adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.492.1335P (fulltext)
showing the distribution of distance values are shown in Fig. 12 . Figure 12. Distance distribution based on methods 1 and 2 described in Section 4.4 . We further analysed the differences in physical parameters computed using distances from methods 1 and 2. The left-hand panel of Fig. 13 shows a comparison of the mass distributions from method 1 (green hatched histogram) and method 2 (solid red histogram). As expected from the differences in the distance distribution, the mass distributions from the two methods are slightly different. However, the overall statistics are similar – while method 1 gives a mean and median mass of 1570 and 930 M ⊙ , the respective values obtained by distances from method 2 are 1785 and 925 M ⊙ . A t -test between the two mass distributions gives a t -value of 0.71 with a significance value of 0.48. Thus, there is no significant difference in the distribution of masses computed using distances from the two methods. Figure 13. Mass distribution using two methods (left-hand panel) and (b) MR plot obtained using the distances obtained with the approach presented by Reid et al. ( 2016 ) (right-hand panel).The dashed green line represents the power-law fit to the data. The upper and lower solid red line shows the surface densities of 1 and 0.05 g cm −2 , respectively. The right-hand panel of Fig. 13 shows the mass-radius relation computed using distances from method 2. With method 2, 287 out of 320 sources satisfy the Kauffmann criterion for potential to form massive stars. This is comparable to method 1 wherein 295 out of 320 sources satisfy the Kauffmann criterion. Thus, although the distances from method 1 are different from that of method 2, the overall statistical properties of sources associated with 6.7 GHz methanol masers are similar between the two methods. We are thus unable to make any distinction regarding accuracy of distances when considering the entire sample statistically. 5 CONCLUSION We have constructed SEDs from 870 to 70 μm for 320 6.7 GHz methanol masers using data from the ATLASGAL and Hi-GAL surveys. The SEDs from 870 to 160 μm were fitted with single-component grey body models. We observe a mean dust temperature of 22 K confirming the later evolutionary stage of the maser sources in comparison to infrared dark clouds, with some sources showing temperatures as high as 48 K. Almost 92 per cent of the methanol maser sources satisfy the Kauffmann criterion for potential to form massive stars. A comparison of the mass–luminosity diagram of the sample with simple evolutionary tracks from the turbulent core model suggest that most methanol masers are associated with massive young stellar objects with over 90 per cent in early evolutionary stages of accelerating accretion. However, there also appears to be a small population of sources that are likely to be associated with intermediate- or low-mass stars suggesting that the association between high-mass star formation and methanol maser emission is not
Paper: 2018MNRAS.478.3627A (acknowledgments)
ACKNOWLEDGEMENTS We thank Peder Norberg and Martin White for helpful discussions. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement No 734374. JA and NP were supported by proyecto financiamiento BASAL CATA PFB-06 and Fondecyt 1150300. BL is supported by the European Research Council (ERC-StG-716532-PUNCA), and UK STFC Consolidated Grants ST/P000541/1, ST/L00075X/1. This work used the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grants ST/H008519/1 and ST/K00087X/1, STFC DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure. YC and JAP are supported by the European Research Council, under grant no. 670193 (the COSFORM project).
Paper: 2017MNRAS.472.3570H (acknowledgments)
Acknowledgements The authors thank Will Hartley for useful comments that have greatly improved this paper. PWH wishes to acknowledge support provided through an STFC studentship, and the Rector and Fellows of Lincoln College for support through the Graduate Research Fund. MJJ acknowledges support from the UK Science and Technology Facilities Council (ST/N000919/1). Based on data products from observations made with ESO Telescopes at the La Silla or Paranal Observatories under ESO programme ID 179.A-2006. Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/IRFU, at the CanadaFranceHawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de lUnivers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at Terapix available at the Canadian Astronomy Data Centre as part of the CanadaFranceHawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS.
Paper: 2016ApJ...833..250W (fulltext)
wavelengths, we can see that there appear simultaneous EUV flashes and enhanced 10830 Å absorption. Actually, the enhanced absorption at 21:31:00 UT has already appeared in Figure 4 . The EUV flashes over the sunspot certainly have a spatiotemporal association with the two valleys on the 10830 Å light curve, and at the same time, they are associated with the illumination of the two micro-flares. Though we need to carry out further detailed quantitative analysis with existing models, the nice correlation will serve as direct observational evidence supporting that the PRM mechanism really works. Here it is worth mentioning that Judge Pietarila ( 2004 ) reported no correlations between helium and He ii emission and overlying coronal emission. Their conclusions were made by analyzing quiet regions and coronal holes. Considering in the paper that the two micro-flares and the excitation site are over 5 Mm away, the excitation of helium atoms by EUV illumination should be a mean effect from overlying coronal emission as well as at least nearby coronal emission. Figure 6. Top and the third rows (panels S1–S8): snapshots for the micro-flare at ∼21:20:30 UT taken on the ground by NST and in space by AIA. AIA images are overlaid with contours of sunspots. The second and fourth rows (panels L1–L8): light curves for 10830 Å and EUV brightness from 21:00 UT to 21:40 UT obtained from the boxed areas shown in the upper corresponding images, with red curves from red boxes (over the sunspot) and blue curves from blue ones (over the sites of the two micro-flares). Two vertical dotted lines indicate the peak times of the two micro-flares. As mentioned in previous sections, the most notable phenomenon is that an oscillating absorption appears inside one flare ribbon when it sweeps into the sunspot. An overall description of this phenomenon is that a dark (absorption) strip rushes out into the sweeping forth sunspot ribbon and then returns back until the next several dark strips repeat the same behavior (Figure 1 , also please watch the online movie). From the space–time diagram, the oscillating behavior is revealed as a sawtooth-like pattern, the oscillating absorption strip actually starts when the ribbon reaches the inner penumbral region. For umbral flashes and even penumbral waves, the sawtooth behavior can be observed from spectrograms taken in certain chromospheric lines like Ca ii H K lines (Beckers Tallant 1969 ). It was also called a Z pattern with fast blueshift and slower redshift excursions, which were attributed to shock waves and post-shock falling back of the material contained in these shock waves. In this event, the observations were made in the He i 10830 Å blue wing −0.25 Å, which certainly highlights the blushift component. However, the 0.5 Å bandpass of the Lyot filter is comparable to the He i 10830 Å line width (∼0.75 Å) and the line is very weak, meaning slower redshift excursions will still be visible. So the features of blueshift and
Paper: 2021A&A...651A..22W (fulltext)
Samsonov et al. (2020) that r 0 would be predicted more accurately by taking the effects of ρ SW and V SW individually into account rather than considering P d alone. However, Samsonov et al. (2020) also indicated that the changes in r 0 for a velocity change were greater than those for a density change for the same P d , especially for a southward IMF, which implied that the power-law index of V SW , M , was lower than that of ρ SW , N . We found a different result, however: A higher solar wind number density (or a lower velocity) results in a smaller r 0 of the Martian MPB for thesame P d ( M > N ). Moreover, these two results are also inconsistent with the results of Ramstad et al. (2017) , who reported that the Martian IMB was mainly pressure-dependent and that changing ρ SW or V SW individually had no significant effect for the equal dynamic pressure. The mechanism for the different effects of ρ SW and V SW on r 0 for the Earth can be explained by the solar wind electric field. It is generally greater for a higher solar wind velocity, which leads to the enhanced magnetopause reconnection rate and Region 1 current, especially for a southward IMF (e.g., Lee Lee 2020 ; Samsonov et al. 2020 ). As a result, the greater changes in r 0 occur for a velocity increase and not for a density increase for the same P d . For Mars, which is a weakly magnetized planet, the different results of ρ SW and V SW effects cannot be explained by the traditional concept of the solar wind electric field and the reconnection conditions at the Earth. On the other hand, the location of the magnetopause can also be affected and described by the pressure balance across it, as we mentioned above. To investigate the reason for the opposite effects of ρ SW and V SW at Mars as compared to Earth, we next studied the pressure balance throughout the MPB in more detail. Following Schield (1969) and Suvorova et al. (2010) , we introduced a factor k and f that describe the contributions of the different components, both thermal and magnetic, of pressure to the total pressure. We define the factor k as the ratio of the total pressure just outside the Martian MPB to the upstream solar wind dynamic pressure as follows: . Here , , and are the plasma thermal pressure, the magnetic pressure, and the solar wind dynamic pressure just outside the subsolar standoff point of the Martian MPB, respectively. is the solar wind dynamic pressure upstream of the bow shock. As the plasma thermal pressure and the magnetic pressure upstream of the bow shock are very low and can be neglected, the factor k can be used to describe how much the upstream solar wind dynamic pressure is converted into thermal and magnetic pressure just outside the MPB. The theoretical value of k in a gas-dynamic approximation assuming a normal shock is 0.881 ( Schield 1969 ). In addition, k also varies with the upstream solar wind and downstream magnetopause conditions (e.g., Suvorova et al. 2010 ). For a higher value of
Paper: 2018A&A...618L...1K (acknowledgments)
The authors thank the referee for valuable feedback. The authors thank Inga Kamp for sharing her ProDiMo model, Christian Rab for sharing prodimopy , Michiel Min for sharing OpacityTool , and Til Birnstiel for sharing twopoppy . We thank the above-mentioned and Rens Waters and Kaustubh Hakim for helpful discussions and Sebastiaan Krijt for his thoughtful comments on the manuscript. L.K. is supported by a grant from NOVA. C.W.O. acknowledges funding by the Netherlands Organization for Scientific Research (NWO; VIDI project 639.042.422). C.D. acknowledges funding by the NWO, project number 614.001.552.
Paper: 2019MNRAS.485.1843W (acknowledgments)
ACKNOWLEDGEMENTS We are grateful to an anonymous referee for valuable suggestions that have helped a lot to clarify our presentation. The study was supported by the Russian Foundation for Basic Research grant 17-02-00644. DS acknowledges support by the Deutsche Forschungsgemeinschaft through SPP 1833: "Building a Habitable Earth'' (SE 1962/6-1).
Paper: 2017MNRAS.469S.805C (acknowledgments)
Acknowledgements Support from the Centre National d'Etudes Spatiales (CNES, France) for this work, based on observations with CONSERT on board Rosetta , is acknowledged. The CONSERT instrument was designed built and operated by IPAG, LATMOS, and MPS and was financially supported by CNES, CNRS, UGA, DLR, and MPS. Rosetta is an ESA mission with contributions from its Member States and NASA. Rosetta 's Philae lander is provided by a consortium led by DLR, MPS, CNES, and ASI. The authors thank the teams of Rosetta (SGS and ESOC) and Philae (LCC and SONC) for making the CONSERT operations possible.
Paper: 2016ApJ...828...46A (acknowledgments)
M.C.A. and J.P.W, were supported by NSF and NASA grants AST-1208911 and NNX15AC92G, respectively. M.C.A. acknowledges student observing support from NRAO. N.M. is supported in part by the Beatrice W. Parrent Fellowship in Astronomy at the University of Hawaii. Leiden is supported by the European Union A-ERC grant 291141 CHEMPLAN, by the Netherlands Research School for Astronomy (NOVA), and by grant 614.001.352 from the Netherlands Organization for Scientific Research (NWO). C.F.M. gratefully acknowledges an ESA Research Fellowship. We thank J.M. Alcalá for providing VLT/X-Shooter spectra for deriving stellar parameters, Ilse Cleeves for providing ALMA continuum and integrated line fluxes for Sz 82 (IM Lup), and Scott Barenfeld for providing ALMA continuum fluxes for Upper Sco sources in advance of publication. We also thank Sean Andrews for his extremely useful comments. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00220.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Paper: 2020ApJ...897...25B (fulltext)
and 3C 273 we get signs of local nonstationarity that are not consistent among the various tests, for Mrk 421 we get multiple clues that may indicate local nonstationarity synchronous with large changes in its average flux. While we do not find any significant change in the PSD between multiple epochs for the same blazar we must note that a small temporal change in the PSD of red-noise variability may not always be detectable with the quality of data that is available. There may be an excess scatter in the binned PSD estimates obtained from light curves spanning hours to years leading to poor fits of a model PSD and subsequently higher rejection probabilities. Sampling window and Poisson noise give rise to distortions in the PSD, which are difficult to quantify, and cause uncertainties in the estimates of PSD slopes, which can eclipse any change in the slope of the PSD of the underlying process. In alternative methods of PSD modeling, such as CARMA, the difficulties in discerning a local nonstationary process persist. Kelly et al. ( 2014 ) showed, using an irregularly sampled simulated light curve stitched together from two separate stationary CARMA processes with differing variances, that a CARMA (5,2) model provided a good fit, as if the complete light curve was a single stationary process. It is as if the PSD of the combined process is a blend of the separate processes weighted according to their respective S/Ns. The local nonstationarity in the PSD of the X-ray variability of Seyfert galaxies such as IRAS 13224−3809 has been explained as being due to having an accretion rate close to the Eddington limit, or deformations in a time-evolving corona (Wilkins Gallo 2015 ; Alston et al. 2020 ). In the blazars the X-ray emission is mostly from the jet, thus if it exhibits local nonstationarity that will imply some important change in the emission process inside the jet. Alternatively, the jet variability may be driven by fluctuations in the accretion disk and in that case the nonstationarity may be due to specific kinds of changes in the disk. We find a linear flux–rms relation in the X-ray light curves of Mrk 421 separated by a period as long as two decades and over timescales of days to months. It is one of the few cases in which a flux–rms relation has been detected in a blazar across such long timescales. This, along with similar demonstrations in other blazars and other wave bands (Edelson et al. 2013 ; Kushwaha et al. 2017 ), implies that the flux–rms relation may be a ubiquitous feature of blazar variability, similar to other accreting black hole systems, e.g., BHXRBs. There is a significant difference in the best-fit slope of the flux–rms relation among the short-term and long-term observations of Mrk 421 across two decades. This may be the result of some systematic effect like error subtraction or it may be due to local nonstationarity in the source itself. For BHXRBs the slope and the flux intercept are known to depend on the
Paper: 2018ApJ...859...85T (acknowledgments)
The authors thank the anonymous referee for useful suggestions. W.P.L. acknowledges support from the National Key Program for Science and Technology Research and Development (2017YFB0203300). W.P.L. and X.K. acknowledge support by the NSFC projects (No. 11473053, 11233005, U1331201, 11333008) and the National Key Basic Research Program of China (No. 2015CB857001, 2013CB834900). X.K. is also supported by the foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20140050). The simulations were run in the Shanghai Supercomputer Center and the data analysis was performed on the supercomputing platform of Shanghai Astronomical Observatory. W.C. is supported by the Ministerio de Economía y Competitividad and the Fondo Europeo de Desarrollo Regional (MINECO/FEDER, UE) in Spain through grant AYA2015-63810-P, as well as the Consolider-Ingenio 2010 Programme of the Spanish Ministerio de Ciencia e Innovación (MICINN) under grant MultiDark CSD2009-00064.
Paper: 2016ApJ...833..209O (acknowledgments)
We thank Matthew S. Povich for his valuable constructive comments that have improved the presentation of the paper. S.O. is financially supported by a Research Fellowship from the Japan Society for the Promotion of Science for Young Scientists. H.V. C. acknowledges financial support from MOST 105-2119-M-007-022-MY3 grant, Taiwan. G.B. acknowledges the support of the Spanish Ministerio de Economia y Competitividad (MINECO) under the grant FPDI- 2013-18204. G.B. is also supported by the Spanish MINECO grant AYA2014-57369-C3-1-P. A.P. appreciates insightful discussions with Enrique Vázquez-Semadeni and Javier Ballesteros-Paredes. A.P. acknowledges financial support from UNAM-DGAPA-PAPIIT IA102815 grant, México. This paper makes use of the ALMA data: ADS/JAO.ALMA 2013.1.00312.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. Data analysis was carried out on a common use data analysis computer system at the Astronomy Data Center, ADC, of the National Astronomical Observatory of Japan. Facility: No:ALMA .
Paper: 2018ApJ...860...70C (fulltext)
definition of the periphery changes with different norms. Therefore, it is possible to overcome this limitation by adopting a different norm. Consistent with Mitschang et al. ( 2013 ), we use the Manhattan distance, or L 1 norm, as the metric for implementing chemical tagging with DBSCAN. Figure 3 illustrates the two-dimensional projections of the chemical space for our sample and the recovered members according to the DBSCAN algorithm with = 0.4 and K = 6. We found that the recovery rate quickly diminishes when we deviate from these values. As compared to chemical tagging performed with K -means, DBSCAN recovers M13 at a similar rate (23.9%) and 18.4% of M5 members. However, the M13 members are recovered in four DBSCAN groups, compared to just one group for K -means. 3.3. Chemodynamical Clustering Previous sections showed that the process of identifying groups in chemical space can retrieve at most 30% of true members in known globular clusters. This result holds independently of the algorithm adopted to identify clusters in the chemical space. This is likely due to insufficient precisions of chemical abundances. We found that radial velocities of members from distinct globular clusters are almost indistinguishable in several cases. Thus, radial velocity alone is likely not sufficient to recover individual globular clusters either. As the next step, we add the kinematic information, i.e., radial velocity, to the chemical space and propose a new algorithm, SNN. We used AstroPy (Astropy Collaboration et al. 2013 ) and various SciPy packages (Jones et al. 2001 ), including Numpy (Walt et al. 2011 ) and Scikit-learn (Pedregosa et al. 2011 ), to build our algorithm. We applied this algorithm to our sample and identified star clusters in the chemistry–velocity space. All clustering techniques rely on assigning stars based on some distance metric. One problem with picking a distance metric is normalization when two spaces with different ranges are considered (e.g., the chemical space and radial velocity space). One of the main advantages of our algorithm is that it identifies clusters in the chemistry – velocity space by taking the intersection of the nearest neighbors in the chemical space and radial velocity space. This feature avoids normalization between chemical abundances and radial velocity and guarantees that the final nearest neighbors are neighbors in both the chemical and velocity space. The algorithm operates as follows. As suggested in Mitschang et al. ( 2013 ), for each set of elements , the distance in the chemical space between two stars, A and B , is computed as the Manhattan distance, defined below:where X i = [Fe/H], [K/Fe], [S/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe]... The distance between the radial velocities of two stars is a simple Euclidean distance: For every star, a list of nearest neighbors in the chemical space and a list of nearest neighbors in the radial velocity space are respectively found. The neighbors for a particular