dataesr/scientific-paragraphs-categorization huggingface dataset:
Most CW searches based on summing strain spectral power from SFTs, including the PowerFlux program used here, apply weightings that disfavor SFTs with high noise. Because the average time interval between loud glitches in O3a data is comparable to or smaller than the coherence time of the SFTs, however, inverse noise weighting of SFTs proved much less effective than in previous runs, especially for the 7200s SFTs. To mitigate the effects of these glitches on O3a CW searches for signals below 475 Hz, a simple glitch-gating algorithm was applied  [34, 35]  to excise the transients from the data. For each 1/16-second for which a whitened version of the H1 and L1 strain data channels had excess RMS power in the 25-50 Hz or 70-110 Hz bands, the strain channel was set to zero. To reduce artifacts from discontinuous data, a 1/4-second half-Hann-window ramping from unity to zero was multiplied against the data stream prior to each zeroed interval, and a 1/4-second half-Hann window ramping from zero back to unity was multiplied at the end of zeroed intervals.
As discussed in section III, the PowerFlux program searches many narrow frequency bands, where the SFT coherence time chosen depends on frequency. In general, longer coherence times give improved sensitivity, but incur larger computational costs from the need to search more finely in parameter space. In addition, efficiency loss from spectral leakage due to Doppler modulations from the Earth's motion increases at higher frequency, especially for sources near the ecliptic plane. Given these tradeoffs, the same coherence-time choices made for the O1 analysis  [8]  are chosen here, as shown in Table  I. The SFTs are created from the C01 calibrated strain data  [36] , using Hann windowing and 50% overlap. Upper limits on the calibration uncertainties over the  Hz band in the O3a epoch are estimated to be < 7% in magnitude and < 4 deg in phase (68% confidence interval).
III. ANALYSIS METHOD This search uses the PowerFlux program  [3, 5, 7, 8]  with loose-coherence follow-up of outliers  [9, 10]. In brief, strain power is summed over many SFTs after correcting for Doppler modulations, for a large bank of templates based on sky location, frequency, frequency derivative and stellar orientation. The maximum strain powers detected over the entire sky and for all frequency derivatives in each narrow frequency sub-band (see Table  II  for the frequency-dependent widths) define strict frequentist upper limits. Initial outliers, defined by high values of SNR, are followed up in a multi-stage search with increasing levels of effective coherence at each stage to increase the expected SNR for true signals. Candidates passing all stages are examined individually to determine their credibility as gravitational wave sources, with further warranted follow-up carried out in the full O3 data set.
The equatorial ellipticity is a convenient, dimensionless measure of stellar non-axisymmetry: ≡ I xx -I yy I zz. (4) The phase evolution of the signal is given in the reference frame of the Solar System barycenter (SSB) by the second-order approximation: Φ(t) = 2π f source • (t -t 0 ) + f (1) • (t -t 0 ) 2 /2 + φ , (5) where f source is the SSB source frequency, f (1) is the first frequency derivative (which, when negative, is termed the spin-down), t is the SSB time, and the initial phase φ is computed relative to reference time t 0. When expressed as a function of the local time of ground-based detectors, Eq. 5 acquires sky-position-dependent Doppler shift terms. We search a frequency band 20-2000 Hz and a frequency derivative ḟ range of [-1 × 10 -8 , +1 × 10 -9 ] Hz s -1. Figure  1  shows this parameter space coverage together with those of previous all-sky searches of the LIGO O2 data using different methods.
As the initial stage 0 sums only powers, it does not use the relative phase between interferometers, which results in some degeneracy among sky position, frequency, and spin-down. The first loose-coherence follow-up stage (1) demands greater temporal coherence (smaller δ) within each interferometer, which should boost the SNR of viable outliers, but combines H1 and L1 power sums incoherently, The subsequent stage (2) uses combined H1 and L1 data coherently, providing tighter bounds on outlier location. Testing of the stages 0-2 pipeline is performed for frequency bands searched via software injections using the same follow-up procedure. The recovery criteria also require that an outlier close to the true injection location (within 2. 5 mHz in frequency f , 3 × 10 -10 Hz/s in spin-down and 28.
Any outlier surviving this full-O3 follow-up is explored via a more sensitive method, implemented in PyFstat  [37, 38]  which uses Markov Chain Monte Carlo (MCMC) techniques  [39, 40]  to explore small regions of parameter space in successive stages of increasing coherence times. The full-O3 PowerFlux follow-up search uses nearly 11 months of data (compared to the 6-month O3a period), leading to an expected increase in SNR by a factor of approximately 11/6 = 1. 35, with an additional ∼40% increase from the use of stage-1 loose coherence, leading to a nominal expectation of SNR gain of a factor of ∼1. 9. Software simulations of signals with amplitudes as weak as 1/3 of the upper limit in a given band, however, reveal large fluctuations above and below this expectation for all search bands. These simulations suggest applying a conservative (negligible efficiency loss) threshold on the SNR increase of only 20%, which is nonetheless effective in suppressing most noise artifacts.
The final follow-up method applied to any survivors of the full-O3 loose-coherence check uses the Pythonbased PyFstat  [37, 38]  software infrastructure to combine a MCMC approach  [39, 40]  with semi-coherent summing of the well known F-statistic detection statistic  [41]. In this approach, the parameter space near to those values from a stage-2 outlier is sampled randomly according to a certain probability density function determined by the F-statistic likelihood function. We closely follow an implementation  [40]  applied to a recent analysis of O2 data to follow up on outliers emerging from several prior O2 continuous wave searches. Briefly, the O3a observation time is divided into N seg segments, for each of which the F-statistic is computed over a coherence time approximately equal to the observation time divided by N seg.
V. CONCLUSIONS We have performed the most sensitive all-sky search to date for continuous gravitational waves in the range 20-2000 Hz while probing spin-down magnitudes as high as 10 -8 Hz/s, using the PowerFlux search program with loose coherence. The overall improvements in strain sensitivity come primarily from the improved noise floors of the Advanced LIGO interferometers over previous LIGO data sets. Improvements in strain sensitivity over our previous O2 results range from a factor of 1. 2 at ∼100 Hz to ∼2 at ∼2000 Hz, with still larger improvements at the lowest frequencies for which spectral line artifacts degraded O2 results. No credible gravitational wave signals are observed, allowing upper limits to be placed on possible source signal amplitudes. Fig. 4  shows the strain amplitude upper limits obtained.
VI. ACKNOWLEDGMENTS 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 GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS) and the Netherlands Organization for Scientific Research, for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
to-Core Program A. 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. This document has been assigned LIGO Laboratory document number LIGO-P2000334-v9. Parameters of the outliers surviving stage-2 follow-up, but discarded after visual inspection of a strain histogram confirmed instrumental contamination. Each outlier listed is the loudest after clustering in frequency, spin-down and sky location.
Acknowledgements. The authors wish to thank the  Kavli Institute for Theoretical Physics (KITP) , at the  University of California, Santa Barbara , for their hospitality and for supporting this research project. The KITP is supported, in part, by the  National Science Foundation  (NSF). Alex Ayet acknowledges the support from the  DGA  and the  French Brittany Regional Council. Juan M. Restrepo  acknowledges the support from the  NSF/OCE  grant. Alex Ayet would like to thank  Bertrand Chapron  for the insightful comments. We also acknowledge the comments and suggestions from the reviewers, which helped improve the clarity of the paper. Financial support. This research has been supported by the  National Science Foundation Directorate for Geosciences (NSF/OCE ; grant no. 1434198 ), the  NSF  (grant no.
EBSD measurements were performed on a JEOL JSM5600 scanning electron microscope coupled to a CamScan X500FE Crystal Probe equipped with a HKL Nordlys camera, and using the HKL Channel 5 suite of programs, installed at Géosciences Montpellier. Textural analysis was performed using MTEX, an open-source MATLAB toolbox for textural analysis (htt ps://mtex-toolbox. github. iol/, Bachmann et al. , 2010). For each sample we obtained a crystallographic orientation map covering a representative part (30 and 60%) of the thin section (Fig. 4 ). Data are presented as one measurement per pixel. The indexation reference phases are Fo 100 (forsterite) for olivine and MgCr 204 for Fig. 3. Structural sketch of studied chromitite deposits and their dunitic enclosure. Marked structures in the peridotite aggregate, foliation and lineation, indicated by spinel shape fabric on outcrop, are the record of asthenospheric mantle flow  (Nicolas et al.
Grain boundary analysis method We have applied to olivine-chromite relationships a method of grain boundary analysis developed in material sciences. Here we use our 2D EBSD data and the MTEX toolbox (https://mtex-toolbox. github. io, Mainprice et al. , 2014). From the grain orientations (g. A) and (g. B) on either side of a boundary, we can calculate the misorientation at that boundary (∆g_AB = g_B. g_A-1). From the ∆g AB matrix we can calculate the misorientation axis, and the misorientation angle about the axis. At each position along a boundary, MTEX calculates misorientations when grains of the same and different minerals are present on either side. The objective in the studied case is to measure the misorientations along the grain boundaries (see Section 4. 2), paying special attention to boundaries that are twins.
Acknowledgements The field data referred in this paper account for the many years of collaboration with the  Directorate of Minerals, Ministry of Commerce and Industry of the Sultanate of Oman. The first author thanks  R. T. Gregory  and  R. G. Coleman  for recent discussions on the  Coast Range ophiolites of California. Andrea Tommasi  provided manuscript clarifications. Reviews by  Qin Wang  and an anonymous reviewer are warmly acknowledged. C. Nevado  and  D. Delmas  supplied high quality polished sections for EBSD measurements. The EBSD-SEM national facility in Montpellier is supported by the  Institut National des Sciences de l'Univers (INSU).
E is the density at a reference temperature 0 E T. Equation 1 then can be rewritten as   0 u ( 4 ) This simplification is known as the extended Boussinesq approximation (e. g. ,  Christensen & Yuen, 1985; Gerya, 2010; Ismail-Zadeh & Tackley, 2010). The equation of conservation of momentum has been integrated using the penalty function formulation, while the Petrov-Galerkin method has been used to integrate the equation of conservation of energy. Numerical integration has been performed by means of the 2D finite elements code SubMar  (Marotta et al. , 2006) , in a rectangular domain 1,400 km wide and 700 km deep, discretized by a non-deforming irregular grid composed by quadratic triangular elements, carrying a denser nodal distribution near the contact region between the plates, where the most significant gradients in temperature and velocity fields are expected.
Data Availability Statement Results are presented and developed also in the frame of the MIUR project "Dipartimento di Eccellenza 2017" (Work Package 3, Task 3. 4). All figures were created using the Generic Mapping Tool (GMT) plotting software  (Wessel & Smith, 1998). All data have been obtained using the numerical code SubMar  (Marotta et al. , 2006) , as described in  Regorda et al. (2017)  and  Marotta et al. (2020).
Intriguingly, above 12 GPa, our K calculated values are much closer to the high frequency data than to the static ones, a behavior which possibly reflects the typical time relaxation (≃ 10 ps, corresponding to GHz frequencies) and the small volumetric variation (<0. 4 nm 3 ) we used for the estimation of K in our MD simulations. For P > 55 GPa, the experimental value (static and high frequency) is ≃ 420 GPa  [48, 49]  in good agreement with our calculations. Affinities with c-SiO 2 polymorphs To identify the SiO 2 polymorph most akin to amorphous phases, we built supercells containing 582 atoms for tridymite, cristobalites, coesites and stishovite, and 432 and 486 atoms for β-and α-quartz, respectively. The unit cells of the polymorphs were obtained from the crystallographic databases  [50].
Acknowledgements: The authors would like to thank the  BioNano-NMRI team (L2C, UM)  for computer facilities. One of us (A. H) acknowledges support from the  CNRS (France)  and thanks  L. C. Rincón  for introducing him in the SCC-DFTB method. This work was done with the support of the  Centre Informatique National de l'Enseignement Supérieur (CINES, France)  via a  Resource Allocation Panel award  (project  A0060910788 ).
Water Chemistry Along the Beagle Channel From Historical CIMAR-FJORD Campaigns Similar spatial coverage of the Beagle Channel but at lower spatial resolution was obtained through two research cruises sponsored by the Chilean Navy CIMAR 3 (1998) and CIMAR 16 (2010) (see Table  1 ). During both cruises, a rosette equipped with 24 Niskin bottles collected water samples at discrete depths for the analysis of macronutrients (nitrate (NO 3 -), phosphate (PO 4 -3 ) and silicic acid (Si (OH) 4 ). Water samples (50 mL) were fixed with mercury chloride, stored at -20 • C in acid-cleaned highdensity polyethylene bottles and analyzed as described by  Atlas et al. (1971)  (for more details see  Valdenegro and Silva, 2003). These data were provided by the Chilean National Oceanographic and Hydrographic Data Center (CENDHOC).
Hourly temperature records at 50 m and 195 m were registered and analyzed for the period July 22, 2017 to September 27, 2019, while temperature at 100 m was recorded from July 22, 2017 to July 19, 2018 only. Wind and atmospheric temperature data were obtained from the fifth major global European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis (ERA5)  (Hersbach et al. , 2020). The data have a horizontal resolution of 0. 25 × 0. 25 degrees. A grid of data points around the Beagle Channel was selected and analyzed. The grid point closest to the location of the mooring was selected to be analyzed in the temporal domain using time series analyses  (Emery and Thomson, 1998). The ERA5 reanalysis, satellite wind products and in situ weather stations had previously been used to determine the spatiotemporal variability of the surface winds along Southern Patagonia  (Pérez-Santos et al.
ACKNOWLEDGMENTS We would like to thank the  Chilean and Argentinean Navy  for providing bathymetry charts, as well as the captain and crew of the Forrest vessel for their professional assistance and help during sampling procedures. We acknowledge the support of  Prefectura Naval Argentina, MINCYT  and thank  Gustavo Ferreyra  for making possible the cruise onboard  M/O Bernardo Houssay  and two reviewers for valuable comments on the manuscript. We would like to express our gratitude and our respects to  Nelson Silva  who passed away in August 2020. His pioneer work and passion for chemical oceanography research in Patagonia ecosystems opened new avenues for those of us who came after him.
FUNDING This research was supported by the  Agencia Nacional de Investigación y Desarrollo (ANID)  through project  FONDAP IDEAL , grant number  15150003 , projects  HYDRO-USHUAIA (ECOS-SUD/MINCyT ) and P-UE 2016:  22920160100077CO  (CONICET), and by the  German Federal Ministry of Education and Research (BMBF) , grant number  LAT16STRUC-039. The project leading to this publication has received funding from the  Excellence Initiative of Aix-Marseille University -A*Midex , a  French "Investissements d'Avenir programme "  AMX-19-IET-012.
We elaborate further on the various data quality categories and their usage in the section 3. 6 Auxiliary channels are also used to subtract post-facto some well identified instrumental noise from the GW strain data. A procedure based on a linear coupling model  [137]  computes the transfer function that couples the witness channels to h(t) and subtracts the contributing noise from the strain amplitude. This procedure was used during the second observing run in Advanced LIGO data. It achieved an increase of up to 30% of the detector sensitive volume to GWs for a broad range of compact binary systems and was most signifi-cant for the LIGO-Hanford detector  [138]. In some cases data are available both before and after noise subtraction is applied (for example in the case of GW170817  [129] ).
Signal injections In addition to data quality, some metadata provide information about hardware injections  [139]  inserted into the detector data for testing and calibration. The detectors' test masses (interferometer mirrors) are physically displaced by an actuator in order to simulate the effects of a GW. 7 A simulated GW signal is introduced into the detector control system yielding a response which mimics that of a true GW. The analysis of a data segment that includes an injection allows an end-to-end test of the ability for the analysis procedure to detect and characterize the GW strain signal. Hardware injections are also used for detector characterization to check that the auxiliary channels used for vetoes do not respond to gravitational-wave-like signals. This is a safety check since a channel that has no sensitivity to GWs is considered safe for use when constructing a veto.
Data records GW open data are distributed under the Creative Commons Attribution International Public License 4. 0 9 through the GWOSC web pages. 10 The files can be directly downloaded one by one from this web page. However, to download large amounts of data (such as an entire observing run) the use of the distributed filesystem CernVM-FS [140, 141] is preferred. 11 Once installed, this filesystem allows access to GWOSC data as files in a directory tree mounted locally on the user's computer. The calibrated strain data of O1  [19]  and O2  [20]  described in this paper are conveniently divided into files of 4096 s. Short segments of 32 s and 4096 s duration for each GW event are also released. 12 The description of the data records that follows is valid both for single event release and for bulk data release.
The strain data are repackaged and resampled by GWOSC to make it more accessible to users both within the LVC and outside. Along with the native 16 384 Hz sampling rate, the data on GWOSC are also made available at 4096 Hz. 13 The down-sampling is performed using the standard decimation technique implemented in scipy. signal. decimate 14 from the Python package 7 Calibration lines mentioned earlier are generated using the same process. 8 See the GWOSC web page http://gw-openscience. org/o1_inj and http:// gw-openscience. org/o2_inj. 9 https://creativecommons. org/licenses/by/4. 0/legalcode 10 http://gw-openscience. org/data/ 11 For installation instructions, see http://gw-openscience. org/cvmfs/ 12 http://gw-openscience. org/eventapi/ 13 In the rest of the paper the sampling rates will be indicated in kHz and rounded to the closest integer, i.
e. 4 and 16 kHz means 4096 and 16 384 Hz, respectively 14 This method applies an anti-aliasing filter based on an order-8 Chebyshev type I infinite impulse response (IIR) filter  [142]  before decimation. scipy  [143]. From the Nyquist-Shannon sampling theorem  [144, 145, 146] , the largest accessible frequency is the Nyquist frequency equal to half of the sampling rate f s. This should be kept in mind when choosing the sampling rate to download from GWOSC, and in general when analyzing these files; in particular, because of the anti-aliasing filter's roll-off, the data sampled at 4 kHz are valid only up to frequencies of about 1700 Hz. The publicly released data are generated from data streams in the LIGO and Virgo data archives uniquely identified by a channel name and a frame type (an internal label that specifies the content of the files).
GWOSC file formats The GW open data are delivered in two different file formats: hdf and gwf. The Hierarchical Data Format hdf  [147]  is a portable data format readable by many programming languages. The Frame format gwf  [148]  is used internally by the GW community. In addition, the data associated with GW events are also released as plain text files containing two columns with the GPS time and the corresponding strain values. There are some differences in the structure of the file names between O1 and O2 due to the evolution of GWOSC itself. For O1 the name of the files has the structure: obs-ifo_LOSC_s_Vn-GPSstart-duration. extension, where obs is the observatory, i. e. the site, so can have values L or H; ifo is the interferometer and can have values H1 or L1; LOSC is the previous name of GWOSC, (the L in LOSC stands for LIGO); s is the sampling rate in kHz with possible values 4 or 16; n is the version number of the file (until now we have only one version, so only V1); GPSstart is the starting time in GPS of the data contained in the file; duration is the duration in seconds of this segment of data, which value is always 4096 in this case; the extension can be gwf or hdf.
The file names in O2 are instead of the type obs-ifo_GWOSC_ObservationRun_sKHZ_Rn-GPSstartduration. extension, with the same meaning of the italic letters, but in this case obs and ifo can have also the values V and V1, respectively, for Virgo data and we added the run name in the file names, so in this case ObservationRun is O2. The folders (or groups) included in the hdf files are: • meta: metadata of the file containing the following fields: -Description, e. g. "Strain data time series from LIGO", -DescriptionURL: URL of the GWOSC website, -Detector, e. g. L1, and Observatory, e. g. L,  15 -Duration, GPSstart, UTCstart: duration and starting time (in GPS and UTC, respectively) of the segment of data contained in the file. In the O2 files it was decided to add also the StrainChannel and FrameType of the original files internally used by the LVC (i.