dataesr/scientific-paragraphs-categorization huggingface dataset:
All spatial filters and slits were removed during acquisition. Table of target samples and experimental parameters used in the presented datasets. The sample material, sample thickness, location (either free standing, FS, or contained at pressure in a Diamond-Anvil Cell, DAC), pressure, and diamond type are tabulated alongside the source parameters, streak window used and number of integrated sweeps used in each experimental run. Samples (S) or Setting 1 5 10 20 50 100 200 Factory measured 0. 994 4. 90 9. 76 19. 60 49. 0 97. 4 200 Pressure Medium (M) is either irradiated by the optical laser, operating in either single-sided (SS) or double-sided (DS) mode, the XFEL, or both. Samples are irradiated under vacuum unless indicated. Re or W gaskets are used for DAC samples. XFEL measured … 4. 73 9.
ACKNOWLEDGMENTS We wish to thank  Paolo Creminelli ,  David Stefanyszyn ,  Yi Wang  and  Masahide Yamaguchi  for helpful discussions. X. T. would like to thank the  Institut d'Astrophysique de Paris  for kind hospitality. S. J. , S. R. P. , and D. W. are supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (Grant Agreement No. 758792 ,  Starting Grant  Project No. GEODESI). X. T. and Y. Z. are supported in part by the  National Key R&D Program of China  (No. 2021YFC2203100 ).
Conclusion In this work, we presented our investigations using the AROMA setup of the carbonaceous molecular content of three different meteorites (Murchison, Allende and Almahata Sitta), and two types of possible stardust analogues. By applying the DBE method we were able to clearly identify chemical families and demonstrate our ability to track chemical changes in samples as a function of experimental parameters. Furthermore, we report for the first time a firm detection of fullerenes in a meteorite ending a long controversy about the existence of fullerenes in meteorites. However, the origin of these species still needs to be investigated. To this end, we demonstrated that the search for fullerenes in meteorites should be expanded beyond investigations of carbonaceous chondrites. Extending this study to a larger collection of meteorites will also allow us to expand our mass spectrometry database (http://aroma.
In particular, after the corrections they decrease by ∼ 37 per cent for the 364 GHz 3 + 2 → 2 - 2 transition, while for the 4241 GHz 4 - 1 → 3 + 1 transition at 300 K the T R -ratio decreases by a factor of ∼40. This strong impact is ho we ver due to the large opacity of the line ( > 100) which makes the convergence of RADEX difficult. We w ould lik e to emphasise that the changes found due to the subsequent corrections do not affect the general discussion or the conclusion of the original paper. The corrected magnitude of the cross sections, rate coefficients, and radiation temperatures fully supports the original findings of the paper and only gives quantitative corrections. The updated set of collisional data is made available within the current supplementary material online as well as through the EMAA , LAMDA , and BASECOL databases.
Using the 6DTV algorithm, DCT reconstructions were successfully obtained and allowed the characterization of the evolution of lattice rotation in the entire microstructure. The results are backed up by comparisons with EBSD and DCT maps acquired at ESRF-ID11 that allowed the validation of the orientation field measurements in the bulk. Difficulties at the grain boundaries are highlighted and discussed in line with increasing plastic strain during the tensile test. Finally, a new outlook is provided on the potential of ff-3DXRD to enrich the present dataset with access to average lattice elastic strain data per grain, on the possibility of performing crystal plasticity simulations from DCT reconstructions, and ultimately on comparisons between experiments and simulations at the scale of the grain.
, 2010; Guo et al. , 2014; Chen & Daly, 2018; Chen et al. , 2018)  or required destructive operations  (Echlin et al. , 2012; Rowenhorst et al. , 2020). Recent progress in synchrotron  (Maire & Withers, 2014; Nygren et al. , 2020a)  and laboratory X-ray techniques  (Bachmann et al. , 2019)  has paved the way to a paradigm shift sometimes called 'diffraction microstructure imaging' (DMI), leading to increasingly complex multimodal in situ experiments that allow observations to be made non-destructively and concurrently on several scales, resulting in significantly richer datasets. In particular, diffraction contrast tomography (DCT), a near-field variant of 3DXRD  (Poulsen et al. , 2001) , allows reconstruction of mesoscopic digital grain maps (on the order of 1 mm 3 ) on which simulations can be computed directly  (Proudhon et al.
, 2016; Shade et al. , 2019). The convergence of experimental and numerical data leads to unified but massive datasets  (Sangid, 2020) ; yet this wealth of information can render manual post-processing untractable. Moreover, modalities are often acquired independently which further hinders analysis. Note that efforts in the scanning electron microscope (SEM) community, also driven by equipment manufacturers, have led to successful correlative experiments using microstructural information in 3D using serial sectioning methods  (Burnett et al. , 2014; Charpagne et al. , 2021). Several recent studies took advantage of DMI for the study of polycrystal plasticity, either with single techniques  (Miller & Dawson, 2014; Pagan et al. , 2017; Hektor et al. , 2019)  or relying on a multimodal approach while focusing on a few grains with manual  (Proudhon et al.
This leads the way for unmatched in situ testing opportunities. For instance, the duration of a single DCT scan has dramatically reduced from 1 h to 3 min. Other synchrotrons such as SOLEIL have already scheduled similar upgrades for the coming years. As part of an effort to promote these developments, the present paper aims to introduce the deployment of an in situ (or 4D) X-ray multimodal technique involving DCT for users on the Psichebeamline at Synchrotron SOLEIL. In addition, the ability to acquire and reconstruct 3D polycrystalline grain maps of initial and deformed microstructures with up to 10 3 grains is demonstrated in hexagonal titanium, allowing access to lattice curvature evolution over large datasets in the bulk of the material.
DCT reconstruction at Psiche Ćurrently, DCT reconstructions are tightly linked to the ESRF computing infrastructure, using the Matlab code developed by the team working at the Materials Science beamline ID11 [https://gitlab. esrf. fr/graintracking/dct (Ludwig et al. , 2009; Reischig et al. , 2013; Viganoet al. , 2014) ]. The key reconstruction steps consist of background correction and normalization of the collected images, diffraction spot segmentation, spot-pair matching, grain indexing, and grainby-grain tomographic reconstruction. Microstructures exhibiting negligible intragranular orientation spread can be reconstructed using the 3D-DCT (single orientation) approach which is based on the algebraic reconstruction algorithm [simultaneous iterative reconstruction technique (SIRT)], implemented in the ASTRA open source tomography library (van  Aarle et al.
Currently the pre-processing and segmentation steps have been implemented at Psiche. Resulting data can be input into the existing Matlab code to complete the DCT reconstruction. With this new pipeline, an acquisition consisting of 3600 images can be processed in less than 1 h (about 15 min for pre-processing and 30 min for segmentation) which can be up to one order of magnitude faster with respect to the current DCT code. In order to ensure that DCT reconstructions are ready for mechanical simulations using finite element or FFT methods, additional numerical cleaning operations are performed with the Python pymicro package (https://github. com/heprom/ pymicro). This includes morphological cleaning to eliminate small-artifact grains and final-grain dilation.
Hence, all these reasons may yield nonreconstructed regions in the final volume. Numerical dilation based on orientation similarity is usually carried out to suppress them, particularly when material simulations are needed. In the present case, one may introduce a simple precision metric describing how well the DCT is able to capture the microstructure, using the ratio of voxels assigned to a grain over the total number of voxels in the illuminated part of the specimen. Using this metric, we found 81% in the initial configuration (step 0), 79% for step 1 and this value drops to 53% for step 2. Meanwhile, the ff-3DXRD data are expected to bring additional information on local deformation mechanisms. For instance it can be processed using the ImageD11 software (https://github. com/FABLE-3DXRD/ImageD11) which allows the user to index the grains (crystal mean orientation) within the illuminated volume.
Further refinement of the diffraction peak positions allows the measurement of the mean elastic strain tensor in each grain. The average stress tensor for each grain can then be obtained using linear elasticity and the elastic constants of the material. The mean values obtained can be compared with simulation results and will be presented in another paper. This will allow us to take full advantage of the multimodal nature of the present experiment which facilitates the collection of richer datasets concurrently, as done on beamline 1-ID at the APS  (Lienert et al. , 2011)  or at CHESS  (Nygren et al. , 2020a). In the present paper, the experiment was conducted step by step but in principle it can be performed continuously as the Bulky stress rig allows movement of the cross head very slowly (50 nm s À1 ); this would require the acquisition of near-field and far-field modalities simultaneously as mentioned previously.
PoS(ICRC2023)972 Investigating high-energy neutrino emission from local U/LIRGs Y. Merckx GOALS objects were originally defined from observations made with IRAS, which had a relatively poor angular resolution, i. e. between ∼0. 5' at 12 𝜇m and ∼2' at 100 𝜇m  [16]. The IRAS emission for a single GOALS object may therefore correspond to the cumulative emission of individual galaxies in an interacting system. However, we model starburst-driven neutrino production in the cores of U/LIRGs based on their EM emission (Section 3). As such, we require the IR luminosity per individual galaxy rather than the total IRAS IR luminosity of the system. For this reason, we use the IR luminosity obtained for 229 disentangled GOALS galaxies, derived from data obtained with the Herschel Space Observatory  [3].
This implies a relation between the IR luminosity and supernova rate R SN ([yr -1 ]), R SN yr -1 = Λ IR • G • [1 -⟨𝛼 AGN ⟩] • 𝐿 IR erg s -1. ( 1 ) Here Λ IR is a calibration factor which is determined using the Starburst99 software  [20]. Following our work in  [18]  we use Λ IR = 5. 97 × 10 -46 for further calculations. In a supernova explosion the outer layer of the progenitor star is ejected with an energy of 𝐸 SN ∼10 51 erg. This drives strong shock waves in the surrounding medium which are capable of accelerating particles to high energies via diffuse shock acceleration. This could drive a rate of high-energy protons 𝑄 p ([(GeV/𝑐) -3 cm -3 s -1 ]) into the central nuclear disk. From diffusive shock acceleration we expect the distribution of high-energy particles to follow a power law, 𝑄 p ∝ 𝑝 -𝛾 SN , with 𝑝 the particle momentum and 𝛾 SN the spectral index.
Acknowledgments This work was supported by the  Flemish Foundation for Scientific Research  ( 1149122N , Y. M. ), the  European Unions   Horizon 2020 research and innovation program  (No  805486 , K. D. de V. ), the  French National Research Agency  ( ANR-21-CE31-0025 , P. C. ), and the  APACHE  grant of the  French Agence Nationale de la Recherche  ( ANR-16-CE31-0001 , K. K. ).
The mass difference between the Ω - b and Ξ - b baryons is measured using protonproton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb -1 , and is found to be The mass of the Ω - b baryon is measured to be m(Ω - b ) = 6045. 9 ± 0. 5 (stat) ± 0. 6 (syst) MeV/c 2. This is the most precise determination of the Ω - b mass to date. In addition, the production rate of Ω - b baryons relative to that of Ξ - b baryons is measured for the first time in pp collisions, using an LHCb dataset collected at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 6 fb -1. Reconstructing beauty baryons in the kinematic region 2 < η < 6 and p T < 20 GeV/c with their decays to a J/ψ meson and a hyperon, the ratio is obtained, where f Ω - b and f Ξ - b are the fragmentation fractions of b quarks into Ω - b and Ξ - b baryons, respectively, and B represents the branching fractions of their respective decays.
LHCb detector and datasets The LHCb detector  [15, 16]  is a single-arm forward spectrometer covering the pseudorapidity range 2 < η < 5, designed for the study of particles containing b or c quarks. The detector includes a high-precision tracking system consisting of a siliconstrip vertex detector (VELO) surrounding the pp interaction region  [17] , a large-area silicon-strip detector located upstream of a dipole magnet with a bending power of about 4 Tm, and three stations of silicon-strip detectors and straw drift tubes  [18, 19]  placed downstream of the magnet. The tracking system provides a measurement of the momentum, p, of charged particles with a relative uncertainty that varies from 0. 5% at low momentum to 1. 0% at 200 GeV/c. The minimum distance of a track to a primary pp collision vertex (PV), the impact parameter (IP), is measured for charged particles reconstructed in all tracking subdetectors with a resolution of (15 + 29/p T ) µm, where p T is the component of the momentum transverse to the beam, in GeV/c.
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors  [20]. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers  [21]. The datasets considered for this measurement were collected by LHCb between 2011-2012 at center-of-mass energies of 7 and 8 TeV (Run 1), corresponding to an integrated luminosity of 3 fb -1 , and between 2015-2018 at a center-of-mass energy of 13 TeV (Run 2), corresponding to an integrated luminosity of 6 fb -1. For the relative production rate measurement, only the Run 2 dataset is used, where the simulated samples describe the reconstruction of long-lived hyperons more precisely. The online event selection is performed by a trigger  [22] , which consists of a hardware stage, based on information from the calorimeters and the muon system, followed by a software stage, which applies a full event reconstruction.
At the hardware stage, the event is required to have a muon with a high transverse momentum or a dimuon pair with a high product of transverse momenta of the two muons. At the software stage, the dimuon pair must have a significant displacement from the PV. The momentum scale is calibrated using samples of J/ψ → µ + µ -and B + → J/ψK + decays collected concurrently with the data sample used for this analysis  [4, 23]. A relative uncertainty of 3 × 10 -4 is estimated using samples of other fully reconstructed b hadrons, Υ mesons and K 0 S mesons. Simulated samples of Ω - b → J/ψΩ -and Ξ - b → J/ψΞ -decays are used to model detector effects and selection requirements. In the simulation, pp collisions are generated using Pythia  [24]  with a specific LHCb configuration  [25]. As the Ω - b production rate in Pythia is very low, its generation is inefficient.
These partially reconstructed processes fail the selection requirements of this measurement due to significant missing energy. Furthermore, all excited Ω -states are located above the ΞK threshold  [1, 31]  and have low partial widths for the Ωγ and isospin-violating Ωπ 0 decays. Therefore, no significant partially reconstructed background is expected in the two analyzed datasets. The final states of interest can also be reached by Cabibbo-suppressed processes such as Ξ - b → J/ψΣ(1385) -and Ω - b → J/ψΞ(1690) -, followed by strong decays such as Σ(1385) -→ Λπ -and Ξ(1690) -→ ΛK -. Since the hadronic resonances produced in these decays are short-lived, these processes are suppressed by the displacement requirement applied on the hyperon candidates. Therefore, the dominant remaining background is combinatorial background, consisting mostly of J/ψ mesons combined with random tracks.
Measurement of the Ω - b -baryon mass The measurement of the Ω - b -baryon mass is performed using an LHCb dataset of 9 fb -1. The collected data are considered separately for the Run 1 and Run 2 samples, which have slightly different invariant-mass resolutions. The mass difference m(Ω - b ) -m(Ξ - b ), rather than the absolute Ω - b mass, is extracted from an unbinned maximum-likelihood fit to benefit from canceling systematic uncertainties due to imprecise calibration of the momentum scale. The fit is performed simultaneously to the two run periods and decay channels. It is implemented in the RooFit toolkit  [33]  within the ROOT framework  [34]. The resulting invariant-mass spectra are shown in Fig. 1. Each signal parametrization is described by a linear combination of two double-sided Crystal Ball functions  [35]  with a common mean.
The parameters of the signal component are determined from large samples of simulated signal decays; most of them are then fixed in fits to data. The remaining free parameters of the signal in the fit to data are the Ξ - b mass for both run periods, the mass difference m(Ω - b ) -m(Ξ - b ), the width of each signal peak, and the signal yields. The background is described in each dataset by an exponential function, with a freely varying normalization and slope. Table  1  shows the resulting signal yields in each dataset. The mass difference from this fit is found to be   m(Ω - b ) -m(Ξ - b ) =.
Decay Dataset Signal yield Ξ - b → J/ψΞ -Run 1 377 ± 21 Run 2 1790 ± 47 Ω - b → J/ψΩ -Run 1 60 ± 8 Run 2 300 ± 18 To exclude dependencies of this result on the different data-taking periods and the track-type categories, the data sample is split into different subsets accordingly, and the mass-difference value measured for each subset. Furthermore, the analysed dataset is split by the charge of the b baryon, as well as by the magnet polarity with which the data was collected. Additional checks are performed by repeating the fit in a narrower invariant-mass range. All these cross-checks returned a mass difference in agreement with the baseline value, demonstrating the stability of this result. The mass difference determined from the fit needs to be corrected for the positive bias (0. 06 ± 0.
To propagate the uncertainty on the world-average values of the Ω -and Ξ -masses, the resulting bias on m constr is estimated. This is performed by varying in simulated datasets the central values assumed in the mass constraints, according to their uncertainties. The average bias on the measured b-baryon mass is evaluated independently for the Ω - b and Ξ - b cases, by varying the value of the Ω -and Ξ -mass, respectively; the two uncertainties are then summed in quadrature. It is expected that the small uncertainties on the Λ and J/ψ mass values cancel in the mass difference. The resulting systematic uncertainty of 0. 35 MeV/c 2 is The systematic uncertainty associated to possible residual backgrounds from the Λ 0 b → J/ψΛ decay, combined with a random pion, is studied with four alternative approaches.
The resulting combined uncertainty due to the fit model is 0. 01 MeV/c 2. Correcting the mass difference for the value of the bias observed in the fit to simulation, and taking into account the systematic uncertainties, the mass difference is determined to be m(Ω - b ) -m(Ξ - b ) = 248. 54 ± 0. 51 (stat) ± 0. 38 (syst) MeV/c 2. The result is in agreement with the previous measurement by LHCb  [6] , but significantly more precise. To obtain the value of m(Ω - b ), the most precise measurement of the Ξ - b baryon mass to date is used: m(Ξ - b ) = 5797. 33 ± 0. 24 ± 0. 29 MeV/c 2  [36]. This measurement uses Ξ - b → Ξ 0 c π -decays, and therefore is statistically independent of the Ξ - b → J/ψΞ -dataset used in this paper. The systematic uncertainties are considered to be fully uncorrelated, except for the uncertainties due to momentum scale calibration and the energy loss correction, which are taken as fully correlated.
[6]  and the measurement presented here), which are already combined in Eq. 2. This is achieved by first extracting m(Ω - b ) from Eq. (  2 ) using the Ξ - b mass value from Ref. [36]. The obtained value of m(Ω - b ) is then combined with that from Ref. [7]. In the Run 1 dataset, the signal yield of the Ω - b → J/ψΩ -decay is limited, and the downstream tracking efficiency is poorly known. Therefore, the relative production ratio of Ω - b and Ξ - b baryons is only reported at a center-of-mass energy of 13 TeV, using the Run 2 dataset. The relative production ratio of the Ω - b and Ξ - b baryons, defined in Eq. (  1 ), is experimentally measured using the observed yields (N ) of the considered decays, corrected for efficiency (ϵ) and branching fractions (B) of hyperon decays: R = N (Ω - b → J/ψΩ -) N (Ξ - b → J/ψΞ -) × ϵ(Ξ - b → J/ψΞ -) ϵ(Ω - b → J/ψΩ -) × B(Ξ -→ Λπ -) B(Ω -→ ΛK -).
(3) Since the decays Λ → pπ -and J/ψ → µ + µ -enter both b-baryon decay chains, their branching fractions cancel in the ratio. The world-average values of B(Ω -→ ΛK -) = (67. 8 ± 0. 7)% and B(Ξ -→ Λπ -) = (99. 887 ± 0. 035)% are used  [1]. The efficiencies are estimated using the relevant simulation samples. In the ratio, many sources of inefficiency and associated systematic uncertainties cancel due to the very similar kinematics and topologies of the two decay modes. However, this cancellation is not expected for the detector acceptance and reconstruction efficiencies due to the large difference in lifetimes between the Ω -and Ξ -hyperons. A simultaneous fit to the Run 2 datasets for both decay modes is performed. The fit setup is nearly identical to that used for the mass measurement, but the signal yield of the Ω - b → J/ψΩ -decays is expressed as a product of the relative production ratio R, the yield of Ξ - b → J/ψΞ -decays and the ratio of efficiencies for the two decay modes.
120 ± 0. 008, where the uncertainty is statistical only. The stability of this result is tested with a dataset-splitting procedure similar to that used in the mass measurement. The obtained results for all subsets are in excellent agreement with the baseline result. As an additional test, the ratio is also extracted with the invariant-mass range in the fit reduced by 50 or 100 MeV/c 2 on both sides, yielding a result consistent with the default value. Possible sources of systematic uncertainty are studied in detail and summarized in Table  4. The size of the simulated samples, used to estimate the signal efficiencies, leads to a 0. 3% relative uncertainty on the efficiency ratio. However, the amount of backgroundsubtracted data used to determine the simulation-calibration weights is rather small, especially for the Ω - b decay.
This result is lower than, but consistent with the value measured by the CDF experiment in pp collisions at the center-of-mass energy of 1. 96 TeV, R = 0. 27 ± 0. 12 (stat) ± 0. 01 (syst)  [3]. This comparison is limited by the very low signal yield of Ω - b → J/ψΩ -decays in the CDF dataset. The CDF measurement was reinterpreted, using the predicted value of the branching fraction of the Ω - b decay, to extract an Ω - b production fraction of f Ω - b ∼ 0. 5 × 10 -2 at the Tevatron  [14]. Similarly, one can estimate the value of f Ω - b /f Ξ - b at LHCb using the predictions for the relevant branching fractions. For instance, Ref. [14]  predicts B(Ω - b → J/ψΩ -) = (5. 3 +5. 0 -3. 4 ) × 10 -4 and quotes B(Ξ - b → J/ψΞ -) = (5. 1 ± 3. 2) × 10 -4. As the ratio of these two branching fractions is close to unity, the resulting value of f Ω - b /f Ξ - b is about 0.