dataesr/scientific-paragraphs-categorization huggingface dataset:
Caused by irrecoverable radiation damages, the total number of telegraphic pixels is expected to increase during mission lifetime. The CCD will be regularly monitored and an updated list of bad pixels will be issued and serve as an entry for DRP. The pipeline then notifies after each observation its own detections from the signal in the subarray window. The location of the subarray will be chosen to avoid hot/dead pixel in the aperture. Simple approaches like sigma clipping to search for outliers in pixel flux time series is not relevant for CHEOPS because of the specific features of its data such as: i) the noise is not stationary due the permanent rotation of the image and ii) at pixel level the noise is dominated by the jitter noise, especially for the peaks of the PSF near the center of the target.
The CR energy is indeed not large enough to stand out among the PSF pixels flux. The 6-σ detection threshold is represented by the vertical line in the lower histogram of residuals. All pixels above this threshold are flagged as CR. The evolution of the light curve of two pixels through the CR detection module is shown in Fig. 11. Once the bad pixel detection is performed, the DRP proceeds to the correction of the pixels hit by CR. This correction is done with a 2D cubic interpolation of neighbor pixels using the Python routine interpolate. griddata of the Scipy library. Hot (dead) pixels are positive (negative) spatial outliers imprinted, in this case, on the temporal average of the residual images. They are detected via a spatial sigma clipping. A threshold as high as 30-σ is necessary to avoid flagging pixels influenced by the peaks of the target's PSF.
Background The zodiacal light, non resolved background objects, stray light from Earth and Moon inject a non constant flux offset over the CCD. This background flux depends primarily on the orbital phase and on the pointing of the telescope. In particular for CHEOPS, the background correction module plays an important role because of the satellite's proximity to the Earth. The classical approach of background estimation based on selected background windows gives poor results for CHEOPS since the displacement of the stars due to the rotation of the image obliges to move them continuously, probing thus not all the time the same pixels and flux distribution. These changes translate into discontinuities in the estimated background time series which is ultimately imprinted in the light curve of the target by the correction.
Image simulations The pipeline builds up simulated images of the whole visit because it needs to estimate smear trails (Sect. 5. 1) and contamination from the resolved nearby stars. The DRP internal simulator starts by making use of the World Coordinate System (WCS) of each exposure (see Sect. 7. 3) jointly with the sky coordinates and the CHEOPS magnitudes of the stars extracted from an input catalogue. This catalogue is built for each observation by extracting from the Gaia DR2 catalogue  (Evans et al. 2018 ) the sky coordinates, the CHEOPS magnitude (obtained from the V-or G-band conversion) and the T e f f of each star in the field of view, and it is provided to the DRP as an input file associated to each observation. The internal simulator then uses this information to spread a reference PSF over the CCD coordinates of the stars with the flux scaled according to their CHEOPS magnitude, resulting in the expected simulated data set.
Performance Two datasets were prepared using CheopSim simulator (Futyan et al. subm. ) to illustrate the performance of the DRP and compare it with the CHEOPS science requirements in terms of photometric precision. The first simulation (case 1 hereafter) represents the observation of a transit of an Earth-size planet orbiting a V-mag=6 (G0V) star with a period of 50 days. The second simulation (case 2 hereafter) corresponds to the observation of a faint star (V-mag=12) with a transit of a Neptune-size planet in a 13 days orbit. CHEOPS photometric requirements for these science cases are 20 ppm in 6 hours of integration time for case 1; and 85 ppm in 3 hours of integration time for case 2. Both simulations were built using an intermediate contamination environment by setting the appropriate options of Cheop-Sim to medium (namely 1673 background stars in the field and ∼ 2 e -pix -1 s -1 of stray light) and taking into account the intrinsic stellar noise of its host star.
Conclusions The CHEOPS data reduction pipeline in its pre-launch version has been presented in this paper with a detailed description of the core processing stages of the calibration, correction and photometry modules. The particularities of CHEOPS data and their treatments by the pipeline have also been discussed. In addition, two representative examples of scientific cases for CHEOPS have been used to evaluate the expected performance of the pipeline. For each case, the achieved photometric precision is given at different time scales. These examples show that the results of the DRP are fully compliant with the scientific requirements of the mission. Even for challenging observations, such as for faint targets (e. g. case 2 in Sect. 8) the light curve derived by the DRP is not far from ideal results.
DRP generates various output products the user will retrieve from the archive: four light curves calculated with different aperture sizes, each with its contamination curve and associated uncertainties. The user will also get the report automatically gen- erated by the pipeline which intends to allow the user to follow what treatment has been done on the data, the quality of the processing and of the final result. In addition to these final products, the user will have the possibility to get additional by-products of the processing, such as, for example, bad pixel maps or the background light curve. After the launch, the pipeline will be tuned and adapted to real in-flight data: algorithms and modules will be improved all along the mission lifetime with our increasing knowledge and understanding of the instrument to allow the best characterization of the transiting planets CHEOPS will observe.
Acknowledgements. The DRP team thanks our referee,  F. Claus , for his very careful reading of the paper and his valuable comments and suggestions which help us to improve the quality of the manuscript. We also thank  D. Futyan  for his valuable support on Cheopsim. We gratefully acknowledge the SOC team and the  CHEOPS  science team members who evaluated the pipeline results, for their constant support in the realization of the CHEOPS pipeline and their helpful suggestions which allowed significant improvements of key reduction steps. We thank  K. Isaak  for her careful reading and valuable feedback on the manuscript. We thank also  A. Cameron  and  D. Queloz  for providing comments that improved the paper. The team at LAM acknowledges CNES funding for the development of the CHEOPS DRP, including grants  124378  for O.
This approach takes full advantage of the complete coverage of these distributions thanks to the large boost of the final state opposite to the high-energy ISR photon detected at large angles. The new analysis will also use full BABAR statistics, double that of the previous analysis. This doubling, together with the increase in efficiency from no longer using particle ID with strong cuts, will produce a final data sample for analysis seven times that of the previous BABAR study. The new and previous results will have a negligible statistical correlation and largely different systematic uncertainties. Work on collecting larger samples of e + e -annihilation data aimed at obtaining much more precise hadronic cross sections is going on in two directions-scan (CMD-3 and SND at VEPP-2000) and ISR (BESIII at BEPCII and Belle II at SuperKEKB) measurements.
Finally, a scan of the CM energy range from 4. 7 GeV to 7. 0 GeV has been recently performed with the KEDR detector at the VEPP-4M e + e -collider in Novosibirsk  [333]. Data were taken at 17 energy points with an approximate step of 150 MeV. The goal is to measure R with an accuracy of about 3%. The ISR program of the BESIII collaboration focuses so far on the three most important channels e + e -→ π + π -, e + e -→ π + π -π 0 , and e + e -→ π + π -π 0 π 0. All investigations are performed on 2. 93 fb -1 of data taken at √ s = 3. 773 GeV  [334]. The analysis of the pion form factor is considered as the flagship project of the ISR program at BESIII. The first measurement, in which sub-percent accuracy has been achieved, is published  [73]. The total uncertainty is dominated by the contributions of the theoretical uncertainty of the radiator function and the uncertainty of the luminosity measurement, each of which is 0.
|F CMD-3 preliminary Figure  37 : Results on the e + e -→ π + π -cross section at SND (left, reprinted from Ref. [171] ) and on the squared pion form factor at CMD-3 (right, preliminary, adapted from Ref. [332] ) measured in energy scans at VEPP-2000. pion form factor measurement. Alternatively, a different normalization scheme can be used, in which the two dominating uncertainties cancel. The normalization to the muon yield is also used in the most precise ISR measurements of the π + π -cross section by the KLOE  [82]  and BABAR  [60]  collaborations. However, based on the data evaluated at BESIII, the final result in this approach is limited by the statistics of the muon yield. The BESIII collaboration is considering acquiring additional 20 fb -1 at √ s = 3. 773 GeV  [335]. A combination of these data and already recorded data sets should provide statistics sufficient to achieve a final accuracy of 0.
The investigation of e + e -→ π + π -π 0  [336]  combines two analysis strategies, involving either explicitly detecting the ISR photon or reconstructing it from the missing four-momentum. In this way, masses of the three-pion system covering the range from the peak of the ω resonance up to the J/ψ resonance peak can be studied from the 2. 93 fb -1 of data at √ s = 3. 773 GeV. As a spin-off, the branching fractions of the narrow resonances are determined. The left panel of Fig. 38  illustrates the BESIII result in comparison with existing ISR and energy scan measurements. The systematic uncertainty of the cross section is found to be 2% at the narrow resonances and 3% in the remaining regions. The dominating background contribution to ISR production in the three-pion channel comes from the fourpion production with two neutral pions, which is studied separately.
5 GeV. With the additional future data mentioned above, this accuracy will also be achieved at smaller masses. In the Belle II experiment the Bhabha tagging at the level-one (L1) trigger was significantly improved by using accurate determination of trigger cell clusters in the trigger electronics. In the L1 trigger there are several independent trigger modes, charged and neutral, to provide a careful measurement and monitoring of the trigger efficiency from  the experimental information during data taking. In spring-summer of 2018 the first physics run was performed in the Belle II experiment. Among other processes, a first look at ISR events was carried out. The invariant mass of two charged particles assuming each to have the pion mass is shown in Fig. 39  in comparison with MC simulations performed with the PHOKHARA code  [347].
It was demonstrated that the trigger efficiency for ISR events was better than 90%. In the second Belle II physics run performed in March-July of 2019, about 5 fb -1 of integrated luminosity was collected. The data analysis is ongoing. It focuses on the π + π -and π + π -π + π -channels with the goal of providing a careful check of the data and their comparison with simulation. Plans for the future suggest extending these studies to other final states, aiming at a determination of the total cross sections for most of the hadronic channels when the collected luminosity achieves a few hundred fb -1. With 50 ab -1 of data planned at Belle II, one hopes to collect an order of magnitude higher data samples of various hadronic final states. Together with new independent measurements at BESIII, two detectors at VEPP-2000, as well as with those already existing at BABAR and KLOE, this should significantly improve the input, and hence also the precision, of the data-driven HVP determination.
Let us consider first the π + π -channel. We will assume that in a few years there will be five new independent data samples (BABAR, Belle II, BESIII, CMD-3, and SND), all giving close results. If each measurement has an uncertainty of 0. 5% dominated by systematic effects, an optimistic average of these data sets results in a π + π --channel uncertainty of the LO hadronic contribution of the order of 1 × 10 -10 (0. 2%). One should also take into account additional uncertainties due to higher-order radiative corrections  [202] , at the level of 0. 2% fully correlated between the data. Thus the total uncertainty due to the π + π -channel is estimated to be 1. 5 × 10 -10. If for other exclusive channels we assume that each of the measurements achieves a 2% systematic uncertainty, the total uncertainty of their contribution is 1.
Acknowledgements We are very grateful to the  Fermilab Directorate  and the  Fermilab Theoretical Physics Department  for their financial and logistical support of the first workshop of the Muon g -2  Theory Initiative (held near Fermilab in June 2017 ) [123], which was crucial for its success, and indeed for the successful start of the Initiative. Financial support for this workshop was also provided by the  Fermilab   Distinguished Scholars program , the  Universities Research Association  through a  URA Visiting Scholar award , the  Riken Brookhaven Research Center , and the  Japan Society for the Promotion of Science  under Grant Number  KAKEHNHI-17H02906. We thank  Shoji Hashimoto ,  Toru Iijima ,  Takashi Kaneko , and  Shohei Nishida  for hosting the the HVP workshop at KEK [124] and the  KEK Theory Center  and the  U.
This workshop was supported in part by the  U. S. Department of Energy, Office of Science , under Award Numbers  DE-FG02-00ER41132 ,  DE-SC0020106 , and by the  U. S. -Japan Science and Technology Cooperation Program in High Energy Physics. This review benefited from discussions with  O. Catà ,  N. Christ ,  L. Y. Dai ,  H. Davoudiasl ,  S. Fayer ,  S. Ganguly ,  A. Gasparian ,  S. Hashimoto ,  T. Iijima ,  K. Kampf ,  D. Kawall ,  I. Larin ,  Z. Pagel ,  M. Petschlies ,  A. Rebhan ,  K. Schilcher ,  K. Shimomura ,  E. Shintani ,  D. Steffen ,  S. Tracz ,  C. Tu , and  T. Yamazaki. The work in this paper was supported by  CNRS , by  Conacyt  ( Ciencia Básica  2015) under Grant Number  250628 , by  CONACyT-Mexico  under Grant Number  CB2014-22117 , by  Coordinación de la Investigación Científica (CIC-UMSNH)  under Grant Number  4.
10 , by  Danmarks Frie Forskningsfond  under Grant Number  8021-00122B , by  Deutsche Forschungsgemeinschaft Collaborative Research Centers CRC   1044 ,  CRC   1044 -204404729 ,  CRC 110 , and under Grant Numbers  HI 2048/1-1 ,  Prisma Cluster  for Excellence PRISMA +  EXC2118/1 ,  STO/876/6-1 , by  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  under Grant Agreement Numbers 668679, 757646, 771971-SIMDAMA,  813942 , by the  European Union   H2020-MSCA-COFUND2016  under Grant Number  754510 , by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement number  843134 , by  European Union Euro-PLEx  Grant  H2020-MSCA-ITN-2018-813942 , by  European Union   STRONG 2020 project  under Grant Agreement Number  824093 , by the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" program , through the  Chaire d'Excellence program  and the  OCEVU Laboratoire d'Excellence  ( ANR-11-LABX-0060 ), by the  Fermilab Distinguished Scholars program , by  Fondo SEP-Cinvestav  under Grant Number  142 , Fundacão para a Ciência e a Tecnologia under Grant Number  SFRH/BPD/109443/2015 , by  Generalitat de Catalunya  under Grant Number  2017SGR1069 , by the  Helmholtz Association (German Federal Ministry of Education and Research) , by the  Helmholtz-Institut Mainz , by the  Istituto Nazionale di Fisica Nucleare (INFN) , by the  Isaac Newton Trust , by the  Japan Society for the Promotion of Science  under Grant Numbers  KAKENHI-15H05742 ,  16K05317 ,  16K05323 ,  16K05338 ,  17H01133 ,  17H02906 ,  18H05226 ,  19K21872 ,  20K03926 ,  20K03960 , by  Junta de Andalucía  under Grant Number  A-FQM-467-UGR18 , by  KEK , by  Ministerio de Ciencia e Innovacion  under Grant Number  CICYTFEDER-FPA2017-86989-P , by  Ministerio de Industria, Economía y Competitividad  under Grant Numbers  FPA2016-78220-C3-3-P, FPA2017-86989-P ,  PGC2018-094857-B-I00 ,  SEV-2016-0588 ,  SEV-2016-0597 , by  Laboratoires d'Excellence FIRST-TF  grants, a  French "Investissements d'Avenir" program , by the  Ministry of Science and Higher Education of the Russian Federation  under Grant Agreement Number  14.
W03. 31. 0026 , by the  National Research Foundation of South Africa , by the  Natural Sciences and Engineering Research Council of Canada , by the  Portuguese Science Foundation (FCT)   Investigator Grant   IF/00898/2015 , by the  Romanian Ministry of Education and Research  under Grant Number  PN19060101 , by the  Russian Science Foundation  under Grant Number  RSF 18-12-00128 , by the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya  under Grant Number  2017 SGR 1069 , by the  Swedish Research Council  under Grant Numbers  2016-05996 ,  2019-03779 , by the  Swiss National Science Foundation  under Grant Numbers  PP00P2 176884 ,  PCEFP2 181117 , by The  Leverhulme Trust  under Grant Number  ECF-2019-223 , by the  UK Science and Technology Facilities Council (STFC)  under Grant Numbers  ST/N504130/1 ,  ST/P000290/1 ,  ST/P00055X/1 ,  ST/P000630/1 ,  ST/P000711/1 ,  ST/P000746/1 ,  ST/S000879/1 ,  ST/S000925/1 , by the  U.
S. Department of Energy, Office of Science, Office of High Energy Physics  under Award Numbers  DE-SC0009919 ,  DE-SC0009998 ,  DE-SC0010005 ,  DE-SC0010120 ,  DE-SC0010339 ,  DE-SC0012391 ,  DE-SC0012704 ,  DE-SC0013682 ,  DE-SC0013895 ,  DE-SC0015655 , by the  U. S. Department of Energy, Office of Science, Office of Nuclear Physics  under Award Numbers  DE-AC02-05CH11231 ,  DE-FG02-00ER41132 ,  DE-FG02-97ER41020 , by the  U. S. -Japan Science and Technology Cooperation Program in High Energy Physics, "Incubation Platform for Intensity Frontier ," by the  U. S. National Science Foundation  under Grant Numbers  NSF-PHY-1316222 ,  PHY14-14614 ,  PHY17-19626 , and  PHY19-23131 , and by the  U. S. National Institute of Standards and Technlology (NIST)   Precision Measurement Grant Program  under Award Number  60NANB16D271.
Proteome Data Analysis The MS/MS spectra were filtered for high confident peptides with strict FDR (1%), with enhanced peptide and protein annotations using the software Proteome Discoverer v2. 3 (Thermo Fisher). Spectra with peptide sequences less than 6 residues were removed. The search parameters allowed for 0. 5 Da mass tolerance and 2 missed cleavage sites. The following modifications were included: modification of Met Oxidation ± 15. 99492 D, Lys Acetyl ± 42. 01057 D, Ser, Thr, Tyr Phospho ± 79. 966333 D, N-Terminus Formyl ± 27. 99492 Da, Pyro-Glu ± 17. 02655 Da, N-Terminus Acetyl ± 42. 01057 Da. The identified peptides were then searched against an aphid proteome database compiled from every aphid genome available on NCBI and AphidBase (Pea aphid, Russian wheat aphid, soybean aphid (Aphis glycines), bird cherry-oat aphid (Rhopalosiphum padi), green peach aphid (Myzus persicae), and black cherry aphid (Myzus cerasi) and other aphid proteins deposited in NCBI in 2017.
These other proteins included six-frame translations of a cowpea aphid transcriptome and the transcriptome of the potato aphid). The 13,330 PSMs identified corresponded to 2,119 proteins and were further filtered to 721 protein group hits. Only high confidence (99%) were considered further filtering the protein groups to 521 protein groups. Spectra that came up when filtering out possible contaminants with a FASTA file containing common contaminants. To accept proteins, they needed to have at least 3 peptides in at least 2 of the 3 replicates (CID, HCD, ETD). The raw peptide spectra were deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) repository with the proteome ID: PXD017323.
Annotation The MS/MS identified proteins were annotated with BLASTP using OmicsBox (V 1. 1. 135 Hotfix) and the NCBI nonredundant protein database with the taxonomy filter for aphids, Aphidomorpha (3380) (e-value = 1e-3)  (Gotz et al. , 2008). The proteins were then subjected to BLASTP to the pea aphid annotation v2. 1b proteins on Aphidbase to identify the corresponding ACYPI homologs (BIPAA, 2017). Gene ontology (GO) was determined for molecular function, biological process, and cellular component using InterProScan (v5. 36-75. 0) (Gotz et al. , 2008; Jones et al. , 2014). The identified proteins were screened with SignalP (V3. 0 and V5. 0) and SecretomeP 1. 0 using eukaryote and mammalian filters, respectively, and by TMHMM V2. 0  (Krogh et al. , 2001; Bendtsen et al. , 2004a,b; Armenteros et al.
Statistical Analyses We used generalized linear models (GLM) with a likelihood ratio and chi-square test to assess whether AcDCXR expression had an effect on aphid survival and fecundity. Data on aphid survival were analyzed with GLM following a binomial distribution and data on aphid fecundity were assumed to follow a Poisson distribution. The fit of all generalized linear models was checked by inspecting residuals and QQ plots. Methylglyoxal levels in plants were analyzed using a nested ANOVA (biological replicates treated as random factor) (package R: 'nlme'). When a significant effect was detected, a pairwise comparison using multiple comparisons of the means (package R: 'multcomp') (Tukey contrasts, p-values adjustment with 'fdr' method) at the 0. 05 significance level was used to test for differences between days after infestation.
Aphid Salivary Proteome Analyses and Annotation To identify the protein composition of the cowpea aphid saliva, aphid saliva was collected in parafilm feeding pouches containing water. The contents of the pouches were concentrated and subjected to proteome analyses. The peptides identified by LC-MS/MS were searched against a custom aphid protein database. The database was composed of proteomes based on all aphid genomes available in the summer of 2017, as well as cowpea aphid-specific transcriptome and a transcriptome from the potato aphid, both with six-frame translations. Around 175 candidate proteins were identified with at least three peptides from at least two replicates and having at least one unique peptide (Supplementary Table  1 ). The identified proteins were then annotated using BLASTP with OmicsBox (TaxID: Aphididiae 27482).
Among these annotated proteins, 18/175 (10. 29%) were uncharacterized. In addition, functional redundancies were recorded among the proteins with annotations. To eliminate these redundancies, the proteins were subjected to BLASTP on AphidBase to identify their corresponding ACYPI homolog using the pea aphid protein database annotation v2. 1b. Among these proteins, 47/175 (26. 86%) shared one of 21 ACYPI top hits. Although these 47 proteins had at least one unique peptide, we grouped them as 21 proteins, resulting in a total of 149 salivary proteins (Supplementary Table  1 ). Annotation of these proteins presented a wide range of functional attributes to the cowpea aphid salivary proteins. Among the 149 identified proteins, 33 proteins with similar functional annotations have been previously reported in the saliva of a cowpea aphid population from Gabon, Africa  (Loudit et al.