dataesr/scientific-paragraphs-categorization huggingface dataset:
Image Preparation The image preparation module automatically downloads all available image scenes in our regions of interest. Landsat 7/8 and Sentinel-2A/B images are downloaded from either Google Cloud, Amazon AWS, USGS EarthExplorer or Copernicus Scihub Copernicus Scihub using their respective API services and depending on the availability of the repository, while Venµs and Pléiades are downloaded manually from the CNES Kalideos platforms and THEIA. Landsat, Sentinel, Venµs and Planet Labs data are provided in the form of ortho-rectified images. No additional ortho-recticification correction is applied. For Pléiades data, an additional step is required to obtain accurate ortho-rectified images useful to calculate surface displacement. Hence, we rely on the Ames Stereo Pipeline  [20, 21]  to generate a DEM and an ortho-image at a resolution of 0.
Glacier Surface Velocity We modified the algorithm ampcor developed by JPL which was part of the Repeated Orbit Interferometry Package (ROI-PAC)  [27]. Our workflow uses the core of the cross-correlation algorithm, written in Fortran-90 and integrated it into a Python environment to maintain both efficiency and flexibility with the rest of the workflow. The python environment extracts the chips from the master and slave images and calls ampcor using the interface numpy. f2py. The Fortran-90 code of ampcor calculates a standardized cross correlation map between the reference image chip and the slave chips. From this correlation map, a peak correlation value is determined. The reference and slave image chips are deramped and oversampled by a factor of two using 2D fast Fourier transform. The correlation procedure is then repeated around the previously determined peak value.
The resulting new correlation map is itself oversampled by a factor of two and the location of correlation peak is then determined. Finally, the outputs ampcor are returned to the python procedure. Thanks to the Python environment, new features can now be more easily implemented and added than if only the Fortran code had been retained, such as the ability to restart a process in the event of a crash, the use of a mask to determine where to calculate the correlation, or an initial guessed displacement map to guide cross correlation in a fast moving region, thus reducing calculation time and avoiding correlation errors due to long distance travel. The mask provided to ampcor is derived from the shapefile of the study region delineated in the first step of the workflow. It includes glaciers and surrounding ice-free areas for calibration purposes.
NetCDF Geo-Cubes Database To facilitate post-processing, our regions of interest are divided into areas of equal size of 10 × 10 km with a resolution of 50 m (or 250 × 250 pixels). For each area, we extract and stack the velocity maps and associated metadata into "cubes" whose dimension z therefore corresponds to the number of calculated speed maps. The cubes overlap by 5 pixels or 1. 25 km to avoid edge problems during post-processing. This standardized dataset where all maps are stored on a common grid can be easily used to extract time series of the surface flow velocity or calculate time-averaged maps. The cubes are stored in the GDAL-compatible netCDF format following the CF (Climate and Forecast) metadata conventions to allow greater portability and facilitate distribution in the community.
The weights are taken as 1/ 2 where is the error on the velocity assuming a sub-pixel matching precision of 0. 1 pixel  [29]. Effective precision on glacier surface velocity mapping is discussed later in the manuscript and justifies our assumption. For efficiency reasons, the code that performs post-processing has been developed in Fortran-90 (integrated in Python using f2py). Each geo-cube can also be processed independently of the others so that the averaging of maps can be done on high-performance clusters. The code returns six output parameters for each component (x and y) of the ice flow: (1, 2) the number of values before and after filtering (Figure  S4 ); (3) the weighted standard deviation after filtering (Figures  1B  and 2B ); (4) the standard error of the weighted mean on the filtered data;  (5, 6)  the mean before and after filtering.
Comparison with Existing Regional Ice Velocity Datasets In Figure  8 , we compare our Sentinel-2 2017-2018 time-averaged speed map with a map generated from Landsat GoLIVE data available over the Mont Blanc massif  [15, 37]. We oversample each GoLIVE ice velocity at a resolution of 50 m and follow the same post-processing methodology described in the Section 2. 4 (Figure  8 ). We also compare the results with a previous Landsat 8 map published by  [38]  that uses a processing chain similar to ours in terms of the parameters used for correlation  [24]. Our map like the one in  [38]  seems to better capture the main glaciers of the Mont Blanc massif such as the Mer de Glace or Leschaux glaciers, with shear margin transitions that are better defined than GoLIVE. It also appears that many small glaciers are not visible in the GoLIVE map, as for example on the Italian side of the massif with the Rochefort and Planpincieux glaciers.
Discussion We implemented a workflow to process datasets from several optical sensors, we generated time-averaged maps of glacier speed from Sentinel-2 data and compared the results with those of other sensors: Landsat 8 with a slightly lower sensor resolution and Venµs, Planet Labs and Pléiades with a higher spatial resolution. We tried to evaluate the performance of Sentinel-2 against other optical sensors to map the surface speed of mountain glaciers in three regions of the world with glaciers having distinct morpho-topographical characteristics. Our statistical analysis shows that the glacier surface velocity precision is exponentially improved with increasing cycle length. We note that calculating velocity fields with pairs that have repeat cycles greater than 335 days provides highly precise velocity maps but does not significantly improve the precision (Figure  4C ).
In the case of Planet Labs' satellite constellation, our analysis shows similar problems to those of Venµs for the quality of orthorectification of images. However, with additional corrections for uncompensated stereo effects, the Planet Labs data have interesting potential for mapping glacier surface velocity. These results highlight the importance of having frequent acquisitions with very high resolution sensors and an accurate ortho-rectification in order to capture the changes in surface velocity of small mountain glaciers. It also shows that Sentinel-2 and Landsat 8 are able to capture with enough details and precision the main glaciers of the world glacier-covered massifs for future studies. Thus, we showed from the comparison with very high resolution sensors and GPS measurements that "conventional" sensors (Sentinel and Landsat) capture surface flow velocity for most glaciers larger than 250 m (Figure  3  and Figures  5 6 7 ).
Currently, Pléiades, Planet Labs or Venµs can locally improve the large scale acquisitions from Sentinel-2 and Landsat 8 and therefore supplement the record of glacier dynamics at even finer spatial and temporal scales. When sufficient amount of data are available and stacked, we found a good agreement between GPS and satellite derived glacier surface velocity for glaciers that are at least 250 m wide (Figure  5 ), suggesting that the accuracy of the Sentinel-2 time-averaged map would be less than 10 m/year. Glacierized areas with persistent shade, snow cover and/or limited number of surface features still remain challenging for Landsat 8 and Sentinel-2 where precision and accuracy are much lower. The use of an orientation correlation algorithm as described in [26], could potentially improve ice velocity mapping in such regions with strong changes in image intensity.
Additional post-processing filtering, for example based on the flow direction of the velocity vector, could be envisioned in future implementations and would certainly improve the final velocity product or time series analysis  [8, 41]. We have shown that, by combining all possible repetition cycles, Sentinel-2 or Landsat 8 can be used to produce time-averaged maps at a spatial resolution of 50-m with enough detail to capture most individual mountain glaciers (Figures  1 2 3 ). Although the native resolution of Sentinel-2 and Landsat 8 remains a limitation for capturing smaller glaciers (<250 m wide), we observe a gain over existing Landsat 8-based databases for mapping mountain glaciers. We attribute this difference to the higher spatial density of the offset maps generated here and the higher resolution of Sentinel-2 compared to Landsat 8.
Conclusions In this manuscript, we describe a new multi-sensor processing chain for processing a large amount of optical satellite data used to map the surface flow velocity of mountain glaciers on a regional scale. We present the first complete maps of glacier surface speed at a resolution of 50 m for the Peruvian Cordillera Blanca (tropical Andes), the European Alps and the Southern Alps of New Zealand. We performed a statistical analysis to establish the precision of Sentinel-2 and Landsat 8 for different repeat cycles. We conclude that the precision of glacier surface velocity mapping is 1. 5 to 2 times better with Sentinel-2 than with Landsat 8 for similar repetition cycles, as expected by the better resolution of Sentinel-2. Our estimation of Sentinel-2 precision indicates that only glaciers with ice velocity fluctuations greater than 10 m/year can be monitored on a seasonal basis.
Acknowledgments: We would like to thank  C. Vincent  and the  Service National d'Observation GLACIOCLIM  (https://glacioclim. osug. fr/)for the availability of in-situ data on Argentière Glacier,  G. Dedieu  (Venµs),  J. P. Dedieu  (Pointing),  NASA ,  ESA  and  CNES  for the data availability. We thank the  Planet Labs company  for providing free images through their  Ambassador program. This work was made possible thanks to the contribution of Labex OSUG@2020 (Investissements d'avenir - ANR10 LABX56 ). Thanks to  E. Berthier  and  F. Brun  for providing support with the  Ames Stereo Pipeline. The computing/storage resources used for this work were provided by  GRICAD (Grenoble Alpes Recherche -Infrastructure de Calcul Intensif et de Données).
We use the results from the ALMA large program ASPECS, the spectroscopic survey in the Hubble Ultra Deep Field (HUDF), to constrain CO luminosity functions of galaxies and the resulting redshift evolution of ρ(H 2 ). The broad frequency range covered enables us to identify CO emission lines of different rotational transitions in the HUDF at z > 1. We find strong evidence that the CO luminosity function evolves with redshift, with the knee of the CO luminosity function decreasing in luminosity by an order of magnitude from ∼2 to the local universe. Based on Schechter fits, we estimate that.
2016). The ASPECS Pilot program was limited by the small area surveyed. Here we present results from the AS-PECS Large Program (ASPECS LP). The project replicates the survey strategy of the ASPECS Pilot, but on a larger mosaic that covers most of the Hubble eXtremely Deep Field (XDF), the region of the HUDF where the deepest near-infrared data are available  (Illingworth et al. 2013; Koekemoer et al. 2013 ; see Fig. 1 ). Here we present and focus on the ASPECS LP 3 mm data, which have been collected in ALMA Cycle 4. We discuss the survey strategy and observations, the data reduction, the ancillary dataset, and we use the CO detections from the 3 mm data to measure the CO LFs in various redshift bins, and to infer the cosmic gas density ρ(H 2 ) as a function of redshift. In  González-López et al.
(2019)  (hereafter, GL19), we present our search for line and continuum sources, and assess their reliability and completeness. Aravena et al. (2019)  place the ASPECS LP 3 mm results in the context of the main sequence narrative. Boogaard et al. (2019)  capitalize on the sensitive VLT/MUSE Integral Field Spectroscopy of the field, in order to address how our CO detections relate with the properties of the galaxies as inferred from rest-frame optical/UV wavelengths. Finally,  Popping et al. (2019)  compare the ASPECS LP 3 mm results to state-of-theart predictions from cosmological simulations and semianalytical models. The structure of this paper is as follows: In Sec. 2, we present the survey strategy, the observations, and the data reduction. In Sec. 3 we summarize the ancillary information available for the galaxies in this field.
Survey design and observations The ASPECS LP survey consists of a 150 hr program in ALMA Cycle 4 (Program ID: 2016. 1. 00324. L). AS-PECS LP comprises two scans, at 3 mm and 1. 2 mm. The 3 mm survey presented here took 68 hr of telescope time (including calibrations and overheads), and was executed between December 2-21, 2016 (ALMA Cycle 4). These observations comprised 17 pointings covering most of the XDF  (Illingworth et al. 2013; Koekemoer et al. 2013 ; see Fig. 1 ). The pointings were arranged in a hexagonal pattern, distanced by 26. 4 (the halfwidth of the primary beam of ALMA 12m antennas at the high-frequency end of ALMA band 3), thus ensuring Nyquist sampling and spatially-homogeneous noise in the mosaic. For reference, the central pointing is cen-   Koekemoer et al. 2013) , in light green; the pointings of the MUSE UDF survey  (Bacon et al.
In all the other frequency settings, the final rms appears consistent with the one computed from the cube provided by the ALMA pipeline. As the cube created with our own procedures is at least as good (in terms of low noise) as the one from the pipeline, we will refer to the former in the remainder of the analysis. We imaged the 3 mm cube with natural weighting using the task tclean. The resulting synthesized beam is ≈ 1. 75 × 1. 49 (PA=91. 5 • ) at the center of the observed frequency range. The lack of very bright sources in our cubes allows us to perform our analysis on the 'dirty' cube, thus preserving the intrisic properties of the noise. The resulting cube is used for the line search, and in most of the following analysis. In addition, we image the dataset after applying a u,v taper of 3 (using the uvtaper parameter in tclean) cleaned to 2-σ.
5 (1. 0 ) for the photometry (spectroscopy). This selection is also cross-matched with the measurements of morphological parameters (size, ellipticity, light concentration index) from van der Wel et al. (  2012 ). The whole catalog, extending over most of the GOODS-South footprint, consists of > 63000 entries. In the 2. 5 × 2. 1 area of the XDF, the catalog includes photometry in >30 broad and medium bands for ∼ 7000 galaxies, 475 of which have a spectroscopic redshift. The photometric dataset is modeled with the highz extension of the Spectral Energy Distribution (SED) fitting code MAGPHYS (da  Cunha et al. 2008 Cunha et al. , 2015)) , in order to infer physical parameters: stellar mass, sSFR (and thus, SFR), dust extinction, IR luminosity, etc. We use the available photometry between 0. 37 µm and 8.
They are listed in Table  2. Fig. 8  compares the observed CO LFs with the fitted Schechter functions. The fitted parameters do not show strong dependency on the choice of binning, with the results being typically consistent within 1-σ uncertainties. We find an indication of a higher L * at z=1-3 (log L * [K km s -1 pc 2 ] ≈ 10. 4) compared to the z > 3 bin, and most importantly, with the local universe (log L * [K km s -1 pc 2 ] ≈ 9. 9, although with a different definition of the Schechter function;  Saintonge et al. 2017). The luminosity-weighted integral of the fitted LFs suggests that the ASPECS LP 3 mm data recover 83%, 91%, and 71% of the total CO luminosity at z = 1. 43, Table  2. Results of the Schechter fits of the observed CO LFs, assuming a fixed α = -0. 2. Line log Φ * log L * [Mpc -3 dex -1 ] [K km s -1 pc 2 ] (1) (2) (3) All L bins CO(2-1) -2.
CONCLUSIONS We presented the ASPECS LP 3 mm survey, an ALMA molecular scan encompassing most of the Hubble XDF over a large fraction of the 3 mm transparent band of the atmosphere. We exploited our data to search for massive molecular gas reservoirs (as traced by CO emission) in galaxies throughout ∼ 90% of cosmic history, with no prior on counterparts at other wavelengths. We detected 70 line candidates with S/N>5. 5, >75% of which with a photometric counterpart at optical/NIR wavelengths. This search allowed us to put stringent constraints on the CO luminosity functions in various redshift bins, as well as to infer the cosmic density of molecular gas in galaxies, ρ(H 2 ). We found that: i-The CO luminosity functions undergo significant evolution compared to the local universe. High redshift galaxies appear brighter in CO than galaxies in the local universe.
We thank the anonymous referee for their useful feedback which allowed us to improve the quality of the paper. Este trabajo contó con el apoyo de  CONICYT + Programa de Astronomía+ Fondo CHINA-CONICYT. J. G. L. acknowledges partial support from  ALMA-CONICYT  project  31160033. D. R. acknowledges support from the  National Science Foundation  under grant number  AST-1614213. F. E. B. acknowledges support from  CONICYT-Chile Basal   AFB-170002  and the  Ministry of Economy, Development, and Tourism's Millennium Science Initiative  through grant  IC120009 , awarded to  The Millennium Institute of Astrophysics, MAS. I. R. S. acknowledges support from the  ERC Advanced  Grant  DUSTYGAL (321334 ) and  STFC  ( ST/P000541/1 ). T. D-S. acknowledges support from  ALMA-CONICYT  project  31130005  and  FONDECYT  project  1151239.
Our statistical treatment is also superior, as we recreate the ATLAS and CMS limit-setting procedures for each analysis rather than comparing the predicted number of signal events to the ATLAS and CMS 95% CL exclusions on the numbers of signal events. This allow us to combine continuous likelihood terms from each analysis, and thus explore possible tensions between analyses in a rigorous fashion. Most significantly, Ref. [69]  is based on searches for electroweakinos using the 8 TeV protonproton collision dataset of the LHC, which have been all but superseded by new ATLAS and CMS searches that use 36 fb -1 of 13 TeV proton-proton collision data. This dramatically extends the possible discovery and exclusion reach of the LHC searches. We begin in Sec. 2 by introducing the model and parameters over which we scan, followed by our sampling methodology, adopted priors and statistical framework.
In Sec. 3, we then give a brief summary of the observables and likelihoods that we employ. We present our main results in Sec. 4  and briefly consider the implications for DM in Sec. 5 before presenting final conclusions in Sec. 6. Appendix A provides additional details for the interested reader on the impact of 8 TeV data on our results, and Appendix B provides best-fit signal predictions for all signal regions of all analyses that we consider. All GAMBIT input files, generated likelihood samples and best-fit benchmarks for this paper are publicly available online through Zenodo  [70].
This problem is made worse by the necessity for some analyses of pre-selecting the signal region to use for a given parameter point, according to which of the available signal regions is expected to have the best sensitivity to the model. As the MC statistics are increased, the signal region with the best expected sensitivity to a given parameter point may change abruptly. When the level of agreement between data and background expectations differs notably between signal regions, a switch in which signal region is pre-selected can cause a large change in the likelihood assigned to the parameter point. To combat this we perform our initial scan of the full parameter space with 100 000 generated events per parameter point, and the targeted scans with 500 000 events. We then carry out a sequential post-processing of the scan results to increase the MC statistics for points within the parameter regions preferred by our fit.
A typical LHC search includes quantifying the impact of a long list of systematic uncertainties, including those related to the jet energy scale and resolution, lepton identification and reconstruction, trigger efficiency, b-tagging, MC modelling (such as the choice of renormalisation and factorisation scales, plus uncertainties related to the choice of parton distribution function), pileup modelling, and particle production cross-sections. These are often correlated across signal regions, and this must be taken into account in determining the likelihood of a SUSY model given the observed data and expected SM background contribution. In addition, for searches with non-orthogonal signal region selections, there will be a correlated number of events in overlapping regions. For most of the analyses that we use, no detailed information is provided by the experiments regarding the correlation of event numbers and uncertainties between the different signal regions (the exceptions will be discussed below).
Best practise in this case is to take the signal region expected to give the highest exclusion power for a given point in the SUSY parameter space, and use that region to calculate a likelihood contribution using the observed LHC data. In previous GAMBIT studies  [73, 87, 88] , our approach has been to select a single such "best expected" signal region across those contained in a given paper, for each point in the SUSY parameter space. However, the division of experimental results into different papers does not always make this a sensible procedure, given that several papers summarise the results of multiple analyses that are thematically similar, but actually orthogonal from the point of view of selecting events. Therefore, in this study, we instead divide the signal regions by final state, and assume that the "best expected" region in each final state can be used to obtain a likelihood contribution independently of other final states (and, of course, a final state in the ATLAS data yields an independent likelihood term from the same final state in the CMS data).
g. as discussed in  [143, 144]. However, our chains are not large enough that they would reliably contain points close to the best fits under pseudodata, because our scans concentrate around the observed best fit but are sparse in other parts of the parameter space where good fits to the pseudodata might lie. Approximate procedures can be applied in lower dimensions, e. g. [145] [146] [147] , however, they are are mainly aimed at reducing the number of pseudodata realisations that are required to perform the trial correction, which is not the issue here. Our problem is instead that obtaining sufficiently good sampling of the possible signal predictions in the EWMSSM is hard. 9 Some smaller level of look-elsewhere effect will remain due to the pre-selection of which signal regions to use for the test.
10 This shows that we find 3σ upper limits on the masses of the two lightest neutralinos and the lightest chargino. At this confidence level, the heavier neutralino and chargino masses saturate the upper limits set by the allowed range for the input parameters. Let us first assume that this pattern of excesses arises from statistical fluctuations, and that there is no production of electroweakinos (or any other sparticle) at the LHC. Under this assumption, it is interesting to determine what limits the present data from LHC direct SUSY searches put on charginos and neutralinos in the EWMSSM. A simple way to do this is to consider a capped version of our LHC likelihood, L cap = min[L LHC (s + b), L LHC (b)], ( 14 ) where L LHC is the combined likelihood from all the simulated 13 TeV SUSY searches.