dataesr/scientific-paragraphs-categorization huggingface dataset:
338 m from the sample in the forward direction. In the diffraction geometry, using the parallel-beam configuration, a 2 µm pinhole carried by the 2D piezo motors was placed at 2. 2 mm downstream the sample in the diffracted beam direction and used for scanning. The smaller, 2 µm pinhole was used for appropriate sampling for ptychography at 2. 171 m. The data was acquired with the Eiger 500K detector, placed at 2. 171 m along the diffracted beam direction. Samples were rocked around the (2 0 2) diffraction peak in a range of ±0. 02 • with an angular step of 0. 002 •. Ptychography scans were performed with a step size of 0. 5 µm and 0. 2 s exposure time following a Fermat spiral pattern  [2].
ACKNOWLEDGMENTS We acknowledge the  Paul Scherrer Institute, Villigen, Switzerland  for provision of synchrotron radiation beamtime at the cSAXS beamline of the SLS. This work was supported by the  SNF  grant No  200021L 169753 ,  ANR-16-CE93-0006 , by " Investissement d'Avenir" (LABEX INTERACTIFS ,  ANR-11-LABX-0017-01 ) and by  Nouvelle Aquitaine Region / European Structural and Investment Funds (ERDF  No  P-2016-BAFE-94/95 ). M. V. was supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sk lodowska-Curie  grant agreement No  701647. K. W. acknowledges the support by the  SNF  grant No 200021  166304. Authors thank  M. Guizar-Sicairos ,  P. O. Renault ,  T. Sadat ,  D. Le Bolloc 'h,  B. Kedjar  and  F. Mignerot  for fruitful discussions.
Binary neutron star mergers offer a new and independent means of measuring the Hubble constant H0 by combining the gravitational-wave inferred source luminosity distance with its redshift obtained from electromagnetic follow-up. This method is limited by the intrinsic degeneracy between the system distance and orbital inclination in the gravitationalwave signal. Observing the afterglow counterpart to a merger can further constrain the inclination angle, allowing this degeneracy to be partially lifted and improving the measurement of H0. In the case of the binary neutron star merger GW170817, afterglow light-curve and imaging modeling thus allowed the H0 measurement to be improved by a factor of three. However, systematic access to afterglow data is far from guaranteed. In fact, though each one allows a leap in H0 precision, these afterglow counterparts should prove rare in forthcoming multimessenger campaigns.
Selection effects in multimessenger cosmology In Appendix A, we make estimates of the selection effects impacting the measurements of H 0 with GW and electromagnetic data. In the circumstances of measuring H 0 with data from GW170817, that is, a Level 3 scenario during the O2 run, the selection effects are less than 2. 0% over the 60-80 km/s/Mpc range, according to below the 14% precision claimed by studies making this measurement  (Hotokezaka et al. 2019). We conclude that no selection effects significantly impacted the measurement of H 0 with the GW170817 data. However, the selection effect is not negligible compared to the 4% precision required to resolve the H 0 tension  (Freedman 2017; Feeney et al. 2018) , particularly with events detected in O3-and O4-type runs, where selection effects reach 2% and 4% in the 60-80 km/s/Mpc range, respectively.
Conclusion The afterglow counterparts of binary neutron star mergers represent viable means to measure the inclination angle of sources, and thereby to improve the standard-siren measurement of the Hubble constant. Afterglows could therefore play the role of narrowing down H 0 and possibly resolve the Hubble tension with fewer events than by leveraging only the gravitational-wave data and source redshift. To quantify how much faster afterglow-enhanced H 0 measurements could solve the Hubble tension, we carried out a realistic population model considering that every future afterglow counterpart could provide a constraint on the source inclination angle at the same level as GW170817. We found that, while each afterglow allows for a jump in H 0 precision, events with afterglow counterparts should prove very rare, to the point that allowing for afterglow-enhanced measurements should not statistically make any difference in the number of events required.
Acknowledgments We thank  H. Chen  for useful comments during the development of this work. RD acknowledges the  Centre National d'Études Spatiales (CNES)  for financial support in this research project. SM is supported by the  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ), of the  European Gravitational Observatory , and the  Paris Center for Cosmological Physics.
From each spectrum at the potential V i, the previously recorded spectrum at the potential V ref is subtracted. The difference spectra are then decomposed by PCA into independent spectral components (PCs), based on their distinct response to the potential increase/decrease. The changes in the total spectrum are then attributed to voltage-dependent changes of the respective partial spectra. One spectral component for the negative potential series and two spectral components for the positive one are sufficient to describe the voltage dependent changes in the spectral dataset. MD Simulations. Classical simulations were performed using the LAMMPS (50) code to simulate 17 aqueous solutions  (17, 486  water molecules) of 20 mM NaCl confined between parallel charged Au(100) walls with imposed 298 K constant temperature to water and ions all along the simulation.
This can explain why a waterrelated spectral component is not observed in the experiments for the negative potential series. Electronic Structure Calculations. All density functional calculations for the cluster models were carried out with the Q-Chem electronic structure package  (53). We applied the RPBE functional (54) for consistency with the periodic slab calculations and the ωB97X-V functional (55) for comparison. The density functional theory calculations for the periodic slab models were carried out on the Au(100) surface using the Vienna Ab Initio Simulation Package  (56, 57). The surface unit cell (periodically extending in the x and y directions) was designed as four layers of eight Au atoms, with a lattice constant of 4. 0782 Å and 20 Å spacing (in the z direction) between two images.
High quality surface tension data obtained from a standard tensiometer are available in the literature, so the period as a function of the surface tension was plotted for all the studied concentrations at different injection rates. The behavior was completely linear, confirming our hypothesis (Fig. 5 ). Then we used just two pure compounds with extreme surface tension values (water for highest and ethanol or cyclohexane for lowest) as a reference and got the scaling factor to transform periods into surface tension values. The results are in excellent agreement with those obtained using standard surface tension instruments. The advantages of using ITC are clear: (i) isothermal calorimeters provide much more information than a standard tensiometer,; (ii) the required amount of sample is very low and it can be recovered at the end of the experiment since the injection of air do not contaminate it; (iii) the implementation of an additional injection system would allow changing the concentration of the sample cell with no need to open and clean it.
Acknowledgements We would like to gratefully thank the help and support from  Mike Brandts , from  Malvern/Microcal , for the information provided on the basic properties of VP-ITC instruments, as well as from  Malin Suurkuusk  and  Peter Vikegard , from  Waters/TA Instruments , for allowing us to use the calorimeters available in their lab. The authors thank the financial support of the  Spanish Ministry of Economy and Competitiveness  (projects MAT2015-71826-P to Á. P. and  BFU2016-78232-P  to A. V. C. ), the  Agencia Estatal de Investigación (AEI)  (project  PID2019-111327GB-I00  to Á. P. ) and Fundação para a Ciência e Tecnologia (FCT), Portugal, for  CIQUP  (project  UIDB/00081/2020  to M. B. ). P. F. G. thanks the  Spanish Ministry of Economy and Competitiveness  and the  European Social Fund  for his predoctoral research grant, reference  BES-2016-076761.
Acknowledgments Thomas Basile ,  Kevin Morand  and  Paolo Saracco  are thanked for useful discussions. The author also thank the  Erwin Schrödinger International Institute for Mathematics and Physics (Vienna)  for hospitality during the thematic programme " Geometry for Higher Spin Gravity: Conformal Structures, PDEs, and Q-manifolds " (August 23rd -September 17th, 2021) during which the final version of this work was completed.
Overview of the Solar Eruption The event on 2014 November 5 was associated with a GOES M7. 9 flare, a filament eruption, and a CME originating from active region NOAA AR 12205 at location N20E68. The flare start, peak, and end times were 09:26, 09:47, and 09:55 UT, respectively, as reported in the online NOAA daily solar event lists and the GOES X-ray light curves from LMSAL. 7 The flare was also accompanied by a slow CME that was observed by the SOHO/LASCO coronagraph with a central position angle (PA) of 87°, a projected line-of-sight speed of 386 km s -1 , and a width of 187°, as reported in the Coordinated Data Analysis Workshops (CDAW) online catalog. 8.
Most importantly, we find that this condition will favor the efficient electron beam acceleration by the shock and the production of type II radio emission. BRAIN-be project SWiM (Solar Wind Modeling with EUHFORIA for the new heliospheric missions). R. V. acknowledges the financial support of the Academy of Finland (projects 309939 and 312357). We thank the RSDB service at LESIA/ USN (Observatoire de Paris) for making the NRH/ORFEES/ NDA data available and the radio monitoring service at LESIA (Observatoire de Paris) for providing value-added data that have been used for this study. We are grateful to Nancay Radioheliograph and Nancay Decameter Array operated by the Observatoire de Paris and funded by the French research agency CNRS/INSU and the ORFEES radio spectrograph (Observations Radio pour Fedome et lEtude des Eruptions Solaires), which is the result of a partnership between the Paris Observatory and the Air Force French, for giving access to the data.
The IRAP team acknowledges support from the  French space agency (Centre National des Etudes Spatiales ; CNES; https://cnes. fr/fr)that funds activity in plasma physics data center ( Centre de Données de la Physique des Plasmas ; CDPP; http://cdpp. eu/)and the  Multi Experiment Data & Operation Center (MEDOC) , the  Solar-Terrestrial Observations and Modelling Service ; (STORMS;https://storms-service. irap. omp. eu). This includes funding for the data mining tools AMDA (http://amda. cdpp. eu/),CLWEB (clweb. cesr. fr/),and the propagation tool (http://propagationtool. cdpp. eu). A. K. acknowledges financial support from the  ANR   COROSHOCK  project ( ANR-17-CE31-0006-01 ). The work of A. W. was supported by  DLR  under grant No. 50 QL 1701. I. C. J. was supported by a PhD grant awarded by the  Royal Observatory of Belgium.
We quantify the calibration requirements for systematic uncertainties for nextgeneration ground-based observatories targeting the large-angle B-mode polarization of the Cosmic Microwave Background, with a focus on the Simons Observatory (SO). We explore uncertainties on gain calibration, bandpass center frequencies, and polarization angles, including the frequency variation of the latter across the bandpass. We find that gain calibration and bandpass center frequencies must be known to percent levels or less to avoid biases on the tensor-to-scalar ratio r on the order of ∆r ∼ 10 -3 , in line with previous findings. Polarization angles must be calibrated to the level of a few tenths of a degree, while their frequency variation between the edges of the band must be known to O(10) degrees. Given the tightness of these calibration requirements, we explore the level to which residual uncertainties on these systematics would affect the final constraints on r if included in the data model and marginalized over.
002 1  , SO will need to tackle both instrument and analysis challenges. This paper presents a comprehensive study of the calibration requirements associated with bandpass and polarization angle systematics in SO. Using a multi-frequency power-spectrum-based analysis pipeline, we try to answer two questions for an SO-like dataset: 1. To what level do these systematics need to be calibrated so as not to significantly bias the final constraints on r if the residual systematic effects are ignored? 2. How much do the final r constraints degrade if the residual systematics are modeled and marginalized over? The results found through our analysis are (summarized in Table  7 ): 1. Gains and bandpass central frequencies must be calibrated to the 0. 5% level, polarization angles must be calibrated to 0.
In general, we use wide top-hat priors on the CMB and foreground parameters such that the data provides all the constraining power on parameters. The priors on systematic parameters were chosen to correspond to previously achieved numbers. The next two sections describe the models used to describe the different sky components and the instrumental systematic effects, and list the fiducial parameter values used in our calculations. We generate a set of power spectra using those fiducial parameters and use them as a data vector when exploring the posterior parameter distribution. The different contributions to our sky and instrument model are described in Figure  1. When simply maximizing the posterior we use Powell's minimization method  [29]  as implemented in scipy 3   [30]. To explore the full posterior distribution we use a (affine invariant ensemble sampler) Markov Chain Monte Carlo approach, as implemented in the emcee package 4   [31].
We used the chain autocorrelation length, Gelman-Rubin statistic, and manual inspection of the chains to ensure convergence. All chains have at least 10,000 times the autocorrelation length in samples. The Gelman-Rubin statistic is enforced to be less than 1. 1, and is generally much smaller than that (<1. 01). We estimate the chain uncertainty by slicing the chains into sub-chains and calculating the standard deviation across sub-chains of the parameter standard deviations within each sub-chain, scaled by the inverse square root of the number of samples in each sub-chain. We find the chain standard deviation of parameter uncertainties to be around 4% for most cases and less than 10% for all cases, depending on the chain length and model dimension. In all cases we use a smooth data vector with no statistical noise as input to the likelihood for simplicity.
The BICEP2/Keck Array data To test the validity of the main results of this analysis on a more realistic setup, we have repeated our analysis on the latest public data from the BICEP2/Keck Array collaboration, presented in  [13]  (BK15X henceforth). The aim of this exercise is threefold: first, it allows us to test our analysis pipeline, verifying that it is able to accurately model realistic levels of foreground contamination. Secondly, we can test the validity of our results on noisy data (recall that our analysis so far has been carried out on mock power spectra with no statistical noise). Finally, doing so allows us to quantify the robustness of the published BK15X results to the presence of residual bandpass/angle systematics. A complete description of the BK15X dataset can be found in  [13]  and references therein.
The data combines information from 12 different frequency bands, including the three BI-CEP2/Keck Array bands (at 95, 150 and 220 GHz), 2 low-frequency WMAP bands (23 and 33 GHz)  [66]  and 7 Planck bands  (30, 44, 70, 100, 143, 217 and 353 GHz)   [67]. Power spectra were computed over the 400 deg 2 BICEP2/Keck Array patch, and all cross-correlations between different frequency and polarization bands are made publicly available together with their covariance matrix as well as all frequency bandpass transmission curves and bandpower window functions  11. Power spectra are computed in 9 bandpowers covering the multipole range 30 ≤ 400. The size of the complete data vector, including all BB, EE and EB cross-correlations is 2700 elements, which reduces to 702 if only B-mode data is used. We first attempt to reproduce the fiducial BK15X results by running our components separation pipeline on the B-mode data with all systematic parameters fixed to zero, and all parameter priors fixed to the same values used by BK15X.
We increased the foreground power spectral index priors from [-1, 0] to  [-4, 1]  to match our SO analysis priors. We observe that the resulting posteriors shift and broaden slightly, especially when allowing for decorrelation, as reported in BK15X. We then explored the impact of bandpass/angle systematics on the BK15X data. While the BK15X data come from several instruments with designs that are different from the SO SAT, bandpass and average polarization angle systematic uncertainties are common to all experiments. We first allowed for changes in the center frequencies and gains of the 12 frequency bands, corresponding to 24 new parameters (∆ν b , ∆g b , b ∈  [1, 12] ), on which we impose a 3% Gaussian prior, motivated by the WMAP calibration uncertainties. The results, in this case analysing only BB data, are shown in orange in Figure  6 , together with our fiducial results.
These parameters are taken from Table  2  by identifying the systematic parameter ∆φ 0 with ∆ψ. The transparent green band indicates the spread expected for f 3dB of the SO detectors. In Table  5 , we summarize the data from Figure  9  and those for the other SO detector bandpasses by computing the median value of the angle shifts measured within the green band for all frequencies in the column labeled "∆ψ fixed". The spread in this column is the average of the differences between the median and the 16th and 84th percentiles, except for the 225 ν b (GHz) ∆ψ fixed ( • ) ∆ψ average spread (  For each band (left column), we show the median and typical spread of ∆ψ values falling within the green band in Fig. 9  (second column). For 225 GHz, we indicate the difference between the median and the 16th and 84th percentiles.
This would then remove any bias on r at the cost of potentially increasing its uncertainty. We find, however, that in most cases, assuming reasonable priors on the systematic parameters, the degradation of σ r from marginalization is at the level of 10%. This is significantly smaller than the degradation expected from additional foreground complexity (at the level of ∼ 30% when accounting for foreground frequency decorrelation). This will allow us to relax the stringent calibration requirements found by our analysis of the induced bias on r. The SO data will be able to self-calibrate the polarization angle in each frequency by constraining the parity-violating EB correlations. We find that this is the case, even in the presence of foreground EB components, which would have to be at least half as large as the BB amplitude in order to induce a significant bias on r.
Our findings have been useful in guiding the design of the SO SATs. We have described the decisions made in the context of 3-versus 5-layered HWPs (where the latter induce an almost frequency-independent polarization angle at the cost of additional mechanical risk) and the impact of detector time constants on the polarization angle uncertainty. These results have also been used to define a calibration strategy for bandpasses and polarization angles in SO. A number of caveats in our analysis must be noted. Our results are based on a multifrequency C -based component separation pipeline. This has been used by BICEP2/Keck Array to derive the current state-of-the-art constraints on B-modes, but may suffer from certain shortcomings when applied to data with higher sensitivity over a wider sky patch.
Acknowledgments MHA acknowledges support from the  Beecroft Trust and Dennis Sciama Junior Research Fellowship at Wolfson College. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 693024 ). DA acknowledges support from the  Beecroft Trust , and from the  Science and Technology Facilities Council  through an  Ernest Rutherford Fellowship , grant reference  ST/P004474/1. The SISSA group acknowledges support from the  ASI-COSMOS network  (www. cosmosnet. it) and the  INDARK INFN Initiative  (web. infn. it/CSN4/IS/Linea5/InDark). EC acknowledges support from the  STFC   Ernest Rutherford Fellowship   ST/M004856/2  and  STFC  Consolidated Grant  ST/S00033X/1 , and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No.
849169 ). JC acknowledges support from a  SNSF   Eccellenza Professorial Fellowship  (No. 186879 ). YC acknowledges the support from the  JSPS KAKENHI  Grant Number  18K13558 ,  18H04347 ,  19H00674. This work was supported by the  ERC Consolidator Grant CMBSPEC  (No. 725456 ) as part of the  European Union 's  Horizon 2020 research and innovation program  and the  Royal Society  grant No. URF\R\191023. JE was supported by the  French National Research Agency (ANR)  grants,  ANR - B3DCMB , ( ANR-17-CE23-0002 ), and  ANR-BxB  ( ANR-17-CE31-0022 ). HN acknowledges the support from the  JSPS KAKENHI  Grant Numbers  JP17K18785  and  JP18H01240. ZX is supported by the  Gordon and Betty Moore Foundation. This work was supported in part by a grant from the  Simons Foundation  (Award # 457687 , B.
Aperture corrections based on either curve of growth analysis for sources in the field (EN1 and LH) or the analytic point spread function (PSF; Boötes) have then been calculated to provide total flux estimates in each filter. For all three fields we make use of 3 apertures for optical to NIR bands and 4 apertures for IRAC (due to the lower resolution in the IRAC imaging). As described in Paper III, the galactic extinction (E(B-V)) for each source has been calculated based on the Milky Way extinction map of  Schlegel et al. (1998) , queried using the dustmaps Python package  (Green 2018). Filter-dependent extinction factors are then calculated by convolving the respective filter response curves with the Milky Way dust extinction law of  Fitzpatrick (1999). In Figure  1  we visually summarise the wavelength coverage and sensitivity of the resulting photometry catalogues used for the photo-z analysis in this paper.
In addition to the processing steps above that are presented in Paper III, for the photo-z estimation and SED fitting work in this paper we include further processing as follows. Due to the combination of large areas, the number of unique bands and depth of the photometry, small numbers of spurious datapoints are inevitable. Therefore, as a final additional step before photo-z estimation, we automatically filter the photometric datasets for spurious datapoints. Specifically, we filter for unphysical colours or extreme datapoints that are likely to be caused by artefacts such as stellar diffraction spikes, cosmic rays, crosstalk etc. Measurements that lie > 2. 5 mag above or < 1 mag below those at both adjacent filters at shorter and longer wavelengths, indicative of extreme excess or deficit, are excluded.