dataesr/scientific-paragraphs-categorization huggingface dataset:
Our method is just as effective (if not more so) at recovering RRL from sub-optimally sampled data than the methods used in comparable surveys. Although we recover most of the RRab in the aforementioned overlapping catalogs, we can see from the AUC of each of these that there is a marked degradation in our algorithm's performance when applied to light curves outside our S82 training set. Thus, we use their AUC values to construct a confidence interval for the performance of our classifier. With the AUC of the training set and all four of these external cross-matches, we find a mean AUC of 0. 728 with a standard deviation of 0. 077. From this, we can determine that our classification methods have a lower effiency for fainter RRab. Unfortunately, we do not have well-characterized training data in a comparable filter system for fainter RRab, so we tested this with simulated light curves.
Detection Biases The strength of DES lies in its wide-field coverage and depth, but the results presented here are limited by the low number of multiband observations. Figure  16  displays histograms of the total number of observations for all objects in the stellar sample (black), all objects passing variability cuts (blue), and all RRab candidates (red). The median number of total observations for each group, marked by a short colored line segment, is 10, 18, and 17, respectively. Note that most of our RRab have fewer observations than the N∼20 observation thresh-old we saw from the simulation results in Figure  14. As future DES data releases will have an increased number of observations, we expect to find more RRL and have a more robust classification of the candidates presented here. We note that the light curves used in this analysis typically had fewer total observations than the number expected from three years of DES data.
We suspect that the total number of observations for the "stellar" sample is skewed by objects near the detection limits of DES, which suffer from noisy photometry and likely have few overlapping observations across all five filters. This low number of observations is also a result of the stringent quality cuts we applied on the single epoch photometry in §2. 2. In future work, we aim to be more judicious in applying our photometric quality cuts so that we do not discard observations unnecessarily. To verify that our sample is not affected by spatial fluctuations in the number of observations, we calculated the median number of total observations in each HEALPix of our Y3Q2 stellar data set. We show the median and the standard deviation of the total number of observations of light curves in each HEALPix in Figure  17.
We expect the addition of DES Y4-Y6 data to increase our detections of RRL considerably. Some additional biases in our RRab sample are results of choices made to exclude non-RRab from our analysis. While we weighted our initial variability cuts by the photometric errors to make the cuts robust against spurious observations (see §3. 2), using these error-weighted metrics biased our variable sample against RRab with smaller amplitudes located at larger distances. We also excluded some real RRab from our sample by limiting Figure  17. Spatial map of the median number of observations across all bands in each HEALPix. As expected, the regions with the fewest observations are near the edges of the survey region. The regions with a relatively large number of observations correspond to Science Verification or supernova fields.
Spatial Distribution of the Candidates The spatial distribution of the 5783 visually validated RRab candidates is shown in Figure  18. We also plot these candidates as a function of their heliocentric distance in Figure  19. In both figures, the overdensities of RRab candidates associated with (in order of decreasing heliocentric distance): the Fornax dSph, the Sculptor dSph, and the the outskirts of the Large Magellanic Cloud are easily visible. We expect that the inclusion of a metallicity term into the model combined with additional epochs of DES observations in the next release of this catalog will enable further characterization of these and other substructures. One of the largest strengths of the DES data set is its depth (see our comparisons to other wide-field surveys in Figure  13 ).
2 kpc) using data from the HiTS Survey  (Förster et al. 2016). Even though the candidates in our RRab sample suffer from a small number of observations and require additional follow-up for confirmation, the fact that there are so many RRab beyond 100 kpc and three RRab beyond 220 kpc provide reasonable evidence that the Milky Way stellar halo extends at least out to 220 kpc. Future DES data releases and other upcoming deep surveys such as LSST will increase the census of known RRL at this distance, enabling further characterization of the outer halo.
Thus, our algorithm would be effective to identify potential RRab candidates (which would need followup for confirmation) within the first year of LSST operations. After the first year, the light curves in the WFD sur-12 See the most current version of the draft of the LSST Observing Strategy white paper located at https://github. com/ LSSTScienceCollaborations/ObservingStrategy. vey will be adequately sampled to use other multiband methods available in the literature.
SUMMARY We have presented a new physically-motivated general multiband RRab template and a computationallyefficient fitting procedure. We combined this method with a random forest classifier to create a powerful technique that can robustly identify these variables even when fewer than 20 observations are available. Despite the poor cadence and sampling of DES data, we detected 5783 RRab candidates, 1795 (31%) of which are previously undiscovered to the best of our knowledge. The large quantity of RRL we recovered in common with overlapping external surveys such as Gaia DR2, Pan-STARRS, Catalina Surveys, and ATLAS provide strong evidence of the effectiveness of this algorithm. Although the number of observations is relatively uniform across the survey footprint in the DES Year 3 data, time series analyses like these will benefit immensely from the additional observations in future data releases.
Facilities: CTIO: Blanco (DECam)  (Flaugher et al. 2015)  Software: astropy(AstropyCollaboration2018),easyaccess (Carrasco  Kind et al. 2018 ), gatspy (VanderPlas & Ivezić 2015) , Matplotlib  (Hunter 2007 ), scikit-learn (Pedegrosa et al. 2011) , SIMBAD  (Wenger et al. 2000)  APPENDIX.
D. TEMPLATE CODE PRODUCTS The RRab template for both SDSS and DES filters and the fitting algorithm presented in this work are available at https://github. com/longjp/rr-templates. The template is originally implemented in R, but can be accessed in Python via the rpy2 module as was done in this analysis. Examples of the template usage are available in both R and Python 3, though it is also compatible with Python 2. When using the template fitting functions, there are two options available to the user that impact the values returned by the fits. As described in the previous section, the template model includes an optional dust term. The model already includes extinction coefficients appropriate for both the DES and SDSS filter systems and can estimate the amount of extinction affecting the light curve as one of the parameters.
E. DATA PRODUCTS To enable further work with similar data, we provide all of the RRab candidate and training light curves at https: //des. ncsa. illinois. edu/releases/other/y3-rrl and an extended version of Table  6. These light curves have already had their photometric uncertainties rescaled as described in §3. 1 and include both the dust-corrected and uncorrected magnitudes in each band. The light curves are indexed by their DES Y3Q2 QUICK OBJECT ID numbers, which are included as a column in the full data table. A description of the columns in the included data table is shown in Table  E  and example sample selection criteria are shown in Table  11. All of this information is also available in the documentation at https://des. ncsa. illinois. edu/ releases/other/y3-rrl.
ported here: brazos. tamu. edu. KMS would like to thank Chris Stringer and Ryan Oelkers for helpful conversations. Part of this work was completed while coauthors ZL and JPL were research fellows in the Astrostatistics program at the Statistics and Applied Mathematical Sciences Center (SAMSI) in Fall 2016. ZL and JPL gratefully acknowledge SAMSIs support. C. Nielsen and J. L. Myron would like to acknowledge support from the NSF grant AST-1263034, "REU Site: Astronomical Research and Instrumentation at Texas A&M University. " This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. Funding for the DES Projects has been provided by the U. S. Department of Energy, the U. S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência,.
studies from Sesar et al. (2010) or Ivezić et al. (2007) Simbad ID Object identifier in SIMBAD database (Wenger et al. 2000) Simbad class Object classification in SIMBAD database (Wenger et al. 2000) OGLE ID OGLE ID from LMC and SMC RRAb catalogs from Soszyński et al. (2016) CEMV ID ID from Martínez-Vázquez et al. (2016) GCVS5 ID Cross matched ID with the General Catalogue of Variable Stars v5. 1 (Samus' et al. 2017) GCVS5 Type Variable type for cross matched object as listed in the General Catalogue of Variable Stars v5. 1 (Samus' et al. 2017) hpx32 Object's Healpix (nside=32) used to subdivide the light curves comments Comments from first author during visual validation followed by a reason code train Identifies objects used to train the random forest classifier. 0 = no, 1 = yes rrab Identified as a high confidence RRab in this study.
ported here: brazos. tamu. edu. KMS would like to thank Chris Stringer and Ryan Oelkers for helpful conversations. Part of this work was completed while coauthors ZL and JPL were research fellows in the Astrostatistics program at the Statistics and Applied Mathematical Sciences Center (SAMSI) in Fall 2016. ZL and JPL gratefully acknowledge SAMSIs support. C. Nielsen and J. L. Myron would like to acknowledge support from the NSF grant AST-1263034, "REU Site: Astronomical Research and Instrumentation at Texas A&M University. " This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. Funding for the DES Projects has been provided by the U. S. Department of Energy, the U. S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, tional Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium.
Based in part on observations at Cerro Tololo Inter- American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) un- der a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Num- bers AST-1138766 and AST-1536171. The DES par- ticipants from Spanish institutions are partially sup- ported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV- 2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is par- tially funded by the CERCA program of the Generali- tat de Catalunya. Research leading to these results has received funding from the European Research Coun- cil under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agree- ments 240672, 291329, and 306478.
Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various features of interest within a sequence of video frames. A scientific application explored in this study is to combine the boosting tracker and the Hough transformation, followed by principal component analysis, to extract the location and trace of grain boundaries within atom probe data. Before the implementation of this method, these information could only be extracted manually, which is time-consuming and error-prone. The effectiveness of this method is demonstrated on an experimental dataset obtained from a pure aluminum bi-crystal and validated on simulated data.
By processing the impact position, the sequence in which ions Data Availability Statement: The simulation setups were initialized with open source tools (http://github. com/mkuehbach/APTTipCarving). All simulation results are accessible open source (http://www. zenodo. org/record/1994509). Code for grain boundary tracking as well as relevent data (The original data has been converted in avi video files, which can be directly used for tracking. ) that has been used in this study can be found in the repository:(https://github. com/DeepHeisenberg/ Object-tracking-in-Al-bicrystal. git). are detected and each ion's elemental identity, a three-dimensional point cloud of the original atomic positions can be reconstructed  [2]. APT is often used to measure the composition of grain boundaries and associated segregation phenomena  [3] [4] [5] [6].
Compared to the conventional Ga FIB, the Xe PFIB show faster material removal rates  [24]. More importantly, Ga is known to segregate at interfaces, especially at grain boundaries, during FIB-milling of aluminum and aluminum alloys. Using an Xe PFIB can avoid such ion implantation damage  [25, 26]. During specimen sharpening, transmission Kikuchi diffraction (TKD) was employed to locate the grain boundary within the needle-shaped APT specimen  [27]  using an EDAX electron backscatter diffraction (EBSD) detector and analyzed using the accompanying OIM software (version 7. 3. 1). APT experiments were conducted using a CAMECA LEAP 5000 XS instrument. It is a straight-flight-path device equipped with a local electrode. Voltage-pulsing mode was used to collect APT data with a pulse fraction of 15% and a pulse repetition rate of 250 kHz.
The target detection rate was set at 1 detection event per 100 pulses while the specimen was kept at a base temperature of 60 K during the measurement. The corresponding reconstruction was crystallographically calibrated using the protocol described by  [28]. The TKD results shown in Fig  1  clearly indicate that the grain boundary region was captured in the analyzed specimen and offer an independent experimental confirmation of the crystallographic character of the interface. Fig  1(a ) is an inverse pole figure map (IPF) along the arbitrarily defined x-direction, which is approximately aligned with the axial direction of the specimen. The A1 and A2 directions show the orientation of the EBSD detector relative to the display view. Grain cleanup was performed using a single iteration of grain dilation in the TSL OIM software with a grain tolerance angle of 5˚and a minimum grain size of 2 pixels.
A thin noisy region between the two grains can be observed, since the boundary plane is not perfectly orthogonal to the viewing plane. For additional clarity between the two grains, a unique grain color map is provided in The same specimen was subsequently analyzed using APT. A detector hit map of 1 million sequentially collected ions from the centre of the dataset is shown in Fig  2(a). The low density features in the map clearly capture crystallographic information from each grain as well as the GB. The dominant crystallographic poles were compared to that in a stereographic projection of the crystal and have been indexed accordingly. 48. 3 million ions were collected in total and reconstructed using IVAS. Crystallographic calibration  [30]  was performed so that the resulting reconstruction was as accurate as enabled by the protocol implemented in the commercial software.
Once a feature has been located on the detector, it should be possible to filter out the points that are associated to this specific feature. In cases where the specimen contains one or more features, one can usually observe their trace as moving objects in a sequence of detector hit maps calculated for a finite number of ions, e. g. typically. 5 to 1 × 10 6 ions. This is demonstrated in  Fig 4)  where the position of the GB line migrates across the detector hit map at different stages of the experiment, corresponding to different depths in the resultant reconstruction. To reconstruct a specific line feature automatically in 3D, an iterative procedure which reads through successive sections of the dataset is required. The problem of object tracking in video can be summarized as the task of finding the position of an object in every frame.
Pre-processing Before introducing machine learning techniques, we developed a feature selection method to ensure that the computer could efficiently learn to track grain boundaries. In the present dataset, the main crystallographic features in detector hit maps comprise poles, zone lines and grain boundaries traces. In order to track the grain boundaries efficiently, we start with reducing the noise (random fluctuation of pixel value in the detector hit map), which can be achieved by convolution of filters with the image  [36]. In BooT-PCA, we use two filters to reduce the noise level in the detector hit map, first, a median filter (size: 7×7) followed by a Gaussian filter (size: 7×7). The median filter computes the median of all the pixels inside a specified area and replaces the central element with the median value.
In addition, data extracted from tracking this complete window would not just contain the GB but also the surrounding matrix. Hence, additional filtering steps in BooT-PCA are required so as to only select the feature of interest within the ROI. Here we aim to track a nearly linear feature, which are known to be efficiently detected by the Hough transform. The Hough transform is widely used in image analysis and computer vision  [39]. It is commonly used for indexing Kikuchi patterns collected using electron backscattered diffraction (EBSD)  [40]. It has also been used previously in three-dimensions to reveal the presence of atomic planes within the atom probe reconstruction  [14, 41]. In this work, the Hough transform implementation is based on Python open-cv package  [38]. Fig 7(a)  shows that linear features can be detected using the Hough transform algorithm within the tracking window, and hence used to refine the feature selection inside the ROI.
Principal component analysis We introduce principal component analysis (PCA) to extrapolate the complete grain boundary surface. PCA is a statistical method which simplifies the complexity in high-dimensional data while retaining trends and patterns  [42]. Applying PCA on our 3D point cloud, formed by all detected GB atoms, gives us a 3 � 3 matrix consisting of three eigenvectors and their corresponding eigenvalues. These three eigenvectors have also been referred to as three principal components, which are perpendicular to each other. Among them, the variance of the positions of all GB atoms along the first principal component is the largest, and along the third component is the smallest. In the case of the aluminum bi-crystal dataset, the final GB plane is set perpendicular to the third principal component and positioned at the average position of all the GB atoms.
Method verification n t ¼ ½À 0:86; 0:47; À 0:20� ð3Þ The discrepancy between n h and n m is only 2. 9˚. Furthermore, around 140000 GB atoms were selected based on the coordinates of the mesh vertex, from which a mass spectrum (mass-to-charge-state-ratio spectrum, often referred to simply as 'mass spectrum') was calculated, which is plotted in Fig  11(c ), alongside a mass spectrum for the entire dataset. Analysis of the mass spectrum extracted from the grain boundary provides direct information on its specific chemical composition. The signal-to-background ratio in the GB mass spectrum is also relatively higher compared to bulk (for example at peak 13. 5 Da, Signal-to-background ratio of the GB mass spectrum is 5:1 whereas for bulk is 2. 3:1), which could allow for the detection of low levels of impurities that would otherwise be hidden in the background when considering the bulk mass spectrum.
Acknowledgments YW is grateful for funding from the  LAPLACE  Project, funded by the  Max-Planck Society. ZP acknowledges funding from the  BIGmax network , funded by the  Max-Planck Society. AB is grateful for funding from the  Alexander von Humboldt foundation. MK  appreciates the funding by  BiGmax , the  Max Planck Society 's  Research Network on Big-Data-Driven Materials Science.
Accurate reconstruction of global solar-wind structure is essential for connecting remote and in situ observations of solar plasma, and hence understanding formation and release of solar wind. Information can routinely be obtained from photospheric magnetograms, via coronal and solar-wind modelling, and directly from in situ observations, typically at large heliocentric distances (most commonly near 1 AU). Magnetogram-constrained modelling has the benefit of reconstructing global solar-wind structure, but with relatively large spatial and/or temporal errors. In situ observations, on the other hand, make accurate temporal measurements of solar-wind structure, but are highly localised. We here use a data assimilative (DA) approach to combine these two sources of information as a first step towards producing a solar-wind "reanalysis" dataset that optimally combines model and observation.
Introduction Scientific progress in climate and meteorological science has been revolutionised by "reanalysis" datasets that merge state-of-the-art physics-based numerical models with available observations through robust data assimilation (e. g. ,  Dee et al. , 2011). Reanalysis provides a means to combine disparate observations with different uncertainty characteristics, as well as enabling interpolation between the available observations using the known physics of the system. More generally, it attempts to merge the global-but-inaccurate information of numerical models with the accurate-but-local information of observations. This approach is beginning to be adopted by space physics, particularly in the radiation belts  (Aseev and Shprits, 2019; Glauert, Horne, and Meredith, 2018)  where there are both strong physical constraints that can be used to reduce the dimensionality of the system and reasonable spatial sampling with in situ observations.