogorov Smirnov (KS) statistic, which yields the probability that two datasets are drawn from the same distribution. We find KS probabilities of   (Tacconi et al. 2013 (Tacconi et al. , 2018)). Due to its uncertain counterpart photometry, 3mm.
Molecular gas budget Inspection of Fig. 9  and the color-coding of the data points, suggests there is a tendency of having more starbursting galaxies with increasing redshift (i. e. , higher values of sSFR with increasing redshift). Conversely, galaxies tend to be more passive at lower redshifts. This effect is expected by standard scaling relations and has been seen by previous targeted CO surveys (e. g. ,  Tacconi et al. 2013 Tacconi et al. , 2018)). The clean CO-based selection of the ASPECS survey now allows us to investigate how the total budget of molecular gas in galaxies evolves as a function of redshift and distance from the MS (i. e. , galaxy type). We divided the ASPECS sample into three sets: galaxies significantly above the MS, with log(δ MS ) = log(sSFR/sSFR MS ) above 0. 4 ("starburst"); galaxies below the MS, with log(δ MS ) < -0.
4 ("passive"); and galaxies within the MS, with -0. 4 <log(δ MS ) < 0. 4 ("MS"). We subdivide these samples into two broad redshift bins: 1. 0 < z < 1. 7; 2. 0 < z < 3. 1, which essentially trace the redshift coverage of ASPECS for CO(2-1) and CO(3-2). Each of these redshift bins contains 10 and 4 sources, respectively (sources 3mm. 8 was excluded due to the ambiguous optical identification and 3mm. 13 due to its redshift outside the defined range). For each redshift bin, we now ask the question of what is the contribution of each galaxy type to the total budget of molecular gas (or what fraction of the total budget they are making up). At each redshift bin, we thus compute this contribution as the sum of all the molecular gas masses from galaxies of this particular type divided by the total molecular gas mass obtained from the recent measurement of cosmic molecular gas density (ρ H2 ) using ASPECS data  (Decarli et al.
Contribution to the total molecular gas budget from galaxies above (starburst), in or below (passive) the MS as a function of redshift inferred from the ASPECS survey. The blue, green and red data points and lines represent galaxies above, in and below the MS, respectively. The black curve shows the contribution of all the CO-selected galaxies considered here to the total molecular gas at each redshift. Each data point is computed from the sum of molecular gas masses of all galaxies in that redshift bin and galaxy type. The redshift measurement of each point is computed as the average redshift from all galaxies in that bin. The shaded region corresponds to the uncertainties of each measurement. The ASPECS CO galaxies are found to tightly follow the SFR-M mol relation, with a typical molecular gas depletion timescale of 1 Gyr, similar to z > 1 PHIBSS1/2 CO galaxies, yet spanning a range from 0.
Figure11. Contribution to the total molecular gas budget from galaxies above (starburst), in or below (passive) the MS as a function of redshift inferred from the ASPECS survey. The blue, green and red data points and lines represent galaxies above, in and below the MS, respectively. The black curve shows the contribution of all the CO-selected galaxies considered here to the total molecular gas at each redshift. Each data point is computed from the sum of molecular gas masses of all galaxies in that redshift bin and galaxy type. The redshift measurement of each point is computed as the average redshift from all galaxies in that bin. The shaded region corresponds to the uncertainties of each measurement.
R. acknowledges support from the  National Science Foundation  under grant number  AST-1614213. I. R. S. acknowledges support from the  ERC Advanced  Grant  DUSTYGAL (321334 ) and  STFC  ( ST/P000541/1 ). This paper makes use of the following ALMA data:  2016. 1. 00324. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ.
With a ten-tonne ν-target placed in front of the vacuum volume and equipped with suitable detectors, about 3 • 10 4 (2 • 10 4 ) interactions of tau (anti-tau) neutrinos are expected within the geometrical acceptance. The first direct observation of the anti-tau neutrino and the measurement of tau neutrino and anti-tau neutrino cross-sections are among the physics goals of the proposed experiment. As charm hadron decays are also a source of electron and muon neutrinos, SHiP will also be able to study neutrino-induced charm production from all flavours with a dataset which is more than one order of magnitude larger than those collected by previous experiments.
Acknowledgements The SHiP Collaboration wishes to thank the  Castaldo company (Naples, Italy)  for their contribution to the development studies of the decay vessel. The support from the  National Research Foundation of Korea  with grant numbers of  2018R1A2B2007757 ,  2018R1D1A3B07050649 ,  2018R1D1A1B07050701 ,  2017R1D1A1B03036042 ,  2017R1A6A3A01075752 ,  2016R1A2B4012302 , and 2016R1A6A3A11930680 is acknowledged. The support from the  Russian Foundation for Basic Research , grant  17-02-00607 , and the support from the  TAEK of Turkey  are acknowledged.
METHODS We follow the statistical framework presented in  Chen et al. (2018)  (see also Del Pozzo 2012;  Gray et al. 2018). We include the role of GW selection effects, galaxy catalog incompleteness, galaxy luminosities, and redshift uncertainties in our analysis. The posterior on H 0 given the GW and EM data, x GW and x EM , is: p(H 0 | x GW , x EM ) = p 0 (H 0 ) β(H 0 ) p(x GW | DL (z, H 0 ), Ω)p(x EM |z, Ω)p 0 (z, Ω)dΩdz, (1) where DL (z, H 0 ) is the luminosity distance of a source at redshift z for a given H 0 (fixing other cosmological parameters to the Planck values; Ade et al. 2016) 1  , Ω is the sky position, and β(H 0 ) is a normalization term to ensure that the likelihood normalizes to 1 when integrated over all detectable GW and EM datasets  (Mandel et al. 2016). The term p 0 (H 0 ) represents the prior on the Hubble constant.
25L B L B > 0. 05L B L B > 0. 01L B Figure 2. Completeness of the GLADE catalog as a function of redshift for galaxies brighter than 0. 25L B (solid blue curve), 0. 05L B (dashed green curve), and 0. 01L B (dotdahsed orange curve), calculated by comparing the redshift distribution of galaxies in GLADE to a distribution that is constant in comoving volume. For galaxies brighter than 0. 626L B , GLADE is complete across the entire redshift range shown. no peculiar velocities or incompleteness, and assigning weights to the galaxies in MICE so that the redshift distribution matches the injected redshift distribution of the F2Y dataset, p(z) ∝ z 2. This last step is necessary in order to ensure that the selection effects are incorporated consistently between the injections and the likelihood. The results are shown in Fig.
1. Even in the bestcase scenario of perfectly-known galaxy redshifts and a complete catalog, the H 0 posteriors from most individual events are nearly flat over the prior range. Combining the 249 individual events, the final H 0 posterior is H 0 = 70. 1 +1. 9 -1. 9 km s -1 Mpc -1 (68. 3% credible interval), corresponding to a convergence rate of ∼ 40%/ √ N , consistent with  Chen et al. (2018). As is visible in the Figure , we confirm that the method is unbiased, with the result for large numbers of detections approaching the true value of H 0. We note that most of the simulated detections in the F2Y dataset have much larger localization volumes than GW170817, which was an unusually loud, nearby source that was detected while all three detectors were operational. Therefore, we expect the statistical H 0 measurement from GW170817 to be unusually informative compared to an average event.
Although these results are suggestive that weighting by stellarmass or star-formation rate may lead to faster convergence, the properties of BNS host galaxies are still uncertain, and it is impossible to establish this definitively with a single event. As the source sample increases it is expected to relate to some combination of these quantities, and incorporating these trends will lead to improvements in the statistical H 0 analysis. In order to quantify the degree of information in the GW170817 H 0 posterior compared to an "average" event as expected from the F2Y dataset, we consider the difference in the Shannon entropy between the flat prior and the posterior (see Appendix;  Shannon 1948). We compare this measure of information for the statistical GW170817 H 0 posterior to the individual statistical H 0 posteriors from each of the simulated BNS events in Section 2.
and the Foundation for Fundamental Research on Matter supported by the Netherlands Organisation for Scientific Research, for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. We are grateful to the LIGO and Virgo collaborations for releasing sky map data for GW170817 at dcc. ligo. org/LIGO-P1800061/public. This work has made use of CosmoHub. CosmoHub has been developed by the Port d'Informaci Cientfica (PIC), maintained through a collaboration of the Institut de Fsica d'Altes Energies (IFAE) and the Centro de Investigaciones Energticas, Medioambientales y Tecnolgicas (CIEMAT), and was partially funded by the "Plan Estatal de Investigacin Cientfica y Tcnica y de Innovacin" program of the Spanish government. We also acknowledge the First Two Years data release (https://www.
We thank  Chihway Chang ,  Alison Coil  and  Risa Wechsler  for valuable discussions about galaxy properties. MF was supported by the  NSF   Graduate Research Fellowship Program  under grant  DGE-1746045. MF and DEH were supported by  NSF  grant  PHY-1708081. They were also supported by the  Kavli Institute for Cosmological Physics at the University of Chicago  through  NSF  grant  PHY-1125897  and an endowment from the  Kavli Foundation. DEH also gratefully acknowledges support from a  Marion and Stuart Rice Award. The authors acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS).
We thank the  KEKB group  for the excellent operation of the accelerator; the  KEK cryogenics group  for the efficient operation of the solenoid; and the  KEK computer group , the  National Institute of Informatics , and the  PNNL/EMSL computing group  for valuable computing and SINET4 network support. We acknowledge support from the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan , the  Japan Society for the Promotion of Science (JSPS) , and the  Tau-Lepton Physics Research Center of Nagoya University ; the  Australian Research Council ;  Austrian Science Fund  under Grant No. P 22742-N16  and  P 26794-N20 ; the  National Natural Science Foundation of China  under Contracts No. 10575109 , No. 10775142 , No. 10875115 , No. 11175187 , No. 11475187  and No. 11575017 ; the  Chinese Academy of Science Center for Excellence in Particle Physics ; the  Ministry of Education, Youth and Sports of the Czech Republic  under Contract No.
II. MICROMAGNETIC SIMULATIONS To understand how geometry affects domain wall propagation in a modulateddiameter cylindrical nanowire when a magnetic field is applied, we used our home-made finite element software feeLLGood  [22, 24]. The non-regular finite element mesh of feeLLGood accurately describes the given geometry. The Landau-Lifshitz-Gilbert equation was solved for magnetization dynamics  [25]  by a micromagnetic approach = - × + × , (1) where m=M/MS is the unit vector in the direction of the local magnetization, and MS is the spontaneous magnetization. The following notations were used: γ0>0 for the gyromagnetic ratio, Heff for the effective field (including magnetostatic, exchange and applied field contributions), α for the Gilbert damping factor. Cylindrical nanowires with a single smooth diameter modulation connecting two straight parts were considered.
ACKNOWLEDGEMENT We acknowledge financial support from the  French National Research Agency (ANR)  (Grant No. JCJC MATEMAC-3D ) and from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 309589  ( M3d ). J. A. F. -R. is grateful for financial support from the  Spanish MINECO  under  MAT2016-76824-C3-1-R  and cosupport from the  ESF  though  BES-2014-068789  and  EEBB-I-16-10934.
Acknowledgments: We thank  T. Ito  and  H. Tsunakawa  for TEM assistance. H. A. thanks the  China Scholarship Council  for financial support. A. K. acknowledges support from the  Leading Initiative for Excellent Young Researchers of MEXT. Funding: Part of this work was financially supported by  JSPS KAKENHI  grant numbers  JP18H05329 ,  JP25107002 ,  JP15H05760 , and  JP15K17984  and  IRENA  Project by  JST-EC DG RTD, Strategic International Collaborative Research Program  (SICORP). Part of this work was based on results obtained from a project commissioned by the  New Energy and Industrial Technology Development Organization (NEDO)  and by the " Nanotechnology Platform"  (project no. 12024046 ) of the  Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. Author contributions: S.
The data are further split into bins of decay time, which are indexed with j. For each, we measure the ratio R + bj (R - bj ) between initially produced D 0 (D 0 ) mesons in Dalitz bin -b and Dalitz bin b. For small mixing parameters and CP -conserving decay amplitudes, which are good approximations here, the ratios are  [12] R ± bj ≈ r b + 1 4 r b t 2 j Re(z 2 CP -∆z 2 ) + 1 4 t 2 j |z CP ± ∆z| 2 + √ r b t j Re[X * b (z CP ± ∆z)] 1 + 1 4 t 2 j Re(z 2 CP -∆z 2 ) + r b 1 4 t 2 j |z CP ± ∆z| 2 + √ r b t j Re[X b (z CP ± ∆z)]. (1) Here t j ( t 2 j ) is the average (squared) decay time of unmixed decays in bin j, in units of the D 0 lifetime τ = /Γ  [2]. The parameter r b is the ratio of signal yields in symmetric Dalitz-plot bins ∓b at t = 0, and X b quantifies the average strong-phase difference in these bins  [12].
CBPF (Brazil), PL-GRID (Poland) and OSC (USA). We are indebted to the communities behind the multiple open-source software packages on which we depend. Individual groups or members have received support from AvH Foundation (Germany); EPLANET, Marie Sk lodowska-Curie Actions and ERC (European Union); ANR, Labex P2IO and OCEVU, and Région Auvergne-Rhône-Alpes (France); Key Research Program of Frontier Sciences of CAS, CAS PIFI, and the Thousand Talents Program (China); RFBR, RSF and Yandex LLC (Russia); GVA, XuntaGal and GENCAT (Spain); the Royal Society and the Leverhulme Trust (United Kingdom); Laboratory Directed Research and Development program of LANL (USA).
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MSHE (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  Aix Marseille Univ ,  CNRS/IN2P3 ,  CPPM, Marseille, France LAL ,  Univ.
Acknowledgments This paper is dedicated to the memory of Christian Duval (1947Duval ( -2018)). Discussions are acknowledged to  Janos Balog ,  Nathalie Deruelle  and  Piotr Kosiński. ME thanks the  Institute of Modern Physics of the Chinese Academy of Sciences in Lanzhou  and the  Denis Poisson Institute of Orléans-Tours University. PH thanks the  Institute of Modern Physics of the Chinese Academy of Sciences in Lanzhou  for hospitality. This work was partially supported by the  Chinese Academy of Sciences President's International Fellowship Initiative  (No. 2017PM0045 ), and by the  National Natural Science Foundation of China  (Grant No. 11575254 ). MC acknowledges CNPq support from projects ( 303923/2015-6 ) and ( 306591/2018-9 ).
2013; Calanog et al. 2014; Negrello et al. 2017). This same approach applied to the Herschel Multi-tiered Extragalactic Survey (HerMES) field extends the areal coverage by a factor of ∼7, resulting in 13 new lensed sources  (Bussmann et al. 2013; Wardlow et al. 2013; Calanog et al. 2014; Nayyeri et al. 2016). By applying similar flux density cuts to the Herschel Stripe 82 Survey (Hers82), an additional three lensed DSFGs are found  (Nayyeri et al. 2016). From the ground, South Pole Telescope (SPT) data enable the selection of strongly lensed DSFGs based on the ratio of flux densities at 1. 4 and 2. 0 mm S 1. 4mm /S 2mm , which are consistent with thermal emission by dusty galaxies  (Vieira et al. 2010; Carlstrom et al. 2011). The brightest sources in the sample are then followed up at higher resolution using most notably the SubMillimeter Array (SMA) and the Atacama Large Millimeter Array (ALMA).
A total of 26 strongly lensed DSFGs are identified, which tend to be at higher redshifts owing to their selection at longer wavelengths, and whose identifications are typically explained as galaxygalaxy lensing events  (Weiß et al. 2013; Vieira et al. 2013). From space, Planck and Herschel Space Observatory (Herschel) data are used to extend the search for lensed DSFGs to all available sky. The search technique relies on the detection of the rest-frame far-infrared thermal dust peak, which is a salient feature of DSFGs. A strict lower limit on the flux density is imposed amounting to 600 mJy at 545 GHz to select only the most extreme sources. The expectation is that the brightest sources that are also compact at Planck resolution, and that remain compact in the Herschel follow-up observations are too faint to be explained by a single-field DSFG.
85 and 0. 06, respectively. We checked the redrizzled images by computing the FWHM of a few stars in each field. In some cases, such as the G165 field, there were only two isolated, unsaturated stars within the field of view, so we substituted a compact and isolated elliptical galaxy as a third source. Redrizzling of the data in each case resulted in a 3 -10% improvement in image quality (FWHM) over the pipeline products. The final reduced images reach comparable depths to the CLASH clusters. For one representative case, G165, we compute 10σ limiting mag-nitudes of F110W AB = 26. 9 mag and F160W AB = 26. 2 mag for point sources inside 0. 4 apertures. We find that the image depth and filters are sufficient to make NIR detections of the strongly lensed DSFG in each of our sample fields. We also identify other examples of giant arcs and/or image multiplicities in some cases.
In Figure  2 , we present the HST color images of the central regions for each of the six fields. We refer the reader to Appendix A for further details regarding the search for the NIR counterparts. The WFC3 F160W images are used as detection images for the matched aperture photometry. We custombuilt our code to cope with the unusual morphologies peculiar to arcs in the central regions of massive lensing clusters. We detect sources by applying a σ-clipping al- c The spectroscopic redshift comes from  Cañameras et al. (2015). d The lensed image is only partially resolved in our HST image (Figure  2 ). These values for the magnification factor, the size of the Einstein radius, and the redshift are drawn from the ALMA data in  Cañameras et al. (2017a,b). e The spectroscopic redshift comes from  Harrington et al.
To ensure robust detection of objects, we smooth the image with a Gaussian filter of FWHM ∼ 0. 2. Objects are deblended using the watershed PYTHON algorithm in astropy. photutils (The Astropy Collaboration et al. 2018 ). Artifacts such as diffraction spikes are visually identified and removed. We assign apertures to each galaxy image by measuring the semi-major/minor axis sizes at 3 times the FWHM lengths of the detected objects, typically amounting to 0. 6. Elliptical annuli are used to get the best estimate of the background, with an inner radius equal to the photometric aperture and an outer radius equal to 1. 2 times the inner radius. We compute the aperture flux and then subtract the area-scaled local background level within the annuli. The flux uncertainty is computed as the quadratic sum of the local smooth background RMS value only.
LBT Observations We acquired imaging of G165 in K band using the LBT Advanced Rayleigh Ground layer adaptive Optics System (ARGOS;  Rabien et al. 2019 ) during instrument commissioning time in December 2016 (2016B, PI: Frye). ARGOS corrects ground-layer distortions in the imaging of the two 8. 4 m apertures using two threebeam constellation lasers as guide stars that are fixed to each aperture. The ARGOS instrumentation operates through the LUCI imager and multislit spectrometer. High-quality corrections of up to FWHM ≈ 0. 25 at K are achievable across a large field of view (4 × 4 ) at a native pixel scale of 0. 12 pix - 1. We acquired LBT/LUCI + ARGOS data in monocular mode on two separate nights: 46 minutes of observation using LUCI2 on 9 December and 42 minutes using LUCI1 on 2016 December 15.
25, with negligible rotation. The WCS information in each object frame is updated accordingly. At this point we resample the images onto the same pixel grid using the flux-conserved, overlapping pixel-area method in the Python routine astropy. reprojection, as needed. We design our own reduction pipeline to ensure high flatness across the chip and to maximize the signal-tonoise ratio of the data. As a first step, we subtract the dark frames from all object frames. We then proceed to find the best estimate of the background. Within a single exposure, the sky background varies by ∼100 ADUs, comparable to the integrated flux of some of the fainter cluster members. Therefore, instead of creating sky frames by taking the median at multiple pointings directly, we apply a "normalizing-rescaling" approach, in which we construct the master sky frames using exposures adjacent in time, which are then scaled to the background level of each object frame prior to the subtraction.
(2018)  for details on the photometric analysis and photometric redshifts. Note that as the planning for this observing run took place prior to receiving the HST data set, we were not able to fine-tune the target list to include new arclet family members. The observations were composed of a single Hectospec pointing with 7 × 1020 s exposures taken under variable seeing conditions of ∼ 1 -2. This was sufficient for our science goal given the 1. 5 fiber widths and the relatively bright magnitudes of the targets of i AB 18 -22 mag. The reductions proceeded in a standard fashion using the IDL/Hectospec Reduction Software package (HSRED) obtained from the Smithsonian Astrophysical Observatory Telescope Data Center. 2 We removed cosmic rays using the code "LA Cosmic" (van Dokkum 2001). Corrections for pixel-to-pixel variations, fringe corrections, and fiber identifications are accomplished using a dome flat.