dataesr/scientific-paragraphs-categorization huggingface dataset:
Band 6 Interferometry from ALMA We present new long-baseline imaging of CO(2-1) from the Atacama Large Millimetre Array (ALMA). This Band 6 observation was taken as part of program 2015. 1. 00370. S (PI: Treister) in three blocks between November 2015 and May 2016 for an aggregate exposure time of approximately 4600 seconds and has a resolution of 0. 06 × 0. 03. To improve the coverage of larger-scale structure, the data from program 2015. 1. 00003. S (PI:  Saito; Saito et al. 2018)  were also included. These data provide the highest resolution view of the cold molecular gas to date, which we contrast with other phases of the ISM at similar spatial resolutions in this paper. A complete analysis of the CO(2-1) and Band 6 continuum dataset is presented in  Treister et al. (2020).
We do not see a trend in [O III]/Hβ along the ∼300 pc of the ridgeline, although we also do not have a measurement for the density of hydrogen along the same spatial scales. (We note that the molecular gas mass surface density from our CO(2-1) emission does not show a significant trend along the ridgeline. ) We summarize the evidence in support of each scenario in Table  2. Although not conclusive, we find the evidence more in favor of a beam of AGN photoionization causing the enhanced [O III]/Hβ in the East arm. We present high spatial resolution emission line maps from the Hubble Space Telescope, ALMA, and the Keck Observatory, and optical integral field spectroscopy from the Wide Field Spectrograph of the Butterfly Nebula of NGC 6240. Our dataset enables a detailed comparison of the multiphase energetics in and around the filaments and bubbles associated with starburst-and AGN-driven winds.
However, towards the edges of the nuclear region and along the filaments, the fraction does increase to roughly 10 -4. Cloud-crushing in the bridge associated with the two nuclei merging is therefore not a significant cause of shock-excitation in NGC 6240 compared to outflows. • A narrow ridge of elevated [O III]/Hβ emission in the eastern arm aligns spatially with the minor axis of the south nucleus' disk. This emission is likely either a tightly-collimated outflow from the AGN or a pencil-beam of radiation that found a path through the otherwise thickly-obscuring interstellar medium. The highly ionized gas shows none of the kinematic signatures of an outflow, favoring the latter scenario. Nebula. Indeed, diffuse [N II] and Hα extend in our WiFeS data > ∼ 5 kpc north of the upper edge of the panels in Figures 16 and 17, as was also seen in the narrowband images of  Veilleux et al.
(2003). This diffuse emission is detected at low significance in our HST F673N and F680N images; the detections are below our 3σ cutoff and are therefore masked out. However, extended diffuse [S II] is either considerably weaker or undetected even in our WiFeS dataset. In Figure  18 , we translate the line ratio maps from Figure  17  into the the optical emission line diagnostic diagrams from Baldwin et al. (1981)  and  Veilleux & Osterbrock (1987). The spectra across the field-of-view show elevated line ratios mostly falling in the LINER classification region from  Kewley et al. (2006). We see evidence of a shock-mixing sequence extending  from lower line ratios to higher in all three panels, likely demonstrating spatial regions where ionization from outflow-induced shocks dominates more (top right) or less (bottom left) over star formation  (Rich et al.
We would like to thank many for helpful discussions related to the interpretation of this rich dataset, including  Phil Appleton ,  Bruce Draine ,  Rosalie McGurk ,  Patrick Ogle ,  Rick Pogge , and  Ralph Sutherland , and  Emanuele Nardini  and  Junfeng Wang  for sharing their processed X-ray maps. We also thank the referee for careful readings and especially the scientific editor for patience while pandemic situations delayed our responses. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the  indigenous Hawai 'ian community; we are privileged to be guests on your sacred mountain. We wish to pay respect to the Gamilaraay/Kamilaroi language group Elders -past, present and future -of the traditional lands on which Siding Spring Observatory stands, and to the  Atacameño community of the Chajnantor Plateau , whose traditional home now also includes the ALMA observatory.
Support for AMM was provided in part by  NASA  through  Hubble Fellowship grant  # HST-HF2-51377  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555 , and in part from the  National Science Foundation  under Grant No. 2009416. AMM and LJK acknowledge the support of the  Australian Research Council (ARC)  through  Discovery  project  DP130103925. Parts of this research were supported by the  Australian Research Council   Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013. CEM acknowledges support by the  National Science Foundation  under award number  AST-0908796. KLL is supported by  NASA  through grants  HST-GO-13690.
Coupling asteroseismic constraints to precise and accurate astrometric data from Gaia, and data from large-scale spectroscopic surveys, enables the study of the chemical and dynamical evolution of our Galaxy with unprecedented temporal resolution. To some extent, results from the exploitation of asteroseismic data went even beyond the expectations of those who, starting from the early 1980s, have been advocating for a European space mission dedicated to stellar seismology (see e. g. Roxburgh, 2002 , for a review on the formidable foresight and decades-long efforts leading eventually to space-based asteroseismology). As discussed in more detail below, results from Kepler and CoRoT have primarily demonstrated the potential of asteroseismology as a versatile and powerful tool. With the benefit of hindsight, i.
The paper is organised as follows: in Sec. 2 we describe in detail the proposed science questions. We then translate scientific requirements into a list of targets and we provide a possible mission design aided by simulations / calibrations using Kepler data (Sec. 3). Before delving into the science objectives, we provide below a succinct summary about the inferences on stellar structure that one can make from the observation of solar-like oscillations (Sec. 1. 1), and clarify the limitations of present and planned asteroseismic missions (Sec. 1. 2). 1. 1 Preamble -what can we infer from high-precision, high-cadence, long photometric time series of solar-like oscillators? Among the various classes of pulsating stars, we focus here primarily on those showing rich spectra of solar-like oscillations, which are global oscillation modes excited and intrinsically damped by turbulence in the outermost layers of convective envelopes.
, 2018; Thomas et al. , 2019). The precision and robustness with which one can achieve such detailed constraints on stars is strongly dependent on the length and quality of the photometric time-series available. The analysis requires, for instance, longduration time series to obtain the requisite frequency resolution for extracting clear signatures of rotation in the oscillation spectrum. Examples of how the different duration of a dataset affects the seismic inferences is presented e. g. in  Davies and Miglio (2016) and , extensively, in Mosser et al. (2019). The mapping from the science questions outlined in Sec. 2 into observational tests and, finally, into a possible mission design (Sec. 3) will in fact be greatly aided by the knowledge acquired on the analysis and interpretation of existing seismic data.
Limitations of present/planned asteroseismic missions In spite of being mainly planet-hunting missions, CoRoT  (Baglin et al. , 2009)  and Kepler  (Borucki et al. , 2010)  have marked a milestone in stellar astrophysics, allowing the inference of stellar properties (global parameters and internal structure) for a few thousands of stars, and opening the way to the synergy between asteroseismology and stellar population studies. Due to different telescope characteristics and observation strategies, the asteroseismic databases differ in spatial and frequency resolution, signal-to-noise level, and sampled regions of the sky. While Kepler observed during four years a 116 deg 2 field towards Cygnus-Lyra, CoRoT collected 3-6 months light-curves from more than ten 4 deg 2 fields in the Galactic disc in the Center and Anticenter directions.
K2  (Howell et al. , 2014)  has observed 19 fields close to the Ecliptic, during 75 days each. The Kepler and CoRoT databases comprise currently more than 20000 G-K red giants (and 600 dwarfs and subgiants) for which at least the seismic global parameters ∆ν and ν max (and hence stellar mass and radius) have been measured (e. g. Yu et al. , 2018; Mosser et al. , 2010; de Assis Peralta et al. , 2018; Chaplin et al. , 2011; Davies et al. , 2016; Michel et al. , 2008; Anders et al. , 2017). For a third of them, period spacing (∆P ) and coupling factor values have been also estimated (e. g. Vrard et al. , 2016; Mosser et al. , 2017a). Finally, the internal rotation profile has been derived for a few main-sequence pulsators (i. e. Benomar et al. , 2015; Aerts, 2015)  and larger samples of evolved stars  (Beck et al.
Kepler observations of NGC 6791 and NGC 6819 -two Galactic open clusters with ages of ∼ 8 Gyr and ∼ 2. 4 Gyr, respectively -have shown an enormous potential clearly revealing, for instance, the presence of nonstandard evolution processes  (Handberg et al. , 2017). However, there are only four stellar clusters in the Kepler database, with the two young and sparse clusters NGC 6811 and NGC 6866 providing less stringent constraints than NGC 6791 and NGC 6819. In addition, the clusters M67 and M4 have been targeted for 70 days with K2, but such data do not allow detailed asteroseismic studies of stellar structures  (Stello et al. , 2016; Miglio et al. , 2016). Overall, we have detailed asteroseismology for just two clusters, covering a small region of the age-metallicity plane. Moreover, the distribution of Kepler and CoRoT targets peaks at slightly sub-solar metallicity, and their selection functions are complex since they have been shaped by planetary science.
Science Objective 1: Unanswered questions in stellar astrophysics Research in stellar astrophysics is now reflourishing, thanks to the availability of the long-sought combination of high-precision spectroscopic and astrometric data, and to the advent of exquisite asteroseismic constraints from space-borne telescopes. However, while Kepler data have started exposing the limitations of current models, robust and more stringent tests of stellar structure are currently limited by the composite nature of the stellar population observed, and by the scarce number of low-metallicity stars with asteroseismic constraints. High-precision tests of stellar physics need to be performed in "controlled environments", and clusters are the best stellar physics laboratories we have access to. Constraints from star counts, photospheric chemical abundances, and asteroseismology, together with strong priors on population-level parameters (e.
Spin alignment in clusters Recent results have suggested that the alignment of stellar rotation axes in clusters is not isotropically distributed, but rather has a preferential inclination angle  (Corsaro et al. , 2017). This result is based on asteroseismology analysis of Kepler data for stars in NGC 6791 and NGC 6819. The generally advocated explanation for the alignment of stellar rotation axes in clusters is that the stars were born within a giant molecular cloud that contained significant amounts of angular momentum, which was inherited by the stars as they formed (e. g. Corsaro et al. , 2017; Rey-Raposo and Read, 2018). While some caution needs to be taken in interpreting these results, due to potential systematic effects in inferring the stellar inclination angle from Kepler analyses  (Kamiaka et al.
g. Saha et al. , 2019) , whose fractions are far from being established. For instance, while the precise HST photometry from  Brown et al. (2010)  indicates only old stars, the spectroscopic masses of microlensing dwarfs from  Bensby et al. (2017)  reveal a ∼15% fraction of young stars. While large spectroscopic surveys are providing excellent kinematics and chemical abundances, the weak point of present bulge studies resides in the lack of accurate distances and ages. For distances, even by combining Gaia DR2 and APOGEE DR16, the Bayesian spectrophotometric code StarHorse  (Anders et al. 2019 , Queiroz et al. 2020)  still has distance uncertainties of the order of 10% in that region -therefore not only insufficient to disentangle the co-existing stellar populations in the bulge region, but also to get large enough samples of stars.
The principal difficulties stem from the high extinction values, its unknown distribution along the line-of-sight, and the position-dependent non-standard extinction curves (e. g  Nataf et al. , 2016; Saha et al. , 2019). Such problems will persist even with Gaia parallaxes, which will not be able to peer through the dust to reveal the real stellar distances over most of the bulge. An even more incomplete situation is the one for age estimates in the bulge region. In this specific front, asteroseismology can be of great help, by providing accurate masses and radii of bulge red giants, and hence their ages and distances. Combined with Gaia's proper motion data, these data enable us to map the age of stars depending on the orbits. Stars in the bar structures, such as the long bar and the shot X-shaped feature, are in the trapped orbit, and the age distribution of stars in the orbit of each feature tells us the formation epoch of each component of the bar structure.
A careful field selection will be needed in this case, and will be aided by combining Gaia data with ongoing/near-future spectroscopic surveys (such as 4MOST, APOGEE South, and MOONS). The observations would be complementary and have a clear synergy with the recently selected Japanese-led Small-JASMINE mission (planned launch in mid 2020s, http://jasmine. nao. ac. jp/index-en. html), which will provide near-infrared astrometry and time-series photometry data in the Galactic nucleus region (within ∼ 0. 7 • ). 2. 4 Additional/complementary science from high-cadence, high-precision long photometric monitoring 2. 4. 1 Solar twins and "solar evolution" from M67 observations With its solar metallicity and ∼4 Gyr age, the star cluster M67 can provide extraordinary insights on the evolution of the Sun.
Activity / flares It has long been established that solar p-mode frequencies respond to the Sun's changing levels of magnetic activity and hence provide a probe of the underlying physical changes driving these variations, i. e. stellar dynamos. Seismic detections of stellar-cycle-related variability are now being extended to other solar-like oscillators thanks to precise, long-timebase photometric data from CoRoT and Kepler (see Sec. 1. 1). However, these detections are in field mainsequence stars. Here, there is a great scientific opportunity in extending studies to counterparts in markedly different stellar environments. The additional constraints provided by common cluster membership opens the exciting possibility to probe the action of stellar dynamos as a function of stellar mass. Stellar activity is likely to have a large influence on the habitability of exoplanets.
Additional targets in the field Asteroseismic surveys carried out in the 2020-2030 era will benefit from extensive new data from wide-area surveys such as Gaia, eROSITA, the Rubin Observatory (previously known as the LSST), SDSS-V, which will provide a wide variety of new targets for more detailed time-series, high-cadence photometry, both within and outside star clusters. It is hard to foresee all the possibilities here, but we might expect: better characterization of classical pulsators, eclipsing binaries, and close binary systems uncovered by "low-cadence surveys" such as Gaia and the Rubin Observatory; improved statistics of FK Com stars, which are rapidly rotating giants probably resulting from recent stellar mergers. Kepler allowed direct measurements of rotation periods for a handful of them  (Costa et al.
Acknowledgements AM, JM and FV acknowledge support from the  European Research Council   Consolidator Grant funding scheme  (project  ASTEROCHRONOMETRY , G. A. n. 772293, http://www. asterochronometry. eu). AM, BM and LG are grateful to the  International Space Science Institute (ISSI)  for support provided to the asteroSTEP  ISSI International Team. AM and WJC acknowledge the support of the  UK Science and Technology Facilities Council (STFC). GB is sponsored by the  Swiss National Science Foundation  (project number  200020 -172505 ). WC acknowledges funding from the  Swiss National Science Foundation  under grant  P400P2 183846. We thank  G. R. Davies  for providing the power spectra used in Fig. 1. N. L. acknowledges financial support from " Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU, France.
Chemical abundances database The present study relies on several samples of objects: globular clusters and field stars, both from the Milky Way and its satellites. We made use of abundances of europium, yttrium, and [Mg/Fe], collected from multiple literature works. The adopted references for the abundances of the different elements and populations analysed in this work are summarized in Table  1. The analysed Milky Way field stars abundances come from three different compilations: a photometric selection of metalpoor stars  (Roederer et al. 2014 ), a study of heavy-element abundances for high-α and low-α stars at intermediate metallicity  (Fishlock et al. 2017) , and a selection of high transversal velocity stars from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey  (Abolfathi et al.
This Milky Way control group allows us to directly study the correlation between heavy elements and α-elements in our Galaxy (cf. Sect. 5). In addition, the chemical abundances of Milky Way satellites were analysed using a compilation with metallicities [Fe/H] < -0. 5 dex, obtained from the SAGA database  (Suda et al. 2008). We gather stars with Y, Eu, and Mg abundance determinations, excluding those with only upper limits, carbon-enriched stars (defined as [C/Fe] < 0. 9 dex if [Fe/H] < -1. 0 dex), and objects reported as binaries. The total sample comprises 115 stars in satellites, with estimates of Fe, Mg, Y, and Eu. Concerning the [Y/Eu] uncertainty estimates, we examined the abundance dispersion of the objects analysed by more than one study, including the dwarf stars database. The mean dispersion in the [Y/Eu] ratio is 0.
Orbital parameter estimations We complemented the chemical analysis of Milky Way objects with orbital parameters based on Gaia data (Gaia Collaboration 2018a). For globular clusters, the orbital parameters were taken from Model-2 in Gaia Collaboration (2018b). These orbital parameters were computed as the average values over 10 Gyr of integration. To this purpose, we used the median values obtained from 1000 orbits for each cluster obtained through Monte Carlo realizations of the initial conditions, considering the observational measurements and their errors. In particular, the average inclination of an orbit was computed as arccos (Lz/Ltot). In our convention, the orbital inclination is defined from the Galactic plane and comprised between 0 • and 180 obtained with different Galactic potentials (defined as Model-1, -2, -3 in Gaia Collaboration 2018b).
In particular, the dispersion in the orbital inclination (estimated as the third quantile value of the differences distribution between two models) is six degrees. In addition to this main dataset of cluster orbits, we completed the sample with six additional objects from  Vasiliev (2019). For our field stars samples, we derived the orbital parameters using the Python package galpy  (Bovy 2015). We assume the MWPotential14 Milky Way mass model included in this package. We derived the action parameters through the actionangle isochrone approximation  (Bovy 2014). As input parameters we used the radial velocities gathered in Simbad, the Gaia DR2 proper motions, and the distances from Bailer-Jones et al. (2018). In addition, we checked the effect of using two different methodologies of the dynamical parameters for clusters and field stars.
To this purpose, we re-computed the clusters orbital inclinations using the field stars methodology calculated the differences with respect to the Model-2 orbital results from Gaia Collaboration (2018a,b). The median absolute deviation of the orbital inclination differences is 2. 5 degrees, confirming the consistency of the two approaches. Finally, we assessed the impact of the detected Gaia kinematic biases  (Schönrich et al. 2019 ) in our data. For the field star samples, only 18 targets had a few parameters outside the Schoenrich et al. quality cuts and were excluded from the analysis. Regarding the globular cluster data, the Gaia Collaboration (2018b) database is within the quality cuts, and the Vasiliev et al. compilation uses literature distances and line-of-sight velocities not concerned by the Gaia parallax bias.
Acknowledgements. This work has made use of data from the  European Space Agency (ESA ) mission Gaia Data Processing and  Analysis Consortium  (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. ARB, PdL and EFA acknowledge financial support from the  ANR   14-CE33-014-01. TA has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement number  745617  and also acknowledges funding from the  MINECO (Spanish Ministry of Economy ) through grants  ESP2016-80079-C2-1-R  ( MINECO/FEDER, UE)  and  ESP2014-55996-C2-1-R (MINECO/FEDER , UE).
Data and simulated event samples The analysed   collision data correspond to the full recorded LHC Run-2 data set at √ 𝑠 = 13 TeV with an integrated luminosity of 139 fb -1 after data quality requirements  [21]. The events were collected with a combination of single-lepton, dilepton, diphoton, and lepton+photon triggers. Standard electron triggers, which require narrow, isolated EM energy clusters, are efficient if the two electrons in the event have a very small angular separation Δ  , as the two electrons produce a cluster that is similar to that of a single electron. For Δ  > 0. 1, two separate EM clusters are typically produced. For the 2017 and 2018 data-taking periods, a dedicated trigger was introduced which requires at least one photon with  T > 35 GeV and at least one EM cluster with  T > 25 GeV matched to at least one ID track.
Executive Summary Impacts due to near-Earth objects (NEOs) -asteroids and comets whose orbits can evolve to cross Earth's -constitute a natural hazard that can cause the extinction of humans and many other species. It is well documented that NEO impacts have already catastrophically altered Earth's evolution by rendering the dinosaurs extinct1 and could have played a role in the four other major mass extinction events preserved in the geologic record. However, we now possess the technical means to prevent most impacts if the potential impactor is detected sufficiently far in advance. While the probability of a devastating event in our lifetimes is low, it is not negligible, and the potential consequences are so catastrophic that society is well justified in addressing the threat. For example, in June 2020, asteroid 2020 LD, estimated to be between 50-200 m, passed inside the orbit of the Moon moving at 27 km/sec relative to the Earth.