dataesr/scientific-paragraphs-categorization huggingface dataset:
The positron flux is measured in 12 rigidity bins from 1. 00 to 41. 9 GV. The binning is similar to that in our electron  [14]  and proton  [15]  daily flux measurements. The small corrections δ j i are estimated by comparing the efficiencies in data and Monte Carlo simulation of every selection cut using information from the detectors unrelated to that cut  [5]. The δ j i are found to have a small rigidity dependence smoothly varying from -5% at 1 GV, to -1% from 2 to 6 GV, to -5% at 41. 9 GV. The trigger efficiency ϵ j i is 100% above 3 GV, decreasing to 83% at 1 GV  [19] , and is stable over time within errors. Extensive studies were made of both the time-dependent and time-independent systematic errors. These errors include the uncertainties in background subtraction, the trigger efficiency, the geomagnetic cutoff, the small correction to the acceptance calculation (δ j i ), the unfolding, and the absolute energy scale.
We also acknowledge the continuous support from  MIT  and its  School of Science ,  Nergis Mavalvala , and the  Laboratory for Nuclear Science, Boleslaw Wyslouch. Research supported by:  Chinese Academy of Sciences, Institute of High Energy Physics, Institute of Electrical Engineering, China Academy of Space Technology, National Natural Science Foundation (NSFC) , and  Ministry of Science and Technology ,  National Key R&D Program  Grants No. 2022YFA1604802  and No. 2022YFA1604803 ,  NSFC  Grant No. 12275158 , the  China Scholarship Council , the provincial governments  of Shandong, Jiangsu, Guangdong, Shandong University , and the  Shandong Institute of Advanced Technology, China ; the  Academy of Finland , Project No. 321882 ,  Finland ;  CNRS/ IN2P3  and  CNES, France ;  DLR  under Grant No.
50OO1803  and computing support on the JARA Partition of the  RWTH Aachen  supercomputer, Germany;  INFN  and  ASI  under  ASI-INFN  Agreements No. 2019-19-HH. 0, its amendments, No. 2021-43-HH. 0, and  ASI-University of Perugia  Agreement No. 2019-2-HH. 0 , and the  Italian Ministry of University and Research (MUR)  through the program " Dipartimenti di Eccellenza 2023-2027 " (Grant  SUPER- C ),  Italy ; the  Consejo Nacional de Ciencia y Tecnología and UNAM, Mexico ;  NWO  under Grant No. 680-1-004 ,  Netherlands ;  FCT  under Grant No. CERN/FIS-PAR/0013/ 2019 ,  Portugal ;  CIEMAT ,  IAC ,  CDTI , and  MCIN-AEI.
The paper is organized as follows: Sect. 2 briefly describes SPIRou 7 and the SPIRou Legacy Survey (SLS 8 ; id P40 and P42, PI: Jean-François Donati). Section 3 describes the generic software called A PipelinE to Reduce Observations APERO 9  (v0. 7. 232, Cook et al. 2022), which is used to reduce the SPIRou observations, in particular, the spectropolarimetric analysis and the derivation of the longitudinal magnetic field. Section 4 explains the results for the stars in our sample, separating them into three categories according to their spectral type. Two examples are treated in detail, and a comparison is made with results from the literature. Finally, Sect. 5 examines our general results and further perspectives.
APERO reduction Our SPIRou data were reduced with the software APERO. APERO first performs some initial preprocessing of the 4096 × 4096 pixel images of the HAWAII 4RG TM (H4RG), applying a series of procedures to correct detector effects, remove background thermal noise, and identify bad pixels and cosmic-ray impacts. It then uses exposures of a quartz halogen lamp (flat) to calculate the position of 49 of the 50 échelle spectral orders recorded on the detector. It optimally extracts (Horne 1986) spectra of the two science channels (fibers A and B) and the simultaneous reference channel (fiber C). This APERO extraction takes the non-Gaussian shape of the instrument profile generated by the pupil slicer into account. APERO corrects the spectra for the blaze signature of the échelle orders obtained from the flat-field exposures, as described in  Cook et al.
(2022). Both a 2D order-by-order and 1 D order-merged spectrum are produced for each channel of each scientific exposure. The pixel-to-wavelength calibration is obtained from exposures of both a uranium-neon hollow cathode lamp and a Fabry-Pérot etalon, generally following the procedure given in  Hobson et al. (2021) , but with differences described in  Cook et al. (2022). We refer to Sects. 5. 4 and 6. 6 of the latter paper for details about the use of a reference night for the whole survey and updates for each survey night, with subtle differences in the wavelength calibration of fibers AB with respect to fibers A and B separately. This procedure provides wavelengths in the rest frame of the observatory, but APERO also calculates the barycentric Earth radial velocity (BERV) and the barycentric Julian date (BJD) of each exposure using the code barycorrpy 10 (Wright & Eastman 2014;  Kanodia & Wright 2018).
(1997). The two polarization states of each subexposure are combined to define the Stokes parame-10 https://github. com/shbhuk/barycorrpy ters, as explained in Sect. 10. 1 of  Cook et al. (2022)  using equations defined in  Bagnulo et al. (2009) : the Stokes I parameter measures the intensity, the Stokes V parameter measures the circular polarization, and the Stokes Q and U measure the linear polarization (not used in thiswork). Another combination defines a null measurement to quantify the quality of the Stokes V detection. The continuum for Stokes I and V can be modeled by an iterative sigma-clip algorithm to fit a polynomial to the data. The spectra are normalized to this continuum before an LSD analysis is performed. More details are given in  Martioli et al. (2020) ,  Cook et al. (2022).
Spectropolarimetry analysis We further analyzed the SPIRou polarized spectra using the code spirou-polarimetry 11  , which we applied to the telluriccorrected 2D spectra. We assumed that the APERO telluric correction is good enough so that we did not need to exclude zones of telluric water bands in this code. The Stokes I, Stokes V, and null-polarization spectra were compressed to one-line profiles using the least-squares deconvolution (LSD) method of  Donati et al. (1997). The line masks used in our LSD analysis were computed using the VALD3 catalog  (Piskunov et al. 1995; Ryabchikova et al. 2015 ) and a MARCS model atmosphere  (Gustafsson et al. 2008 ) with a grid of effective temperatures between 3000 K and 4000 K by steps of 500 K, a constant surface gravity of log g = 5. 0 dex, and a micro-turbulent velocity υ mic of 1 km s -1.
For other stars, the detection rate does not significantly change when the median-subtracted profiles are compared to the original Stokes V profiles. This is the case of PM J21463+3813, Gl 412A, PM J08402+3127, Gl 480, and GJ 1148 among the stars without a detected rotation period. We plan a more detailed study of the magnetic topology of the stars in our sample to further investigate the reasons for these nondetections. [d] µ [G] σ [G] α [G] l [d] β [G] Priors U(2, 300) U(-∞, +∞) U(0, +∞) U(0, +∞) U( A52, page 10 of 24 Table  3. Best six to four parameters fit of a quasi-periodic GP model obtained in our analysis of the stellar activity from the SPIRou B data of the mid-range M dwarfs in our sample.
Acknowledgements. Based on observations obtained at the  Canada-France-Hawaii Telescope (CFHT)  which is operated from the summit of Maunakea by the  National Research Council of Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France , and the  University of Hawaii. The observations at the  Canada-France-Hawaii Telescope  were performed with care and respect from the summit of Maunakea which is a significant cultural and historic site. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the  Gaia Multilateral Agreement.
This research has also made intensive use of the SIM-BAD database and of the VizieR catalog access tool, operated at  CDS, Strasbourg, France  and of  NASA 's Astrophysics Data System (ADS). We made an extensive use of the DACE (Data Analysis Center for Exoplanets) software from the  University of Geneva  (Díaz et al. 2014;Delisle et al. 2016). This work has made use of the VALD database, operated at  Uppsala University , the  Institute of Astronomy RAS in Moscow , and the  University of Vienna. X. D. , A. C. , P. C. Z. , E. M. and S. B. and more generally most of the French authors of this paper acknowledge funding from the  French National Research Agency (ANR)  under contract number  ANR18CE310019  (SPlaSH). This work is supported by the  ANR  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the  "Origin of Life" project of the Grenoble-Alpes University.
Time-resolved studies Figure  3  shows the time evolution of the transmission signal upon impulsive excitation of the system at ~4. 7 eV and for different soft X-ray probe photon energies and circular polarizations. All traces reveal periodic intensity modulations (5-10 % amplitude of the signal) around a mean value of the transmission. The data in panel (a) were obtained at a probe energy of 285. 7 eV and with RC polarized light. Panels (b) and (c) show time scans acquired using a probe photon energy of 285. 0 eV with LC and RC polarization, respectively. All three panels exhibit clear modulations with a sine dependence, as expected for Raman-induced processes  56. We refrain from plotting the time traces of the CD signal (difference between left and right polarized signal) because, due to the low S/N and the fact that the modes have the same phase, this would completely wash out the modulations.
This way, it is possible to estimate the expected I 1 for any given I 0 value and to calculate the IBP layer transmission shot-by-shot, as the ratio between the measured I 1 and the one calculated with the second order polynomial defined above. The procedure was repeated for every probe energy/ polarization (experimental configuration). Time traces were collected by continuously scanning the pumpprobe delay (at a velocity of 0. 5 ps/s) and recording the transmission of the soft X-ray beam. The measurements were repeated three times for each experimental configuration. The data were subsequently merged and binned in 50 fs steps; the data points displayed in the time traces are the mean values of the bin content while error bars are the standard deviations of the bin entries. Supplementary Fig.
Figure3shows the time evolution of the transmission signal upon impulsive excitation of the system at ~4. 7 eV and for different soft X-ray probe photon energies and circular polarizations. All traces reveal periodic intensity modulations (5-10 % amplitude of the signal) around a mean value of the transmission. The data in panel (a) were obtained at a probe energy of 285. 7 eV and with RC polarized light. Panels (b) and (c) show time scans acquired using a probe photon energy of 285. 0 eV with LC and RC polarization, respectively. All three panels exhibit clear modulations with a sine dependence, as expected for Raman-induced processes 56. We refrain from plotting the time traces of the CD signal (difference between left and right polarized signal) because, due to the low S/N and the fact that the modes have the same phase, this would completely wash out the modulations.
Acknowledgements M. C. acknowledges support by the  Swiss NSF  via the NCCR:MUST and by the  European Research Council  Advanced  H2020  Grant  ERCEA 695197  DYNAMOX. S. M gratefully acknowledges the support of  National Science Foundation  (Grant  CHE-1953045 ) and computational support of the  Department of Energy  ( DE-FG02-04ER15571 ). J. R. R. was supported by the  LABEX MANUTECH-SISE  ( ANR-10-LABX-0075 ) of  Université de Lyon  and by the  Fédération de Recherche André Marie Ampère (FRAMA). S. C. also thanks the research project " FSE-REACT EU " financed by  National Social Fund-National Operative Research Program and Innovation   2014-2020 (D. M. 1062/2021 ), personal Grant number  23-G-15445-3. We acknowledge  Prof. S. Lupi  and  CNR-IOM  for the use of the FTIR interferometer Thanks to  A.
2 2 Figure3shows the time evolution of the transmission signal upon impulsive excitation of the system at ~4. 7 eV and for different soft X-ray probe photon energies and circular polarizations. All traces reveal periodic intensity modulations (5-10 % amplitude of the signal) around a mean value of the transmission. The data in panel (a) were obtained at a probe energy of 285. 7 eV and with RC polarized light. Panels (b) and (c) show time scans acquired using a probe photon energy of 285. 0 eV with LC and RC polarization, respectively. All three panels exhibit clear modulations with a sine dependence, as expected for Raman-induced processes 56. We refrain from plotting the time traces of the CD signal (difference between left and right polarized signal) because, due to the low S/N and the fact that the modes have the same phase, this would completely wash out the modulations.
ACKNOWLEDGMENTS Calibration of the LIGO strain data was performed with GstLAL-based calibration software pipeline  [40]. Calibration of the Virgo strain data is performed with C-based software  [48]. Data-quality products and event-validation results were computed using the DMT  [396] , DQR  [69] , DQSEGDB  [397] , gwdetchar  [398] , hveto  [399] , iDQ  [400]  and Omicron  [59]  software packages and contributing software tools. Analyses in this catalog relied upon the LALSuite software library  [401]. The detection of the signals and subsequent significance evaluations in this catalog were performed with the GstLAL-based inspiral software pipeline  [70] [71] [72] [73] , with the MBTA pipeline  [74, 75] , and with the Py-CBC  [78] [79] [80]  and the cWB  [55, 81, 82]  packages. Estimates of the noise spectra and glitch models were obtained using BayesWave  [362, 364, 368].
Source parameter estimation was performed with the Bilby and Parallel Bilby libraries  [130] [131] [132]  using the Dynesty nested sampling package  [402] , and the RIFT library  [133] [134] [135] , with the LALInference  [361]  libraries used for initial analyses. PESummary was used to post-process and collate parameter-estimation results  [403]. The various stages of the parameter-estimation analysis were managed with the Asimov library  [404]. Plots were prepared with Matplotlib  [405] , seaborn  [406]  and GWpy  [407]. NumPy  [408]  and SciPy  [409]  were used in the preparation of the manuscript. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO 600 detector.
These candidates were cataloged in the Gravitational Candidate Event Database (GraceDB). Each entry into GraceDB is known as an event, and a collection of these within a specific time window is referred to as a superevent. The time window for CBC events was variable based on the spread of events, with a typical value of 1 s symmetric around the merger time. The duration of the time window for cWB was variable and was reported by the search pipeline for each event. One candidate event belonging to the superevent was identified as the preferred event and its attributes (time, localization, significance, classification and properties)  [116, 410, 411]  were inherited by the superevent. The HasRemnant property indicator was related to the probability of having an electromagnetic counterpart  [410] , and the p astro classifier assigned a source-category based astrophysical probability under the assumption that astrophysical and terrestrial triggers occurred as independent Poisson processes  [104, 411].
We use Bilby for inferences using the IMRPhenomXPHM  [122]  model. For events where more computationally expensive analyses were required, for example, using waveforms that include matter effects, we used the Parallel Bilby code  [131]. This employs a highly parallel distributed approach to nested sampling that can be run over a large number of processing cores, reducing the wall time of the required computation. To improve the sampling performance of Bilby and Parallel Bilby, the posterior distribution is analytically marginalized over luminosity distance  [116]  and geocenter time  [132, 647]  prior to sampling. We reconstruct posterior distributions for marginalized parameters in post-processing: for each sample, we interpolate over a one-dimensional likelihood computed at discrete points within the prior of the marginalized parameter, and draw one value from this posterior probability curve  [132, 136].
Advanced Research Networks ,  JSPS Grant-in-Aid for Scientific Research  (S)  17H06133 , the  joint research program  of the  Institute for Cosmic Ray Research, University of Tokyo ,  National Research Foundation (NRF)  and  Computing Infrastructure Project of KISTI-GSDC in Korea, Academia Sinica (AS) ,  AS Grid Center (ASGC)  and the  Ministry of Science and Technology (MoST) in Taiwan  under grants including  AS-CDA-105-M06 ,  Advanced Technology Center (ATC) of NAOJ , and  Mechanical Engineering Center of KEK. We would like to thank all of the essential workers who put their health at risk during the COVID-19 pandemic, without whom we would not have been able to complete this work.
A two-level trigger system is used, with a hardware low level trigger, L0, and a software high level trigger, L1  [17, 18]. Auxiliary trigger lines are operated concurrently with the main trigger line that is dedicated to the K + → π + ν ν decay. This analysis uses the non-µ and the control trigger lines described below. • The non-µ line requires signals in the RICH and CHOD detectors at L0, compatible with the presence of at least one charged particle in the final state, and no signal in the MUV3 detector in time coincidence. A signal in the KTAG detector is required at L1, compatible with the presence of a charged kaon. In a fraction of the dataset, a track reconstructed by the STRAW is also required at L1. This trigger line is downscaled by a factor of 200. • The control line requires the presence of signal in the NA48-CHOD detector at L0, compatible with the presence of a charged particle in the final state.
The radiative photon contributes to the photon veto simulation in the normalization case, but not in the signal case where it is included in the signal event reconstruction. Additional radiative photons are not generated in the simulated samples, leading to a systematic underestimation of the acceptance ratios A Ke3 /A Ke3γ j. However, special signal and normalization MC samples with multiple radiative photons are generated using the PHOTOS program  [21] , providing a less accurate description of the radiative effects than the standard MC samples and therefore used only to evaluate the photon veto effects. The resulting correction factors f PV j are reported in table  3. The acceptance ratios A Ke3 /A Ke3γ j (table  2 ) must be multiplied by f PV j. The uncertainties in f PV j include MC statistical uncertainties, variations of the simulated LAV and SAV efficiencies when replaced by the measured energy-dependent values (evaluated with data using photons from π 0 → γγ decays)  [16] , and the variation of the LKr veto efficiency with the LKr response tuning.
T-asymmetry measurement The T-asymmetry A ξ is measured using the K e3γ samples selected for the R j measurements. The distributions of the ξ observable are shown in figure  4 , for data and simulation. For each selected signal sample, a raw asymmetry measurement A Data ξ is obtained using eq. (1. 2). An offset A MC ξ , possibly introduced by the detector and the selection, is measured by comparing the reconstructed and generated asymmetry values in the K e3γ simulated sample. The generated asymmetry value is checked to be zero within a O(10 -4 ) precision in the three K e3γ kinematic regions considered, in agreement with the ChPT O(p 6 ) calculations  [11]. The final measurement is obtained as A NA62 ξ = A Data ξ -A MC ξ.
Cosmic rays are mostly composed by protons accelerated to relativistic speeds. When those protons encounter interstellar material, they produce neutral pions which in turn decay into gamma rays. This offers a compelling way to identify the acceleration sites of protons. A characteristic hadronic spectrum was detected in the gamma-ray spectra of four Supernova Remnants (SNRs), IC 443, W44, W49B, and W51C, with the Fermi Large Area Telescope. This detection provided direct evidence that cosmic-ray protons are (re-)accelerated in SNRs. In this review, we present the results from a comprehensive search for low energy spectral breaks. We use 8 years of data from the Fermi Large Area Telescope between 50 MeV and 1 GeV. This search is based on the 4FGL catalog from which we extracted the unidentified sources or those associated to SNRs with a significance above 3 sigma at low energy in both cases.
This signature of protons was detected in five SNRs interacting with molecular clouds (MCs) and detected at gamma-ray energies by Fermi-LAT: IC 443 and W44  [2, 3] , W49B  [4] , W51C  [5] , and HB 21  [6] , although in this last source both the leptonic and hadronic processes are able to reproduce the γ-ray emission. Here, we use 8 years of Pass 8 data to analyse 311 Galactic sources detected in the 4FGL catalog and search for significant spectral breaks between 50 MeV and 1 GeV. The analysis and the results are fully described in  [7].
Analysis of the Fermi-LAT observations The Fermi-LAT is a γ-ray telescope which detects photons with energies from 20 MeV to more than 500 GeV by conversion into electron-positron pairs, as described in  [8]. For this analysis, we used exactly the same dataset as that used to derive the 4FGL catalog of sources, namely 8 years (2008 August 4 to 2016 August 2) of Pass 8 SOURCE class photons with the Instrument Response Functions P8R3_SOURCE_V3. The data reduction and exposure calculations are performed using the LAT f ermitools version 1. 2. 23 and f ermipy  [9]  version 0. 19. 0. We accounted for the effect of energy dispersion by using edisp_bins1 = -3. We performed a summed likelihood binned analysis with three logarithmically spaced energy bins between 50 MeV and 100 MeV including only PSF3 events, and 9 energy bins between 100 MeV and 1 GeV including PSF3 and PSF2 events.
Acknowledgments The  Fermi-LAT Collaboration  acknowledges support for LAT development, operation and data analysis from  NASA  and  DOE (United States) ,  CEA/Irfu  and  IN2P3/CNRS (France) ,  ASI  and  INFN (Italy) ,  MEXT ,  KEK , and  JAXA (Japan) , and the  K. A. Wallenberg Foundation , the  Swedish Research Council  and the  National Space Board (Sweden). Science analysis support in the operations phase from  INAF (Italy)  and  CNES (France)  is also gratefully acknowledged. This work performed in part under  DOE  Contract  DE-AC02-76SF00515. MLG acknowledges support from  Agence Nationale de la Recherche  (grant  ANR-17-CE31-0014 ).