dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with Gaia parallaxes We compared our measured orbital parallaxes from this study and from our previous works  (Gallenne et al. 2016 (Gallenne et al. , 2018b (Gallenne et al. , 2019) )  with the Gaia third data release (Gaia Collaboration 2023). We applied a zero-point offset to each Gaia value following the correction recipe from  Lindegren et al. (2021a). As we can see in Fig. 8 , we found that ∼50% (8/16) of the sample is more than 1σ away. To check if Gaia would detect the astrometric signature of the primary star around the centre of mass, that is to say the influence of the secondary star on the primary, we calculated a S/N as follows: a phot (mas) = a(mas) M 2 M 1 + M 2 - 1 1 + 10 0. 4∆m V (3) S /N = a phot σ fov , (4) where a phot is the photocentre semi-major axis related to the angular semi-major axis a, the mass of the components M 1 , M 2 , and the magnitude difference ∆m V = m 2m 1.
This parameter is a good metric to distinguish between a good or bad single-star astrometric solution, whose threshold is usually set to be around 1. 4. First, we notice that most systems have a large S/N and they will be detected by Gaia in the next data release (DR). DR3 provides results for about 800 000 binary stars, including solutions for astrometric, spectroscopic, and eclipsing binaries (Gaia Collaboration 2023;  Halbwachs et al. 2023 ). Unfortunately, only the known eclipsing system AK For has published full orbital solutions, with a corrected parallax improving the agreement with our measurement from 0. 7σ to 0. 6σ. However, their given eccentricity of 0. 287 ± 0. 098 is not at all consistent with the zero eccentricity we measured or previously published. This discrepancy likely comes from the fact that the Gaia solutions only fitted the astrometry, which poorly constrained some orbital parameters of eclipsing systems (as seen with the A119, page 17 of 30 given eccentricity precision).
Conclusions We have reported new interferometric and spectroscopic observations of double-lined binary systems. We simultaneously fitted the astrometry and RVs to obtain extremely precise and accurate masses and distances for ten systems. We reached uncertainties as low as 0. 03% and an average precision of ∼0. 2%. A comparison with previous studies and different datasets demonstrated that our measurements are both precise and accurate. This was possible thanks to the precision and sensitivity of the GRAV-ITY instrument, which provided exquisite differential astrometry, with a median rms of ∼16 µas. We confronted our measurements with additional observables to four stellar evolution models and we show that theory is clearly deficient for most of the systems when fitting one common isochrone for the components in a system.
Acknowledgements. We thank  F. Widmann  and  J. Stadler  for making public the code to create the GRAVITY aberration maps. A. G. acknowledges the support of the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-15-CE31-0012-01  (project UnlockCepheids). A. G. also acknowledges financial support from the  ANID-ALMA  fund No. ASTRO20-0059. The research leading to these results has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No. 695099  (project  CepBin ), No. 951549  (project  Uni-verScale ) and from the  National Science Center, Poland  grants  MAESTRO UMO-2017/26/A/ST9/00446  and  BEETHOVEN UMO-2018/31/G/ST9/03050. B. P. acknowledges support from the  Polish National Science Center  grant  SONATA BIS 2020/38/E/ST9/00486.
Over the last few years, many studies have found an empirical relationship between the abundance of a star and its age. Here we estimate spectroscopic stellar ages for 178 825 red-giant stars observed by the APOGEE survey with a median statistical uncertainty of 17%. To this end, we use the supervised machine learning technique XGBoost, trained on a high-quality dataset of 3060 redgiant and red-clump stars with asteroseismic ages observed by both APOGEE and Kepler. After verifying the obtained age estimates with independent catalogues, we investigate some of the classical chemical, positional, and kinematic relationships of the stars as a function of their age. We find a very clear imprint of the outer-disc flare in the age maps and confirm the recently found split in the local age-metallicity relation.
(2020)  through Gaussian Process regression on LAMOST data (again using the APOGEE-Kepler data as a training set), or by  Hasselquist et al. (2020)  using The Cannon  (Ness et al. 2015)  for high-luminosity red giants in the Galactic bulge (using the local giant sample of  Feuillet et al. 2018  as a training set). In this work, we build upon the success of the works of  Hayden et al. (2022)  and  He et al. (2022)  and use XGBoost to estimate ages for 178 825 red-giant stars contained in the APOGEE DR17 catalogue  (Abdurro'uf et al. 2022). As a training set, we use the carefully produced APOGEE-Kepler catalogue of  Miglio et al. (2021). The paper is structured as follows: In Sect. 2 we present the APOGEE data used in this work (including the APOGEE-Kepler training set). In Sect. 3 we explain the XGBoost algorithm.
Data The outcome of a machine-learning regressor can only be as reliable as its training set. Therefore, it is often preferable to use a smaller, but statistically significant and highest-quality, dataset for the training phase. For red-giant stars, the longest asteroseismic time series have been obtained by the Kepler satellite  (Borucki et al. 2010)  during its first phase of observations in which it was continuously observing one large sky area in the sky, towards the constellation of Cygnus. Using APOGEE DR14  (Abolfathi et al. 2018 ) spectroscopic follow-up observations of Kepler targets  (Pinsonneault et al. 2018; Yu et al. 2018) ,  Miglio et al. (2021)  inferred masses and ages for more than 5000 giants with available Kepler light curves and APOGEE spectra using the Bayesian isochrone-fitting code PARAM  (da Silva et al.
2006; Rodrigues et al. 2017). This is arguably an asteroseismic+spectroscopic dataset that is of such a high quality that it can be used for training a machine-learning regressor. The median nominal uncertainties of the asteroseismic ages reported in the  Miglio et al. (2021)  catalogue are 23% for RGB stars and 10% for RC stars. For this study we crossmatched the results of  Miglio et al. (2021)  with the APOGEE DR17 allStarLite table  (Abdurro'uf et al. 2022) , resulting in a preliminary training dataset of 3315 stars. We clean the APOGEE-Kepler sample to contain only stars with APOGEE signal-to-noise ratio (SNREV) above 70 and nominal age uncertainties smaller than both 30% and 3 Gyr. We also exclude the few apparently young α-rich stars  (Chiappini et al. 2015; Martig et al. 2015 ; here defined as [α/Fe] > 0.
14 + 0. 0001 • (age -4. 1) 5 ), since their ages (inferred from single-star evolutionary models) are most probably flawed by binary interactions (e. g. Fuhrmann & Chini 2017; Jofré et al. 2023 ). As we are interested in using the APOGEE abundance measurements as features for the age estimator, we require that the abundance flags 2  for each of the used elements are equal to zero (meaning that their abundance estimates are unproblematic). The chosen chemical features are: When applying our age estimator to the full APOGEE DR17 dataset, we restrict our predictions to the stellar-parameter space covered by the training data (black dots in Fig. 1 , top panel), and to [Fe/H] > -1. We also require clean abundance flags for all included elements, but allow for lower signal-to-noise ratios (SNREV > 50).
Method Supervised machine-learning regression models can be trained to fit arbitrarily complex relationships between the introduced data. In our case of tabular data, we want to predict an output column (the so-called "label") based on a set of introduced input parameter columns (so-called "features"). The features (in our case stellar parameters and abundances) are the variables of the data that the model uses to train itself to predict the label (stellar age). Once the model is trained, it can be used to make predictions (age estimates) based on data for which reliable labels do not yet exist. Borisov et al. (2022)  recently benchmark-tested several regression algorithms for tabular data. They found that the bestperforming algorithm, in terms of accuracy and speed, was the well-known tree-based algorithm XGBoost.
Finally, in contrast to other gradient-boosted methods, XGBoost also performs parallel processing and tree-pruning, handles missing values, and regularises the data to avoid over-fitting. In consequence, the training time of XGBoost is often much smaller compared to other methods with similar predictive power (see  Borisov et al. 2022). Since the algorithm is very well explained in the original paper  (Chen & Guestrin 2016)  as well as in several online resources, we abstain from a deeper explanation here and only mention that XGBoost has a number of hyperparameters that can be tuned to optimise the performance for a given dataset or problem. For our use case, we use scikit-learn's  (Virtanen et al. 2020 ) GridSearch 3  to find the optimal hyperparameters for our machine-learning model. The optimal hyperparameters used in our fiducial XGBoost 4  regressor are: learning_rate = 0.
3. SHAP "bee-swarm" plot (https://shap. readthedocs. io/en/latest/example_notebooks/api_examples/plots/ beeswarm. html). The rows correspond to the input features, ordered by importance. In each row, one dot corresponds to a star in the test dataset, coloured by its (normalised) feature value. The position of each dot indicates how much and in which way each feature contributes to its output label (age). ical abundances [C/Fe], [Mg/Fe], [N/Fe], and the surface gravity, log g. In addition, Fig. 3  shows the exact effect each of the features has on the estimated age: for example, for the case of effective temperature (redder dots in the first line), we observe that an increase in its value implies a negative SHAP value, i. e. a decrease in the inferred age. The usefulness of carbon and nitrogen abundances for the prediction of red-giant ages in the absence of asteroseismology is discussed in detail by  Martig et al.
Biases and uncertainties The performance of our default model (trained on 80% of the  Miglio et al. 2021 data)  is shown for the test dataset (the 20% of the data not used in the training phase) in Fig. 2. We observe a very clear linear trend with little wiggles ( 0. 5 Gyr for almost the full age range), and a determination coefficient of R 2 = 0. 91. Our spectroscopic ages tend to slightly overestimate (by <0. 5 Gyr) ages for stars around 2 Gyr, while for stars between 5 and 7 Gyr our age scale tends to underestimate the seismic age by similar amounts. Beyond seismic ages of ∼11 Gyr, our method provides more seriously underestimated ages compared to the seismic scale. Considering that the age uncertainties associated with the training data are of order 10% for RC and 25% for first-ascent red-giant branch (RGB) stars 5  , the amount of scatter around the one-to-one relation in Fig.
Duplicate APOGEE observations More than 11 000 stars in our selected region of the Kiel diagram (Fig. 1 ) are contained more than once in APOGEE DR17 catalogue (for example, stars that have been observed in different fields or even by both the SDSS Telescope and the du Pont Telescope). These duplicate observations can serve to estimate realistic internal uncertainties for stellar parameters and elemental abundances (e. g. Jönsson et al. 2020, Tables   10  and 11 ). In our case we can use these multiple observations also to assess the uncertainties of our age estimates: the scatter in the stellar parameters and abundances naturally leads to a scatter in the inferred ages. The results of this experiment are A158, page 5 of 21 shown in the bottom panel of Fig. 4  (orange dashed curve). Reassuringly, the dispersion among multiple observations (with often significantly different signal-to-noise ratios and, therefore, elemental-abundance precisions) is always lower than the uncertainty estimated from the test dataset.
Asteroseismic ages in other fields (CoRoT, K2, TESS) Asteroseismology coupled with spectroscopy can significantly reduce the uncertainties of red-giant ages (e. g. Miglio et al. 2013; Pinsonneault et al. 2014). This is exactly the reason why we have chosen the APOGEE-Kepler dataset of  Miglio et al. (2021)  as a training set for our method. The concordance with this age scale is inherent to the machine-learning method used in this paper, and was demonstrated in Fig. 2  for the unseen test data. Here we compare our age estimates for the full APOGEE DR17 data with other age estimates that used similar techniques as  Miglio et al. (2021) , but on other asteroseismic and spectroscopic data. The main reason is that we need to assess whether the stellar populations contained in the training set (i.
(2017b) ,  Zinn et al. (2022), and Mackereth et al. (2021). The plot shares some similarities with the comparison plot for test sample shown in Fig. 2 : in the youngish regime, the XGBoost ages appear overestimated with respect to the seismic scale for all three external datasets, albeit much more pronounced than in the Kepler sample. In the intermediate regime (between 4 and 10 Gyr), the concordance is better on average, and there is no consistent trend among the three external seismic samples (in this regime, the age scale of Zinn et al. 2022 is lower, while the one of  Mackereth et al. 2021  is higher than ours). For the oldest ages (>10 Gyr), the XGBoost ages appear underestimated with respect to the seismic scale, at least for the case of the TESS-CVZ sample. Finally, it is important to recall that also the asteroseis-mic+spectroscopic catalogues used for comparison here are prone to sizeable statistical and systematic uncertainties.
Due to the shorter time series of especially CoRoT and K2, these uncertainties are typically much larger than the ones associated to the Kepler training data. To illustrate this, the typical statistical age uncertainties for each of the considered catalogues are shown as error bars in Fig. 6. Keeping this in mind, the concordance between our age estimates and the OC ages is not too bad: on average, our ages are greater than the OC ages by 0. 48 Gyr, and the dispersion is 1. 37 Gyr (∼40% larger than the uncertainties estimated from the test dataset in Fig. 2 ). In spite of the statistical concordance, however, we observe a tendency to overestimate age for a group of clusters younger than 2. 5 Gyr. Queiroz et al. (2023).
While traditional chemical clocks such as [α/Fe] or [Y/Mg] should be quite robust also for close binaries, post-commonenvelope phase binaries, and binary merger products, our age estimates also rely on stellar parameters and chemical abundances that are heavily affected by non-binary evolution. This is the reason why, despite the fact that we eliminated the conspicuous "young α-rich" stars (a. k. a [α/Fe]-enhanced over-massive stars) from the training set, we still find some of them in the full APOGEE sample. In particular, we find 706 stars (0. 4% of the total sample) with [Mg/Fe] > 0. 2 and spectroscopic ages 6 Gyr. These stars are very likely products of binary evolution; their ages should not be used. Another small part of our dataset (561 stars) that is most likely affected by effects of binary evolution are rapidly rotating (v sin i > 10 km s -1 ) red giants -they were recently analysed using APOGEE DR16 by  Patton et al.
Acknowledgements. This work was partially funded by the  Spanish MICIN/ AEI /10. 13039/501100011033  and by the " ERDF A way of making Europe " funds by the  European Union  through grant  RTI2018-095076-B-C21  and  PID2021-122842OB-C21 , and the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia "María de Maeztu")  through grant  CEX2019-000918-M. F. A. acknowledges financial support from  MCIN/AEI/10. 13039/501100011033  through grants  IJC2019-04862-I  and  RYC2021-031638- I (the latter co-funded by  European Union  NextGen-erationEU/PRTR). B. R. and I. M. acknowledge support by the  Deutsche Forschungsgemeinschaft  under the grant  MI 2009/2-1. C. C. acknowledges support from the  Jesús Serra  foundation. J. A. S. A. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
Pipeline Construction Our analysis relies on parent quasar catalogs generated via three tools: Redrock (RR; S. Bailey et al. 2023, in preparation) ,  31  which is a PCA-based template classifier that is part of the main DESI spectroscopic pipeline  (Guy et al. 2023) ; QuasarNet (QN; Busca & Balland 2018), 32  which is a neural network based quasar classifier (see also  Farr et al. 2020) ; and an Mg II-emission-based code, which is designed to identify AGN-like spectra that show both strong galactic emission features and broad Mg II emission. The results from the visual inspection of quasar spectra informed the need for, and use of, these tools  (Alexander et al. 2023). Initial spectral types (QSO or non-QSO for our purposes) as well as initial redshifts are determined by RR. QN and the Mg II-emission code are then run as afterburners.
g. ,  Pickering et al. 1998) , then this doublet is regarded as being a likely candidate. A similar detection method was used in  Raghunathan et al. (2016) ; however, we find that our approach improves the detection of relatively low-signal absorption systems in highsignal QSO spectra. Note that our rest-frame line separation uncertainty value, 1. 5 Å, has been chosen to consider as many candidate absorbers as possible without encompassing the restframe separation of Si IV, which is another absorber doublet that is commonly strong in QSO spectra. To further verify these systems, we next perform an MCMC analysis using the emcee software (Foreman-  Mackey et al. 2013). The decision to use MCMC to fit the relatively simple model of a doublet absorption line was made in the interest of fully understanding the posterior distributions of our parameters, as well as increasing the likelihood of recovering lowsignal absorbers.
Catalog Format and Access We describe the data columns that we catalog for each detected absorption system in the Appendix. We will now give a brief overview of these columns and their use. We first retain sufficient information to uniquely identify each analyzed healpix coadded spectrum, specifically the DESI TARGETID (see  Myers et al. 2023) , R. A. , decl. , and phase of the DESI survey. We additionally store the Redrock ZWARN bitmask, which details possible redshift warnings, as well as various TSNR2 values of each spectrum, and the best quasar redshift for each spectrum (derived from the parent quasar catalogs, as discussed in Section 2. 2). We also record the rest-frame EWs of both lines, as well as the central posterior distribution values for all five MCMC fit parameters, as described in Section 2.
Conclusion In this paper, we have presented the methods by which we detect and verify a sample of Mg II absorption systems in the data collected during the survey validation phase of the DESI survey. We model these absorption systems by first identifying possible systems in smoothed residuals and then characterizing them using Gaussian lines profiles in an MCMC process. In total, we have characterized 23,921 absorption systems in 16,707 unique quasar spectra. The parent quasar catalogs utilized in this study contain 83,207 entries, implying that 20. 1% of DESI quasars will contain an identifiable Mg II absorber. The total number of expected identifiable absorbers will then be equal to 28. 8% the total number of observed quasars (accounting for spectra with multiple absorbers). Assuming that DESI ultimately obtains spectra for three million quasars, our pilot study implies that DESI will eventually compile a sample of over 800,000 Mg II absorption systems across ∼560,000 quasar spectra-by far the largest such sample ever constructed.
Acknowledgments We thank  Guangtun Zhu  for sharing the NMF eigen-spectra of SDSS quasars. We thank the referee for their valuable feedback that has helped us improve the quality of the paper. L. N. and A. D. M. were supported by the  U. S. Department of Energy, Office of Science, Office of High Energy Physics , under Award Number  DE-SC0019022. L. N. was also partially supported by  Wyoming NASA Space Grant Consortium  award # 80NSSC20M0113. A. P. was supported by the  University of Wyoming Science Initiative Wyoming   Research Scholars Program. T. W. L. was supported by the  Ministry of Science and Technology  ( MOST 111-2112-M-002-015-MY3 ), the  Ministry of Education, Taiwan  ( MOE Yushan Young Scholar  grant  NTU-110VV007 ),  National Taiwan University  research grants ( NTU-CC-111L894806 ,  NTU-111L7318 ).
Data Reduction We have reduced the NIRSpec data for the TWA sources as well as similar observations of the planetary mass companion VHS J125601. 92-125723. 9b (VHS 1256b; Gauza et al. 2015)  from program 1386  (Hinkley et al. 2022) , which were first reported by  Miles et al. (2023). It is useful to compare the spectra of TWA 27B and VHS 1256b since they have roughly similar spectral types but different ages. For the data reduction, we began by retrieving the uncal files from the Mikulksi Archive for Space Telescopes (MAST; doi:10. 17909/jzze-5611). We performed ramps-to-slopes processing on those files using the NIRSpec Instrument Pipeline Software, 11  which features a correction for "snowballs"  (Böker et al. 2023 ) and a correction for residual correlated noise in IFU data. The resulting count rate maps (rate files) were used as inputs to the JWST calwebb_spec2 pipeline with the default processing steps, including assignment of the world coordinate system, flat-fielding, and aperture correction.
We used the JWST Science Calibration pipeline version 1. 9. 4 under Calibration Reference Data System context jwst_1063. pmap. We flagged outliers in the resulting cal files and then constructed the IFU data cubes using the calwebb_spec3 pipeline with outlier detection disabled. The latter was necessary as currently the pipeline's outlier detection is not working properly, resulting in many erroneously flagged data points and poor final cubes. We selected a spaxel size of 0 1 for the cubes and used the "ifualign" coordinate system to provide the same point-spread function (PSF) orientation among observations of different targets. Spectra for TWA 27A, TWA 28, and VHS 1256b were extracted from these cubes with an aperture radius of 4 spaxels. Before extracting TWA 27B, PSF subtraction was applied to the primary using a version of the data cube for TWA 28 that had been scaled and shifted appropriately.
In Figure  1 , we show the 2D images of TWA 27A and B for each of the three gratings before and after PSF subtraction. Flux calibration was performed with observation 62 for standard star P330E from program 1538 (PI: Karl Gordon), which utilized the NIRpec IFU with the same gratings and filters as our observations. The reduced spectra of TWA 27B and VHS 1256b are available at doi:10. 5281/ zenodo. 7876159. We have compared the reduced spectra of VHS 1256b from this work  and Miles et al. (2023). For some wavelength ranges, our version of the spectrum has less low-order structure and exhibits a more smoothly varying continuum. For instance, the absorption feature near 1. 66 μm that was noted by  Miles et al. (2023)  is absent from our reduction. We have derived synthetic photometry from our reduced spectra of the TWA sources and VHS 1256b, which we use to assess the accuracy of the absolute fluxes in these data.