dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Studying the present-day structure and substructures of the Milky Way is one of the most direct ways for understanding the true nature of the Galaxy formation mechanism and evolutionary history. Gaia is an ambitious space mission of the European Space Agency (ESA) to primarily provide a threedimensional map of the Milky Way with an unprecedented volume and precision  (Gaia Collaboration 2016). It represents a revolution in galactic archaeology and is a leap forward in revealing how galaxies take shape and investigating the excit-Our catalogue is available from the Gaia Archive and partner data centres https://gea. esac. esa. int/archive/documentation/ GDR3/. Corresponding author: M. Fouesneau, e-mail: fouesneau@mpia. de ing complexities of our own Galaxy. Although it observes only one percent of the stellar population of our own Galaxy, Gaia still characterises ∼1.
The non-stellar content is presented in  Delchambre et al. (2023). For more technical details of the Apsis modules, we refer to the online documentation 1  (Gaia Collaboration 2022) and specific publications describing some of the modules (GSP-Phot in  Andrae et al. 2023 , GSP-Spec in Recio-Blanco et al. 2023 , and ESP-CS in Lanzafame et al. 2023). We list the relevant module acronyms in Table  1. We only processed stellar sources down to G = 19 mag for which Gaia provides a BP/RP or RVS spectrum, except for ultra-cool dwarfs (UCDs). We selectively processed 78 739 UCDs fainter than this limit (see Fig. 1 ). This limiting magnitude choice was driven primarily by the limited processing time of the BP/RP spectra. The astrophysical parameter dataset contains stellar spectroscopic and evolutionary parameters for 470 million sources.
These comprise T eff , log g, and [M/H] (470 million using BP/RP, and 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (up to 5 million), diffuse interstellar band analysis (0. 5 million), activity indices (2 million), Hα equivalent widths (200 million), and further classifications of spectral types (220 million) and emission-line stars (50 thousand). The work described here was carried out within the Gaia Data Processing and Analysis Consortium (DPAC) within Coordination Unit 8 (CU8; see Gaia Collaboration 2016 for an overview of the DPAC). We realise that one can create more precise and possibly more accurate estimates of the stellar parameters by cross-matching Gaia with other survey data, such as GALEX  (Morrissey et al. 2007 ), Pan-STARRS  (Chambers et al.
2016) , or catWISE  (Eisenhardt et al. 2020) , and spectroscopic surveys such as LAMOST  (Luo et al. 2019) , GALAH  (Buder et al. 2021) , or APOGEE  (Jönsson et al. 2020). For example,  Fouesneau et al. (2022) ,  Anders et al. (2023) , and  Huang et al. (2022)  combined Gaia data with other photometry and spectroscopic surveys to derive APs for millions of stars 2. However, the remit of the Gaia-DPAC is to process the Gaia data. Further exploitation, for instance, including data from other catalogues, is left to the community at large. These Gaia-only stellar parameters will assist the exploitation of Gaia DR3 and the validation of such extended analyses, however. We continue this article in Sect. 2 with a brief overview of our assumptions and key processing aspects. In Sect. 3, we describe the Gaia DR3 AP content, the validation of our results, and their internal consistenc.
Overview of stellar APs in GDR3 The goal of Apsis is to classify and estimate astrophysical parameters for the Gaia sources using (only) the Gaia data  (Bailer-Jones et al. 2013; Creevey et al. 2023 ). In addition to assisting the exploitation of Gaia DR3, the DPAC data processing itself uses these APs internally, for example, to help extract template-based radial velocities from the RVS spectra, identify quasars that were used to fix the astrometric reference frame, or the optimisation of the BP/RP calibration. We designed the Apsis software to provide estimates for a broad class of objects covering a significant fraction of the Gaia catalogue, rather than treating specific types of objects. Apsis consists of several modules with different functions and source selections. Creevey et al. (2023)  presented the architecture and the modules of Apsis separately.
Following these two independent general analyses, Apsis refines the characterisation of Gaia sources with specific modules. FLAME operates on a subset of sources with APs of sufficient precision from GSP-Phot (G < 18. 25 mag) and GSP-Spec (G < 14 mag), based on their reported uncertainties. MSC analyses all sources with G < 18. 25 mag and treats every source as though it were a system of two unresolved stars. The remaining modules, specifically ESP-CS  (Lanzafame et al. 2023) , ESP-HS, ESP-ELS, and ESP-UCD only analyse objects of their class, that is, active cool stars, hot stars, emission-line stars, and ultra-cool dwarfs. Apart from ESP-UCD, which analyses UCDs fainter than G = 19 mag, the other specific modules only produce results for sources with G < 17. 65 mag. Finally, GSP-Phot also provides the A 0 estimates used by TGE to produce an all-sky (two-dimensional) map of the total Galactic extinction, meaning the cumulative amount of extinction in front of objects beyond the edge of our Galaxy (see  Sect.
3. 4 and Delchambre et al. 2023). The various quoted magnitude limits are independent of the physical properties of the star and of the quality of the spectra. Instead, these limits came from the Apsis processing scheme and from processing time limitations 3. In addition to and in contrast with the classifications from some of these analysis modules, Apsis comprises two modules dedicated to empirical classifications of sources. DSC classifies sources probabilistically into five classes: quasar, galaxy, star, white dwarf, and physical binary star. It is primarily intended to identify extragalactic sources, however, and OA complements this classification by clustering the sources with the lowest classification probabilities from DSC. See Sect. 3. 6 and details in  Creevey et al. (2023)  and  Delchambre et al.
(2023). We summarise the target selection of the Apsis modules in Fig. 3. We used the inverse parallax as a proxy to emphasise the stellar loci of the targets. Even though we did not explicitly select for G BP -G RP colours, we note that most of the sources with G BP -G RP < -0. 8 mag in Gaia DR3 are not stellar objects according to the Apsis processing definitions. This selection means that stellar evolution models (e. g. PARSEC  4  ) do not predict bluer stars than G BP -G RP < -0. 6 mag in the absence of noise in the measurements and within the chemical abundance regime of our analysis.
Stellar processing modules and stellar definition(s) A principle of Apsis in Gaia DR3 is to use only Gaia data on individual sources when inferring the APs. We only used non-Gaia observations for validation and calibration. We defined stellar objects as those that remain after removing other types of objects: for instance, extragalactic sources (i. e. galaxies and quasars; Gaia Collaboration 2023b) through dedicated modules such as DSC and with proper motion, Gaia brightness, and colour selections. Apsis currently ignores morphological information  (Ducourant et al. 2023)  and does not take stellar variability  (Rimoldini et al. 2023)  into account. As it works with combined epoch spectra (BP, RP, and RVS), some time-variable sources (e. g. Cepheids) received spurious APs from Apsis. Eyer et al.
(2023)  summarises the characterisation of variable sources with dedicated pipelines. In the future, we plan to investigate using epoch data and determine whether variability information could improve the quality of our results. A consequence of our analysis design is that Apsis can assign multiple sets of APs to any given source. Figure  2  illustrates the overlap between modules, which for example, leads to four temperature estimates for some main-sequence stars. The values we derive not only depend on the data we measure, but also on the stellar models we adopt (as embodied in the training data) and other assumptions made, see  Creevey et al. (2023)  for a brief overview and the online documentation for details. We can never know the true APs of a star with 100% confidence. Which estimate to use inevitably remains a decision for the user.
phot_g_mean_mag. Apsis derived some of the APs from the analysis of the RVS spectra. The RVS processing pipeline provided us with time-or epoch-averaged spectra, also called mean spectra, after removing potential cosmic rays and the deblending of overlapping sources. The pipeline delivers the spectra in their stellar rest-frame, that is corrected for the radial velocity of the star (gaia_source. radial_velocity), and normalised at the local (pseudo-)continuum (T eff ≥ 3500 K). Our analysis used these final spectra resampled from 846 to 870 nm, with a constant spacing of 0. 01 nm. Seabroke et al. (in prep. ) described the processing of RVS spectra in detail. However, Apsis modules rebin the spectra to their optimal use-cases to increase the signalto-noise ratio of their relevant spectral features  (Creevey et al.
2023, for details). Most of the Apsis modules produced APs from the analysis of the BP and RP spectra (see examples in Fig. 4 ). Gaia 5 Gaia eDR3 provides the parallax zero-point code https://www. cosmos. esa. int/web/gaia/edr3-code A28, page 4 of 36 DR3 provides the (epoch) mean BP and RP spectra in a series of coefficients associated with Gauss-Hermite polynomials. This format results from the complexity of the prism observations. Carrasco et al. (2021)  described the processing of the spectra. These coefficients contain a flux-calibrated (mathematical) continuous representation of the spectra that the Apsis pipeline internally samples 6 approximately uniformly in pseudo-instrumental pixel space, but non-uniform in wavelengths (see Fig. 4  from  Creevey et al. 2023 ). 6 Gaia DR3 provides GaiaXPy, a Python package to sample the BP and RP spectra; https://www.
g. Andrae et al. 2023; Creevey et al. 2023; Delchambre et al. 2023; Recio-Blanco et al. 2023; Lanzafame et al. 2023). For guidance, Appendix D compiles the various estimates of stellar parameters from Gaia DR3 cast into the categories described above (corresponding to the following subsections). The compilation indicates which Apsis module produces them, and which table and fields store the values in the Gaia catalogue. We emphasise that the field names correspond to the catalogue in the Gaia Archive but names may differ when using partner data centers.
Distances Two Apsis modules provide distance estimates: GSP-Phot for single stars and MSC for unresolved binary stars. Both modules analyse the BP and RP spectra with the Gaia parallaxes to derive distance estimates simultaneously with other astrophysical parameters. We list the catalogue fields related to the distance estimates of the two modules in Table  D. 1. For GSP-Phot, the distances are reliable out to ∼2 kpc. Beyond 2 kpc, GSP-Phot systematically underestimates distance, as is evident from star clusters, for example. Figure  7  compares the median GSP-Phot distances of stellar members for each cluster with their literature values by Cantat-Gaudin et al. (  2020 ) derived using Gaia DR2 data through maximum likelihood. We included the Gaia DR3 variable zero-point on parallaxes mentioned in Sect.
Consequently, the atmospheric parameters span a great variety of spectral types, from O to M, and even some L-type stars, some of which require target-specific treatment (partly handled by the ESP-modules in Apsis). Depending on the star (spectral and luminosity) class, we used either empirical or theoretical atmospheric models to estimate the atmospheric parameters of the stars, and sometimes both. The theoretical models try to model the relevant physical processes of the matter-light interaction in stellar atmospheres, while the empirical models capture some hard-to-model observational effects. The overlap between   2020 ) (blue points). We quote the mean absolute differences (MAD), the absolute difference at 90% confidence (AD90%) and the rms differences (RMSD) to the measured parallax for the two samples in both panels.
The panels show the same set of stars with estimates from both modules. The anti-diagonal dashed line highlights the parallax as inverse distance. models and application ranges of Apsis modules allows us to confirm consistency or the lack thereof (see overlaps in Figs. 2  and 11 ). Below we summarised our validation results for the parameters T eff , log g, [M/H], and [α/Fe] that various modules of Apsis estimate (see Table  D. 2). We first focus on the FGK-type stars as these constitute the majority of stars in the Gaia data set. Mainly GSP-Phot and GSP-Spec overlap on this stellar type interval. We emphasise that the application range of the Apsis modules varies significantly. To facilitate, we thus organise the description per module. One way to validate the Gaia-based APs and simultaneously quantify their precision is to compare them with large stellar surveys in the literature.
Unresolved binaries In Apsis, the MSC module aims to distinguish between the two components of binaries by analysing their composite BP/RP spectra. It assumes that these sources are blended coeval stars (same distance, extinction, and metallicity). We were unable to create sufficiently high-quality synthetic models of BP and RP spectra of unresolved binaries; they did not fully model the instrumental (and data reduction) effects for these sources. A28, page 23 of 36 Instead, MSC implements an empirical set of models constructed from observed BP and RP spectra of spectroscopic binary stars (see  Creevey et al. 2023, for details). As a result of the limited number of unresolved binaries for reference with APs, MSC adopted a strong [M/H] prior centred on solar values. MSC analysed all sources with G < 18.
25 mag and therefore inherently analyses single stars as well (assuming a binary source). Similarly, GSP-Phot takes all sources to be single stars. As internally MSC operates very similarly to GSP-Phot, we can compare their overlapping results more robustly than any other Apsis module. Figure  36  compares APs from MSC and GSP-Phot parameters with those from the binary sample of  El-Badry et al. (2018). We find an expected negative bias in temperature and log g from GSP-Phot because it assumed that these sources are single stars. They correspond to a luminosityweighted average between the primary and the secondary. Commonly, this leads to a lower T eff and log g to reach the observed brightness of the binary system with a single star. We find that despite its strong solar metallicity prior, the posterior of [M/H] from MSC are broad.
However, these derive from supervised classifications and comparisons against models, which limits discoveries of peculiar objects. On the other hand, the OA (outlier analysis) software is an Apsis module that aims at identifying groups of similar objects in the Gaia DR3 sample exclusively according to their BP and RP spectra. The approach of OA to unsupervised clustering is entirely empirical by implementing self-organising maps  (Kohonen 2001). One can further explore the resulting clusters, label them, and identify new classes of objects. However, OA analyses only 10% of the sources that DSC processed, those with the lowest DSC combined probabilities of membership to astronomical classes. They represent about 56 million sources in Gaia DR3. We note that the analysis scope will expand in Gaia DR4.
The poorer the data, the more strongly our prior dominates our estimates. Our priors vary significantly from one Apsis module to the next. None of the modules included a threedimensional extinction map or a detailed Milky Way model. One should expect significant differences with other AP catalogues when the prior dominates. However, in reality, if the actual stellar population, extinction, or reddening distributions are very different from those of Galactic models, these differences may partially indicate these deviations. To derive stellar APs, we implicitly assumed that all Gaia sources are single stars in the Galaxy (apart from MSC). These estimates are most likely incorrect for any non-single star (binaries, extended sources, or extragalactic sources). Furthermore, our stellar models also had intrinsic limitations in the range of parameters they covered.
These works helped to validate our results. In addition, it provides the community with values of reference to explore and understand the content of Gaia DR3 better. Gaia DR3 is not an incremental improvement of the Gaia data. It multiplies the quantities of multi-messenger information of Gaia with new data products (e. g. BP, RP, RVS, and APs). We increased the volume of sources with APs by a factor of 5, but we also increased the number of APs from 2 to ∼40. Gaia DR3 represents a significant step forward to anchor all current and future spectroscopic surveys in a common ground, and it provides the most comprehensive view of our Galaxy.
2022), Gaia-ESO (Gilmore et al. 2012; Blomme et al. 2022) , GALAH (Buder et al. 2021) , LAMOST (Wu et al. 2011 (Wu et al. , 2014)) , and RAVE (Steinmetz et al. 2020)  catalogues. We characterised the GSP-Phot results by a median absolute error in T eff of 119 K, and a mean absolute error of 180 K across the various mentioned datasets (details in Andrae et al. 2023 ). The difference between the two statistics translates into the complexity of the distributions. The variations in log g and [M/H] affect the BP and RP spectra only weakly in contrast with the temperature. Although GSP-Phot analyses the BP/RP spectra with the Gaia parallax information and isochrone models, this combination allows GSP-Phot to determine log g and [M/H] estimates. In Andrae et al. (2023) , we compared our values to seismic log g values of solar-like oscillators from Serenelli et al.
Acknowledgements. We would like to thank the anonymous referee for the review of the manuscript and  Carme Jordi  for the very constructive feedback during the internal review of the manuscript. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the  Gaia Multilateral Agreement. This research has used  NASA 's Astrophysics Data System, the VizieR catalogue access tool ( CDS, Strasbourg, France ). This publication made extensive use of the online authoring Overleaf platform (https://www.
overleaf. com/). The data processing and analysis made use of matplotlib (Hunter 2007),  NumPy  (Harris et al. 2020), the IPython package (Pérez & Granger 2007),  Vaex  ( Breddels & Veljanoski 2018 ), TOPCAT (Taylor 2005), pyphot (http:// github. com/mfouesneau/pyphot),QuantStack xtensor (http://xtensor. readthedocs. io/)and R (R Core Team 2013). Part of the calculations have been performed with the high-performance computing facility SIGAMM, hosted by the  Observatoire de la Côte d'Azur. C. B. J. , R. A. , M. F. , and J. R. acknowledge the support from the  DLR (German space agency)  via grants  50QG0602 ,  50QG1001 ,  50QG1403 , and  50QG2102. A. B. , A. R. B. , A. C. , G. C. , G. K. , Pd. L. , C. O. , P. A. P. , M. S. , and F. T. acknowledge financial supports from the french space agency (CNES),  Agence National de la Recherche  ( ANR 14-CE33-014-01 ) and  Programmes Nationaux de Physique Stellaire & Cosmologie et Galaxies (PNPS & PNCG)  of  CNRS/INSU.
Introduction Thermal Scattering Laws in the international libraries were all produced with the LEAPR module of NJOY  [1]. However, the formalism in LEAPR is limited especially to account for the structure of the molecules. In addition, it is rather painful to accommodate new physics within the LEAPR formalism through its static input file. In our case, we encountered issues to analyse neutron scattering experiments for which some experimental corrections are needed, such as texture. It was also di cult to investigate the complexity of the dynamical structure factor for light water or to add an anharmonic correction to the nominal vibrations of the atoms. For these reasons, we started to developed the CINEL code to avoid using LEAPR in the generation of the TSL. Models implemented in the CINEL code are presented in section 2.
Presentation of the CINEL code The CINEL code  [2]  is composed of three modules. The CUBIC module treats the coherent and incoherent neu-⇤ e-mail: gilles. noguere@cea. fr tron scattering cross sections. The INELASTIC module calculates the inelastic scattering contribution. The third module, namely SVT, is devoted to study various Doppler models above the thermal cuto↵ energy. TSL provided by CINEL are stored in ENDF-6 format and tested thanks to the Monte-Carlo code TRIPOLI4  [3]. In the INELASTIC module, the theoretical description of the inelastic dynamic structure factor comes from the  LEAPR module. It relies on the phonon expansion method enriched by specific models for light water. In order to reproduce the experimental quasi-elastic neutron scattering (QENS) peaks, we implemented a roto-translational di↵usion model with a random jump di↵usion correction and a fludicity factor (Fig.
1 ). In the CUBIC module, the incoherent elastic scattering cross sections rely on the LEAPR model. For the coherent contribution, a generalized formalism was implemented to account for structure information stored in CIF files, textured materials and anharmonicity via a third-cumulant coe cient as proposed by Willis  [4]  For the propagation of the TSL uncertainties, we use the CONRAD code  [5] , which contains a mathematical framework for generating covariance information from model parameters. Figure  2  shows the results obtained for ice and light water. Example of elastic scattering cross sections of 16 O and 240 Pu in PuO 2 are shown in Fig. 3. The so-called thermal cuto↵ energy delimits the low energy neutron cross section, which is reconstructed with the CINEL code and the resolved resonance range, which is reconstructed with a R-Matrix formalism.
Conclusions Results summarized in this paper show that the CINEL code is able to provide thermal scattering laws for materials of interest for nuclear applications with temperatures ranging from 40 K to 1000 K. The combination of INS and neutron di↵raction experiments probes the consistency of the CINEL models. However, some experimental limitations make the use of ab initio simulations essential. Evaluations for Zry4, ThO 2 and ↵-U are still in progress. The UO 2 and PuO 2 files were delivered at the NEA data bank in the test version JEFF-4T1 [8]. The CINEL code and validation tests will be made available at the NEA data bank before the release of JEFF-4 in 2024.
[14] [15] [16]  It is proposed that machine learning's expertise in pattern recognition and feature extraction  17  is complementary if not substitutive to manual inspection of electrochemical data. 11, 12, 18  For example, our recent work reported a deeplearning (DL) model based on the architecture of ResNet (Residual Neural Network)  19  that automatically analyzes cyclic voltammograms (Fig. 1A ), assuming the presence of only one redox event, and designates the probable mechanism among five of the most common ones in homogeneous molecular electrochemistry. 12  The ResNet model yields a probability distribution for five mechanisms, represented as a vector y = {yi} (i = 1 to 5) in which yi refers to the mechanistic propensity of the i-th mechanism. Such a probability-driven analysis provides a more satisfying accommodation given the finite amount of available electrochemical data and the finite measurement resolutions of instrumentations.