dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The mass difference between the Ω - b and Ξ - b baryons is measured to be m(Ω - b ) -m(Ξ - b ) = 248. 54 ± 0. 51 (stat) ± 0. 38 (syst) MeV/c 2 using an LHCb dataset corresponding to an intergrated luminosity of 9 fb -1. This result is consistent with the previous direct determination of this quantity in Ref. [36]  is used as a reference. This measurement represents the most precise determination of the Ω - b -baryon mass, and is consistent with the current world average of 6045. 2 ± 1. 2 MeV/c 2  [1]. It supersedes the value measured in Ref. [4]. The combination of the m(Ω - b ) value determined in this paper with the previous LHCb measurements in Refs. [6, 7]  leads to m(Ω - b ) LHCb comb = 6045. 7 ± 0. 5 (stat) ± 0. 6 (syst) MeV/c 2. In addition, the relative production rate of the Ω - b to Ξ - b baryons multiplied by the branching fractions of their decays to J/ψΩ -and J/ψΞ -final states is reported for the first time at the LHC.
Acknowledgements We express our gratitude to our colleagues in the  CERN accelerator departments  for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST and NSFC (China) ;  CNRS/IN2P3 (France) ;  BMBF ,  DFG and MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW and NCN (Poland) ;  MEN/IFA (Romania) ;  MICINN (Spain) ;  SNSF and SER (Switzerland) ;  NASU (Ukraine) ;  STFC (United Kingdom) ;  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP (United Kingdom) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  Polish WLCG (Poland)  and  NERSC (USA).
The green dots indicate sightings of auroral activity reported between 28th of August and 4th of September, 1859  25. These maps were generated in Python 3. 8 with the library cartopy (version 0. 18. 0). City, Leeds, Dunedin, Edmonton) or for being popular aurora-watching destinations (Dunedin, Edmonton, Salekhard, Tromso, Yellowknife). From Fig. 3  we can see a more detailed picture of what already indicated in Fig. 1. For example, both Tromso and Yellowknife will remain inside the auroral zone over the next 50 years, and the likelihood of aurora sightings is not expected to vary (on average). By the end of the century, however, some of the forecasts predict that both cities might find themselves at the edge of the auroral zones, and that perhaps aurorae sightings could be less frequent than today.
Furthermore, following the results of  26  , we define the regions most exposed to severe space weather events (and hereby named 'danger zones') as the geographical regions of Earth's surface where the highest geomagnetically induced currents (GIC) are expected to be driven by temporal geomagnetic variations. For catastrophic space weather events with 200-years return period,  26  estimated that the auroral zones can be defined by the geomagnetic latitudinal band between 50 and 60 • , with peak geomagnetically induced currents (GIC) being expected at about 55 • (see Figure  6  of  26  ). We therefore define the danger zones as being bound by AACGM latitudes 50 • and 60  •. In order to calculate AACGM latitude, m , we make use of the freely available Python package aacgmv2 version 2. 6. 2 (https:// github.
The MPG geomagnetic field forecast dataset, analyzed during the current study, is freely available at https:// edmond. mpdl. mpg. de/ datas et. xhtml? persi stent Id= doi: 10. 17617/3. MZGFPM. The IGRF and gufm1 geomagnetic field models, analyzed during the current study, are both freely available as part of the aacgmv2 Python package (version 2. 6. 2) at: https:// github. com/ aburr ell/ aacgm v2. The auroral and danger zones boundary datasets, as well as the geomagnetic field forecasts and field models generated and analysed during the current study are available in the auroral_forecast repository: https:// github. com/ smaff ei/ auror al_ forec ast. The dataset for the aurorae sightings during the Carrington event displaied in Figure  2  have been provided by James L. Green and Scott Boardsen (NASA), and it is available from NASA upon reasonable request.
Acknowledgements This research was funded by  NERC  Grant Number  NE/P016758/1  (S. M. , P. W. L and J. E. M. ). S. M. also acknowledges funding contribution from the  European Research Council  (agreement 833848-UEMHP) under the  Horizon 2020 programme. J. W. B. E and J. P. E acknowledge funding by  NERC  Grant Number  NE/P017142/1. The authors are grateful to  Julien Aubert  ( IPGP ) for making the coefficients of the IPGP forecast available. The authors would like to express their gratitude to  James L. Green  and  Scott Boardsen  ( NASA ) for providing the dataset containing auroral observations during the Carrington event.
Observations The NRT dataset consists of observations carried out with the Berkeley-Orléans-Nançay (BON) backend until August 2011 and the NUPPI (a version of the Green Bank Ultimate Pulsar Processing Instrument, GUPPI, made for the NRT) backend afterwards. The BON observations of PSR J1528-3146 started in November 2006, and were carried out at a central frequency of 1398 MHz. These observations were coherently dedispersed and carried out over a frequency bandwidth of 64 MHz until July 2008, and over a bandwidth of 128 MHz after that date. The NUPPI instrument, which became the primary pulsar timing instrument at Nançay after August 2011, increased the bandwidth of NRT pulsar observations to 512 MHz that are also coherently dedispersed. NUPPI observations were conducted at a central frequency of 1484 MHz.
In this study we considered NUPPI data recorded until May 2022. Although pulsar observations with the BON backend were maintained for another few years after 2011 for testing purposes, we discarded BON observations made simultaneously with NUPPI observations, as they represented duplicated astrophysical information. The Parkes radio telescope dataset included the timing data presented in  Jacoby et al. (2007) , recorded between September 2001 and September 2004, and more recent data taken with the UWL recorded from October 2019 to January 2021. The former observations were conducted with the 21-cm multibeam receiver  (Staveley-Smith et al. 1996 ) at a central frequency of 1390 MHz, using the 2 × 0. 5 × 512 MHz analogue filterbank backend (AFB) and were thus incoherently dedispersed. Observations with the UWL receiver were carried out using the medusa backend, that performed coherent dedispersion over a much larger bandwidth of 3328 MHz, centered at 2368 MHz.
The MeerKAT dataset we used for the study is comprised of observations made between March 2019 and April 2021, as part of the RelBin program of the MeerTime large science project. The RelBin program aims at improving the measurement and detecting new relativistic effects in known pulsars, increasing the number of pulsars with mass measurements, and testing gravity theories, with the excellent timing capabilities of MeerKAT  (Kramer et al. 2021a). Pulsar observing set-ups, verifications and early science results with MeerKAT are presented in  Bailes et al. (2020). Observations were calibrated in polarization as described in  Serylak et al. (2021). MeerKAT observations were conducted at a central frequency of 1284 MHz, and covered a frequency bandwidth of about 800 MHz. While the bulk of the observations had a typical duration of about 30 min, an intensive orbital campaign was conducted around 2019 December 12 (MJD 58829).
On that day, the system was observed for about four hours to cover a wide range of orbital phases centered on superior conjunction in order to significantly increase the sensitivity of our subsequent timing analyses to a faint Shapiro delay  (Shapiro 1964 ) signal that had already been detected in Nançay data. Additionally, three other 30 min observations of the pulsar were conducted within three days centered on MJD 58829, in order to sample orbital phase ranges away from superior conjunction. In Fig. 1  we show the MeerKAT pulse profile for PSR J1528-3146 from the latter observation, which represented the highest S/N observation in our total dataset. The main radio component in the pulse profile is at rotational phase ∼0. 54 in this graph. As can be seen from Fig. 1 , the unprecedented sensitivity of MeerKAT observations has enabled us to detect a previously unseen weak component separated from the main pulse by approximately half a rotation.
Data analysis All the data reduction steps for the datasets described above were done using the psrchive analysis library  (Hotan et al. 2004). Observations were cleaned of radio frequency interference (RFI) and also polarization-calibrated. The typically ∼1 h long Nançay observations with the BON and NUPPI backends were split into sub-integrations of 10 min each. For the BON dataset, these sub-integrations were split into two frequency sub-bands of 32 MHz each for the data recorded before July 2008, and two sub-bands of 64 MHz for the data taken after the bandwidth increase. Sub-integrations for A71, page 2 of 13  Notes. ( a) These observations were scrunched in time to single sub-integrations. See  Jacoby et al. (2007)  for details on the corresponding observations. ( b) For this dataset the TOAs were generated using the wide-band template matching technique implemented in PulsePortraiture (see  Pennucci et al.
2014 ). ( c) The bulk of the MeerKAT observations covered a total of 856 MHz of bandwidth. For these observations, we grouped the channels into eight sub-bands of 107 MHz each. Other observations covered a slightly shorter bandwidth of 776 MHz -for these, we also grouped the channels in eight sub-bands of 97 MHz. ( d)  the NUPPI data were divided into eight frequency sub-bands of 64 MHz each. For the BON and NUPPI data, we combined 10 observations with the highest signal-to-noise ratios (S/N) that we then smoothed to produce noise-free standard profiles at 1. 4 GHz with the same frequency resolution. Times of arrival (TOAs) to be used in the timing analysis (described in Sect. 4) were derived from the Nançay data using the Matrix Template Matching (MTM) method of the pat routine of psrchive  (van Straten 2006).
The TOAs were extracted from the "historic" Parkes dataset by comparing observations with a high S/N template profile, as described in  Jacoby et al. (2007). Each observation was split into two frequency sub-bands of 128 MHz, so that two TOAs were extracted from each observation. On the other hand, the very large frequency bandwidth covered by Parkes observations with the UWL prompted us to consider a different approach for extracting TOAs. Instead of splitting the very large bandwidth into several sub-bands (which often results in sub-bands containing no clear detections at the highest frequencies), we used the wide-band template matching technique implemented in the PulsePortraiture library 1   (Pennucci et al. 2014 ) and extracted one TOA for the entire bandwidth for every 10 min of observation.
Finally, the MeerKAT observations were divided into eight frequency sub-bands 97 MHz (resp. 107 MHz) for observations with a 759 MHz (resp. 856 MHz) bandwidth and the subintegrations were grouped into segments of 10 min. For each resulting element of time and frequency, we extracted a TOA using the standard Fourier Domain Markov chain Monte Carlo (FDM) method of pat, comparing the profiles with a smoothed version of the highest S/N MeerKAT observation with the same number of frequency sub-bands. A summary of the TOA dataset produced for the timing analysis is presented in Table  1.
Timing analysis The timing analysis was carried out by analyzing the topocentric TOA data presented in Sect. 2, using the Tempo2 pulsar timing package  (Hobbs et al. 2006 ). The measured TOAs were first converted to Barycentric Coordinate Time (TCB), taking the known clock corrections for the different telescopes and using the JPL DE438 solar system ephemeris 3. The orbital motion of the pulsar was described using the DDH orbital model, which uses the orthometric parameterization of the Shapiro delay  (Freire & Wex 2010). This parameterization is well-suited for systems with low orbital inclinations (which is the case of PSR J1528-3146, as shown later in this section). The reason is that it describes the Shapiro delay using two parameters: the orthometric amplitude (h 3 ) and the orthometric ratio (ς), which are, in such systems, much less correlated than the range (r) and shape (s) parameters of the DD orbital model  (Damour & Deruelle 1985 , 1986).
We used Tempo2 to fit for the pulsar's sky position and proper motion, its spin parameters (spin frequency and first and second time derivatives), orbital parameters, and PK parameters describing perturbations to the classical Keplerian orbit. The TOAs from the different datasets were aligned with each other using time offsets, which were fitted for as part of the timing analysis. The dispersion measure (DM) and its variations were modeled as a piecewise-constant function, using the "DMX" model. The TOA dataset was divided into segments of 100 days, and constant DM values were fitted in each of the intervals containing timing data. In a first iteration of the analysis, we obtained a preliminary timing model for PSR J1528-3146 which we then used to determine "EFAC" factors for each of the individual datasets, namely, scaling factors applied to the TOA uncertainties such that the individual reduced χ 2 values become equal to unity.
4  and 5. The residuals are normally-distributed around 0, demonstrating that the parameters in Table  2  describe the timing data appropriately. Figure  6  shows the best-fit piecewise-consant DM values obtained from the timing analysis. A71, page 6 of 13 Fig. 4. Timing residuals as a function of time (top) and orbital phase (bottom), with the datasets considered in our analysis shifted by different amounts for ease of comparison. In the bottom plot, the orbital phase corresponding to superior conjunction (i. e. , the moment of the orbit when the pulsar is on the opposite side of the companion) was shifted to occur at 0. 25. The DM displays long-term variations around the value of 18. 163 (6) measured by  Jacoby et al. (2007). The analysis of ∼16 yr of timing data, which includes data of unprecedented quality from the MeerKAT telescope, enabled us to measure the pulsar's proper motion and several PK parameters for the first time.
The preferred value for cos i of -0. 55 corresponds to an inclination of i = 123. 3 +4. 8 -4. 1 • , the other solution corresponds to i = 56. 7 +4. 1 -4. 8 •. The fact that the two maxima have such similar probabilities indicates that the annual orbital parallax is not detected in the considered dataset. As noted in Sect. 3, the polarimetry suggests that the latter may be the preferred solution. Figure  8  shows four distinct solutions in the cos i-Ω space, which are listed in Table  3. Again, none of the solutions is strongly preferred over the other, as can be seen from Fig. 8. Furthermore, there is a plateau of high probability between these peaks. The high-probability regions are well described by the ẋ and ς from the timing analysis, the broad distribution of probability can thus be understood to result from the relatively low precision of ẋobs.
Acknowledgements. The MeerKAT telescope is operated by the  South African Radio Astronomy Observatory , which is a facility of the  National Research Foundation , an agency of the  Department of Science and Innovation. SARAO acknowledges the ongoing advice and calibration of GPS systems by the  National Metrology Institute of South Africa (NMISA)  and the time space reference systems department department of the  Paris Observatory. MeerTime data is housed on the OzSTAR supercomputer at  Swinburne University of Technology. The Parkes radio telescope is funded by the  Commonwealth of Australia  for operation as a National Facility managed by  CSIRO. We acknowledge the Wiradjuri people as the traditional owners of the Observatory site. This research has made extensive use of NASAs Astrophysics Data System (https: //ui.
adsabs. harvard. edu/)and includes archived data obtained through the  CSIRO Data Access Portal  (http://data. csiro. au). Parts of this research were conducted by the  Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav) , through project number  CE170100004  and the  Laureate  fellowship number  FL150100148. The Nançay Radio Observatory is operated by the  Paris Observatory , associated with the  French Centre National de la Recherche Scientifique (CNRS). We acknowledge financial support from the  "Programme National de Cosmologie et Galaxies" (PNCG)  and " Programme National Hautes Energies" (PNHE)  of  CNRS/INSU, France. The authors acknowledge the constant support from the  Max-Planck-Society. M. B. acknowledges support through  ARC  grant  CE170100004  (OzGrav).
875 GHz-bandwidth, two in the lower side band (LSB) and two in the upper sideband (USB). This setup allowed us to observe the CO(J = 3-2) line (345. 796 GHz at rest) and the continuum emission (344. 0 GHz at rest) in the LSB bands, as well as HCO + (4-3) (356. 734 GHz at rest) and the continuum emission (358. 1 GHz at rest) in the USB bands. We calibrated the data making use of the ALMA reduction package CASA  (McMullin et al. 2007)   1. In this work we make use of the line and continuum data sets from the compact, extended, and the merged configurations. The calibrated uv-tables of the merged configurations were exported to GILDAS 2  to proceed with the continuum subtraction and imaging procedures, as detailed below. We first subtracted the continuum from each of the spectral (u, v) data sets using the GILDAS task UV-BASELINE.
VLT/SINFONI In this section we present new near-IR IFU spectroscopic observations obtained with the SINFONI instrument  (Eisenhauer et al. 2003; Bonnet et al. 2004 ) at the VLT, as part of program 093. B-0057(B) (PI: R. Davies). NGC 7172 was observed during the nights of 16 and 20 July 2014. The H + K grating was used with laser guide star adaptive optics to achieve a spectral resolution R 1500 over a 3 × 3 FoV. A standard observing sequence of Object-Sky-Object was used with detector integration times of 300 (12 exposures) and 50 s (6 exposures) and small dithering between each Object exposure resulting in a total exposure time of 65 min. The raw detector images were reduced with SPRED  (Abuter et al. 2006) , a custom data reduction package for SINFONI developed at MPE. SPRED performs all standard reduction steps needed to reconstruct NIR IFU cubes.
Discussion In Sect. 4. 2, we showed that the CO(3-2) minor axis p-v diagram (Fig. 10 ) goes across significantly strong emission from the ring. Under the commonly assumed hypothesis that the bulk of molecular gas shares a coplanar geometry, the v rad term is the only component of gas motions that has a nonzero projection along the disk minor axis. If the molecular gas is following circular orbits, for the known orientation of the galaxy, the derived sign of the radial velocity from the global 3D BAROLO best fit to the CO(3-2) data cube implies the presence of a prevalent outward radial component throughout the ring region and out to r 700 pc in the disk. In this section, we derive the properties of this molecular outflow, compare them with those of the ionized outflow, and look for evidence of the impact of the outflow on molecular gas in the nuclear regions of NGC 7172.
Acknowledgements. We thank the referee for some insightful comments. A. A. -H. and S. G. -B. acknowledge support from grant  PGC2018-094671-B-I00  funded by  MCIN/AEI/10. 13039/501100011033  and by  ERDF  A way of making Europe. A. A. H. also acknowledges support from  PID2021-124665NB-I00  funded by the  Spanish Ministry of Science and Innovation  and the  State Agency of Research   MCIN/AEI/10. 13039/501100011033 PID2021-124665NB-I00  and  ERDF  A way of making Europe. S. G. B. also acknowledges support from the research project  PID2019-106027GA-C44  of the  Spanish Ministerio de Ciencia e Innovación. C. R. A. acknowledges the projects " Feeding and feedback in active galaxies ", with reference  PID2019-106027GB-C42 , funded by  MICINN-AEI/10. 13039/501100011033 , " Quantifying the impact of quasar feedback on galaxy evolution ", with reference  EUR2020-112266 , funded by  MICINN-AEI/10.
13039/501100011033  and the  European Union NextGenera-tionEU/PRTR , and from the  Consejería de Economía, Conocimiento y Empleo del Gobierno de Canarias  and the  European Regional Development Fund (ERDF)  under grant " Quasar feedback and molecular gas reservoirs ", with reference  ProID2020010105 ,  ACCISI/FEDER , UE. C. R. acknowledges support from the  Fondecyt Iniciacion  grant  11190831  and  ANID BASAL  project  FB210003. E. B. acknowledges the  María Zambrano program  of the  Spanish Ministerio de Universidades  funded by the  Next Generation European Union  and is also partly supported by grant  RTI2018-096188-B-I00  funded by  MCIN/AEI/10. 13039/501100011033. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2019. 1. 00618. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO  and  NAOJ. Based on observations collected at the  European Southern Observatory  under  ESO programme   093. B-0057 (B). This research has made use of ESASky, developed by the  ESAC Science Data Centre (ESDC)  team and maintained alongside other  ESA science mission 's archives at  ESA's European Space Astronomy Centre (ESAC, Madrid, Spain). This research has made use of the  NASA /IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. We acknowledge the usage of the HyperLeda database (http://leda. univ-lyon1. fr). This research made use of NumPy (Harris et al. 2020),  Matplotlib  (Hunter 2007), and Astropy (Astropy Collaboration 2013, 2018).
2020). These authors discussed the possibility that the different conclusions may be due to different intermediate data reduction steps (e. g. , sky subtraction or frame stacking). The main objectives of this first paper on the JWST data of XID 2028 are to characterize the warm ionized outflow in this system, and assess the impact of the outflow and quasar radiation field on the host galaxy. This paper is organized as follows. In Section 2, we describe the observations and steps taken to reduce the JWST data. Our use of the software package q3dfit to analyze these data is discussed in Section 3. We present the results from this analysis in Section 4. We discuss the properties of the outflow and impact of this outflow on the host galaxy in the context of quasar feedback scenarios in Section 5, taking into account the large set of ancillary data on this object.
Observations XID 2028 was observed on 2022 November 20 by JWST using the NIRSpec Instrument in IFU mode  (Böker et al. 2022; Jakobsen et al. 2022). These data are publicly available on the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The specific observations analyzed can be accessed via doi:10. 17909/04tb-mn90. The NIRSpec field of view (FOV) in IFU mode is ∼3″ × 3″ or ∼25 × 25 kpc for this object. We used the filter/grating combination F100LP/G140H, with corresponding wavelength coverage of 0. 97-1. 89 μm or ∼0. 37-0. 73 μm at the redshift of XID 2028. The grating has a near-constant dispersion Δλ = (2. 30 -2. 40) × 10 -4 μm per pixel, corresponding to a velocity resolution ∼80-180 km s -1. This allows us to easily spectrally resolve the profiles of the emission lines in XID 2028, which have typical velocity widths of several hundred km s -1.