dataesr/scientific-paragraphs-categorization huggingface dataset:
The ALICE apparatus A detailed description of the ALICE detectors can be found in  [15, 16]  and references therein. In the following more information is given on the sub-detectors used to perform the analysis presented in this work, namely the V0, the Inner Tracking System (ITS), the Time Projection Chamber (TPC) and the Time-of-Flight (TOF). All of them are located inside a solenoidal magnet creating a magnetic fields parallel to the beam line, with an intensity of 0. 5 T for the data sample here considered. The V0 detector  [17]  is formed by two arrays of scintillation counters placed around the beam pipe on either side of the interaction point. They cover the pseudorapidity ranges 2. 8 ≤ η ≤ 5. 1 (V0A) and -3. 7 ≤ η ≤ -1. 7 (V0C). The collision multiplicity is estimated using the signal amplitude in the V0 detector, which is also used as a trigger detector.
Efficiency and acceptance correction The raw yield must be corrected to take into account the tracking efficiency and the detector acceptance. This correction is evaluated from Monte Carlo (MC) simulated events, which are generated using the event generator PYTHIA8. 21 (Monash2013 tune)  [23]. However, since PYTHIA8 does not handle the production of nuclei properly, it is necessary to inject (anti)nuclei on top of each generated event. In each pp collision, one deuteron, one antideuteron, one helion or one antihelion are injected, randomly chosen from a flat rapidity distribution in the range |y| < 1 and a flat p T distribution in the range p T ∈ [0, 10] GeV/c. The GEANT4  [24]  transport code is exploited to describe the hadronic interaction of the particles propagating through the detector material.
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics In-.
Acknowledgements The authors acknowledge funding from  TopDyn ,  SFB TRR 146  (project # 233630050 ),  SFB TRR 173 Spin+X  (project  A01  # 268565370  and project  B12  # 268565370 ). The work was additionally funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) Project No. 403502522  ( SPP 2137 Skyrmionics ) as well as the Emmy Noether project # 320163632. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No. 863155  ( s-Nebula ) and No. 856538  ( ERC-SyG 3D MAGIC ). Open access funding is enabled and organized by Projekt DEAL. J. Z. acknowledges the support of  Charles University  grant  PRIMUS/20/SCI/018.
Plasma quasi-neutrality is guaranteed by the electric field generated by the charge separation. Due to the cylindrical symmetry of the RAID plasma, the magnetic DC field as well as the electric field generated by the plasma potential have no azimuthal components. However they produce a E × B and a diamagnetic drift along the azimuthal direction of the charged fluids with associated tangential fluxes. If we consider axial invariance, the total flux can be expressed as the sum of a radial and an azimuthal component, namely  2 ), (  3 ) and (  4 ) are then solved with the PDE (partial differential equation) module of the finite element calculator COMSOL multiphysics. 21 ssuming cylindrical symmetry of the plasma discharge, the continuity equation can be written as: Γ = Γ r + Γ φ. Equations ( 1 r ∂ (rΓ r ) ∂ r = S(r) -2 Γ z (r) L z , (6) where S(r) is the source term of any species along the radial direction (it takes into account the sum of sources and losses in volume), and 2 Γ z (r) L z is the term describing the particle losses along the axial direction z due to the free streaming along the magnetic field lines up to both endplates.
E. Structure of the model A complete fluid model describing all species would require the inclusion of a balance equation for electrons and an equation for the power deposition. In RAID, the electron temperature and density profiles can be measured by Langmuir probes and microwave interferometry across the entire volume of the device. 4 These data are introduced as a physical constraint on the basis of which transport and chemistry of the other ion species and atomic hydrogen can be computed. A typical electron density and temperature profile at 1 m from the center of the antenna, 3 kW RF power and 0. 3 Pa of gas pressure is shown in Fig. 1 , and will be taken as input profile for the modeling. Fig. 2  provides a diagram description of the structure of the model.
We present the key results from an intensive and coordinated ten-year amateur observational programme designed to uncover and confirm Galactic planetary nebulae (PNe). This was undertaken and led by a dedicated group of largely French amateur astronomers in collaboration with professional colleagues. Over this period this group has uncovered a total of 209 spectroscopically confirmed True, Likely, and Possible Galactic PNe, with the work accelerating over the last three years in particular. These new discoveries represent ∼5% of all 3831 True, Likely, and Possible Galactic PNe currently known according to the HASH database as of March 2022. A further 610 PNe candidates are awaiting follow-up. These figures demonstrate the power and value of the amateur community in undertaking a coordinated and focused programme such as this.
⋆ Tables 1-5 are only available at the CDS via anonymous ftp to cdsarc. u-strasbg. fr  (130. 79. 128. 5)  or via http://cdsarc. u-strasbg. fr/viz-bin/cat/J/A+A/666/A152 ⋆⋆ A dedicated online database was created by this group to record and present this work: www. planetarynebulae. net Their complex shapes provide clues to their formation, evolution, mass-loss processes, and the shaping role played by binary central stars  (De Marco 2009; Miszalski 2009; Jones & Boffin 2017; Boffin & Jones 2019). These characteristics make PNe excellent objects with which to study late-stage stellar evolution and Galactic structure. Over the last 20 yr the number of Galactic PNe discovered has more than doubled, with more than 2400 PNe added to the current total Galactic inventory of ∼3800 since 2006, as recorded in the gold standard Hong-Kong/AAO/Strasbourg/Hα Planetary Nebulae (HASH) database (see e.
The production and dissemination online of publicly available wide-field digital surveys, particularly in the optical and infrared regime has facilitated large-scale coordinated amateur PN hunting programmes (see below). This effort has uncovered 209 spectroscopically confirmed True (122), Likely (51), and Possible (36) Galactic PNe, which represent ∼5% of the PNe currently in HASH. A further 610 PNe candidates await follow-up. So far some 104 candidates have been shown to be a variety of PN mimics giving a current PN confirmation success rate of ∼67%. All the discoveries and associated information have been compiled into an interactive online database by the amateur group 2. These discoveries demonstrate the power and value the amateur community can have in undertaking a coordinated, combined imaging and spectroscopic observing programme, as described in Sect.
The search for CSPN To help verify whether a PN candidate is a bona fide PN we systematically search for a central star (CSPN) in the PanSTARRS and DECaPS surveys or in the GAIA2 photometric catalogue. GAIA often provides additional information on these possible CSPN, such as distances or temperatures. A blue star in the centre of a weak nebulosity is potential evidence of a PN nature. Once our candidates have been incorporated into HASH all available imagery can be examined for traces of a CSPN including, where available, the GALEX UV imagery. We also consult the American Association of Variable Star Observations (AAVSO) database 19 and available white dwarf catalogues (e. g. McCook & Sion 1999; Gentile Fusillo et al. 2021). Variable stars can mimic a compact PN candidate by showing an excess in red photometry that can be mistakenly attributed to a Hα signal.
The contents of Tables -the name given to the object at the time of its discovery; -the PNG name based on Galactic coordinates (IAU Commission 5 recommendation); -J2000 Coordinates in RA (HH:MM:SS. ss) and DEC (DD:MM:SS. ss); -the dimension or apparent dimension in arc minutes, as estimated from the best available optical imagery; -the publications where the object is designated (nonexhaustive list); -if available, the origin of the spectra obtained for the object (non-exhaustive list) including the SAAO 1. 9 m and Grantecan 10 m telescopes provided by our professional colleagues; -if available, the origins of the images made with Hα and/or [OIII] filters; -the HASH unique object identification number in the HASH database; -the HASH status abbreviation of T (True), L (Likely), or P (Possible) PN.
Dissemination of our data Once updated on the planetarynebulae website, the tables are sent to the HASH team at the Laboratory for Space Research (LSR 25  ) at the University of Hong Kong to be checked and migrated to the HASH PN database. Classification of the objects in our tables is independently assessed before inclusion in HASH. Our latest discoveries are also published in the French Science magazine L'astronomie every year, and then sent to the CDS for insertion into VizieR and Simbad.
The planetarynebulae website The planetarynebulae website 24 was created to bring transparency and utility to the combined amateur work carried out and to make our work accessible to the PN research community. The website allows users to visualise the candidates discovered and to follow the work of our collaborators. A data sheet is constructed for each object that provides detailed information on the candidate and selected images at different wavelengths centred on the object. Relevant amateur images and/or spectra may also be added. Published sources are indicated. Documentation and advice is also provided on how to search for PNe candidates, how to contribute to the analysis and how to communicate with the French-led amateur group. All spectra obtained by the group are available on a specific page of the website.
HASH and the integrity of our database The HASH database 26  currently maintained by the LSR at the University of Hong Kong is the final arbiter of PN identification. It contains over 11,466 entries, as of March 2022, including over 3800 PNe in the Galaxy and 821 in the Magellanic Clouds. The remaining entries consist of the various mimics that have at one time or another been classified as PNe, emission objects found by the MASH and IPHAS teams but not PNe (spread across 40 different classifications), and a further 2802 candidates awaiting verification (see  Parker et al. 2016; Bojičić et al. 2017). A perfect complementarity has been established between the French-led amateur group and the professional team at the LSR that curates HASH and provides additional followup spectroscopy where possible on professional telescopes like the SAAO 1.
Kinematic information is effectively absent. The lack of telescope aperture is at least partially compensated for by very long exposure times (e. g. several hours on faint objects). This is something we amateurs can afford to do, unlike our professional counterparts. Furthermore, we do not have to justify our need to carry out an observing campaign on specific objects and do not depend on the availability of professional observatories or on diverse programme priorities. Unfettered availability and responsiveness to take spectra on demand, coupled with the long time that can be spent to acquire data on a single object, makes this the key strength and advantage in pursuing this amateur programme. We have shown that such objectives are achievable with our equipment for candidates detected on broad-band DSS2 red or blue wide-field survey data.
The equipment in our backyard observatories Most spectroscopy (2018-2021) was performed from private backyard observatories (mainly Cornillon and Kermerrien in France). These provided ∼60% of the data at the time of writing. Spectra are mainly acquired with small aperture Newtonian telescopes (0. 2 m mirror and F/5) equipped with lowresolution spectrographs, for example the Alpy 600, with a 23 µm slit, cooled ATIK 414 EX CCD camera with dispersions of ∼3. 0 Å pixel -1 at 6560 Å.
Amateur data reduction and follow-up Our amateur spectroscopy is reduced using the Integrated Spectrographic Innovative Software (ISIS) software 29. All spectra are dark, bias, and flat-field corrected as standard. The instrumental 29 http://www. astrosurf. com/buil/isis-software. html response is derived from a reference star spectrum acquired under the same observing conditions as the target. Wavelength calibration is done via an argon-neon calibration lamp, and is checked against absorption lines of the reference star in the blue part of the spectrum. Fully processed 2D spectra are first produced after dark, bias, flat-field, instrumental correction, wavelength calibration, and the removal of atmospheric lines and cosmic rays. This often reveals the PN emission lines clearly in this case prior to extraction of the 1D spectrum.
Amateur spectroscopy of some of our PNe candidates and other PNe candidates from the HASH database have revealed a further 62 PN mimics. These mostly comprise HII regions, emission-line galaxies, supernova remnants, and emission-line stars. These objects are presented in Table  5  together with the preliminary identifications from their spectra to prevent future confusion. Figure  6  presents the distribution of True, Likely, and Possible PNe from our list, from the DSH list (only Kn and Pa objects), and from the HASH database (which includes the LMC PNe) on an all-sky Aitoff projection in Galactic coordinates (powered by Aladin 31 ). The Galactic Centre is in the middle of the map.
The biggest challenge is perfecting and improving our tools and creating new ones that will assist future PN hunting activities. New spectrographs developed by Shelyak Instruments (UVEX in particular) will bring improvements to the quality of the spectra produced by amateurs. The planetarynebulae. net website will continue to grow and will offer improved interfaces for data synthesis and retrieval. The sustainability of the existing observatories is essential in order to continue making quality observations. Hardware will be kept up to date and operational. Finally, 2021 marked the arrival in Chile of the observatory of the 2SPOT association (created in 2019 by five French amateur astronomers). The infrastructure of the Deep Sky Chile (DSC) company, located just a few dozen kilometres from the Cerro Tololo observatory in Chile, was chosen so as to benefit from very high-quality sky.
Acknowledgements. Our pro-amateur group was forged with time and perseverance to build a team able to work together and bring the knowledge and competence of each to this research field with the discovery and analysis of PN candidates. The people involved are numerous and each one contributes, according to their degree of involvement and know-how to the relevance of the work and the reactivity that amateurs can show. Q. A. P. thanks the  Hong Kong Research Grants Council for General Research Fund  research support under grants  17326116  and  17300417. A. R. thanks H. K. U. for the provision of a postdoctoral fellowship. We would also like to thank  Prof. Agnès Acker  whose involvement in the amateur community greatly contributed to the birth of this pro-am collaboration. We also thank all the discoverers, photographers, and.
Generally, the data sets and model functions are derived from experimental data for cross sections and/or theoretical approaches. The latter play a fundamental role in the extreme cases of very high energetic (above several tens of MeV) or very low energetic particles (below 1 keV), where direct experimental determination of cross sections is not feasible. EURADOS Task Group 6. 2, which is concerned with computational micro-and nanodosimetry, began an intercomparison exercise a few years ago to evaluate and investigate the origin of the dispersion of results that can occur when previously validated Monte Carlo codes are applied to microdosimetric and nanodosimetric problems. A simple simulation setup was chosen for the investigation so that differences between results would predominantly reflect the impact of the cross-section models used in the codes.
Background and Rationale of the Exercise Microdosimetry and nanodosimetry are concerned with the quantitative characterization of the stochasticity of ionizing radiation interactions at the microscopic scale of biological targets as cells and subcellular structures and how this connects to the biological effectiveness of radiation. Measurements in conventional microdosimetry and in nanodosimetry are performed with experimental devices (gas counters) that simulate micrometric or nanometric targets in condensed matter based on a density scaling principle  ( 5, 6). The relevant quantities (frequency distributions of imparted energy or number of ionizations in a defined target volume) in biological matter are assessed by simulations using MC codes. Direct validation of these simulations is generally not possible, since corresponding experiments cannot be performed; the codes can be validated indirectly by benchmarking results of simulations of microdosimetric or nanodosimetric experimental setups with experimental data obtained in gas  ( 7, 8, 9).
The best-fit parameters to the data in the ESTAR database for electron energies between 10 keV and 100 keV were = 2. 53 µm and = 1. 7586. Using the mean energy spectrum of emitted electrons shown in Supplementary Fig. S3 (a) and aforementioned power law regression, one can determine the mean distributions of the CSDA range and the mean energy imparted within a given distance around the point of electron emission that are shown in Supplementary Fig. S3 (b) and S3(d). Convoluting the latter with the chord length distributions for the three geometries gives estimates for the mean specific energy of 3. 78 mGy for the volume source, 4. 14 mGy for the point source and 2. 05 mGy for the surface source. This supports the plausibility of the values shown in the last two columns of Table  4  as well as the results shown for the surface source obtained with track structure codes.
These low uncertainties are of the same order of magnitude as those recommended by the ICRU or NIST databases for stopping powers of electrons with energies from 1-10 keV. Nevertheless, for even lower energies, the uncertainties involved in the calculations significantly increase, and the relative spread of inelastic cross-sections reported in the literature for electrons down to 10 eV exhibits discrepancies of as much as 20-100%, or in some cases even more  ( 44). It, therefore, seems logical to consider within the sensitivity analysis modifications in the inelastic cross-sections that depend on the electron energy. For this study, we adopted the following expression: * = [1+f(E)] (1) where, is the cross-section as implemented in the code, * is the modified cross-section and f(E) is an energy-dependent relative change.
The consensus ICSDs and underlying cross-section dataset could be the ones minimizing such differences. Such a procedure has some complexity associated with it, as it originates from the attempt to infer the consensus ICSD with respect to a given 'parameter' (i. e. the cross-sections used) by varying this parameter using discrete steps. This procedure would require the participants to perform a large number of simulations (thus increasing the risk of possible errors), which may also introduce a bias in the consensus ICSD depending on the number of discrete steps used for the study. An alternative approach would be to obtain the consensus ICSD directly by using a "consensus" crosssection data set. To this end, participants would need to provide their inelastic and elastic crosssections for a pooled analysis to produce a table of mean values of the cross-sections at each considered value of independent variables (projectile energy and, if applicable, scattering angle, energy loss, secondary particle energy and emission angle).
Two different procedures have been tested that modified the inelastic cross-section dataset using the Geant4-DNA option 4 and an energy-dependent function that took into account the fact that uncertainties are larger at lower electron energies. From the above discussion of the limitations of such an approach, it was concluded that the contribution of crosssections to the uncertainty in nanodosimetric results is difficult to achieve from a comparison of ICSDs obtained with different codes due to the difficulty of defining a consensus result among the codes. The proposal of a pooled analysis of different cross-section data to establish a mean/consensus data set is a promising alternative for estimating uncertainties associated with simulated micro-and nanodosimetric quantities. With this goal in mind, the second part of this activity is currently underway, where users of the MC track-structure codes are welcome to participate.
Photometry and Frequency Extraction EPIC 220422705 had been observed by K2 in short-cadence (SC) mode over a period of 78. 72 days during Campaign 8. Its assembled light curves were downloaded from Mikulski Archive for Space Telescopes 7  (MAST). These archived data were processed through the EPIC Variability Extraction and Removal for Exoplanet Science Targets (EVEREST) pipeline. 8 he photometry corrected by EVEREST has comparable precision to the original Kepler mission for targets brighter than K p ≈ 13  (Luger et al. 2018). EPIC 220422705 is within this brightness range. The EVEREST flux was first shifted to the relative fraction of their mean value. We then used a sixth-order polynomial fitting to detrend the whole light curve due to residual instrumental drifts. To avoid discontinuities in the light curve across gaps longer than 0.
02 days, we separated the light curve piecewise where such gaps occurred for our fitting. This detrending method will flatten the light curves and dismiss signals with a period (2 days) in Fourier transform, which will not have an impact on the modulating patterns for our prime aim. We note that those signals are not concerned here due to the fact that the EVEREST pipeline may not recover those signals correctly. Then the light curves were iteratively clipped of a few outliers three times by filtering at 4. 5σ around the light curve before we produced a Fourier transformation. We used the specialized software FELIX 9  to perform frequency extraction from the light curves. The frequencies were prewhitened in order of decreasing amplitude until the value of 5. 2 times the local noise level, a value that is the median amplitude in the LSP  (Zong et al.
75% (D 2 -D 1 , N -D 2 ). For each AM/FM, we prefer to keep the fitting function with a lower freedom degree as indicated by the parameter λ. The results of our fitting for each AM/FM are listed in Table  2 , where most frequencies have a sinusoidal component, G 3 (t), or with an additional linear fitting, G 4 (t). After the fitting function was set, we adopted the posterior distributions based on the Bayesian frame to estimate the best fitting parameters and their uncertainties. The posterior distributions of parameters are sampled by the Markov Chain Monte Carlo (MCMC), which is implemented by the EMCEE code (Foreman-Mackey et al. 2013). The MCMC sampling is performed with more than 22 chains, according to the number of free parameters. It proceeds until the chains converge to  values that are inferred by the autocorrelation time module of the EMCEE.