dataesr/scientific-paragraphs-categorization huggingface dataset:
The lack of agreement between IRS data and WISE fluxes made us decide to exclude WISE data from the model analysis in Sec. 5. The first step before modelling the whole SED is to derive the stellar parameters. For each star in the sample, we selected photometric points with a pure photospheric origin (typically in the 0. 3-3. 0 µm range) and used them to compare the non-excess SED with theoretical models using the Virtual Observatory SED Analyzer 1  (VOSA,  Bayo et al. 2008). VOSA compares the photometric data with a grid of models to match the photosphere that produces the best fit, based on a χ 2 minimisation, and, if desired, on Bayesian analysis. The values for T eff and L * from the best fits are summarised in Table  1. In Fig. 1  we plot the Hertzsprung-Russell diagram for the stars in the sample.
Spectroscopic observations and data reduction Four BPMG members  (HD 164249, HD172555, HD 181296, and HD 181327)  were observed with PACS in LineScan spectroscopic mode, targeting [OI] emission at 63. 18 µm. Riviere-Marichalar et al. (2012b)  reported the detection of o-H 2 O emission at 63. 32 µm in the PACS spectra of eight T Tauri stars in Taurus, therefore we also searched for line emission at this wavelength. All the targets were also observed in RangeScan mode, aiming to detect [CII] emission at 157. 74 µm. PACS spectra were reduced using HIPE 9 with the latest version of the pipeline and the proper calibration files. Spectra were extracted from the central spaxel and aperture-corrected to account for flux spread in the surrounding spaxels. The line spectra from PACS typically show lower S/N near the spectrum edges.
Acknowledgements. We acknowledge the anonymous referee for a very interesting discussion that helped improve the quality of the paper. This research has been funded by  Spanish  grants  BES-2008-003863 ,  AYA 2010-21161-C02-02 ,  AYA 2012-38897-C02-01 ,  AYA 2011-26202 , and  PRICIT-S2009/ESP-1496. I. Kamp  and  P. Riviére-Marichalar  acknowledge funding from an  NWO   MEERVOUD grant. We also acknowledge support from  ANR  (contract  ANR-07-BLAN-0221 ) and  PNPS of CNRS/INSU, France. C. Pinte  acknowledges funding from the  European Commission 's 7 th  Framework Programme  (contract  PERG06-GA-2009-256513 ) and from  Agence Nationale pour la Recherche (ANR) of France  under contract  ANR-2010-JCJC-0504-01.
Acknowledgements We would like to thank  Samir Mathur  and  David Turton  for extensive discussions and for providing us with an advance copy of [1]. We would also like to thank  Stefano Giusto ,  Rodolfo Russo  and  Masaki Shigemori  for useful discussions. NPW is grateful to the  IPhT, CEA-Saclay  and to the  Institut des Hautes Etudes Scientifiques (IHES) ,  Bures-sur-Yvette , for hospitality while this work was done. NPW would also like to thank the  Simons Foundation  for their support through a  Simons Fellowship in Theoretical Physics. SFR would like to thank the  UPMC  for hospitality while this work was done. NPW and SFR are also grateful to CERN for hospitality while this work was initiated, and we are all grateful to the  Centro de Ciencias de Benasque  for hospitality at the "Gravity-New perspectives from strings and higher dimensions" workshop.
ACKNOWLEDGEMENTS The authors would like to thank  E. Dudas ,  A. Falkowski ,  K. Olive ,  M. Goodsell ,  T. Hambye ,  M. Tytgat ,  E. Fernandez-Martinez  and  M. Blennow  for very useful discussions. This work was supported by the  French ANR TAPDMS   ANR-09-JCJC-0146  and the  Spanish MICINNs  Consolider- Ingenio 2010 Programme  under grant  Multi-Dark   CSD2009-00064. X. C. acknowledges the support of the  FNRS-FRS , the IISN, the  Belgian Science Policy  ( IAP VI-11 ). Y. M. and J. Q. acknowledge partial support from the  European Union   FP7 ITN INVISIBLES (Marie Curie Actions ,  PITN-GA-2011-289442 ), the  ERC  advanced grant Higgs@LHC.
The momentum resolution of the tracks in this data is δp/p = 1. 10%⊕1. 16%p, where p is the total momentum measured in GeV/c. Electrons can be identified from associated hits in the Ring Imaging Čerenkov (RICH) detector and the Electromagnetic Calorimeters (EMCal). Electrons above 17 MeV/c passing through the CO 2 -filled RICH will emit Čerenkov radiation. The EMCal comprises eight sectors, six of leadscintillator and two of lead-glass, used to collect the energy from electron and photon showers. The nominal energy resolution for the lead-scintillator and lead-glass is 8. 1%± E[GeV]⊕2. 1% and 6. 0%± E[GeV]⊕0. 9% [17] , respectively. The North muon spectrometer is located at 1. 2 < η < 2. 4 and covers 2π in azimuth. The spectrometer measures tracks in the muon tracker (MuTr) and the muon identifier (MuID).
[19]. The MuID roads are then projected and matched to MuTr tracks at the 1st MuID gap. An identified muon candidate is the closest MuTr track that matches a MuID road within at least 10 • in slope and 10 cm in distance. Muon candidates are further restricted to 1. 4 < η < 2. 1. During both the p+p and d+Au data taking periods, there were backgrounds primarily from beam-related particles interacting with material in the accelerator upstream of PHENIX. Collimators were used in the accelerator to reduce this background but it was not totally eliminated. Restricting the η range of the muon candidates helped minimize this background.
However, these uncertainties are highly correlated between p+p and d+Au, because the same cuts are applied to both data sets. Another source of uncertainty is in the evaluation of the single particle efficiencies. The single particles were generated flat in p T and then weighted to match the measured PHENIX heavy flavor lepton spectra  [24]. For the uncertainty determination, the single particle efficiency was re-evaluated without the weighting applied. This was estimated to be 1. 0% for the electrons and 0. 8% for the muons. For muons there is an additional 2. 0% uncertainty due to the run-by-run variation in muon acceptance. The final portion of the type C systematic uncertainty is due to the trigger efficiency. To evaluate this uncertainty, the data were analyzed for several data-taking periods defined by the muon trigger performance.
ACKNOWLEDGMENTS We thank the staff of the  Collider-Accelerator and Physics Departments at Brookhaven National Laboratory  and the staff of the other PHENIX participating institutions for their vital contributions. We acknowledge support from the  Office of Nuclear Physics in the Office of Science of the Department of Energy , the  National Science Foundation , a sponsored research grant from  Renaissance Technologies LLC, Abilene Christian University Research Council, Research Foundation of SUNY, and Dean of the College of Arts and Sciences, Vanderbilt University (U. S. A) ,  Ministry of Education, Culture, Sports, Science, and Technology  and the  Japan Society for the Promotion of Science (Japan) ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado de São Paulo (Brazil) ,  Natural Science Foundation of China (P.
ACKNOWLEDGEMENTS Adrian Tanasa is partially supported by the  ANR JCJC CombPhysMat2Tens  grant and by the grant  PN 09 37 01 02. Razvan Gurau ,  James P. Ryan  and  Adrian Tanasa  ackowldge the E. Schroödinger Institute  for the work conditions offered to them during the " Combinatorics, Geometry and Physics" programme.
ACKNOWLEDGMENTS M. D. and A. B. acknowledge the support of the  Deutsche Forschungsgemeinschaft (DFG)  through Grant No. Bl 142/11-1 , of the  DAAD  through the  PROCOPE program  (Project No. 55853833 ), of the  Marie Curie International Research Staff Exchange Scheme Fellowship  within the 7th European Community  Framework Program SPIDER  ( PIRSES-GA-2011-295302 ), as well as additional funding from the  Fonds der Chemischen Industrie. T. G. , O. B. , and R. V. acknowledge the support of  Campus France  (Project No. 28252XE ) and of the  European Research Council  starting Grant No. FPTOpt-277998.
We present a panoptic view of the stellar structure in the Galactic disk's outer reaches commonly known as the Monoceros Ring, based on data from Pan-STARRS1. These observations clearly show the large extent of the stellar overdensities on both sides of the Galactic disk, extending between b = -25 • and b = +35 • and covering over 130 • in Galactic longitude. The structure exhibits a complex morphology with both stream-like features and a sharp edge to the structure in both the north and the south. We compare this map to mock observations of two published simulations aimed at explaining such structures in the outer stellar disk, one postulating an origin as a tidal stream and the other demonstrating a scenario where the disk is strongly distorted by the accretion of a satellite. These morphological comparisons of simulations can link formation scenarios to observed structures, such as demonstrating that the distorted-disk model can produce thin density features resembling tidal streams.
2014). Most of the models of the Monoceros Ring can produce qualitative matches to the available data, which has left little leverage to distinguish between these scenarios. In particular, the lack of a contiguous map of the MRi has forced models to rely on matching the distances and depth of the structure as seen by the available sparse pointings. This limited dataset has made it difficult to see a correspondence (or disagreement) between the Nbody simulations and the actual MRi structure, since the complex density structure predicted by these simulations can be masked when only a limited number of discontiguous pointings are available for comparison. In this work we present a panoramic view of the Monoceros Ring using Pan-STARRS1 (PS1;  Kaiser et al. 2010) , extending both north and south of the Galactic disk and covering 160 • in Galactic longitude.
Although the survey does produce stacked images and the resulting photometry, in this work we use data that is the merger of the photometric catalogs of all individual exposures. Stacked data from PanSTARRS-1 will reach more than a magnitude deeper, but the processing pipeline for the single epoch data is currently more mature and reliable. MSTO stars in Monoceros are easily detected in the PS1 single epoch images, and so we accordingly use that data in this work. We use the most recent consistent reprocessing of the 3π observations, termed Processing Version 1 (PV1), which contains observations obtained primarily between May 2010 and March 2013. As measured against the SDSS stripe 82 coadd catalog, our 50% completeness levels range from roughly g P 1 = 21. 4 to 22. 0, and r P 1 = 21. 2 to 21.
g. , NGC 4565, Radburn-Smith et al. 2014). The behavior of the gas may also provide additional points of comparison between observations and simulations, as the warping of the HI disk is well-studied (Kalberla & Kerp 2009). 2009 ) model, approximating the distance cut and uncertainties from the PS1 data in Figure  2. The feature annotations from Figure  3  have been included to give a sense of scale of the out of plane features. The hatched region indicates the area south of declination -30 • , which is not observed by PS1. For clarity and as the model already includes a galactic disk, we have not overplotted it on the existing data. While the simulation is not designed to replicate individual features, there is a striking similarity between the model and the observed MRi in the presence of thin wisp-like features, but also a clearly excessive level of warping of the disk midplane beyond that seen in the Milky Way.
We thank  S. Kazantzidis  for generously providing his simulation outputs. CTS and EFB were supported during this work by  NSF  grant  AST 1008342. EFS, EM, and NFM acknowledge support from the  DFG 's  SFB 881 grant  " The Milky Way System " (sub-project  A3 ). NFM gratefully acknowledges the  CNRS  for support through  PICS  project  PICS06183. HWR acknowledges support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP 7) ERC Grant  Agreement n. [321035]. The  Pan-STARRS1 Survey  has been made possible through contributions of 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, Queens University Belfast , the  Harvard-Smithsonian Center for Astrophysics , and the  Las Cumbres Observatory Global Telescope Network, Incorporated , the  National Central University of Taiwan , and the  National Aeronautics and Space Administration  under Grant No.
LDA In the LDA calculation we use the Wien2K code  [1] , that is an implementation of the full-potential linearizedaugmented plane wave method (FP-LAPW)  [2]. As we are not interested in total energies nor magnetic states, we adopt the simplest local density approximation to represent the exchange-correlation potential  [3]. Results using the generalized gradient approximation are completely equivalent since band structures are, in general, insensitive to this choice. Even thought within the DFT schemes the eigenvalues and eigenfunctions formally have no direct physical meaning, it is, nevertheless, broadly accepted that they provide a good approximation for the quasi-particle energies and band-structure. Thus, here we shall adopt them as the reference for the calculation of the localized Wannier orbital basis  [4].
These Wannier orbitals represent the molecular orbitals arising from the Ta 4 clusters, which are well defined as the distances between the four metal atoms in the cluster are significantly shorter than the inter-cluster ones. We restrict the energy window to the three t 2g bands that cross the Fermi energy. We note that these three bands are quite well separated from the rest of the band manifold, which is very advantageous in order to successfully obtain an accurate local orbital basis representation with short ranged hopping amplitudes. The localized Wannier orbital basis is computed following the procedure described by Marzari, Vanderbilt and coworkers in a series of papers  [5]  and implemented in the code Wannier90  [6]. As the interface between the two programs we use the code wien2wannier  [7].
We thank  V. Ta Phuoc ,  V. Guiot , and  P. Stoliar  for useful discussions. We acknowledge support from the  French Agence Nationale de la Recherche  ( ANR-09-Blan-0154-01 ) and (R. W. and C. A. ) from  CONICET  and  ANPCyT  ( PIP 114-201101-00376 ,  PICT-2012-0609 , and  PIP 112-201101-00536 ).
Mars Global Surveyor (MGS) magnetic data were analyzed to obtain the electromagnetically induced response of the Martian mantle. The time-varying part of the MGS magnetic data analysis was obtained by removing a model of the static field. Only night data were selected and binned on a regular grid. The binned time series were processed using a proxy of the transient magnetic field. Two proxies were designed, one based on the MGS data themselves and one built from the Advanced Composition Explorer (ACE) dataset. The internal induced response of the planet was obtained at periods ranging from more than 1 to 200 days. The induced responses were inverted to infer the mantle's electrical conductivity down to about 1,300 km. The comparison of our conductivity profiles with previous theoretical studies confirms an aerotherm close to the cold boundary of thermal models of the Mars interior.
This study aims to discriminate the thermal profile (hot or cold), to obtain crucial information about the mineralogical assembly (peridotic or pyroxenic), and to determine the mineralogical transition zone of the Martian mantle from those proposed in the literature  (Dreibus and Wanke 1985; Bertka and Fei 1997; Sohl and Spohn 1997; Sanloup et al. 1999; Mocquet and Menvielle 2000). As no seismic observations are available yet, mantle structures in planet Mars may be directly inferred by studying the observed magnetic field. The most extensive magnetic dataset outside the Earth was obtained during the Mars Global Surveyor (MGS) mission  (Acuña et al. 2001). These data are comparable to Earth satellite data in length (several years) and quality, but with a complex time-varying field  (Dubinin et al.
Mars Global Surveyor measured quasi-continuous time series of the three components of the Martian magnetic field for 8 years in a quasi-circular and polar orbit configuration  (Acuña 1999 ) at a mean altitude of 380 km during the mapping phase period  (1999 to 2006). The orbit plane was fixed in local time (2 a. m. to 2 p. m. ) during the mapping phase. Datasets were acquired both in planetocentric coordinates (X toward longitude 0, Airy crater) and in Sun-state system coordinates (X toward the Sun). The MGS magnetic dataset has an ideal spatial coverage to infer the overall induced response to external electromagnetic forcing. Magnetic dataset was acquired at a nominal sampling rate of 32 samples per second and averaged to 0. 75, 1. 5, or 3. 0 s according to the memory allocation. The memory allocation were determine with respect to the Mars-Earth distance (see Figure  1 ) with a dynamic accuracy from 0.
002 to 32. 768 nT with respect to the amplitude of the field. EI analysis is based on the study of the timevarying part of the recorded magnetic field. The first step in the data processing was therefore to reduce the magnetic field from the static crustal field. The latter was removed using the equivalent dipole model calculated by  Langlais et al. 2004  for each position of the spacecraft. Thus, from now on, the magnetic field refers to the MGS field minus the static crustal field. The purpose of EI analysis is to determine the induced part of the magnetic field carrying information about the electrical conductivity of the Mars mantle. With satellite measurements, the magnetometer samples both the time-varying and the spatial geometry of the transient field simultaneously. The discontinuous sampling impacts the recovery of the EI response functions.
The sampling of MGS magnetic data is presented in Figure  1  expressed in daily percentage of available data compared to a continuous nominal maximum sampling. It can be seen in Figure  1  that the sampling rate varies from 100% to 25% with respect to the distance Earth-Mars with a 2-year periodicity. The MGS magnetic mission was not designed to perform electromagnetic induction studies, and the dataset contains gaps from a few seconds to several days mainly during communication with the Earth, due to the presence of high-gain antennae close to the magnetometers, or during orbit adjustments  (Acuña et al. 2001). The MGS data were analyzed using the technique proposed by  Civet and Tarits (2013)  which combines satellite data binning on a regular grid and a time-series proxy of the time-varying field (see Additional file 1).
This method called 'self-proxy method' led to similar results for the Earth's mantle conductivity. We applied this method to MGS. In practice, each magnetic field component was averaged every half-orbit containing more than 500 data points. The data were then re-interpolated to obtain a regular hourly time series. For gaps shorter than 5 h, a http://www. earth-planets-space. com/content/66/1/85 Lagrangian polynomial interpolation was applied. With these criteria (half orbit of more than 500 samples and gaps less than 4 to 5 h) six continuous time windows of 125 days and two time windows of 210 days were obtained (Figure  1 ). These proxies were noted proxy-MGS-1 for the 125-day windows and proxy-MGS-2 for the 210-day windows. The self-proxy technique was tested to represent the temporal variability of the EM source on Earth using Ørsted magnetic data  (Neubert et al.
2001 ). The results were in agreement with previous studies  (Olsen 1999a; Semenov and Jozwiak 1999; Kuvshinov and Olsen 2006; Velimsky 2010; Civet and Tarits 2013 ); more information is available in Additional file 1. The only other dataset available and usable during the MGS mission is the solar wind set of parameters, which has been continuously recorded by the Advanced Composition Explorer (ACE) satellite located at the L1 Lagrange point of the Earth-Sun system since 1997  (Stone et al. 1998). Vennerstrom (2003)  showed that the interplanetary magnetic field (IMF) deduced from the ACE dataset may be extrapolated to the Mars region when Mars and Earth are close to the same arm of the Parker spiral  (Parker 1958 ). In contrast with Earth's ionized environment, Mars magnetic activity is a fairly direct function of the IMF  (Vennerstrom 2003) , and we assumed that the time-varying magnetic field recorded by MGS was approximately proportional to the fluctuations of the IMF.
Following  Vennerstrom (2003) , the ACE time series was time-shifted to the Mars position assuming a constant velocity of V m = 400 km s -1 for the particles carrying the IMF (see Additional file 1 for details). Four periods of 200 days were determined (Figure  1 ) when the time shift between ACE and Mars was shorter than 4 days. These time series corresponded to time spans when the sampling rate of the MGS data was maximal. This proxy was called proxy-ACE. Figure  2  shows four simultaneous time series of the time-shifted ACE data for the three magnetic components in heliocentric coordinates, which are compared to the MGS Sun-state dataset B x , B y , and B z (proxy-SU). The Sun-state coordinate system is defined by the following characteristics: the X-axis lies along the instantaneous Mars-Sun vector and is positive toward the Sun, the Yaxis is the vector cross product of X, and Z is the vector parallel to the northward (upward) normal of the orbit plane of Mars.
The dataset was first averaged to obtain daily values and remove shorter periods in the signals. Linear (Pearson test) and non-parametric (Kendall and Spearman) correlation coefficients  (Press 2007)  were calculated between the proxy-ACE and proxy-SU time series (see Additional file 1 for details). We hoped to find a satisfying correlation for at least one component of the magnetic field in order to define a second proxy of the external variability, as predicted by  Vennerstrom (2003). Only the B y component was significantly correlated. Correlation coefficients were greater than 50% for all sections (see Additional file 1). This level of correlation is remarkable given the strong assumptions that were used to extrapolate the IMF near the Mars environment. For short time spans (less than a day), the correlation was weaker probably because of a draping phenomena close to the planetary bow shock which induces tension and elongation of magnetic field lines.
For some specific periods, degree, and order, internal potentials were larger than the external ones. Hence, SH coefficients with values larger than the physically acceptable values were removed (maximum values of the induced/inducing = β/α ratio are 0. 5 for l = 1, 0. 66 for l = 2, and 0. 75 for l = 3 for perfect conductive body). Examples of the remaining magnetic external α m l and internal β m l potentials at SH degree l and order m are synthesized in the form of a spectrogram in Figure  3  for two sets of data using proxy-MGS-2 (210 days) and proxy-ACE (200 days). The energy of SH coefficients α m l and β m l was calculated by l * |α m l | 2 and (l + 1) * |β m l | 2 , respectively. For both datasets, the energy was stronger for periods greater than 2 days. There was a dominant amplitude in the external potential α m l at SH degrees 1 to 3 and order 0 for both proxy-MGS and proxy-ACE with a maximum for l = 2.