dataesr/scientific-paragraphs-categorization huggingface dataset:
In that case we use the Planck temperature and polarization power spectra  [46] , the JLA SNe dataset  [206]  and a set of isotropic and anisotropic BAO data (see Section 3. 3. 1 of  [51]  for details). To generate the catalogs discussed in section 4 we assume a fiducial cosmological model, that we always take to be ΛCDM with H 0 = 67. 64 and Ω M = 0. 3087, which are the fiducial values obtained from the CMB+BAO+SNe dataset that we use. So, in particular, our fiducial values for the extra parameters in the DE sector are w 0 = -1 and Ξ 0 = 1, and we use ΛCDM and standard perturbation theory within GR as fiducial theoretical framework for treating and analyzing our dataset. Our aim is to evaluate to what accuracy LISA, alone or in combination with other datasets, can reconstruct these fiducial values.
Thus, depending on the specific realization of the catalog, the contours in parameter space of the likelihood obtained from standard sirens could show mild tensions with those obtained from other datasets. This is unavoidable, and will also happen in the actual experimental situation. Thus, in the plots shown below, the overlap (or lack of it) between different contours has little meaning, as it depends on the specific random realization of the catalog. What carries the important information is the relative size of the contours, that will tell us to what extent the addition of standard sirens to other datasets can improve our knowledge of the cosmological parameters. However, when combining contours from different datasets to obtain a combined estimate on the error on a parameter, one must be careful not to use a specific realization where the separate contours are in mild tension among each other.
14  shows the results for the scenario  (2). In particular in the most favorable scenario ("hnd" seeds and optimistic errors on d L ), from the corresponding one-dimensional marginalized likelihood we find that, with standard sirens only, the relative error on H 0 is ∆H 0 H 0 = 3. 8% , (5. 2) (which raises to 7. 7% in the "realistic" scenario with "hnd" seeds) and the one on Ω M is ∆Ω M /Ω M = 14. 7%; using our CMB+BAO+SNe dataset we get instead ∆H 0 /H 0 = 0. 7% and ∆Ω M /Ω M = 2. 1%; combining CMB+BAO+SNe+standard sirens we get ∆H 0 H 0 = 0. 7% , ∆Ω M Ω M = 2. 0%. (5. 3) Therefore, from MBH binaries at LISA, we do not find a significant improvement on the accuracy on H 0 , compared to current results from CMB+BAO+SNe. It should however be observed that the measurement from standard sirens is still useful because it has completely different systematic errors from that obtained with CMB, BAO and SNe.
However, as for H 0 , one should be aware of all the assumptions and uncertainties that entered in the construction of the source catalogs, and that could significantly alter the results. In particular these estimates can either be improved by combining the cosmological data collected with SMBHBs with the ones collected from other LISA sources, such as EMRIs and SOBHBs, or by extending the observational period of LISA. In the first case the different redshift ranges where EMRIs and SOBHBs are expected to be observed, will help breaking some degeneracies in the cosmological parameters, such as for example the degeneracy between H 0 and Ω M in ΛCDM. In the second case instead the estimated errors are expected to improve roughly as ∼ √ N or better, where N is the number of SMBHB merger with EM counterpart observed by LISA, which linearly depends on the observational period.
Under this hypothesis, it was found in  [16]  that, by combining ET with the same CMB+BAO+SNe dataset that we are using in this paper, and extending the dark energy sector through the parameters Ξ 0 and w 0 (and fixing n = 2. 5, as in this paper), at ET one can get the accuracy ∆Ξ 0 = 0. 008 and ∆w 0 = 0. 032. Comparing with the results for LISA in Table  2  we see that the accuracy at ET would be slightly better than that in the 'hnd' scenario for LISA, although quite comparable. However, this comparison relies heavily on the assumption of having 10 3 sources with counterpart, which at the present stage is only a reasonable working hypothesis. 16 One should also observe that ET and LISA observations are complementary because most of the ET events are at redshift z < ∼ 1, while, as we see from Fig.
Acknowledgments It is a pleasure to thank  Chiara Caprini  for useful discussions, the LISA Cosmology Working Group Chairs - Robert Caldwell ,  Chiara Caprini ,  Germano Nardini  -for their input and support during the project, and  Vuk Mandic  and  Alberto Sesana  for carefully reading our manuscript and for their useful suggestions. The work of E. B, S. F. and M. M. is supported by the  Fonds National Suisse  and by the  SwissMap National Center for Competence in Research. The MCMCs have been run on the Baobab cluster of the  University of Geneva. This project has received funding (to E. Barausse) from the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation programme  (grant agreement no. GRAMS-815673 ; project title " GRavity from Astrophysical to Microscopic Scales ").
This work was supported by the  H2020-MSCA-RISE-2015  Grant No. StronGrHEP-690904. N. B. , D. B. , A. G and S. M. acknowledge financial support by  ASI  Grant  2016-24-H. 0. G. C. is supported by the  I+D  grant  FIS2017-86497-C2-2-P  of the  Spanish Ministry of Science, Innovation and Universities. M. C. is supported by the  Labex   P2IO  and the Enhanced Eurotalents Fellowship. C. D. and L. L. were supported by a  Swiss National Science Foundation   Professorship grant  (No. 170547 ). J. M. E. is supported by the  Spanish FPU  Grant No. FPU14/01618 , the Research Project  FPA2015-68048-03-3P  ( MINECO-FEDER)  and the  Centro de Excelencia Severo Ochoa Program   SEV-2016-0597. M. R. F. acknowledges support from  STFC  grant  ST/N000668/1. J. G. B. acknowledges support from the  Research  Project  FPA2015-68048-03-3P [MINECO-FEDER]  and the  Centro de Excelencia Severo Ochoa Program   SEV-2016-0597.
All pipelines assume that, while the gravitational wave signal dwells in the detector sensitive band, the spins of the compact objects are aligned or antialigned with the orbital angular momentum, and that orbital eccentricity is negligible. Additional details of the PyCBC Live and GstLAL banks can be found in Dal Canton & Harry 2017 and  Mukherjee et al. 2018, respectively. In the case of PyCBC Live, the online and final offline analyses covered exactly the same space. For GstLAL, the offline bank extended to a larger total mass of 400M. A matched-filtering analysis is performed by each pipeline, producing triggers for each detector's data stream whenever the matched-filter single-detector signal to noise ratio (SNR) peaks above a threshold given in Table  1. Due to the small probability of a signal being detectable in Virgo and not in LIGO during O2, PyCBC Live did not use Virgo to produce triggers; Virgo's data was nevertheless still analyzed and used for the sky localization of candidates from LIGO.
Matched filtering alone is insufficient in non-Gaussian detector noise, producing frequent non-astrophysical triggers with large SNR  (Abbott et al. 2016f). Pipelines can choose among different techniques to mitigate this effect: calculating additional statistics based on the template waveform (signal-based vetoes), explicitly zeroing out loud and short instrumental transients before matched-filtering (gating), and vetoing triggers based on known data-quality issues which are reported with the same latency as the strain data itself. In O2, all matched-filter searches employed signal-based vetoes; PyCBC Live and MBTAOnline applied vetoes based on low-latency data-quality information, while GstLAL applied gating. The trigger lists produced by matched filtering and cleaned via the aforementioned procedures are searched for coincidences between detectors.
PyCBC Live's ranking of coincident triggers in O2 was somewhat simpler than the final offline analysis: it did not account for the variation of background over the parameter space  (Nitz et al. 2017 ) and it did not include the sine-Gaussian signal-based veto  (Nitz 2018). Py-CBC Live estimated the background of accidental coincidences by using time shifts between triggers from different detectors, as done by the offline analysis (Usman et al. 2016). The amount of live time used for background estimation in PyCBC Live was 5 hours, to be compared with ∼5 days of the offline analysis. This choice limited the inverse false-alarm rate of online detections to ∼100 years maximum, insufficient for claiming a very significant detection, but adequate for generating rapid alerts for astronomers. On the other hand, this choice gave the background estimation a faster response to variations in noise characteristics, which is useful considering the limited data quality flags available to the online analysis.
Online unmodeled searches The two unmodeled signal searches (burst), cWB and oLIB, work by looking for excess power in the timefrequency (TF) domain of the GW strain data  (Klimenko et al. 2016; Lynch et al. 2017). The cWB pipeline does this by creating TF maps at multiple resolutions across the GW detector network and identifying clusters of TF data samples with power above the baseline detector noise. Excess power clusters in different detectors that overlap in time and frequency indicate the presence of a GW event. The signal waveforms and the source sky location are reconstructed with the maximum likelihood method by maximizing over all possible timeof-flight delays in the detector network. The cWB detec-tion statistic is based on the coherent energy obtained by cross-correlating the signal waveforms reconstructed in the detectors.
GraceDb and LVAlert The Gravitational-wave Candidate Event Database (GraceDb 3 ) is a centralized hub for aggregating and disseminating information about candidate events from GW searches. It features a web interface for displaying event information in a human-friendly format, as well as a representational state transfer application program interface (RESTful API) for programmatic interaction with the service. A Python-based client code package is also maintained to facilitate interactions with the API; this set of tools allows users to add new candidate events to the database, annotate existing events, search for events, upload files, and more. During O2, GraceDb sent push notifications about candidate event creation and annotation to registered listeners via the LIGO/Virgo Alert System (LVAlert), a real-time messaging service based on the Extensible Messaging and Presence Protocol (XMPP) and its publish-subscribe (pubsub) extension.
Supervised follow-up process Several follow-up processes responded to the entry of a GW candidate into GraceDb, notified by the arrival of an LVAlert message. Three of these processes were of immediate relevance to the EM follow-up effort: the low-latency sky localization probability map (skymap) generator for CBC triggers, BAYESTAR  (Singer & Price 2016) , the tracker of candidate event status/incoming information and alert generator/sender (approval processorMP ), and the tracker of other followup processing (eventSupervisor ). In particular, approval processorMP was responsible for the decision to send alerts based on the following incoming state information: basic trigger properties from the pipelines (FAR, event time, detectors involved with the trigger), data quality and data products (sections 2.
2. 3 and 3. 3. 2), detector operator and advocate signoffs determining the result of human vetting (section 2. 2. 4), and other labels identifying time-correlated external triggers or signal injections performed in hardware at the sites. Hardware signal injections are simulated GW signals created by physically displacing the detectors' test masses  (Biwer et al. 2017). Triggers with false alarm rates below an agreed-upon FAR threshold and with no injection or data quality veto labels generated alerts to the astronomers involved in the LIGO and Virgo EM follow-up community via the GCN network (see section 3. 2) and GraceDb web services.
These vetoes were defined using sensors that measured the behavior of the instruments and their environment. This data quality information was applied in several steps. A set of data quality vetoes were generated in real time and provided to the low-latency pipelines alongside the detector state information. If a candidate GW event occurred during a time that had been vetoed, it was not reported for EM followup. Given that these vetoes could potentially prevent a true GW signal from being distributed, this category of data quality information was reserved for severe noise sources. In parallel with this effort, low-latency algorithms searched LIGO data for correlations between witness sensors and the GW strain data to identify noise sources that might not have been included in defined data quality vetoes.
) did not happen at the time of the GW trigger and that the interferometer status was nominal. Second, the experts and on-duty advocates met during an on-call validation process organized immediately after being notified of the trigger. All previously mentioned algorithms in section 2. 2. 3 and additional data quality information not accessible at low-latency timescales were considered. For example, the Omega scan and Omicron scan algorithms  (Chatterji et al. 2004; Robinet 2016 ) created time-frequency visualizations of witness sensor data around the time of the candidate event. This allowed for detailed views of instrumental or environmental noise that could potentially influence the detectors' GW strain data. In O2, this information was used to identify false triggers due to noise and veto them before they were reported for EM follow-up.
e All skymaps excluding those using Virgo data round sky localization areas to the nearest 10; otherwise, to the nearest 1. 1. 0 2. 83 10. 0 100. 0 been generated simply by noise at a rate higher than once per two months was rejected for the EM follow-up program. The FAR estimation is specific to the pipeline that triggered the event (see section 2. 1 and Table  3  for distributed alerts and the associated FARs). In Table  2 , the eight triggers which were not confirmed as confident events were reported by five different pipelines. This is consistent with the expected number of false alarms from five pipelines in 118 days of coincident data from LIGO-Hanford,  LIGO-Livingston, and Virgo (Abbott et al. 2018a ). m1[M ] 1. 0 2. 83 10. 0 m2[M ] M c = 1. 5 M M c = 3. 5 M M c = 6. 0 M χ B H = 0.
Three-Detector Observations The Virgo contribution to O2 is noteworthy in significantly improving the localizations of the events GW170809, GW170814 and GW170817. 8 As an example, for GW170814, the 50% sky localization area is confined in a single region of tens of square degrees in the southern hemisphere. We note that Virgo data was used to produce updated skymaps of GW170823  (LIGO Scientific Collaboration & Virgo Collaboration 2017x,d)  soon after identification of the signal. Subsequent data validation studies identified problems with the Virgo data around GW170823 which made it unreliable for use in parameter estimation: the final sky localization relies only on the LIGO data. A note should be made regarding the initial and refined skymaps of GW170814  (Abbott et al. 2017b,d) : it is expected that the initial and updated skymaps for compact binary coalescence events are similar unless there are significant changes in data calibration, data quality or glitch treatment or low-latency parameter estimations.
The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also grate-fully acknowledge research support from these agencies as well as by the  Council of Scientific and Industrial Research of India , the  Department of Science and Technology, India , the  Science & Engineering Research Board (SERB), India , the  Ministry of Human Resource Development, India , the  Spanish Agencia Estatal de Investigación , the  Vicepresidència i Conselleria d'Innovació ,  Recerca i Turisme  and the  Conselleria d'Educació i Universitat del Govern de les Illes Balears , the  Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana , the  National Science Centre of Poland , the  Swiss National Science Foundation (SNSF) , the  Russian Foundation for Basic Research , the  Russian Science Foundation , the  European Commission , the  European Regional Development Funds (ERDF) , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , the  Hungarian Scientific Research Fund (OTKA) , the  Lyon Institute of Origins (LIO) , the  Paris Île-de-France Region , the  National Research, Development and Innovation Office Hungary (NKFIH) , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  Natural Science and Engineering Research Council Canada , the  Canadian Institute for Advanced Research , the  Brazilian Ministry of Science, Technology, Innovations , and Communications, the  International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR) , the  Research Grants Council of Hong Kong , the  National Natural Science Foundation of China (NSFC) , the  Leverhulme Trust , the  Research Corporation , the  Ministry of Science and Technology (MOST), Taiwan  and the  Kavli Foundation.
Acknowledgements C. L. , F. M. , C. B. , T. S. and J. M. D. acknowledge support from the  French Investissements d' Avenir programme , project  ISITE-BFC  (contract  ANR-15-IDEX-0003 ) and the project  EUR  ( ANR-17-EURE-0002 ). G. G. acknowledges support from the  Academy of Finland  (grants  298463  and  318082 ).
Acknowledgments The work of JE was supported in part by the  United Kingdom STFC  Grant  ST/P000258/1 , and in part by the  Estonian Research Council  via a  Mobilitas Pluss grant. The work of SH was supported in part by the  MEINCOP (Spain)  under contract  FPA2016-78022-P , in part by the  Spanish Agencia Estatal de Investigación (AEI) , in part by the  EU Fondo Europeo de Desarrollo Regional (FEDER)  through the project  FPA2016-78645-P , in part by the " Spanish Red Consolider MultiDark "  FPA2017-90566-REDC , and in part by the  AEI  through the grant  IFT Centro de Excelencia Severo Ochoa   SEV-2016-0597. The work of KAO was supported in part by  DOE  grant  DE-SC0011842  at the  University of Minnesota. The work of SP was supported by the  ANR  grant " HiggsAutomator " ( ANR-15-CE31-0002 ).
INTRODUCTION The CHARMed PyMca protocol, which is described in detail in Part I of this article  (Heginbotham and Solé 2017) , is designed for users of energy-dispersive X-ray fluorescence spectrometry (ED-XRF) who wish to maximize interlaboratory reproducibility of quantitative results for the wide range of copper alloys found in heritage materials. By maximizing reproducibility and providing robust error estimations, this protocol should facilitate collaboration and allow for reliable comparison of data collected by different instruments. The name of the protocol refers to its two essential components, namely: PyMca fundamental parameters software  (Solé et al. 2007 ) used in conjunction with the so-called copper CHARM (Cultural Heritage Alloy Reference Materials) set of certified reference standards  (Heginbotham et al.
2015). The primary purpose of this second article is to provide a detailed characterization of the interlaboratory reproducibility that is to be expected if the CHARMed PyMca protocol is followed for ED-XRF analysis of heritage copper alloys. It thus reports the detailed statistical results of the ASTM standard interlaboratory reproducibility study introduced in Part I. This study used nine ED-XRF data sets generated by seven different instrument types, in five laboratories, all using the CHARMed PyMca quantification protocol. The results provide, for any measurement made according to the protocol, a robust confidence interval within which measurements from other laboratories can be expected to fall. Importantly, because the reproducibility study used reference materials of known composition, the results can also show that the protocol yields accurate quantitative measurements and that the defined reproducibility interval is also a robust estimate of the protocol's real precision.
In the case of this study, the lead author instructed all the co-authors in the use of both PyMca and the calibration software, and personally conducted the analysis using four of the nine different instruments. As a result, the results from the nine instruments in the most recent reproducibility study are not strictly independent and it is possible that analysts with less experience using PyMca and preparing calibrations might experience less favourable results than those presented below. However, it is hoped that the detailed description of the method presented in Part 1 of this article, along with the supplementary material provided here, provide sufficient information for any diligent analyst to achieve comparable results.
Element suite A very significant benefit of the CHARMed PyMca protocol evident in these results is that all instruments were able to report consistently results for a common suite of 15 elements. This is a significant improvement over the 2010 baseline study in which only 15 of 19 respondents were able to report consistently on the suite of eight elements shown in Table  4  and only five elements (Cu, Sn, Zn, Pb and Fe) were reported consistently in all 19 submitted data sets. Participants in the 2010 study often reported that they were unable to manipulate their quantification software to include easily elements that were not originally included in the manufacturer's default configuration. Furthermore, those participants were often unaware of the range of concentrations covered by the calibration standards used for their instrument.
Lower limits Many of the participants in the 2010 baseline study reported a significant number of results as 'b. d. l. ' (below detection limit), 'n. d. ' (not detected), 'tr. ' (trace) or some other non-quantitative result based on an unfavourable relationship to some often unspecified lower limit, for example, LOQ (limit of quantification), LOD (limit of detection, or sometimes limit of determination) or CV (critical value). Follow-up discussions with the analysts confirmed that in many cases where On average, the CHARMed PyMca protocol resulted in a reduction in the method calculated lower limit of 56% and the reduction in the mean per cent relative reproducibility of 76%. proprietary software was being used, it was difficult or impossible to access the actual quantitative result that lay behind the qualitative result given.
In contrast, the instruments used for the CHARMed PyMca testing were all able to report quantitative results for every element and every sample, regardless of the relationship of the result to any lower limit. This is appropriate when the objective of the data collection is to build a database of reference observations, or to compare results with an existing database. From the point of view of collaborative data sharing, lower limits are of limited and there is a strong argument to be made that quantitative results should always be reported, whether or not they are below any particular lower limit. From an analytical point of view, the reporting of results as 'below detection limit', 'not detected', etc. results in needless information loss  (Currie 1999; Burgess 2000; Analytical Methods Committee 2001).