dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on a compilation of 509 Co-bearing deposits and occurrences identified in 25 European countries, a wide distribution for Co across Europe can be assumed that might be a viable future source  [87]. The first Pan-European Map of Submarine Energy Critical Elements was compiled in 2018  [88]  and has been improved periodically since 2020  [89]. Due to the societal needs, the first attempt put focus on the CRMs required for the energy transition (Figure  9 ). Together with the assessment of the European seabed, new potential sources of supply are identified. As part of the European Marine Observatory and Data Network (EMODnet), MINDeSEA adds to the comprehensive information of Europe's maritime issues  [90]. These new data and information merged with information on resource statistics (Minerals Yearbook), on demands, and others allow for strategic investments to develop Europe's domestic resources that abide to the high ethical, social, and ecological standards that are further recurring recommendations of the European Commission.
The scientific research in these projects solidifies and innovates on existing premises, models, and strategies for mineral deposit and CRM exploration and establishes the first stepping stones to secure a reliable and responsible sourcing of mineral raw materials from domestic sources as viable future sources. These new data and information merged with information on resource statistics (Minerals Yearbook), on demands, and others allow for strategic investments to develop Europe's domestic resources that abide to the high ethical, social, and ecological standards that are further recurring recommendations of the European Commission. The scientific research in these projects solidifies and innovates on existing premises, models, and strategies for mineral deposit and CRM exploration and establishes the first stepping stones to secure a reliable and responsible sourcing of mineral raw materials from domestic sources as viable future sources.
FP7-NMP-2013-CSA-7. Grant agreement ID: 608,921 [32]. • BlueMining (Sustainable extraction of raw materials from oceanic depths), 1 • MinFuture (Global material flows and demand-supply forecasting for mineral strategies), 1 December 2016-30 November 2018, aims to identify, integrate, and develop expertise for global material flow analysis and scenario modeling. MinFuture is funded by the Horizon 2020 Framework Programme of the European Union under Grant Agreement no. 730,330 [34].
CHARA/MIRC Observations Over the course of 5 years, we observed AZ Cyg using MIRC at the Georgia State University's CHARA Array. The CHARA Array is located on Mount Wilson, CA and consists of six 1 m telescopes with 15 baselines ranging from 34 m to 331 m and angular resolutions reaching ∼0. 5 mas in the H-band  (ten Brummelaar et al. 2005). The MIRC instrument, which has since been upgraded to MIRC-X, was a six beam combiner operating in the H-band (1. 5-1. 8 m). The instrument was capable of providing 15 visibilities, 10 independent closure phases, and 9 independent closure amplitudes per spectral channel. In the low spectral resolution mode used for our observations (R=30), eight 30 nm wide spectral channels are acquired  (Monnier et al. 2004 (Monnier et al. , 2012)). We recorded 21 data blocks for AZ Cyg, as listed in Table  1 , with each data block corresponding to a continuous observation of a target for 10 minutes.
The integration time on each exposure was 29. 65 s. We used Spextool, an Interactive Data Language (IDL) package  (Cushing et al. 2004 ) to reduce data. During this process, the standard star spectrum was used to generate a model telluric spectrum by adjusting a convolved model spectrum of Vega to match the standard star, as described in  Vacca et al. (2003) , and dividing the standard spectrum by this model so that only telluric lines remained. To get a spectrum corrected for telluric lines, we divided the AZ Cyg spectrum by this derived telluric spectrum. We shifted the telluric spectrum roughly 0. 1-0. 2 pixels in order to minimize systematic errors around sharp telluric lines. Telluric correction using this method within the SpeX pipeline calibrates flux to 10% accuracy  (Rayner et al.
2009). Calibration frames also included a flat field and Argon arc lamp exposure. In the final step of data reduction, we merged the order separated spectra into a single spectrum. In SpeX spectra, the offset between neighboring orders is between 1-3%  (Rayner et al. 2009). Following reduction, we followed the procedure of  Villaume et al. (2017)  to absolutely flux calibrate our data. In short, we determined a calibration factor, 𝐶 = 𝑋 𝑤 𝑋 Max[ 𝑓 (𝐶 𝑋 )] 𝑋 𝑤 𝑋 (1) that we used to scale the entire spectrum, whilst preserving the relative flux calibration between each order. In equation 1,  (  ) = 10 ( AB -N obs )/2. 5 where  AB is the AB magnitude determined from the observed spectrum, N obs is a random normal distribution determined using 2MASS magnitudes and errors from  Cutri et al. (2003) , and   are weights comprised of 1 1  deviations from  (  ).
The reduced  2 in Table  5  and residuals in the bottom panels of Figure  13  indicate a close match between reconstructed images and data and low frequencies, but that there are notable deviations at higher frequencies and in datasets with longer temporal coverage. Although the spectral resolution of the observations is rather low, we also reconstructed images for each wavelength channel in order to test for wavelength dependence. These images were produced using the same reconstruction parameters as used in reconstructing images using data from all wavelength channels. We present these images in Figure  14  in the Appendix. In order to test the results of our reconstructions, we also used OITOOLS. jl to reconstruct the data using total variation and a centering prior. We used the same masks and initial images as used with SQUEEZE.
We display the reconstructions made using OITOOLS. jl in the top row of Figure  5. We note that prominent features found in the images produced using SQUEEZE are present in the reconstructions made with  OITOOLS. jl, which suggests that these features are not the result of a bias in the reconstruction process. In the following analysis section, we will use only the images produced using SQUEEZE. As further tests of our images, we split up the observations from the 2016 epoch into two parts and reconstructed images with these split data. We find that the prominent features in the image produced with the complete dataset remain in the split data sets. We depict the result of this experiment in Figure  7.
Overall, it seems like the surface of this RSG, and likely others, is dominated by a pattern of large, long lived convection cells and smaller features consisting of hot granules of rising gas which persist for months, but less than one year. This research has made use of the Jean-Marie Mariotti Center OIFits Explorer 5  and Aspro 6  services. This research has also made use of the SIMBAD database and the VizieR catalogue access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). The original description of the VizieR service was published in  Ochsenbein et al. (2000). This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.
and RN would like to acknowledge funding of this research through NSF Awards #1616483 and #1814777. RN would also like to acknowledge start-up funding from the Office for Research at New Mexico Tech. This work is based upon observations obtained with the Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The CHARA Array is supported by the National Science Foundation under Grant No. AST-1636624 and AST-1715788. Institutional support has been provided from the GSU College of Arts and Sciences and the GSU Office of the Vice President for Research and Economic Development. This work is based upon observations obtained with the Infrared Telescope Facility, which is operated by the University of Hawaii under contract NNH14CK55B with the National Aeronautics and Space Administration.
FB and RN would like to acknowledge funding of this research through NSF Awards #1616483 and #1814777. RN would also like to acknowledge start-up funding from the Office for Research at New Mexico Tech. This work is based upon observations obtained with the Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The CHARA Array is supported by the National Science Foundation under Grant No. AST-1636624 and AST-1715788. Institutional support has been provided from the GSU College of Arts and Sciences and the GSU Office of the Vice President for Research and Economic Development. This work is based upon observations obtained with the Infrared Telescope Facility, which is operated by the University of Hawaii under contract NNH14CK55B with the National Aeronautics and Space Administration.
To complete our data and give fully simultaneous data, we interpolate the observed light curves using Gaussian Processes (GP;  Rasmussen & Williams 2005). These were implemented using the package  (Ambikasaran et al. 2015) , following the process outlined in  Angus et al. (2019). The observed photometry without any corrections is detailed in Table  1 , and the corrected restframe light curves are shown in Fig. 1  and also detailed in Table  1. The quoted uncertainties are purely statistical without incorporating any systematic uncertainties. Due to the high signal-to-noise observations of DES14X2fna, these uncertainties are very small and reach millimag levels at peak. In practice, the uncertainties will be larger than this -for our analysis, we add the statistical errors in quadrature with a value of 0.
Distances are estimated by cross-matching SNe with their hosts using the Nasa Extragalactic Database (NED 3 ) and Table  1. Observed and rest-frame photometry of DES14X2fna, quoted in AB magnitudes in the natural DECam system. Note that this photometry has not been corrected for Milky Way extinction and quoted uncertainties are purely statistical. For our analysis, these have been added in quadrature with an error of 0. 05 mag to represent systematic uncertainty, and these are included in the rest-frame photometry. Phases are given with respect to explosion. obtaining a luminosity distance in our assumed cosmology corrected for Virgo infall. We choose this approach for consistency as some of these hosts do not have redshift-independent distance estimates and where they do these are often calculated for different cosmologies.
2013). As a result, we fix this to the lowest value considered reasonable. As an independent check, we also considered an alternative semianalytic magnetar code based on the model outlined in  Inserra et al. (2013) , hereafter I13. Unlike MOSFiT, I13 fits directly to bolometric luminosities rather than observed photometry -as such, we utilise our black-body estimated luminosities from Section 3. 3. For this fit, we have again assumed a fixed value of  ,mag = 0. 01 cm 2 g -1. Our fit to this data using I13 is shown in Fig  9  -overall, this model does provide us with a good fit to our data both around peak and in the tail. As previously noted, the -band light curve of DES14X2fna shows strong resemblance peak to that of SN 1998bw (SN Ic-BL), which  Wang et al. (2017)  finds can be accurately fit with a combination of a magnetar and nickel-cobalt decay.
DES14X2fna as a photometric contaminant DES14X2fna has a very high luminosity at peak, reaching a peak M  -19. 3, and therefore into the range of peak luminosities expected for type Ia SNe. While the H and He features of DES14X2fna clearly distinguish it from a SN Ia, it is a possibility that a similar object could be mistakenly included in a photometric sample of SNe Ia in the absence of spectroscopy. To assess this, we use the SNe Ia light curve template fitter SALT2  (Guy et al. 2007 ) to fit our observed photometry using the Python package. SALT2 calculates the stretch and colour parameters for a SN Ia light curve,  1 and. For the DES cosmological sample of SNe Ia, only SNe with -0. 3<<0. 3 and -3< 1 <3 are included. Fitting the observed light curve between -15 and +45 days relative to explosion gives a poor quality fit, particularly in higher wavelength bands, with  2   = 6041.
(2019). Any objects outside the redshift range of [0. 05,1. 2], with a Milky Way E(B-V) > 0. 25, without data both before and after peak or without data points with a signal-to-noise ratio > 5 in at least two bands are excluded from the SALT2 fits entirely, leaving a total of 18,520 objects. (ii) Select only SNe with -0. 3<<0. 3 and -3< 1 <3 based on SALT2 fits: As previously mentioned, this cut is used in  Brout et al. (2019)  to select the cosmological sample. Performing this cut leaves a total of 15,101 objects in the sample. (iii) Select only SNe with a high value of P Ia : This identifies only the synthetic SNe with a high probability of being classified as a SN Ia by S NN. Note that S NN has two models, one for photometric classification with a host spectroscopic redshift and one without.
A3. 1 Prior Table  A2  gives the details of the priors used on parameter values in our MOSFiT simulation for the magnetar model. As mentioned in section A1. 1, most flat and log-flat priors were left at their default values and Gaussian priors were selected to restrict the parameter space to physically reasonable solutions based on our observations. We have raised the minimum value of P from the default of 1 ms to 5 ms as with the lower threshold some walkers were ending in a local minimum with an unphysically small ejecta velocity. We fix the neutron star mass at 1. 4 M. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U. S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.
ACKNOWLEDGEMENTS We thank the anonymous referee for the comments and suggestions that have helped to improve the paper. M. G. , C. P. G. and M. S. acknowledge support from  EU/FP7-ERC  grant No. [615929]. This work was supported by the  Science and Technology Facilities Council  [grant number  ST/P006760/1 ] through the  DISCnet Centre for Doctoral Training. MS acknowledges support  from  EU/FP7-ERC  grant  615929. L. G. was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 839090. This work has been partially supported by the  Spanish  grant  PGC2018-095317-B-C21  within the  European Funds for Regional Development (FEDER). Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U.
We report the detection of X-ray pulsations from the rotation-powered millisecond-period pulsars PSR J0740+6620 and PSR J1614-2230, two of the most massive neutron stars known, using observations with the Neutron Star Interior Composition Explorer (NICER). We also analyze X-ray Multi-Mirror Mission (XMM-Newton) data for both pulsars to obtain their time-averaged fluxes and study their respective X-ray fields. PSR J0740+6620 exhibits a broad double-peaked profile with a separation of ∼ 0. 4 in phase. PSR J1614-2230, on the other hand, has a broad single-peak profile. The broad modulations with soft X-ray spectra of both pulsars are indicative of thermal radiation from one or more small regions of the stellar surface. We show the NICER detections of X-ray pulsations for both pulsars and also discuss the phase relationship to their radio pulsations.
The NICER observations of eight other MSPs were presented in  Ray et al. (2019)  and  Guillot et al. (2019). In the present paper, we describe NICER observations of the two nearby binary MSP systems hosting massive neutron stars, PSR J0740+6620 and PSR J1614-2230, and report the detection of thermal X-ray pulsations from both. The paper is organized as follows. In Section 2 we describe the X-ray observations and the data reduction procedures. Because the photon event data for PSR J0740+6620 have been made available for the NS radius inference analyses of  Miller et al. (2021)  and  Riley et al. (2021) , the data extraction procedure is more stringent for that pulsar than for PSR J1614-2230. In Section 3 we present our findings from the X-ray observations together with the known radio properties of these two pulsars.
OBSERVATIONS The Neutron Star Interior Composition Explorer (NICER; see Gendreau & Arzoumanian 2017) is a NASA astrophysics explorer mission that has been operating on the International Space Station (ISS) since 2017 June. The NICER X-ray Timing Instrument (XTI) is an array of 52 active silicon drift detectors each paired with concentrator optics that are nominally sensitive to photons in the 0. 2-12 keV range and have absolute timing precision of ∼100 nanoseconds rms for tagging of photon detection times  (Prigozhin et al. 2016). The event data for both sources were processed using HEASOFT v6. 28 and the NICER-specific package NICERDAS v6, together with the NICER calibration files v20200202. However, somewhat different criteria for extracting NICER event data were used for the two pulsars.
Furthermore, the XMM-Newton data were obtained under a number of different circumstances, as described below. 2. 1. NICER PSR J0740+6620 was observed by NICER for 1867. 5 ks between 2018 September 21 and 2020 April 17, while PSR J1614-2230 was observed by NICER for 852. 5 ks over the period spanning 2017 July 4 to 2021 March 1. Both pulsars are challenging to observe because of their faintness, although PSR J1614-2230 is somewhat brighter than PSR J0740+6620. Because both are faint, everything possible has been done to minimize backgrounds, including making observations primarily during ISS orbital night. Special care is warranted for PSR J0740+6620 because data sets are being used to obtain a NS radius constraint from the light curve modeling of the pulsed component (which is derived from NICER) relative to the total phase-averaged flux (which is derived from XMM-Newton imaging).
Furthermore, there are nearby sources of comparable brightness that the NICER concentrating detector optics do not exclude and their contributions must be estimated. A summary of NICER observations of our target pulsars can be found in Tables  1  and 2. For the pulsar PSR J0740+6620, all events included in our and subsequent analyses were obtained when the pulsar was at least 80 • from the Sun. This minimized the effects of scattered solar X-rays and detector noise resulting from photons whose energies are just below the NICER energy range. We extract the event data in an ObsID by ObsID manner. We first filter out event data outside the 0. 25-3. 0 keV energy range. We also filter the observations keeping only those times that have a Cosmic Ray Cutoff Rigidity greater than 2. 0 GeV/c and a space weather Kp index less than 5.
0. We further require that the observations by NICER include the full complement of 52 detectors active during the observations. We exclude the detector with DET ID 34 since this detector tends to be noisy. This ensures that the event data passed to the light curve analysts have an internally consistent set of 51 out of 52 available detectors and thus utilize a constant effective area configuration. We also demand that NICER is always pointed to better than 15 arcseconds of the source in order to include the data. The NICER vignetting function is known to be relatively flat-topped within 2 arcminutes of the instrument boresight so the pointing accuracy filter removes rare, large pointing errors as a possible significant source of brightness variation. In August 2020 maintenance on the individual detectors was begun and this involved cycling individual detectors off for a period of annealing.
Thus, for our initial investigation of PSR J0740+6620, utilizing event data obtained after the detector maintenance was started would have created a NICER XTI effective area inconsistency across this date boundary and we choose to avoid this complication. The pulsar phase for each event is calculated with the photonphase tool of the PINT software package  (Jing et al. 2020 ) using the timing solutions for each pulsar described in §2. 3. The end result here is a set of good time intervals (GTIs) with event data and low background noise. For PSR J1614-2230, because we are not yet at the stage of performing a full light curve analysis to obtain an estimated NS radius, we slightly relaxed our event acceptance criteria. Namely, we employ a Kp index constraint of 4. 0 and a Sun angle constraint of 60 •.
Radio pulsar timing To search for pulsations, we require pulsar timing models that provide sufficient precision over the full NICER observation span to assign pulse phases to the NICER data without needing any additional trials over folding periods. These models are provided by ground-based pulsar timing programs at radio frequencies. PSR J0740+6620 is regularly observed as part of the NANOGrav pulsar timing program  (Alam et al. 2021 ) with both the Green Bank Telescope (GBT) and Canadian Hydrogen Intensity Mapping Experiment (CHIME). Fonseca et al. (2021)  have performed an analysis of these data, extending the work done by  Cromartie et al. (2020). Since this covers the entire span of the NICER data, we use that timing model to assign pulse phases for the present work. For PSR J1614-2230, we constructed a timing model using a merged dataset from the NANOGrav 12.
CONCLUSIONS The event data from NICER and XMM-Newton we described here for PSR J0740+6620 were used by  Miller et al. (2021) ;  Raaijmakers et al. (2021) ;  Riley et al. (2021)  to investigate the radius and EOS of the pulsar in this system. The event list we derive for this pulsar is available for download on Zenodo. org website 1. For PSR J0740+6620 the NICER observations have shown X-ray pulsations with two broad thermal X-ray peaks separated in phase by about ∼0. 4. In the case of PSR J1614-2230, we have confirmed the X-ray pulsations originally reported by  Pancrazi et al. (2012)  and find one broad thermal X-ray peak in the phase diagram. We show the fields of these pulsars in XMM-Newton images and find that both pulsars are faint and nearby X-ray sources will contribute significantly to their respective NICER concentrator count rates.
ACKNOWLEDGMENTS This work was supported in part by  NASA  through the  NICER mission  and the  Astrophysics Explorers Program. NICER  science team members at NRL are supported by  NASA  under Interagency Agreement  NNG200808A. S. B. was funded in part by  NASA  grants  NNX17AC28G  and  80NSSC20K0275. M. C. M. thanks the  Radboud Excellence Initiative  for supporting his stay at  Radboud University. W. C. G. H. acknowledges support through grant  80NSSC20K0278  from  NASA. This research has made use of data and/or software provided by the  High Energy Astrophysics Science Archive Research Center (HEASARC) , which is a service of the  Astrophysics Science Division at NASA/GSFC  and the  High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. We acknowledge use of  NASA 's Astrophysics Data System (ADS) bibliographic services and the  ArXiv.
Facilities: NICER, XMM-Newton Software: astropy (Astropy Collaboration et al. 2013Collaboration et al. , ascl: 1304. 002). 002 ),PINT (ascl: 1902. 007 ,https://github. com/nanograv/pint), HEAsoft (ascl: 1408. 004 ,https://heasarc. nasa. gov/lheasoft/),  DS9  (  Joye & Mandel  ( 2003 ), ascl: 0003. 002 ),emcee (ascl: 1303. 002 ,https://github. com/dfm/emcee),and the XMM-Newton Scientific Analysis System (SAS, ascl: 1404. 004 ,https://www. cosmos. esa. int/web/xmm-newton/sas).