dataesr/scientific-paragraphs-categorization huggingface dataset:
Reconstruction performance for test-beam data In order to compare the triggered test-beam particle reconstruction and identification efficiency between data and MC, a less strict definition of efficiently reconstructed test-beam particles is used in comparison to the one described in Fig. 18  Primary beam particle reconstruction and identification efficiency (top row) and the reconstructed particle completeness (middle row) and purity (bottom row) for 1 GeV/c π + (left column) and 1 GeV/c e + (right column) beam. The black distributions show the performance for the full ProtoDUNE-SP simulation, and the red and cyan curves show events with no cosmic rays, and no cosmic rays and no beam halo, respectively. In a number of places the red distribution is exactly covered by the cyan points 123 Table  1  The reconstruction and identification efficiency for the triggered test-beam particle in ProtoDUNE-SP simulation for positrons, charged pions, protons and charged kaons for different beam momenta.
Momentum Reconstruction efficiency (%) Sect. 6. 1. The following selection is used to obtain the sample of events that are highly likely to contain a beam particle, and hence form the denominator for the efficiency calculation. Data: 1. The beam trigger is active. 2. A single track is reconstructed in the beam monitors immediately upstream of ProtoDUNE-SP. 3. The high-voltage applied to the cathode is stable at -180 kV. 4. The readout electronics on the beam-side of the detector are active. 5. There are at least 10 3D hits in the region where the beam particle enters the detector. These 3D hits are produced as part of the disambiguation procedure described in Sect. 3, not within the Pandora software. Simulation: 1. There is a triggered test-beam particle in the MC particle hierarchy 2. There are at least 10 3D hits in the region where the beam particle enters the detector.
5 Physics analysis is possible as positrons are reconstructed as showers and can be easily selected. However, calculation of the absolute efficiency in data is not possible. Fig. 19  The beam particle identification efficiency for the test-beam particle in ProtoDUNE-SP simulation for different beam momenta. No data were collected at 4 GeV/c or 5 GeV/c, so no entries are shown for these momenta Two additional simulation samples were produced to investigate the effect of potential systematic uncertainties. Furthermore, to account for potential differences in the 3D hit finding between data and MC, the selection criterion requiring 10 3D hits in the region where the beam enters the detector was varied for the simulation to 8 and 12 and the change in efficiency was taken as a systematic uncertainty, giving the higher and lower limits on the systematic uncertainty band, respectively.
7 Conclusions A summary of Pandora, a pattern-recognition software package, has been presented alongside relevant modifications allowing it to be applied to simulated and experimental data interactions in the ProtoDUNE-SP LArTPC detector. Pandora is the primary event reconstruction used in all ProtoDUNE-SP physics analyses and enables the measurement of hadronic cross sections on liquid argon, the primary physics goal of the experiment. The performance of Pandora has been extensively evaluated for simulated charged test-beam and cosmic-ray interactions in the ProtoDUNE-SP detector. Several pattern-recognition metrics have been evaluated enabling a comparison of data and simulation. It is not trivial to extrapolate the performance measured in ProtoDUNE-SP due to the much higher detector occupancy compared to the FD, but it will provide a lower bound on the expected performance and the results presented here demonstrate the potential of Pandora to provide accurate and efficient event reconstruction.
Over the coming years, developments to the patternrecognition are expected from the introduction of new algorithms and the incorporation of deep-learning techniques to drive some key decisions within the Pandora multi-algorithm approach. Examples include improved vertex finding, event slicing and hit classification. Whilst many of these new algorithms are being developed for the DUNE FD, they will be tested on the ProtoDUNE-SP simulation and data. ProtoDUNE-SP has been dismantled but a very similar upgraded detector called ProtoDUNE-HD is under construction in the same cryostat, which is due to commence data taking in 2023. or the data will not be deposited. [.
We gratefully acknowledge the support of the CERN management, and the  CERN EP, BE , TE, EN and IT Departments for NP04/ProtoDUNE-SP. This document was prepared by the DUNE collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. This work was supported by  CNPq ,  FAPERJ ,  FAPEG  and  FAPESP, Brazil ;  CFI ,  IPP  and  NSERC ,  Canada ; CERN; MŠMT, Czech Republic; ERDF,  H2020-EU  and  MSCA ,  European Union ;  CNRS/IN2P3  and  CEA, France ;  INFN, Italy ; FCT, Portugal; NRF, South Korea;  CAM, Fundación "La Caixa ",  Junta de Andalucía-FEDER ,  MICINN , and  Xunta de Galicia, Spain ;  SERI  and  SNSF, Switzerland ;  TÜB İTAK, Turkey ;  The Royal Society  and  UKRI/STFC ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
These multi-PGS models can be trained on thousands of different PGS such as for health outcomes, body measurements, and behavioral phenotypes which are not necessarily genetically correlated with the outcome. Multiple PGS and covariates can be combined using either a linear model (lasso penalized regression) or a nonlinear model (XGBoost  21  ) into a multi-PGS model. This model is then evaluated in an independent dataset in terms of the prediction accuracy of the multi-PGS. We apply our multi-PGS framework to the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH)  22, 23  , one of the largest datasets on the genetics of major psychiatric disorders. These disorders are genetically correlated with many other psychiatric and neurological disorders as well as other behavioral phenotypes  24, 25  , which are precisely the circumstances under which the proposed multi-PGS might boost the polygenic prediction accuracy.
We then trained multi-PGS models for 6 major psychiatric disorders: attentiondeficit/hyperactivity disorder (ADHD), affective disorder (AFF), anorexia nervosa (AN), autism spectrum disorder (ASD), bipolar disorder (BD) and schizophrenia (SCZ). We focus the first part of the results section on these 6 psychiatric disorders and extend the multi-PGS application to other 62 ICD10 code disease definitions, continuous phenotypes and case-case classification in the last result section.
PGS library construction A detailed description of the PGS library construction and file filtering process is provided as Supplementary Methods and all code used will be available. A number of resources were used to obtain an initial list of GWAS summary statistics for generating the PGS library. The majority of the GWAS summary statistics were downloaded from publicly available databases (GWAS Catalog  26  , GWAS Atlas 27 , the Psychiatric Genomics Consortium Website (https://www. med. unc. edu/pgc). For the specific PGC GWAS summary statistics where iPSYCH was used in the discovery dataset, we used in-house GWAS results where these samples were excluded from the calculation. The GWAS files were specifically selected to be based on European ancestry individuals, to not be overly redundant (only the latest GWAS for each same phenotype) and to not contain iPSYCH samples.
The set of 952 GWAS summary statistics that passed QC and kept over 200,000 SNPs were used to derive PGS with LDpred2-auto  16. Polygenic score weights were derived using LDpred2-auto, a method within the LDpred2 framework  16  that does not require a validation dataset to fit the hyperparameters (SNP-h2; SNP-based heritability estimate and p; proportion of causal SNPs), but these are fitted as part of the Gibbs sampler instead. We used the provided Europeanancestry independent LD blocks as reference panel  46. For each GWAS summary statistics file, LDpred2-auto was run with 30 Gibbs sampler chains, 800 burn-in iterations and 400 iterations. The SNP-h2 initial value was set to the LD score regression estimate  47  from the GWAS summary statistics after. Each of the chains was initialized with a different prior for the proportion of causal variants: [1e-4, 0.
iPSYCH data Genotypes and imputation. The iPSYCH 2015 case-cohort sample is a genotyped dataset from neonatal dried blood spots (DBS) nested within the entire Danish population born between 1981 and 2008, including 1,657,449 persons  22, 23. After genotyping and sample quality control, it includes 92,765 individuals diagnosed with a major psychiatric disorder i. e. attention-deficit/hyperactivity disorder (ADHD), affective disorder (AFF), autism (ASD), schizophrenia (SCZ) and bipolar disorder (BD). We also included the anorexia nervosa (AN; ANGI-DK) samples from the Anorexia Nervosa Genetics Initiative (ANGI)  48  , as they were samples within the same framework as iPSYCH 2015. The dataset also includes 42,912 individuals randomly sampled from the same birth cohort, making it representative of the general Danish population.
The genotype data was imputed using the Haplotype Reference Consortium (HRC)  49  as the reference panel and following the RICOPILI pipeline  50. After removing SNPs with minor allele frequency (MAF) < 0. 01 and Hardy-Weinberg p value < 10-6, we restricted to the HapMap3 variants in the LDpred2 LD reference panel, resulting in 1,053,299 SNPs. Principal components & relatedness. We performed principal component analysis (PCA) following Privé et al. 51  and obtained 20 PCs. The process has already been described in Albiñana et al. 32. Using the set of 20 PCs, we defined genetically homogeneous individuals as having <4. 5 log distance units to the multidimensional center of the 20 PCs (calculated using the function dist_ogk from the R package bigutilsr  51, 52  ). We also computed the KING-relatedness robust coefficient of the sample and excluded the second of each pair with >3rd degree relatedness.
Multi-PGS models We used the library of 937 PGS to train multi-phenotype predictors (multi-PGS) using two different algorithms (1) L1 penalized regression (lasso) as implemented in the R package glmnet  53  and (2) tree gradient boosting as implemented in the XGBoost (eXtreme Gradient Boosting) algorithm in the R package xgboost  21. For both models, we trained a base model using only the covariates (sex, birth year and 20 PCs) and a full model using the covariates plus the 937 standarized LDpred2-auto PGS. For the base model, we used the glm function with the option family = "logistic". For lasso, we used the function cv. glmnet from the glmnet R package with the options alpha = 0 and family = "binomial" for binary phenotypes. The covariates were not regularized by giving them a penalty factor of 0 with the option penalty.
Acknowledgements C. A. , B. J. V. and F. P. were supported by the  Danish National Research Foundation  ( Niels Bohr Professorship  to  Prof. John McGrath ), the  Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH  ( R102-A9118 ,  R155-2014-1724  and  R248-2017-2003 ), and a  Lundbeck Foundation  Fellowship ( R335-2019-2339 ). C. A. was supported by a  Willam Demant Fonden  fellowship. I. B. was also supported by the  Swedish Brain Foundation  and  Fredrik och Ingrid Thurings Stiftelse. AN data are from the  Anorexia Nervosa Genetics Initiative , an initiative of the  Klarman Family Foundation , and extendented with support from the  Lundbeck foundation  ( R276-2017-4581 ). High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the  Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing , iSEQ,  Aarhus University, Denmark  (grant to A.
Data availability The multi-PGS lasso weights generated in this study have been deposited in the figshare database (https://doi. org/10. 6084/m9. figshare. 23597019. v1). The iPSYCH and Danish ANGI data are available under restricted access as the data are protected by Danish legislation, access can be obtained after approval by the iPSYCH Data Access Committee and can only be accessed on the secured Danish server GenomeDK (https://genome. au. dk). For data access and correspondence, please contact C. A. (albinanaclara@gmail. com) or B. J. V. (bjv. ncrr@au. dk). The PGS library metadata generated in this study is provided in the Supplementary Information/Source Data file. The GWAS summary statistics data used in this study are available in the GWAS Catalog database (https://www. ebi. ac. uk/gwas/, downloaded on 09/09/2020), GWAS Atlas UKB2 data freeze v20191115 (https://atlas.
Grain Size Determination by Numerical Modeling and Simulations Given that it was not possible to directly constrain the GSD of the scraped regolith by image analysis due to the insufficient resolution, it was found relevant to determine it by using the wind as a natural sorter. The principle of sorting the poured regolith particles submitted to wind is based on the fact that particles are simultaneously  subjected to the effects of weight, buoyancy, and wind drag. As a consequence, finer and lighter particles will be transported and deposited further than larger and heavier ones. This physical consideration is the basis of the computational fluid mechanics models developed and implemented in this work. In order to compute the trajectory and the distance at which the grains are settled on the Martian ground from the scoop, we have used two independent numerical modeling packages: the first model solves the first-order force equations using a multi-physics system modeling software called "Simulink" integrated in Matlab  (Moler, 1980) ; the second one uses a comprehensive general purpose computational fluid dynamics (CFD) software package for engineering analysis and design optimization of enterprise applications, based on ENGYS' own open-source CFD simulation engine (ENGYS, 2022).
Finally, the lift force is neglected here because the particles are considered spherical in the grain size range (i. e. , 100-1,000 microns) of our model, in conjunction with the sub-rounded to rounded shapes of sand grains observed at the surface of Mars (e. g. ,  Goetz et al. , 2010  in the Phoenix landing site); so, at low Reynolds number R e as in the Martian conditions due to atmospheric pressure, temperature, and density, the flow is stationary. Given that Equation  1 , which can be rewritten as a differential equation for particle velocity, is nonlinear, a Matlab Simulink model was developed to solve it using the projected equation derived from Equation 1 as follows: 𝑑𝑑𝑑𝑑𝑧𝑧 𝑑𝑑𝑑𝑑 = ( 𝜌𝜌atm 𝜌𝜌𝑠𝑠 - 1 ) ⋅ 𝑔𝑔 + 3 4 ⋅ 𝜌𝜌atm 𝜌𝜌𝑠𝑠 ⋅ 𝐶𝐶𝑑𝑑 𝐷𝐷 ⋅ 𝑑𝑑𝑧𝑧 2 (7) 𝑑𝑑𝑑𝑑𝑥𝑥 𝑑𝑑𝑑𝑑 = 3 4 ⋅ 𝜌𝜌atm 𝜌𝜌𝑠𝑠 ⋅ 𝐶𝐶𝑑𝑑 𝐷𝐷 ⋅ ( 𝑤𝑤𝑠𝑠(𝑧𝑧) 2 -𝑑𝑑𝑥𝑥 2 ) ( 8 ) 𝑑𝑑𝑑𝑑𝑦𝑦 𝑑𝑑𝑑𝑑 = - 3 4 ⋅ 𝜌𝜌atm 𝜌𝜌𝑠𝑠 ⋅ 𝐶𝐶𝑑𝑑 𝐷𝐷 ⋅ 𝑑𝑑𝑦𝑦 2 (9) By integrating Equations 7-9, the position of the grain can be known until reaching the final condition 𝐴𝐴 𝐴𝐴= 0 , that ends the simulation of the trajectory.
Simulations We used the numerical solver Simulink™ integrated in Matlab™ to solve Equations 7 to 9 because (a) these equations are not linear, and (b) to take into account the initial conditions on Mars (Section 3. 1. 1. 3). Simulink is a block diagram environment used to design systems with multidomain models, to simulate before moving to hardware, and to deploy without writing any code. The simulation is launched automatically by a Matlab script for each grain and stopped when the grain reaches the floor. The main outputs of the simulation are the coordinates of grain impacts on the floor in the scoop frame (Figure  15 ). In addition, the ballistic range of the grain, that is,  the horizontal distance of the impact relative to the scoop can be reported (Figure  16 ) with error bars taking into account the variability and accuracy of the input parameters.
Comparison Between Matlab-Simulink Basic Model and CFD Simulations The main output of the CFD simulations is the map of grain impact locations on the ground superimposed on the vertically projected differentiated ICC image (Figure  19 ). We observed a good correlation between the direction of the main deposit area and the wind direction for each dump. A small discrepancy between the width of the deposited grain areas observed on the ICC images and that computed by the CFD model is observed. This discrepancy is likely due to the 1-way coupling approximation (i. e. , no particle-particle interaction or no action from the particles on the fluid is taken into account in the simulation). As observed in the Simulink simulation, the distribution of grains on the ground shows a decrease in grain size with distance from the pouring point, with a size ranging from 1 mm near the dump point to ∼100 μm in the farthest area observed in the ICC images (Figure  19 ).
In addition, the ballistic range of the grains (i. e. , the distance of the impact relative to the scoop) is plotted as a function of their size (Figure  20 ). The CFD simulation performs a random draw in the ranges of variations of input parameters (Tables  2, 4 , and 5) and on the grain size for 100,000 grains. The result is a point cloud on the grain size range curve. For each grain size, a probability density function (pdf) is visualized as the number of points in the vicinity. The maximum of the distribution is then compared with the same initial conditions as the Matlab Simulink model (black line in Figure  20 ) for the whole size range. For all dumps, we observe that the Matlab model gives results close to the CFD model, with Matlab curves remaining within the probability distribution of the CFD model, which is satisfactory and provides some confidence in both approaches.
e. , 115 μm) with, however, possible content of smaller particles blown away by the wind and not quantifiable with the available data. The Matlab Simulink method, that can be carried out on a standard desk computer, is more cost-effective than the CFD approach. The calculation time of the trajectories of the 5,500 grains (11 runs of 500 grains each) lasts 45 min on an Intel I7-6500@2. 5GHz, 8 GB RAM, while the CFD model, that simultaneously calculates the trajectories of 100,000 particles of size between 100 and 1,000 microns, runs during 2. 5 hr using a 2x AMD EPYC 7351, 16 × 8 GB DDR4 2666 MHz computer with 32 cores. Because the results are comparable, the results of Matlab Simulink simulation were less consuming and were used to feed the granulometry estimation process.
Discussion Significant wind sorting is inferred during the regolith pouring sequence of the SEIS tether covering operation, with a significant proportion (around 40%) of smallest particles blown away by the wind, whereas only 60% of the poured regolith was deposited on piles above the tether. Since wind data were accurately monitored by the TWINS sensors, the tether covering process provided a unique opportunity to further constrain the grain size distribution (GSD) of the poured regolith thanks to wind sorting, with smaller particles deposited further away from the tether. The length of the plumes of transported and deposited particles, observed from image analysis, is comprised between 1, 5 and 3 m, with wind speeds between 5 and 7 m. s -1. The two models used provided comparable data. Four GSD curves were derived with the MS model from dumps 3 to 6, with smallest particles having diameters between 115 μm (dump 6) and 145 μm (dump 3).
Both calculations showed that, intuitively, smallest particles were deposited further away with stronger wind, at distances between 2 and 3 m. Extrapolating the Matlab Simulink data of Figure  20  shows that 100 μm diameter particles would be deposited at 3 m under 5 m. s -1 and 5 m under 7 m. s -1. These results support the hypothesis that fine sand can be driven by low velocity wind on Mars when the grains are already detached from the ground and blown off (e. g. ,  Andreotti et al. , 2021; Kok, 2010; Sullivan & Kok, 2017). On Mars, we find such fine sand grains in the aeolian bedforms (dunes, ripples, sand sheets…) like those measured by the various instruments on board Mars rovers (e. g. ,  Ehlmann et al. , 2017; Lapotre & Rampe, 2018; Weitz et al. , 2018 Weitz et al. , , 2022) )  showed that they have been mobilized by the wind.
Wind speed and direction are measured by the TWINS sensors of the lander (about 1. 2 m above the ground and 1. 3 m away from the scoop). Wind data could be somewhat different at the scoop level during pouring because of turbulence caused by the lander and the various instruments (seismometer, HP 3 , IDA…), resulting in lateral spreading of the grains at the surface. In our models, we considered that the composition of the grains was essentially basaltic with a grain density of 3,000 kg. m -3. This may appear restrictive with respect to the chemical and mineralogical compositions of regolith at other Martian sites (e. g. ,  Berger et al. , 2016; Weitz et al. , 2022)  where fine grains could easily self-aggregate. Sand-sized dust aggregates, much lighter due to their porous nature, could represent a non-negligible fraction of surface materials.
Conclusion The tether covering operations, initiated by the InSight Science Team to improve the quality of the signal provided by the SEIS seismometer, showed that a non-negligible fraction of the regolith poured from the IDA scoop at heights around 45 cm did not reach the tether. More detailed observation and analysis of the ICC images evidenced, by image differentiating, that a plume of blown particles was observed downwind of each pouring, the stronger the wind, the longer the plume. It was then decided to better constrain the GSD of the regolith by using the wind as a natural sorter, able to blow finer particles further. The physical laws governing the phenomenon are relatively simple, and the effects of the various forces acting on the particles (gravity, buoyancy and drag) could be modeled through two approaches: a simpler one based on the MATLAB-SIMULINK tool, and another one using a Computational Fluid Dynamic tool.
Acknowledgments The authors also thank the JGR Editor  Laurent Montési , associate editor  German Martinez  and both the anonymous referees for their insightful suggestions and constructive review on greatly improving this manuscript. This work was supported by the  French space agency ,  Centre National d'Etudes Spatiales (CNES). Portions of the research were carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). This paper is InSight Contribution Number 230.
Data Availability Statement The authors acknowledge NASA, UK Space Agency, CNES, their partner agencies and Institutions (UKSA, IC, SSO, DLR, JPL, IPGP-CNRS, ETHZ, and MPS-MPG) and the flight operations team at JPL, SISMOC, MSDS, IRIS-DMC, and PDS for acquiring and providing InSight data, including SEED SEIS data. InSight Lander camera images are publicly available through the IDC/ICC Bundle  (Deen et al. , 2019)  and through the NASA Planetary Data System (PDS) Geosciences node and MarsInSight mission. The atmospheric measurement data presented here are also publicly available in the APSS TWINS Bundle  (Mora, 2019). Seismic raw data are in "InSight Mars SEIS Data Service (2019a) together with IPGP, JPL, CNES, ETHZ, ICL, MPS, ISAE-Supaero, LPG, MFSC" (2019) and Insight Mars SEIS Data Service (2019b) SEIS Bundle.