dataesr/scientific-paragraphs-categorization huggingface dataset:
Decay time [ps] [rad] The systematic uncertainty on the decay time acceptance is evaluated from the difference in the decay time acceptance evaluated from B 0 s → φφ and B 0 s → D - s π + simulated events. The simulated datasets are generated with the decay time acceptance of B 0 s → φφ simulation and then fitted with the B 0 s → D - s π + decay time acceptance. This process is repeated and the resulting bias on the fitted parameters is used as an estimate of the systematic uncertainty. The uncertainty on the mass model is found by refitting the data with signal weights derived from a single Gaussian B 0 s → φφ model, rather than the nominal double Gaussian. The uncertainty due to peaking background contributions is found through the recalculation of the signal weights with peaking background contributions varied according to the statistical uncertainties on the yields of the Λ 0 b → φpK -and B 0 → φK * 0 contributions.
Fit bias arises in likelihood fits when the number of events used to determine the free parameters is not sufficient to achieve the Gaussian limit. This uncertainty is evaluated by generating and fitting simulated datasets and taking the resulting bias as the uncertainty. Uncertainties due to flavour tagging are included in the statistical uncertainty through Gaussian constraints on the calibration parameters, and amount to 10 % of the statistical uncertainty on the CP -violating phase. A summary of the systematic uncertainties is given in Table  6. 9 Triple-product asymmetries.
Likelihood In order to determine the triple-product asymmetries, a separate likelihood fit is performed. This is based around the simultaneous fitting of separate datasets to the four-kaon invariant mass, which are split according to the sign of U and V observables. Simultaneous mass fits are performed for the U and V observables separately. The set of free parameters in fits to determine the U and V observables consist of the asymmetries of the B 0 s → φφ signal and combinatoric background (A U (V ) and A B U (V ) ), along with their associated total yields (N S and N B ). The mass model is the same as that described in Sec. 3. The total PDF, S TP is then of the form S TP = i∈{+,-} f S i G S (m K + K -K + K -) + j f j i P j (m K + K -K + K -) , (16) where j indicates the sum over the background components with corresponding PDFs, P j , and G S is the double Gaussian signal PDF as described in Sec.
Results The background-subtracted distributions of the U and V observables are shown in Fig. 7  for the mass range 5246. 8 < m K + K -K + K -< 5486. 8 MeV/c 2. Distributions are found to agree between 2011 and 2012 datasets and show qualitatively symmetric distributions. The triple-product asymmetries found from the simultaneous fit described in Sec. 9. 1 are measured to be A U = -0. 003 ± 0. 017, A V = -0. 017 ± 0. 017. Statistical uncertainties are therefore to have approximately halved with respect to the previous LHCb measurements  [17] , due to more efficient selection requirements and a larger data sample, and are verified through fits to simulated datasets. No evidence for CP violation is found.
Systematic uncertainties As for the case of the decay time dependent fit, the largest contributions to the systematic uncertainty arise from the decay time and angular acceptances. Minor uncertainties also result from the mass model and peaking background knowledge. The effect of the decay time acceptance is determined through the generation of simulated samples including the decay time acceptance obtained from B 0 s → D - s π + data, and fitted with the method described in Sec. 9. 1. The resulting bias is used to assign a systematic uncertainty. The effect of the angular acceptance is evaluated by generating simulated datasets with and without the inclusion of the angular acceptance. The resulting bias found on the fit results of the triple-product asymmetries is then used as a systematic uncertainty.
Summary and conclusions Measurements of CP violation in the B 0 s → φφ decay are presented, based on the full LHCb Run 1 dataset of 3. 0 fb -1. The CP -violating phase, φ s , and CP violation parameter, |λ|, are determined to be φ s = -0. 17 ± 0. 15 (stat) ± 0. 03 (syst) rad, |λ| = 1. 04 ± 0. 07 (stat) ± 0. 03 (syst). Results are found to agree with the theoretical predictions  [1] [2] [3]. When compared with the CP -violating phase measured in B 0 s → J/ψ K + K -and B 0 s → J/ψ π + π -decays  [6] , these results show that no large CP violation is present either in B 0 s -B 0 s mixing or in the b → sss decay amplitude. The polarisation amplitudes and strong phases are measured to be |A 0 | 2 = 0. 364 ± 0. 012 (stat) ± 0. 009 (syst), |A ⊥ | 2 = 0. 305 ± 0. 013 (stat) ± 0. 005 (syst), δ 1 = 0.
METHODOLOGY In the framework of the KUN-SHEN project, 7 months of monitoring (from November 2011 to September 2012) provided offshore wind and wave, tide and surf to swash free surfaces measurements as well as topographic data of the emerged beach. Offshore wind and wave conditions were obtained from the Cigu buoy (from Taiwanese Central Weather Bureau) located in 18 m of water depth. Wave conditions from surf zone to the swash zone were respectively acquired from one RDI current profiler (2Hz acquisition frequency) deployed in 4 m of water depth and pressure sensors (5Hz of measure frequency) deployed in the subtidal zone (P3) and buried in the dune crest (P1). Each instrument is fixed to a stainless steel frame (6 m long, 10 cm of diameter and 3 mm of thickness) deeply buried vertically in the sand (figure  2 ).
Spatial resolution is maximal for areas of interest and breaking slopes (dune, cliff, sand bar, crescentic dune, washover fan). All the DEMs (figure  3b  or c), profiles (figure  3h ) and sand volume changes (DEMs differential; figure  3d ) maps were performed using Bash script and Generic Mapping Tools (GMT,  (Wessel and Smith, 1998) ). We use the Akima interpolation (based on spline functions providing smoother and much more natural curvatures,  (Akima, 1970) ) to build the DEMs. Isobaths and reference points (points number 1, 2, 3 and L) are plotted in order to improve the reading. Isobaths Z = 0 m and 1 m above MSL are boundaries of subtidal (below 0 m of elevation), intertidal and supratidal (above 1 m of elevation) zones. Because of the lack of continuous morphological marker along the barrier (like dune-foot, dune-crest or berm), we defined a shoreline at the exact location of the isobath Z = 1 m.
CONCLUSION This study focuses on the Wan-tzu-liao sand barrier morphodynamics at different time scales ranging from the typhoon event to entire typhoons and monsoon seasons. The three main objectives were, (i), to provide detailed description and quantification of hydro-morphodynamic changes under a single typhoon and a typhoons/monsoon seasons, (ii), to better understand relationships between extreme forcings and sand barrier dynamics and, (iii), to discuss these in light of existing conceptual models mainly developed from moderate storms data. Seven months of field work provided a robust dataset of hydrodynamics conditions from offshore to the swash zone coupled with a high frequency surveys of the emerged beach. Based on this dataset, we observed an accretive morphological pattern of typhoon (Talim tropical storm) and typhoons season (2012).
ACKNOWLEDGEMENTS This work was funded by the  KUN-SHEN-ANR  project  between French and Taïwanese laboratories  ( SIMI6-2011 ) and the GLADYS (www. gladys-littoral. org)/ SO LTC (www. soltc. org)research groups. We are grateful to the  Taïwanese field team of Tainan Hydraulics Laboratory  for their contribution to the field work.
Introduction This paper, one of a set associated with the 2013 release of data from the Planck 1  mission (Planck Collaboration I 2014), describes the Low Frequency Instrument (LFI) data processing that supports the first Planck cosmological release based on the nominal Planck survey (15. 5 months of observation). This paper represents an updated version of the LFI data processing description  (Zacchei et al. 2011 ) that was part of the first wave of astrophysical results published in early 2011 (Planck Collaboration VIII-XXVI 2011). This work describes the overall data flow of the pipeline implemented at the LFI DPC, from instrument scientific telemetry and housekeeping data to frequency maps, as well as the test plan applied to validate the data products. Detailed descriptions of critical aspects of the data analysis and products, including justifications for choices of algorithms used in the pipeline, are given in three companion papers: Planck Collaboration III (2014) discusses systematic effects and gives the overall error budget; Planck Collaboration IV (2014) describes determination of main beams and uncertainties from inflight planet-crossing measurements; and Planck Collaboration V (2014) describes photometric calibration, including methods and related uncertainties.
Instrument performance update Table  1  gives a top-level summary of instrument performance parameters measured in flight during the nominal data period. Optical properties have been successfully reconstructed using Jupiter transits (Planck Collaboration IV 2014), and the main parameters are in agreement with pre-launch and early estimates  (Mennella et al. 2011 ). The white noise sensitivity and parameters describing the 1/ f noise component are in line with ground measurements  (Mennella et al. 2010 ), and agree with the values in  Mennella et al. (2011). Photometric calibration based on the CMB dipole yields an overall statistical uncertainty of 0. 25 % (Planck Collaboration V 2014). Variations due to slow instrumental changes are traced by the calibration pipeline, yielding an overall uncertainty between 0.
Time ordered information (TOI) processing The Level 1 pipeline receives telemetry data as a stream of packets that are handled automatically in several steps: -uncompress the retrieved packets; -de-quantize and de-mix the uncompressed packets to retrieve the original signal in analog to digital units (ADU); -transform ADU data into volts using a conversion factor stored in the packet header; -cross-correlate time information to time stamp each sample uniquely; -store the resulting timelines in a database interface to the Level 2 pipeline. We made no change in Level 1 software during the mission. Detailed information on how each of the steps listed above was applied is provided in  Zacchei et al. (2011). To avoid strong gradients in the signal and signals that do not project correctly in the maps, we established the procedure to flag a single scientific sample described in Sect.
Input flags For each sample we define a 32-bit flag mask to identify potential inconsistencies in the data and to enable the pipeline to skip that sample or handle it differently. The TOI from all LFI detectors are archived in the Level 1 database, and regularly checked to identify and flag events that can affect the scientific analysis. These events include missing or anomalous data, and data acquired during the manoeuvres regularly performed to repoint the telescope according to the Planck scanning strategy. Table  2  summarizes the percentage of time associated with these events for the nominal mission. The table also reports the total percentage of Level 1 TOIs usable in the scientific analysis. Most of the missing data are from telemetry packets in which the arithmetic compression performed by the Science Processing Unit (SPU) is incorrect, causing a decompression error.
Corrections for electronic spikes Electronic spikes in the signal are caused by an interaction between the housekeeping electronics clock and the scientific data line in the on-board data acquisition system. The spikes are synchronous with the on-board time, with no changes in phase over the entire acquisition period, allowing the construction of dedicated templates that are then removed from the timelines. Spikes are present in all frequencies, but are significant only at 44 GHz due to the high gain of these detectors. Consequently, electronic spikes are removed only in this channel. This process and the evaluation of the effect at map level are described in Planck Collaboration III (2014).
Main beams and the geometrical calibration of the focal plane The profiles and locations of the beams are determined from the four observations of Jupiter listed in for the definitions of these angles). Only θ uv and φ uv , which are the beam pointing in spherical coordinates referred to the line of sight, can be determined with Jupiter observations. The polarization orientation of the beams, defined by ψ uv + ψ pol , is not estimated from flight data but is derived from main beam simulations based on ground measurements. For each beam, the pointing is determined by the location of the maximum of an elliptical Gaussian fit to that beam. This was done for each beam in each single scan. Results are reported, with errors, in Planck Collaboration IV (2014). In addition, the beams are stacked in pairs (J1J2 and J3J4) and all together (J1J2J3J4) in order to improve the signal-tonoise ratio of the measurements.
Before the stacking, each beam is artificially repointed along the direction given by the arithmetic average of the center of each beam to be stacked. Then a Planck Collaboration: LFI data processing fit is performed again on the stacked beams and the resulting parameters recorded. For single scans it has been found that there is an agreement within 2 ′′ in the pointing direction between J1 and J2. The same agreement occurs between J3 and J4. In contrast, a ∼ 15 ′′ systematic deviation of the beam center was detected when comparing J1J2 to J3J4. Figure  3  shows the reconstructed beam positions and errors in the line-of-sight frame magnified by a factor of 100. The shift is evident for the 70 GHz beams, as well as in all the J1J2 and J3J4 stacked beam centers. The change in the location has been found mainly in the scan direction (i.
Scanning beams Scanning beams are defined as the beams measured in flight on planets. The scanning beam derives from the optical beam coupled with the radiometer response, and smeared by the satellite motion. With four Jupiter transit measurements we were able to reconstruct the beam shape down to -20 dB from the peak at 30 and 44 GHz, and down to -25 dB at 70 GHz. From the beam shape we estimated the main beam parameters using a bivariate Gaussian fit on the four stacked beams (J1J2J3J4). The fitting procedure, described in  Zacchei et al. (2011) , was slightly mod-ified to correct for offsets in the data and to avoid noise contamination. We refer to the companion paper on LFI beams (Planck Collaboration IV 2014) for details on procedures and results. Table  6  gives the average values of the FWHM and ellipticity, with errors.
Effective beams The effective beam at a given pixel in a map of the sky is the average of all scanning beams that observed that pixel during the observing period of the map given the Planck scan strategy. We compute the effective beam at each LFI frequency scanning beam and scan history in real space using the FEBeCoP (Mitra et al. 2011) method. Details of the application of FEBeCoP to Planck data will be discussed in a future paper. Effective beams were used to calculate the effective beam window function as reported in Planck Collaboration IV (2014) and in the source detection pipeline necessary to generate the PCCS catalogue (Planck Collaboration XXVIII 2014). Table  7  lists the mean and rms variation across the sky of the main parameters computed with FEBeCoP. Note that the FWHM and ellipticity in Table  7  differ slightly from the values reported in Table  6.
Photometric calibration Conversion of time-ordered streams of voltages into timeordered streams of thermodynamic temperatures is modelled by V = G × T sky + T noise , ( 4 ) where V is the voltage measured by the ADC, T sky is obtained by convolving the sky temperature with the beam response of the instrument at a given time, and T noise is the noise temperature of the radiometer. In general, we are interested in K = G -1 , as the purpose of the calibration is to convert V back into a temperature. As described in Planck Collaboration V (2014), two different algorithms are used for calibrating the LFI radiometers in this data release: The overall accuracy in the calibration is reported in Table  1. The reasons why we used two different algorithms are discussed in Planck Collaboration V (2014).
Iterative calibration The main features of the iterative calibration algorithm used for 44 and 70 GHz are the following: 1. We combine the speed of the spacecraft with respect to the Sun, v Planck , and the speed of the Sun with respect to the CMB, v Sun. The angle between the velocity vector and the axis of the relevant beam is θ. The dipole is then evaluated considering the relativistic correction ∆T = T CMB 1 γ(1 -β cos θ) -1 , (5) where T CMB = 2. 7255 K. We produce discrete time ordered data (TOD) of the expected overall dipole signal for each sample in a pointing period. 2. Using pointing information, we project both V i and ∆T i on a HEALPix map (N side = 256). Multiple hits on the same pixels are averaged. The result is a pair of maps, V map k and ∆T map k , with k being the pixel index 2.
This allows extension of the destriping approach to shorter baseline lengths, well below the scanning period of 1 minute. The baseline length is a key parameter in the destriping technique. We chose the baseline length to be an integral number of samples near 1 s, specifically, 33, 47, and 79 samples for 30, 44, Planck Collaboration: LFI data processing and 70 GHz, respectively, corresponding to 1. 0151, 1. 0098, and 1. 0029 s. A baseline length of one second is a reasonable compromise between computational burden and the quality of the final map. Shortening the baseline below one second has very little effect on the residual noise. When mapmaking is run with a short baseline, the noise prior plays an important role. There are not enough crossing points between the one-second data sections to determine the baselines without additional constraints.
Low-resolution data set To fully exploit the information contained in the large scale structure of the microwave sky, pixel-pixel covariances are needed in the maximum likelihood estimation of the CMB power spectrum. Full covariance matrices are impossible to employ at the native map resolution, because of resource limitations. A low-resolution dataset is therefore required for the low-ℓ analysis. This dataset consists of low-resolution maps plus descriptions of residual noise present in those maps given by pixelpixel noise covariance matrices (NCVMs). At present, the lowresolution dataset can be used efficiently only at resolution N side = 16 or lower. All the low-resolution data products are produced at this target resolution. We first discuss production of the low-resolution maps, and then discuss the NCVMs.
5 s baselines. The number of idealizations in the noise-only simulations decreases from top to bottom. The first set of simulations (plotted in black) contains the same approximations that are made in the NCVM calculation. The last set of simulations (plotted in purple) corresponds to the standard mapmaking options: the horns are weighted uniformly; destriping resolution, N Destr. , is 1024; and a destriping mask is applied. their hit-count-weighted difference maps is described in more detail in  Zacchei et al. (2011)  and Planck Collaboration (2013). The use of half-ring maps in the validation of data and noise estimates will be explained in Sect. 12. In addition, the half-ring maps were an integral part of the component separation process (Planck Collaboration XII 2014) and likelihood codes (Planck Collaboration XV 2014).
Such mismatch arises from three main causes: -differences between the beam profiles of M and S; -errors in the gain calibration; -differential color corrections between M and S due to differences in their bandpasses (the "bandpass leakage effect"). These effects are described in  Leahy et al. (2010). Polarization imposes stringent requirements on the accuracy of gain calibration that have driven our choice of calibration scheme (Planck Collaboration V 2014). The control of systematics for polarization also requires precise cancellation of the M and S signals. This underlies our use of identical pointings for the data from the two radiometers in each horn, despite a small amount of beam squint between the polarizations, and also the decision to use horn-uniform weighting. Neither of these choices results in significant degradation to the total intensity maps, although they are slightly sub-optimal from the standpoint of noise.
The primary LFI channel for cosmology is 70 GHz, which has the least foreground contamination of all Planck channels. Our likelihood pipeline estimates these spectra using a conservative Galactic mask, and corrects for residual foregrounds based on the Planck 30 GHz map or WMAP maps. With our current calibration, both these spectra contain residuals at a level comparable to the expected E-mode signal from reionization. Hence, although the latter is apparently detected, we cannot be confident that the signal is real. This situation is better illustrated in Fig. 11 , where we report, at all three LFI frequencies, null-test spectra from survey-survey differences. At all frequencies, the null-test EE spectra are in good agreement with the noise level as determined by half-ring difference maps at multipoles larger of few tens; this is an indication of the data quality at these multipoles.
Null test results It is important to set pass-fail criteria for such null tests. In general, test failures reveal problems in the data or in the data analysis that must be carefully studied so that specific actions can be taken to mitigate the problems. A simple figure of merit is the actual level of noise in the data derived from half-ring difference maps. Any departure from this noise level would indicate a problem. For example, null-test power spectra are used in Planck Collaboration III (2014) to check the total level of systematic effects in the data. Figure  11  gives results at frequency level of survey-difference null tests for both TT and EE spectra, compared to the noise level derived from half-ring difference maps. Based on such results we have analyzed our calibration pipeline, particularly the treatment of sidelobes, which has been updated with the inclusion of both the intermediate beam and the in-band beam behavior based on simulations.
Half-ring test The middle panels of Figs. 8, 9 , and 10 show the noise calculated from the hit-count-weighted half-ring difference maps at N side = 1024, as described by  Zacchei et al. (2011)  and Planck Collaboration (2013). As a first quality check of the maps, and as one of the tests of the whole data processing pipeline up to the maps, we verify both numerically and visually that the values obtained by dividing the half-ring difference map pixel-by-pixel by the square root of the white noise covariance map (Planck Collaboration (2013)) are approximately Gaussian-distributed with rms near unity. The results are 1. 0211, 1. 0089, and 1. 0007 for 30, 44, and 70 GHz, respectively. The half-ring difference maps n m are a direct measure of the noise in the actual maps. Models of the noise, required for NCVMs and MC noise realizations, must be validated by comparison to the half-ring difference maps.