dataesr/scientific-paragraphs-categorization huggingface dataset:
3) was found to produce photo-z estimates with the smallest scatter and OLF for star-forming or quiescent galaxy populations (D18a). Here we make use of the new template set derived from the Flexible Stellar Population Synthesis code (FSPS;  Conroy et al. 2009; Conroy & Gunn 2010). While designed to reproduce the same representative combination of stellar emission dominated galaxy SEDs, the revised FSPS templates now include dust reprocessed emission in the mid-IR. The emission from dust extends the range of rest-frame wavelengths that can reliably be used to constrain the photo-z estimate, which is particularly valuable in the deep field datasets that includes photometry out to 8 µm. Extended Atlas Library: The second template set, the 'Atlas of Empirical SEDs  ' Brown et al. (2014)  was recently extended to incorporate a wide range of the AGN and quasistellar object (QSO) populations not previously present in the original library  (Brown et al.
Photometric zero-point offsets The inclusion of small magnitude offsets, or zero-point offsets, to the observed photometry of some datasets has been shown to improve photo-z estimates from template fitting (e. g. see  Dahlen et al. 2013). While typically small ( 10%), these additional offsets can often substantially reduce the overall scatter or OLF for photo-z estimates. In  Hildebrandt et al. (2012) , detailed comparison between the resulting photo-z performance for different levels of photometry precision indicates that zero-point flux offsets serve largely to correct for PSF effects. Given the nature of the photometry used in the work, we would therefore expect that the inclusion of zero-point offsets during template fitting will be beneficial. Zero-point offsets for all template sets are derived for each photometric dataset following the method outlined in D18a.
Within a given field, we find good agreement between the largest zero-point corrections derived for the different template sets (e. g. the UKIRT Infrared Deep Sky Survey, UKIDSS, K-band data in EN1 and LH are consistently found to require a correction of ∼ 10%). We conclude that for the datasets employed in this analysis, the zero-point corrections are likely correcting for small offsets in the photometry itself, rather than large systematic errors in the templates themselves (we note that the three different template sets encompass both model and empirically derived templates).
A key change to our method from previous implementations is the choice of input magnitudes. While the use of standard logarithmic magnitudes has produced good photoz results in our previous efforts (D19), they are however not suited to datasets containing large numbers of nondetections (i. e. flux measurements with S/N < 2) or low S/N measurements. In multi-wavelength forced photometry catalogues such as those employed here where depth varies significantly between filters (Fig. 1 ), non-detections in individual bands are inevitable and therefore a number of sources may have zero or negative flux measurements and hence are undefined in standard logarithmic magnitudes. In many cases, most notably high-redshift galaxies, those flux limits provide valuable colour (and hence redshift) information.
For individual photometric bands within the deep fields optical datasets, this assumption is valid due to the relatively homogeneous depth within our datasets (the heterogeneity within the fields is largely a result of different spatial coverage of different bands). Due to the differing range and sources (i. e. telescopes or surveys) of optical to mid-IR photometric imaging across the three fields, GPz must be trained separately for each of the corresponding spectroscopic datasets. The primary GPz classifier for each field is trained on optical sources that do not satisfy any of the AGN selection criteria -corresponding to the significant majority of both the training sample and photometric catalog. In Boötes, we also use additional GPz classifiers trained on the IR, optical and X-ray selected AGN subsets.
Few spectroscopically confirmed sources at z > 4 exist in the EN1 and LH but we find that our photo-z estimates in Boötes provide accurate estimates out to z > 6 (including the confirmed radio-loud quasar at z = 6. 1  McGreer et al. 2006). Given that hybrid photo-z estimates in this regime are found to be dominated by the template estimates (see D18b), we would expect the performance in the EN1 and LH datasets to therefore be comparable in quality (or potentially better in the case of EN1). Nevertheless, we caution against the use of the photozs above z ∼ 4 without careful analysis of the individual estimates.
After accounting for any small global offset between the two flux measurements based on the median flux ratio for the source types used for aperture corrections, the resulting flux ratio can be used to estimate an approximate aperture to total flux conversion for each source. In the faint limit where a source has a S/N less than 5σ or is undetected in Legacy Surveys imaging, we assume that sources in the LOFAR optical catalogue are unresolved and the aperture corrected fluxes provide a reliable estimate of the total flux. In the stellar mass catalogue presented in this data release we provide the stellar mass and rest-frame magnitude values as measured, alongside total flux corrections where available.
For the HSC-Mizuki template based estimates  (Tanaka 2015) , we find a median offset of just 0. 01 dex and robust scatter of 0. 32 dex. We note that the typical difference between our estimates and either of the HSC estimates is smaller than the difference between the two HSC estimates themselves when calculated for the same galaxy sample (a median offset of 0. 14 dex). Given the good agreement and consistency between our three fields at the redshifts of interest, we are therefore confident, that with appropriate sample selection and quality cuts, our stellar mass estimates are suitable for robust quantitative studies across all three fields. Furthermore, the large samples and volumes probed by the combined deep fields dataset over a wide range of cosmic history offer reliable reference samples for studying radio properties as a function of history, mass, and environment.
Radio source properties The combination of exquisite radio continuum observations and extensive multi-wavelength data covering > 20 deg 2 provided by the LoTSS Deep Fields data release offers the potential for a wide range of studies of the radio population -ranging from studies of the cosmic star-formation history through to obscuration free studies of the black hole accretion history (e. g. Smolčić et al. 2017b; Novak et al. 2017). In addition to the SED fitting presented in this paper, extensive detailed physical modelling of the radio population that incorporates additional FIR information (e. g. Calistro Rivera et al. 2016) can offer further insight into the physical properties of the faint radio source population. Building upon the multi-wavelength datasets presented in Paper III and the photo-zs presented here,  Best et al.
Acknowledgements KJD and HJAR acknowledge support from the  ERC   Advanced Investigator programme NewClusters 321271. RK acknowledges support from the  Science and Technology Facilities Council (STFC)  through an  STFC studentship via grant   ST/R504737/1. PNB and JS are grateful for support from the  UK STFC  via grant  ST/R000972/1  MB acknowledges support from  INAF  under  PRIN SKA/CTA FORE-CaST  and from the  Ministero degli Affari Esteri della Cooperazione Internazionale -Direzione Generale per la Promozione del Sistema Paese Progetto di Grande Rilevanza   ZA18GR02. RAAB acknowledges support from the  Glasstone Foundation. MJH acknowledges support from the  UK STFC  via grant  ST/R000905/1. MJJ acknowledges support from the  UK Science and Technology Facilities Council  [ ST/N000919/1 ] and the  Oxford Hintze Centre for Astrophysical Surveys  which is funded through generous sup-port from the  Hintze Family Charitable Foundation.
MKB and AW acknowledge support from the  National Science Centre, Poland  under grant no. 2017/26/E/ST9/00216. K. M. has been supported by the  National Science Centre  ( UMO-2018/30/E/ST9/00082 ). IP acknowledges support from  INAF  under the  SKA/CTA PRIN "FORECaST " and the  PRIN MAIN STREAM "SAuROS " projects. This paper is based (in part) on data obtained with the  International LOFAR Telescope (ILT)  under project codes  LC0 015 ,  LC2 024 ,  LC2 038 ,  LC3 008 ,  LC4 008 ,  LC4 034  and  LT10 01. LOFAR (van Haarlem et al. 2013) is the  Low Frequency Array designed  and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and which are collectively operated by the ILT foundation under a joint scientific policy.
The ILT resources have benefitted from the following recent major funding sources:  CNRS-INSU, Observatoire de Paris and Université d'Orléans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The  Netherlands , The  Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland. The  Pan-STARRS1 Surveys  ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
The offline taggers are also the most sophisticated taggers developed by the ATLAS Collaboration and therefore provide the best available signal selection and background rejection. The -tagging of jets is described in Section 6, where the performance of the -jet triggers is also shown. ATLAS successfully used -jet triggers throughout the Run 1 data-taking campaign, and several improvements to the -jet triggers were implemented during the long shutdown period (2013-2014) to further improve performance for Run 2 (2015-2018) data-taking. The new -jet triggers were commissioned during 2015, while the Run-1-style -jet triggers (i. e. those that used the same software and -tagging algorithms as were used in Run 1 but benefited from other upgrades to the ATLAS detector and trigger system) were the primary triggers for physics analyses using the data taken that year.
Datasets and simulated events The results presented here use data from proton-proton ( ) collisions with a centre-of-mass energy √  = 13 TeV, collected during Run 2 of the LHC, between 2016 and 2018. The -jet triggers were monitored during the on-going runs as an early-warning alert mechanism to spot problems and improve data quality. Monitored variables include the jet and track multiplicities, the primaryand secondary-vertex positions, the variables used as inputs to the -tagging algorithms, and the output discriminants of the taggers. Histograms of these variables were compared with reference histograms, using an automated evaluation system and checked by shift-personnel in the ATLAS Control Room. A more in-depth evaluation of the data quality was performed offline, soon after the data were recorded, and used as the input to a per-luminosity-block 2  evaluation of the suitability of the data for use in physics analysis.
This mismatch caused the online primary-vertex-finding algorithm to fail in cases where the mean -position of the interaction region ( online beamspot ) was far from the origin. The problem was resolved during 2016 data-taking and a -jet-trigger-aware Good Run List (GRL)  3  is provided to reject events with | online beamspot | > 2 mm in the affected data. Further information is available in Ref. [17]. The application of the -jet-trigger-aware GRL reduces the integrated luminosity of the 2016 dataset from 32. 9 fb -1 to 24. 6 fb -1. A more stringent GRL is provided for use in precision measurements and tightens this requirement so as to reject events with | online beamspot | > 1 mm, which reduces the integrated luminosity further to 20. 6 fb -1. In all years, luminosity blocks at the start of each run associated with an out-of-date or invalid beamspot position are discarded.
This additional requirement reduces the integrated luminosity of the 2017 and 2018 datasets by approximately 1. 5% compared with the baseline ATLAS GRL. The maximum instantaneous luminosity, and therefore the average number of   interactions per bunch crossing under constant beam conditions,  , commonly referred to as 'pile-up', increased by a factor of four during Run 2. This information, together with the integrated luminosity of the datasets after requiring stable beam conditions and the -jet-trigger-aware GRL described above, is summarised for each year of Run 2 data-taking in Table  1. Uncertainties in the integrated luminosity are obtained using the methods discussed in Ref. [18]  and the LUCID-2 detector  [19]  for the primary luminosity measurements. Monte Carlo (MC) simulations of top-quark pairs ( t) produced in   collisions are used throughout this paper to provide a sample of simulated -, -, and light-flavour jets.
The -jet trigger software is run on the jets and all associated -tagging information is kept, but no selection is made on the online -tagging weight of the jets. The triggers used for these measurements were run unprescaled, but in 2016 they were only run for part of the year and the integrated luminosity of that dataset is 13. 1 fb -1. Table  9 : Details of the triggers used to select a data sample to perform the calibrations. Electrons (muons) are required to be isolated and pass a 'Tight'  [59]  ('Medium'  [62] ) identification working point. Jet  T thresholds denoted * indicate that the GSC was applied and the value quoted is the calibrated  T threshold. All triggers were run unprescaled, but in 2016 they were only run for part of the year and the integrated luminosity of that dataset is 13.
Acknowledgments We thank  Emil Martinec ,  Stefano Massai ,  Sami Rawash ,  Michele Santagata  and  Jeremías Aguilera-Damia  for discussions. The work of D. B. was supported by the  Royal Society   Research Grant   RGF\R1\181019. The work of S. I. was partially supported by the  CONICET  fellowships  PICT-2016-1358  and  PUE-IAFE22920160100060CO. The work of N. K. was supported by the  Leverhulme Trust  under grant no. RPG-2018-153  and by the  ERC  Consolidator Grant 772408-Stringlandscape. The work of D. T. was supported by a  Royal Society Tata University Research Fellowship.
Here, simulations have been performed with the code TOKAM3X  [33]. This code has been largely used in former studies, mostly in its isothermal version, assuming a single value of collisionality and examining the effect of turbulence on complex geometries and/or neutral physics  [34] [35] [36] [37] [38] [39]. The paper is organized as follows. The non-isothermal TOKAM3X model is briefly introduced in Sec. 2 together with the numerical setup. Sec. 3 defines the collisionality and discusses the related parameters in the fluid framework. In Sec. 4, flow properties are investigated in details in a simulation of reference performed at a given collisionality value. Sec. 5 analyses how these proprieties are modified when varying the collisionality. Finally, some concluding remarks are given in Sec.
This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. This work has been carried out thanks to the support of the A*MIDEX project (ANR-11-IDEX-0001 02, TOP project) funded by the 'Investissements d'Avenir' French Government program, managed by the French National Research Agency (ANR). This work was granted access to the HPC resources of IDRIS under the allocations A0030506912 & A0050506912 made by GENCI, of Aix-Marseille University, financed by the project Equip@Meso (ANR-10-EQPX-29 01), and of the EUROfusion High Performance Computer (Marconi-Fusion) under the project HEAT.
(B4) We determine from a sinusoidal fit the values M ↑ (φ) and M ↑ and thus compute the magnetization S φ using eqs. (B1) to (B4). The amplitude A of the sinusoidal fit, normalized by N ↑+↓ , corresponds to the magnetization in the xy-plane. Following this procedure, we deduce that the number of atoms in the auxiliary states N a increases linearly in time with a rate of 7 kHz, consistent with the blackbody decay of the spin states toward Rydberg states above the ionization threshold. Here, s (i) = (s x , s z ) are classical spins and {. } denotes the Poisson bracket. The system of ordinary differential equations is solved by Tsitouras 5/4 Runge-Kutta method  [56]  using the Julia Differential Equations package  [57]. For a perfect initial state where all spins are aligned in x-direction, mean-field theory does not predict any dynamics.
ACKNOWLEDGEMENTS We thank  J. Berges ,  A. Piñiero Orioli ,  M. Rigol  and  J. Schachenmayer  for fruitful discussions. This work is part of and supported by the  DFG Collaborative Research Centre "SFB 1225 (ISOQUANT) ", the  DFG   Priority Program "GiRyd 1929 ", the  European Union   H2020  projects  FET Proactive project RySQ  (Grant No. 640378 ) and  FET  flagship project  PASQuanS  (Grant No. 817482 ) and the  Heidelberg Center for Quantum Dynamics. S. W. acknowledges support by " Investissements d'Avenir" programme through the Excellence Initiative  of the  University of Strasbourg (IdEx). T. F. acknowledges funding by a graduate scholarship of the Heidelberg University (LGFG) and R. F. from the  Brazilian fund Ciência sem Fronteiras.
2019). Photometric analyses of the equatorial and southern midlatitudes using Hapke's model show that while, to first order, the single-scattering albedo properties are homogeneous, there are localized brighter and darker regions  (Pilorget et al. 2021). The darker regions tend to be associated with a slightly deeper 2. 72 μm feature, which  Pilorget et al. (2021)  ascribe to enrichment of dark fines coupled to hydrated phases. In this study we present an examination of the opposition observations acquired by the NIRS3 instrument to gain further insight into the structural properties of Ryugu's regolith. This study examines the effects of footprint resolution, spectral variability across the surface, and variations as a function of wavelength within this opposition data set. The goal is to gain an understanding of the nature and magnitude of the surface spectral variations, the global photometric properties, and their combined implications for the regolith on Ryugu.
The opposition observations provide insight into the nature and properties of Ryugu's surface under unique observing conditions. This study begins with a presentation of the spectral characteristics of Ryugu's opposition data set followed by the examination and modeling of the photometric properties of this data set. Both spectral and photometric properties are compared with similar examinations from the Optical Navigation Camera (ONC) imaging data set, and the results are placed in context with other small bodies studied in our solar system.
The Data Set The NIRS3 is a point spectrometer with a 0. 1°field of view, which obtained continuous point-target spectra over Ryugu's surface. The observations used in this study are a subset of those made over the entire mission and acquired by the NIRS3 prior to the first touchdown (TD1) for sample collection in 2019 February. This study focuses on the infrared spectra of Ryugu acquired under opposition (phase angles, α, <5°) and near opposition (α < 30°) geometries at various spatial scales over the near-equatorial, low-latitude regions (Figure  1 ). For this study, all observations acquired prior to TD1 were binned according to footprint resolution in consecutive bins of 2 m, beginning with the first bin of 4-6 m footprint size. This resolution range (2 m) was chosen as the smallest resolution with sufficient data to cover the incidence, emission, and phase angle space for modeling.
All data for which geometric information (latitude, longitude, altitude, incidence, emission, and phase angle) was available were examined for this analysis. Each bin was examined for phase angle coverage. Only four footprint size bins contained opposition observations: bin 4-6 m, bin 6-8 m, bin 8-10 m, and bin 38-40 m. These four bins were used for this study of the opposition surge. All data within each footprint size bin, regardless of phase angle value, were used for the study. Each spectrum ranges in wavelength from 1. 8 to 3. 2 μm  (Iwata et al. 2017 ). This analysis focuses on the 76 channels ranging in wavelength from 1. 801 to 3. 149 μm. This wavelength range was selected because those channels above 3. 149 μm are affected by thermal emission and those channels below 1. 8 μm are within the wavelength region where the NIRS3 detector sensitivity decreases  (Iwata et al.
Inversion Modeling Technique The modeling methodology used was a least-squares grid search that minimized the value of eta (η), defined by ( ) () å h = - = r r N , 4 i N 1 measured model 2 where N is the number of measurements, r measured is the NIRS3 measured reflectance, and r model is the calculated reflectance using the Hapke model. This assumes that the errors in the measured reflectance are the same for each measurement within a single wavelength. Wavelength-dependent variations in η are a result of both different wavelength-to-wavelength standard deviation differences in the observations and spectral dispersion (reflectance variability of the surface) within the different wavelength data sets. The grid search was defined by a range and increment (grid size) for each parameter, and all combinations across the parameter values were examined.
General Spectral Properties: Regolith Structural Implications Comparisons of the NIRS3 opposition observations were made with the ONC-T color observations. Two sets of mosaics have been created from the ONC-T data set. One set of mosaics were constructed from images photometrically standardized to 30°, 0°, and 30°in incidence, emission, and phase, respectively  (Tatsumi et al. 2020). This set consists of a global mosaic for each filter band of the camera system, and we hereafter refer to these global mosaics as the "standard mosaics". The second set of mosaics was constructed from the ONC-T opposition observations, and they represent the normal albedo of the surface  (Yokota et al. 2021). This set also consists of global mosaics, one for each band, and we hereafter refer to these global mosaics as the "normal albedo mosaics".
Phase reddening (P r , also called spectral reddening in the literature) is defined as a a = - - a a P slope slope 1 2 , r 1 2 where α 1 and α 2 are the phase angle values, and slope α1 and slope α2 are their corresponding slopes. Phase reddening occurs when P r is larger than zero (slope increases with increasing phase angle), and phase bluing occurs when P r is less than zero (slope decreases with increasing phase angle). The spectral slope at the NIRS3 opposition footprint sites can be examined in both the visible and the near-infrared. Using the ONC-T data, the visible spectral slope was calculated using the b-and p-band reflectance values normalized to the v band. The slope was calculated for the locations of the NIRS3 opposition data from both the normal albedo mosaics of  Yokota et al.