dataesr/scientific-paragraphs-categorization huggingface dataset:
TIEGCM underestimates density Fig. 6. The mean l and standard deviation r of the 24 correlation coefficients per phase (black) and overall (red), using data from three satellites. Fig. 7. The absolute relative amplitude difference (top) and the modeled minus observed delay in time with respect to the maximum density peak, i. e. negative values mean that the model peaks too early (bottom) for 11 storms, using data from three satellites. S. Bruinsma et al. : J. Space Weather Space Clim. 2021, 11, 12  in Phase 2, but the Weimer model radically improves performance in the decaying storm phase (Phase 3), enhancing the mean from 0. 90 to 0. 97 and reducing the standard deviation from 0. 16 to 0. 09. The mean standard deviations of the density ratios (SD; %) per phase are displayed in Figure  5. The mean standard deviations for GOCE (green symbols) is smallest, followed by CHAMP (red symbols), and GRACE (blue symbols) has the largest mean standard deviation; this nicely demonstrates that relative variability increases with altitude, although it is partly due to data becoming noisier too.
Summary and conclusions The density data and indices for the 13 selected storms as well as updated metrics for thermosphere model assessment have been described in this paper. All storms are divided in four phases, which are relative to the time of minimum Dst. The mean and standard deviation of the density ratios, the correlations and the amplitude and timing of the peak density, are computed using available CHAMP, GRACE and GOCE density data and the CIRA models NRLMSISE-00, JB2008 and DTM2013, and the first principles models CTIPe and TIE-GCM using Heelis or Weimer drivers. The best results over the entire 4-phase storm period are obtained with DTM2013 and TIEGCM-W, while the oldest model, NRLMSISE-00, is the least precise. Compared to the assessments presented in  (Bruinsma et al. , 2018) , in which the same models except TIEGCM-W were evaluated using entire years of data, this study confirms that best results are obtained with DTM2013 and that NRLMSISE-00 is trailing.
The current model assessment is far from comprehensive. A more thorough model assessment, e. g. solar cycle, local time and altitude effects, under storm conditions requires more and better-distributed observations of density. In this study, with data from two (or one) satellites, density variations are monitored in four local time planes at best, and that only at two altitudes. Only four Kp = 9 storms were observed since 2000, thanks to CHAMP, GRACE and GOCE; bear in mind that the assessment is possible only thanks to data of opportunity, the objective of those missions was geodesy.
Acknowledgements. We thank  ESA  ( GOCE densities at earth. esa. int), NASA  (GRACE), and  DLR  (CHAMP and GRACE) for making their data publicly available. We thank  WDC Kyoto  for the Dst index (http://wdc. kugi. kyoto-u. ac. jp/), and  GFZ Potsdam  for the Kp index (https://www. gfz-potsdam. de/en/kp-index/). SB and CB received support from  CNES/ SHM  and the  SWAMI  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  776287. MF was supported by  NSF  grant  AGS1651459  and  NASA  grant  NNX17AI34G. ES was supported by the  University of Colorado Chancellor's Office Grand Challenge Initiative "Our Space. Our Future" via the Space Weather Technology, Research, and Education Center. We acknowledge the reviewers for their valuable comments that helped to improve the manuscript.
■ EXPERIMENTAL METHODS The nuclear and magnetic structures of the Tb oxides were determined by neutron powder diffraction (NPD) using the D2B and D20 instruments at the ILL (Grenoble, France). High-resolution diffraction patterns were collected on D2B at 300 and 1000 K using a wavelength λ = 1. 594 Å. To determine the thermal evolution of the magnetic structure, patterns were collected between 300 and 1000 K every 3. 5 K on D20 with neutrons of λ = 2. 41 Å, using a quartz tube open to air inside a furnace under vacuum. The data were fitted using FullProf. 19 Magnetic symmetry analysis was done using the program BASIREPS. 20, 21  57  e Mossbauer spectra were measured to confirm the oxidation states and oxygen environments of the Fe cations, and to study the magnetic states in the compounds.
The Tb and Gd compounds are isostructural. The crystal structures have been determined by transmission electron microscopy techniques with atomic resolution in the Gd oxides. 17, 18 The nuclear structures have been confirmed and refined in the Tb oxides by means of neutron powder diffraction (NPD) experiment data collected at 300 and 1000 K. The results of the structure refinements are collected in the Supporting Information (Figure  S1  and Tables  S1-5 ), and in ref 17. Figure  S2  displays the representation of the building blocks of the crystal structures of the three compounds. Analogous to the previously reported structure of Gd compounds,  17, 18  these crystal structures consist of layered ordering of the RE 3+ , Ba 2+ , and Ca 2+ cations in combination with layered ordering of the different coordination polyhedra of the Fe atoms along the stacking c-axis.
DATA AND OBSERVATIONS The events discussed in this manuscript are primarily analysed using imaging data from SDO/AIA. AIA provides fulldisk images of the Sun in 10 filters, imaging plasma with temperatures between ≈10 4 to 10 7 K. The spatial resolution of AIA is ≈1. 5  (Boerner et al. 2012 ) and its cadence is 12-24 s depending on bandpass. We processed level-1 AIA data using the aia prep routine. Flares located on solar disk were corrected for the differential rotation. Where appropriate, we supplement AIA data with observations performed by the Extreme Ultraviolet Imager (EUVI;  Wuelser et al. 2004; Howard et al. 2008 ) onboard STEREO-B, as well as the X-Ray Telescope (XRT;  Golub et al. 2007 ) onboard the Hinode mission. These datasets were processed using standard routines available within SolarSoft and coregistered with AIA.
75 GHz, rendering their observation from the ground very difficult. Hence observations of CO + have generally targeted excited transitions where the sky transmission is much better. We chose to search for the N = 2 → 1 Notes. ( a) The spectroscopic data are extracted from the CDMS database  (Müller et al. 2001 (Müller et al. , 2005;; Endres et al. 2016 ). ( b) For an excitation temperature of 2. 73 K. (c) For an excitation temperature of 5 K. transitions because the frequencies of this first excited transition near 236 GHz are easily accessible from ground-based radio observatories and the level population remains significant even in diffuse interstellar gas. This N = 2 → 1 transition is split into three hyperfine components, with the strongest component at 236. 06 GHz. Table  1  lists the three targeted transitions, their frequencies, Einstein A-coefficient and the conversion factor between the integrated line opacity and the molecule column density N(CO + )/ τ dv assuming that the energy level population is determined by the cosmic microwave background at 2.
ALMA observations The observations were performed with ALMA band 6 receivers tuned near 236 GHz during ALMA Cycle 7 under the project code 2019. 1. 00120. S. The CO + lines were observed with a channel spacing of 244 kHz, corresponding to a velocity resolution of 0. 31 kms -1. The channel spectral resolution is twice the channel spacing. As in our earlier observations toward these sources, the  bandpass calibrator was fixed to J1924-2914. Absorption spectra were extracted from the standard pipeline-processed data products at the peak of the continuum map in each spectral window. The ALMA spectra of 13 CO, CO + and HCO + are displayed in Fig. 1. The H 2 CO (3 0,3 -2 0,2 ) line was detected in both directions and will be discussed elsewhere.
2003). It is straightforward to show that the relative abundance of CO + is too small to provide a major source of the observed HCO + in diffuse molecular gas, even independent of the HCO + abundance. In chemical terms, the reaction CO + + H 2 → HCO + + H forming HCO + can be compared with the rate at which HCO + recombines with electrons with rates as given in Table  5. Equating formation and recombination of HCO + , neglecting photodissociation that is four orders of magnitude slower according to the Kinetic Database for Astrochemistry (KIDA)  (Wakelam et al. 2015 ) k 2 n(CO + )n(H 2 ) = α(T ) n(HCO + )n(e) and rearranging, we have n(CO + )/n(HCO + ) = (α(T )/k 2 )n(e)/n(H 2 ) or equivalently n(CO + )/n(HCO + ) = (2/f H 2 ) (α(T )/k 2 ) x(e) where f H 2 is the fraction of H-nuclei in H 2 , x(e) is the electron fraction, n(H 2 ) is the density of H 2 molecules, and n(H) is the total density of H-nuclei.
The thermal chemistry of CO + , HOC + and HCO + Our upper limits show that CO + cannot be responsible for forming the observed CO in diffuse molecular gas because CO + does not exist in sufficient quantity to replenish HCO + as it recombines with ambient electrons. However, beyond that, our limits combined with the observed relative abundance N(HOC + )/N(HCO + ) = 1. 4 ± 0. 2 × 10 -4  (Gerin et al. 2019 ) can be used to gain further insights into the thermal chemistry that is working in diffuse molecular gas. Most of the reactions that form HCO + also form HOC + , yet their observed abundances differ by a factor 70. To explore this, we extended the thermal CO + and HOC + chemistry discussed by  Gerin et al. (2019)  to include some hypotheticals corresponding to uncertainties in reaction rates in the KIDA reaction rate database  (Wakelam et al.
By contrast the abundance of CH + is locally variable and CH + may not co-exist with the other species if it is predominantly formed in the nonthermal chemistry  (Godard et al. 2014; Valdivia et al. 2017). We take X(CH + ) as a free parameter with X(CH + ) = 10 -9 by default in the following discussion. HOC + can be formed in the reaction of H 2 with CO + and in the reaction of C + with H 2 O whose fixed relative abundance X(H 2 O) = 2. 4 × 10 -8 we take from  Gerin et al. (2016). We ignore HOC + formation by the reaction of O with CH 3 + because the formation of CH 3 + is an aspect of the non-thermal chemistry. HOC + recombines with electrons and is susceptible to photodissociation, but most importantly is isomerized to HCO + in reaction with H 2. The rate constant for the isomerization reaction of H 2 and HOC + has alternatives in the KIDA database as discussed previously.
The option of a low rate for this reaction in the KIDA database should probably be ignored. The predicted HOC + abundance is in better agreement with observation if the reaction of C + and H 2 O preferentially forms HCO + , rather than HOC +. We placed a weak limit on the relative abundance of CH + in quiescent gas, N(CH + )/N(OH) < ∼ 3 or X(CH + )/X(H 2 ) < ∼ 3 × 10 -7 such that the reaction O + CH + does not become an important source of CO + , substantially overproducing it. In Section 5 we derived characteristic number densities n(H) at which CO would form in diffuse molecular gas with observed abundances. For E(B-V) > ∼ 0. 5 mag the densities are modest, n(H) ∼ 100 -300 cm -3 but for CO-bright regions of low extinction E(B-V) ∼ 0. 1 -0. 2 mag they are an order of magnitude higher n(H) ∼ 1 -2 × 10 3 cm -3 and such heavily over-pressured gas is more properly considered in the context of a turbulent dissipation chemistry.
Acknowledgements. The authors thanks the referee  Helmut Wiesemeyer  for his thoughtful comments and suggestions. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2019. 1. 00120. S. 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. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. This work was supported by the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES.
An appropriate poststripper foil (C 3 H 6 or Carbon) was located downstream the target position in order to minimize the amount of unwanted 20 Ne 8+ and 20 Ne 9+ elastically scattered events reaching the focal plane  [18]. The various ejectiles were momentum analyzed by the MAGNEX spectrometer  [19]  whose optical axis was set at θ opt = -3 • , allowing the measurement in the angular range of scattering angles 0 • ≤ θ lab ≤ 9. 5 •. The different ions were detected by the MAGNEX Focal Plane Detector (FPD)  [20]. Adopting a particle identification technique as described in Ref. [21]  and thanks to the excellent Z, mass and charge state resolution of MAGNEX, the 20 O 8+ ions were appropriately selected. A software highorder trajectory reconstruction was applied to the data  [22]  and, the excitation energy (E x = Q 0 -Q, where Q 0 is the g.
The SPD and PS are designed to discriminate between signals from photons and electrons, while ECAL and HCAL provide the energy measurement and identify electromagnetic radiation and neutral hadrons. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. The pPb data were collected with the LHCb experiment in two distinct beam configurations. In the forward configuration, the particles produced in the direction of the proton beam are measured in a center-of-mass rapidity region 1. 5 < y * < 4. 0, while in the backward configuration, particles produced in the lead-beam direction are measured at center-of-mass rapidity -5. 0 < y * < -2. 5. The forward (backward) data sample corresponds to an integrated luminosity of about 14 µb -1 (21 µb -1 ).
SfM photogrammetry is particularly well suited for geological purposes and is commonly used both on Earth  [25] [26] [27] [28] [29] , and in the planetary community  [30, 31]. In the case of the Gale crater, several successful examples of DOM reconstruction to characterize the sedimentary record have already been performed using Navcam  [20] , MAHLI  [32] , Mastcam  [18]  or a combination of these instruments  [12, 33]. In this work, we apply this method to images taken by the RMI telescope, in order to reconstruct the 3D shape of remote and inaccessible outcrops situated several hundred meters away from the rover. For the SfM process, we are using the commercial Agisoft Metashape Professional software v. 1. 7. 1 (Agisoft LLC, St. Petersburg, Russia) [34].
Processing Chain 4. 1. Imagery Dataset Individual images taken by ChemCam's RMI can be downloaded either from the JPL's MSL RAW images library (https://mars. nasa. gov/msl/multimedia/raw-images/, accessed on 5 October 2021) or from the Planetary Data System (PDS; https://pds-geosciences. wustl. edu/missions/msl/, accessed on 5 October 2021). Images from the PDS are provided in the IMG exchange format, and therefore need to be converted beforehand using the small IMG2PNG command-line software utility (available at: http://bjj. mmedia. is/utils/ img2png/, accessed on 5 October 2021). Preprocessed mosaics are also provided on the PDS in PNG format at https://pds-geosciences. wustl. edu/msl/msl-m-chemcam-libs-4_5rdr-v1/mslccm_1xxx/extras/rmi_mosaics/, accessed on 5 October 2021. The RMI individual frames used for the photogrammetric treatment have been radiometrically corrected following the pipeline described in  [7].
Individual frames of each selected mosaic are imported in PNG format within the SfM software (Figure  4a ). from the Planetary Data System (PDS; https://pds-geosciences. wustl. edu/missions/msl/). Images from the PDS are provided in the IMG exchange format, and therefore need to be converted beforehand using the small IMG2PNG command-line software utility (available at: http://bjj. mmedia. is/utils/img2png/). Preprocessed mosaics are also provided on the PDS in PNG format at https://pds-geosciences. wustl. edu/msl/msl-m-chemcam-libs-4_5rdr-v1/mslccm_1xxx/extras/rmi_mosaics/). The RMI individual frames used for the photogrammetric treatment have been radiometrically corrected following the pipeline described in  [7]. Individual frames of each selected mosaic are imported in PNG format within the SfM software (Figure  4a ).
Dense Cloud Generation With entry images aligned, a dense cloud can be generated. This cloud, composed of up to several millions of points (e. g. , ~2 million for mesh #1, Figure  4d ), gives a very precise representation of the reconstructed area. The sharpness of the 3D reconstruction is once more linked to the degree of overlapping and overall quality of the entry images but is also a function of the quality setting in the software. In the case displayed in Figure  4 , we used a high precision setting in Metashape. The resolution of this dense cloud is sufficient to fulfill our scientific needs and to perform accurate characterization of the shape and structures on the outcrop.
Mesh Generation and Texturing The next step in the photogrammetric reconstruction process is the generation of a 3D mesh. While the dense point cloud could be exported and used to perform measurements, a mesh is computed for easier handling and visual characterization, especially when associated with a photorealistic texture, providing an accurate representation of the actual geologic object. The mesh is composed of several thousand faces or polygons (e. g. , 500,000 in mesh #1, Figure  4e ) representing the surface relief of the reconstructed object. These polygons can be generated either by triangulation of the dense cloud points (with each point tied to neighbors by a vertex, creating the polygons), or by computation of a depth map. Choice of either of these methods is left to the user, depending on the capacities of the SfM software.
In this work we therefore tested the possibility of recreating the 3D shape of distant outcrop by using long-distance images obtained with the RMI telescope of Curiosity. Even if RMI was not specifically designed for long distance stereo imaging, it is the most powerful imager in terms of pixel size. We demonstrate for the first time that individual frames from overlapping long-distance RMI mosaics can be used to successfully compute DOMs of these distant outcrops on two test cases using SfM photogrammetry (performed by the Metashape software). To maximize the variation in point of view, we are using a "virtual baseline" implied by successive stops of the rover along its traverse, separated by several hundred meters. The resulting models computed from these RMI mosaics allow examination of the overall accurate 3D shape of the geologic objects that are out of the rover's reach.
Data Availability Statement: All Mars Science Laboratory data are freely accessible to the public. Raw RMI images can be accessed on the Jet Propulsion Laboratory's raw images archive at: https://mars. nasa. gov/msl/multimedia/raw-images/, accessed on 5 October 2021. RMI images can also be accessed on the Planetary Data System database at: pds-geosciences. wustl. edu/msl/ msl-m-chemcam-libs-4_5-rdr-v1/mslccm_1xxx/data/, accessed on 5 October 2021. Preprocessed mosaics made from individual RMI images can be accessed on the Planetary Data System database at: https://pds-geosciences. wustl. edu/msl/msl-m-chemcam-libs-4_5-rdr-v1/mslccm_1xxx/extras/ rmi_mosaics/, accessed on 5 October 2021. All 3D models discussed in this article are freely accessible and visualizable on the public Sketchfab web platform.
V. COMPUTATIONAL DETAILS 1] [102]  (Note that the calculation of the second-order perturbative correction is also restricted to the seniority zero subspace. 100 ) A similar modification has been employed by Shepherd et al. 67  to perform DOCI calculations within FCIQMC. 103 In our DOCI implementation, the roots are located with the standard Davidson diagonalization method. 104 The pCCD method and the corresponding orbital optimization algorithm was also implemented in quantum package following Ref. 63. 9] 102  Solving the CC equations and optimizing the orbitals for excited states are central to our discussion and are thus discussed in separate sections below. Details regarding the particular applications are also given in their respective sections. Complementary CC3, EOM-CCSDT, and EOM-CCSDTQ calculations were also performed with the CFOUR package.
ACKNOWLEDGMENTS This work was performed using HPC resources from  GENCI-TGCC  ( 2020-A0080801738  and  2021-A0100801738 ) and from  CALMIP (Toulouse)  under allocation  2021-18005. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 863481 ).
The strong interaction with the IMBH breaks the binary producing high-velocity stars (e. g. ,  Hills 1988; Fragione & Gualandris 2019; Šubr et al. 2019). In this work, we study the effects that the presence of an IMBH has on the binary population of its host GC. Motivated by the recent observations of binaries in NGC 3201 with VLT/MUSE by  Giesers et al. (2019) , we use a sample of simulated GCs from the MOCCA-Survey database I  (Askar et al. 2017)  with two goals: (1) explore the contamination of binaries in the line-of-sight velocity dispersion and (2) use the detected binary sample to study the binary fraction in GCs with and without an IMBH. The first point is effectively extending the discussion of Figure  4  in  Aros et al. (2020)  where we show that binaries can systematically increase the observed line-ofsight velocity dispersion.
Simulations and mock data We have selected a sample of 284 simulated clusters from the MOCCA-Survey Database I  (Askar et al. 2017)  to study the effects of binaries in the observed kinematics and the interaction of binaries with a central IMBH. These simulations were evolved to 12 Gyr using the MOCCA code  (Hypki & Giersz 2013; Giersz et al. 2013) , which follows a state-of-the-art implementation of the Monte Carlo method first proposed by  Hénon (1971a,b). All 284 simulations have different initial conditions (see Appendix A), but share the same initial binary fraction  bin = 10%, consistent with observations  (Sollima et al. 2007; Milone et al. 2012). As described in detail by  Giersz et al. (2015) , IMBHs in MOCCA simulations form via dynamical interactions in two kinds of scenarios. The "FAST" scenario starts from the beginning of the simulation and requires an extremely high initial central density ( 10 6  /pc 3 , see also  Hong et al.
This set a lower limit in magnitude around 1 mag below the main-sequenceturn-off (or ∼ 18 mag), similar as used by  Aros et al. (2020)  and motivated by current observations  (Libralato et al. 2018; Giesers et al. 2019). From the sample of 284 simulated GCs in the MOCCA-Survey database I with an initial binary fraction of 10%, we selected a subsample of clusters that have: (a) more than 1000 stellar systems (binary and single) within the selected sample of sufficiently bright stars, and (b) an intrinsic velocity dispersion higher than 4 km/s, which is twice the median velocity error in line-of-sight velocities and proper motions at the assumed distance of 5 kpc. These two criteria help to reduce the stochasticity due to low numbers (binary fraction), and to measure velocity dispersion values that are not dominated by observational errors.