dataesr/scientific-paragraphs-categorization huggingface dataset:
The first searches over a v sin i range of 0-100 km s -1 , sampled at 1 km s -1 intervals, while the second samples the best value from the first search within a range of ±1 km s -1 at 0. 1 km s -1 intervals. We then use parabolic interpolation to obtain the final v sin i value of 0. 53 km s -1 from the second grid search. This value is within the v sin i upper limit of 1. 8 km s -1 set by half the spectral resolution of CHIRON. The top panel of Figure  8  shows as an example the 7100 Å TiO bands from the 2019 August 27 spectrum and the cross-correlation function that yields our RV measurement for that date and set of bands. RVs were derived using all 6 available TiO band apertures in the spec-11 https://github. com/mdwarfgeek/tres-tools 6 ). The vertical, dashed lines labeled s and t in the cross-correlation plot indicate where the sky and telluric lines would be; our template does not correlate with those lines.
(2017b). These include the S-Index, multiple lines in the Hydrogen Balmer series (Hα, Hβ, Hγ), and the sodium D doublet (NaD). We show LS periodograms of the RV time series and each additional diagnostic in Figure  9. These are computed using the LombScargle algorithm in the Python package astropy. We calculated 1% and 5% FAPs for each time series using a bootstrap approach. The LS periodogram of the RV data exhibits no significant peaks (above 1% FAP), including at the known periods of the planetary signals. Several other diagnostics exhibit The strongest signal in the RV data, with a 5% FAP, appears at 41. 7 days (vertical dotted line). The signal may be associated with the stellar rotation period or a harmonic, but the data are insufficient to place a firm constraint. This candidate signal does not appear with any significance in the activity diagnostics.
Fortunately, LHS 1678 is relatively uncrowded, and the transit depth in these Sectors was biased to be ∼1% deeper, while the radius was biased to be ∼0. 5% larger, both an order of magnitude smaller than our error bars. To estimate the planet properties, we jointly modeled the exoplanet transits from TESS and ground-based data, stellar variability in the light curves, along with other systematics and the HARPS RV data using the exoplanet package (Foreman-Mackey et al. 2020). All datasets were modeled simultaneously to preserve covariances between parameters. The ground-based photometry included in the model is listed in Table  4  with a "Y" in the "In Fit?" column. Each TESS sector is modeled with a mean offset and a white noise variance term described using a log-normal distribution. We modeled residual stellar variability using a Gaussian Process (GP) model (Foreman-Mackey et al.
VALIDATION The detection of multiple TESS transits combined with ground-based follow-up and precision RV observations provides a compelling dataset that indicates there is little parameter space remaining for false positives that may mimic the transit signals. To place a numerical value on the remaining false positive probability (FPP) and statistically validate the planets, we use the vespa tool  (Morton 2015). vespa combines the host star properties, the observed TESS transits, and follow-up constraints and compares to simulated false positive scenarios allowed by the remaining parameter space in a probabilistic framework. The output of vespa is the likelihood that the detected transit signal may be mimicked by the simulated false positive scenarios, the FPP. We ran the algorithm on each planet candidate individually.
Search for Additional Planets After we completed the main analysis of the TESS and follow-up data presented here, LHS 1678 was observed for two additional sectors (UT October 21 to December 17, 2020) in Cycle 3 of the TESS extended mission, as • • • (pc) (mag) • • • (K) (M ) • • • • • • (days) (R⊕) (AU) (S⊕) (M⊕) • • • • • • TRAPPIST-1 12. 4 10. 296±0. 023 M8. 0V±0. 5 2566±26 0. 0898±0. 023 G00, C06, A20, M19 b 1. 510826   part of Guest Investigator Programs G03228 16 , G03272 17 , G03274 18 , and G03278 19 , the Cool Dwarf target catalog  (Muirhead et al. 2018) , and the TESS CTL  (Stassun et al. 2018b). Because at the time of writing, the complete two-Cycle, four-Sector set of TESS two-minute cadence measurements for this star had not yet been searched by the SPOC pipeline, we performed our own search of this combined dataset to see if any additional planet signals would be revealed.
This is likely due to the relatively short duration of TOI-696. 03, which compromises the transit depth when undersampled with the longer FFI cadences. To verify that the transit signal originates from the target star, we compute difference images from the TESS FFIs (Fig. 19 ) and the publicly available Python package TESS-plots 20. First, TESS-plots uses TESSCut to create a pixel cutout of the FFIs centered on the target star. Then, each transit's in-and out-of-transit cadences are identified using the signal's ephemerides, and the means of inand out-of-transit frames are calculated. The mean out-oftransit image represents the target star with no transits, while the difference between out-of-transit and in-transit images reveals the location of the largest source of variability during a transit.
(QLP) at the TESS Science Office at MIT, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by NASA's Science Mission directorate. We acknowledge the use of public TESS data from pipelines at the TESS Science Office and at the TESS Science Processing Operations Center. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center for the production of the SPOC data products. This research has made use of the Exoplanet Follow-up Observation Program website, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exo-We reiterate that the three methods used in this paper to derive stellar effective temperature, luminosity, and radius fall into better agreement when all three assume subsolar metallicity, which is consistent with other properties of LHS 1678 (e.
We calculated a Box Least Squares(BLS,  Kovács et al. 2002)  periodogram, as implemented and optimized by Vanderburg et al. (2016)  on the full Cycle 1 (Sectors 4 & 5) and Cycle 3 (Sectors 31 & 32) two-minute datasets. Our search confirmed the detection of the two planets found by the SPOC pipeline presented earlier in this work, and also detected an additional sub-Earth-sized tran-16 High Frequency Quasi-Periodic Pulsations In M Dwarf Flares -PI C. Million. 17 Two Minute TESS Data For Thousands Of Promising New Radial Velocity Target Stars -PI J. Burt. 18 Understanding The Physical Origin Of The Rocky/Non-Rocky Transition Around Mid-To-Late M Dwarfs With TESS -PI R. Cloutier. 19 Enriching Our View Of Multiplanet Systems Using TESS -PI A. Mayo.
Figure18. Phase-folded light curves of TOI-696. 03 identified in a search of the full two-Cycle, four-Sector TESS dataset using two independent analyses. Top (Analysis 1): The grey and purple symbols are averages of TESS two-minute observations in intervals of 3. 6 and 12 minutes in phase, respectively. The best-fit model is plotted in red. Bottom (Analysis 2): Grey green symbols represent 2-min cadence and 7. 5-min binned measurements, respectively. The brown symbols are drawn from the best fit transit model from this analysis. Both analyses consistently yield a small planet candidate on a 4. 965-day period. If confirmed, TOI-696. 03 would be sub-Earth-sized and near the 4:3 mean motion resonance with LHS 1678 c.
ACKNOWLEDGMENTS We thank  Daniel Foreman-Mackey  for valuable suggestions on sampling parameterization. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation. This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.
C. ). This material is based upon work supported by the  National Science Foundation  under grants  AST-0807690 ,  AST-1109468 ,  AST-1004488  ( Alan T. Waterman Award ), and  AST-1616624 , and upon work supported by the  National Aeronautics and Space Administration  under Grant No. 80NSSC18K0476  issued through the  XRP Program. This work is made possible by a grant from the  John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation. N. A. -D. acknowledges the support of  FONDECYT  project  3180063. TD acknowledges support from  MIT's Kavli Institute  as a Kavli postdoctoral fellow. KH acknowledges support from  STFC  grant  ST/R000824/1. E. A. G. thanks the  LSSTC Data Science Fellowship Program , which is funded by  LSSTC, NSF  Cybertraining Grant # 1829740 , the  Brinson Foundation , and the  Moore Foundation ; her participation in the program has benefited this work.
planet Exploration Program. This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by  NOIRLab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by  NSF. This research has made use of "Aladin sky atlas" developed at  CDS, Strasbourg Observatory, France  (Bonnarel et al. 2000), the SIMBAD database, operated at  CDS, Strasbourg, France  (Wenger et al. 2000), and the VizieR catalogue access tool, CDS, Strasbourg, France (Ochsenbein et al. 2000). This research made use of the TOPCAT database visualization and management software, available at http://www. star. bris. ac. uk/~mbt/topcat/ (Taylor 2005). This research made use of Lightkurve, a Python package for Kepler and TESS data analysis (Lightkurve Collaboration et al. 2018). This work made use of tpfplotter by  J.
Lillo-Box  (publicly available in www. github. com/jlillo/tpfplotter),which also made use of the python packages astropy, Lightkurve, matplotlib and numpy. This research made use of exoplanet (Foreman-Mackey et al. 2021) and its dependencies (Agol et al. 2020; Kumar et al. 2019; Astropy Collaboration et al. 2013, 2018; Kipping 2013;Luger et al. 2019;Salvatier et al. 2016; Theano Development Team 2016).
Analysis 1 : 1 We calculated a Box Least Squares(BLS,  Kovács et al. 2002)  periodogram, as implemented and optimized by Vanderburg et al. (2016)  on the full Cycle 1 (Sectors 4 & 5) and Cycle 3 (Sectors 31 & 32) two-minute datasets. Our search confirmed the detection of the two planets found by the SPOC pipeline presented earlier in this work, and also detected an additional sub-Earth-sized tran-16 High Frequency Quasi-Periodic Pulsations In M Dwarf Flares -PI C. Million. 17 Two Minute TESS Data For Thousands Of Promising New Radial Velocity Target Stars -PI J. Burt. 18 Understanding The Physical Origin Of The Rocky/Non-Rocky Transition Around Mid-To-Late M Dwarfs With TESS -PI R. Cloutier. 19 Enriching Our View Of Multiplanet Systems Using TESS -PI A. Mayo.
Results To capture the small-boulder distributions and shape of Bennu at high resolution, we model Bennu as a self-gravitating rubble pile consisting of approximately 41,000 spherical particles with radii ranging from about 4 to 12 m following a power-law size distribution (Fig. 1a ; see Methods Section Bennu rubble-pile model). The SSDEM N-body code PKDGRAV with quantitatively controllable material strengths is applied to integrate the motion of particles (see Methods Section Softsphere discrete element method). A quasi-static spinup procedure is designed to model the YORP spinup process (see Methods Section Modelling of YORP-induced quasi-static spinup). To explore a wide range of possible geological material properties, numerical experiments are run with four friction angles ϕ (i. e. , 29 ∘ , 32 ∘ , 35 ∘ , and 40 ∘ ) and a series of macroscopic cohesion values C ∈ [0, 22] Pa.
Soft-sphere discrete element method We use the high-efficiency parallel N-body tree code, PKDGRAV  52, 53  , and its SSDEM framework  26  , including an improved rolling friction  51  and cohesion 10 model, to solve the gravity and contact interactions between spherical particles representing the components of a simulated rubble pile. The movement and rotation of each particle are obtained by solving the equations of motion with a second-order leapfrog integrator. Two compiled versions of PKDGRAV are provided as Supplementary Software 1. Briefly, the SSDEM model includes a linear spring-dashpot normal contact force, a normal cohesive force, a spring-dashpot-slider tangential contact force, and two spring-dashpot-slider rotational torques in the rolling and twisting directions. A set of interparticle parameters is used to adjust the mechanical properties, including: two stiffness constants, (k N , k S ), for controlling the compressive strength along the normal and tangential directions; two coefficients of restitution, (ε N , ε S ), for controlling the contact energy dissipation along the normal and tangential directions; three friction coefficients, (μ S , μ R , μ T ), for controlling the material shear strength along the tangential, rolling, and twisting directions; a shape factor, β, to take into account the fact that real particles are not spherical; and an interparticle tensile strength coefficient, c, for controlling the material cohesive strength along the normal direction.
The SSDEM model and the relation between the parameter sets and mechanical properties have been calibrated with laboratory experiments on real sands  10, 26. The performance and reproducibility of this SSDEM model has been verified by comparisons with another SSDEM package, i. e. , the open-source code Chrono  54. Based on the compressive strength estimated for metre-sized boulders on Bennu  42, 55  , i. e. , about 0. 1-1 MPa, the normal stiffness k N is set to 2. 0 × 10 7 N/m. The tangential stiffness k S is taken to be (2/7)k N to maintain the same oscillation frequencies along the normal and tangential directions  26. The two coefficients of restitution, ε N and ε S , are both taken to be 0. 55, which is close to the low-speed collisional energy dissipation of terrestrial rocks  56. μ R and μ T are taken to be 1.
Acknowledgements Y. Z. and P. M. acknowledge funding from the  French space agency CNES , the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ), and the  Doeblin Federation  (project  Impact-Granular Simulations ). O. S. B. was supported by the  NASA   Solar System Working Program  under grant number  NNX16AQ13G. K. J. W. was supported by the  NASA   Solar System Exploration Research Virtual Institute node Project ESPRESSO , under the cooperative agreement number  80ARC0M0008. Simulations were performed on Mésocentre SIGAMM hosted at the  Observatoire de la Côte d'Azur  and on the Deepthought2 HPC cluster administered by the  Division of Informational Technology at the University of Maryland. We are grateful to the entire OSIRIS-REx Team of engineers, operators, scientists and administrators for making the encounter with Bennu possible.
Data availability The OSIRIS-REx Laser Altimeter (OLA) Bennu shape model (v20) is available from the Small Body Mapping Tool (SBMT) at https://sbmt. jhuapl. edu/Object-Template. php?obj=77. The datasets generated during and/or analysed during the current study are available. Source data are provided with this paper. All necessary files to reproduce the outcome of the 1st, 16th and 24th cases given in Supplementary Table  1  and shown in Fig. 2 , are provided with the Supplementary Software file, as examples. The input data for other cases presented in this study are available from the corresponding author on reasonable request. Source data are provided with this paper.
Code availability The simulations are performed by the code PKDGRAV in its custom version that includes the Soft-Sphere Discrete Element Method (see Methods). Two compiled versions of PKDGRAV (one with the spinup module and one without) are provided as Supplementary Software 1. Visualisation support is provided by the open-source POV-Ray raytracing package (https://www. povray. org; Fig. 1a  and all the supplementary movies).
In Sect. 2 we describe the observational details of the data obtained with the IRAM 30 m telescope. In Sect. 3 we present the determination of the collisional rate coefficients required for the modelling, and we describe the non-LTE radiative transfer code, the pre-stellar core model, and the fractional abundance profiles used. The results are presented in Sect. 4. In Sect. 5 we discuss the results obtained in the context of past works, and our conclusions can be found in Sect. 6.
Both horizontal and vertical polarisations were observed simultaneously. The final spectrum is shown in Fig. 1. 1 ). The vertical dotted line represents the LSR velocity of L1544 (7. 2 km s -1 ). The observational data was processed and then averaged with the Continuum and Line Analysis Single-dish Software (CLASS), an application from the GILDAS 1 software  (Pety 2005). A F eff /B eff ratio of 1. 17 was used for the T * A to T MB conversion. Figure  1  shows the observed HC 17 O + (1-0) rotational transition split into three hyperfine components that present a clear double-peaked structure. Each hyperfine level is labelled by a quantum number F (F = I + J) varying between |I -J| and I + J. The quantum number F corresponds to the lower level while the F corresponds to the upper level of the transition.
C 17 O The J = 1-0 transition of C 17 O has been observed towards L1544 by  Chacón-Tanarro et al. (2019). Contrary to HC 17 O + , C 17 O presents single-peaked hyperfine components, even though it is also an optically thin transition. The critical densities of HC 17 O + (1-0) and C 17 O(1-0) are 1. 5 × 10 5 cm -3 and 2. 0 × 10 3 cm -3 , respectively, as calculated from the Einstein A119, page 4 of 32  and collisional coefficients at 10 K from the Leiden Atomic and Molecular Database (LAMDA) 2. To model the hyperfine C 17 O emission the hyperfine collisional rate coefficients were required, which we approximated from the non-hyperfine collisional rate coefficients available at LAMDA  (Yang et al. 2010 ) with a M j randomisation approach (Appendix D). We note that Dagdigian (2022) has recently also computed hyperfine C 17 O collisional rate coefficients using the recoupling technique.
This fit results in a hyperfine component model that follows the statistical intensity ratios. In non-CT ex conditions, the relative intensity amongst hyperfine components can differ from the statistical value. In our case, however, the ratio of intensities does not differ significantly from the statistical values. With the parameters derived from HFS fittings (Table  B. 1) and the HC 17 O + molecular constants extracted from the Cologne Database for Molecular Spectroscopy (CDMS) 3 , we calculated the column density of the molecule assuming that the transition is optically thin. For this purpose, we use Equation B. 2 (Mangum & Shirley 2015), 3 https://cdms. astro. uni-koeln. de Table  B. 2. Derived column densities from HFS fitting. V lsr N col (T ex =5 K) N col (T ex =7 K) N col (T ex =9 K) (km s -1 ) (cm -2 ) (cm -2 ) (cm -2 ) 7.
Full details of the methodology are available in the reference; here we present a summary of the steps to process the proton VDF data of one 192-s IMA scan: 1. Fit a 3D drifting Maxwellian VDF with isotropic temperature to determine the proton bulk-velocity vector 2. Shift the data into the proton bulk-velocity frame (plasma frame) 3. Convert from Cartesian (v x , v y , v z ) to spherical (v, φ, θ) velocity coordinates 4. Rotate into a frame where the average local-magnetic-field vector lies along the θ = 0 axis (i. e. , θ is the pitch angle) 5. Convert from spherical (v, φ, θ) to local-magnetic-field (v ⊥ , v ∥ ) velocity coordinates (we do not distinguish between different perpendicular directions) 6. Fit a gyrotropic Maxwellian VDF to determine the proton density and the thermal velocities in the directions perpendicular and parallel to We use the orthogonal distance regression algorithm  (Boggs & Rogers, 1989)  as implemented in SciPy's ODRPACK software package (SciPy, 2021) to fit the data.
The detector used was an Andor iKon-M CCD camera operated with a standard PSI Midi-box detector design, with a mirror and commercial optical lens system (100 mm Nikkor). The scintillator was a 200 µm thick LiF:ZnS screen. The image area was 1024 × 1024 pixels corresponding to an approximately 67 × 67 mm 2 field of view. The phase-stepping method was adopted by a G 1 scan performed in eight phase steps for each DFI image. The exposure time was 30 s for each raw image, and three images were recorded per phase step, which were combined by a median filter operation during data reduction. The DFI contrast was extracted from the resulting images pixel by pixel using Fourier analysis. In addition, the sample distance, L s , was scanned to probe the local correlation function, G(ξ), for the correlation length ξ (Equation (  1 )) from 130 nm to 3.
8 < η < 5. 1 (V0A), and is used for triggering, event selection, and centrality determination. A minimum-bias interaction trigger was provided by the coincidence of signals in the V0A and the V0C. Furthermore, two separate trigger classes, which consist of an online event selection based on the V0 signal amplitude, were used during the 2018 Pb-Pb data taking period in order to enrich the sample of central and semicentral collisions. Events due to the interaction of the beams with residual gas in the vacuum pipe were rejected offline using the V0 and the ZDC timing information. Only events with a primary vertex reconstructed within ±10 cm from the centre of the detector along the beam axis were considered in the analysis. Collisions were divided into centrality classes, defined in terms of percentiles of the hadronic Pb-Pb cross section, using the sum of the V0 signal amplitudes, as described in details in Ref.
It was computed as the ratio between the generated Dmeson yield in |y| < y fid (p T ) and that in |y| < 0. 5 using a data-driven p T shape and a rapidity distribution from fixed order plus next-to-leading logarithms (FONLL)  [53, 54]  perturbative QCD calculations, after verifying that the D-meson rapidity distributions in FONLL are consistent with those from PYTHIA 8  [55]. The (Acc × ε) correction was obtained separately for prompt and feed-down D mesons using simulations with the GEANT3 transport package  [56] , including a detailed description of the detector geometry and response as well as of the LHC beam conditions. The Pb-Pb collisions at √ s NN = 5. 02 TeV were produced with the HIJING v1. 36  [57]  event generator and D-meson signals were added by injecting cc or bb pairs generated with the PYTHIA v8.