adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.487.4721Y (fulltext)
in Column 6. The uncertainties are at the 1σ level and are calculated with behr (Section 3.2 ). (9) Logarithmic 0.5–7 keV peak flux converted from the peak count rate in Fig. 6 with the method in Section 2.2.1 . (10) The Method(s) responsible for identifying the transient candidate. In Fig. 6 (left), we show the light curves of the transient candidates during the observation when the transient happens. The light curves are derived from the X-ray events extracted in Section 3.2 , and are binned by 5-count intervals. The data points in these light curves indicate total count rates, including contributions from the source and background. The estimated average background count rate is marked as the dashed line in each panel of Fig. 6 (left). The durations of XT1 and XT2 tend to be shorter than for other transient candidates (Fig. 6 left). The T 90 values of XT1 and XT2 are and ks, respectively (see Bauer et al. 2017 and Xue et al. 2019 for details). We do not derive T 90 for other sources, because T 90 cannot be derived for many transients that extend beyond the Chandra exposures (e.g. ID3 and ID9 in Fig. 6 left). Also, unlike XT1 and XT2, many of the other transient candidates have non-zero fluxes in the quiescent states, and thus their T 90 calculation requires careful subtraction of the quiescent fluxes, which is beyond the scope of this work. Figure 6. Light curves for each transient candidate. The left-hand panels are light curves for the observation with the transient, with each bin including 5 counts. The horizontal dashed lines indicate the estimated average background count rates. The right-hand panels are long-term light curves with each data point representing a Chandra observation. The transient observation is highlighted in red colour. The horizontal dashed lines indicate a net count rate of zero. We plot the long-term light curves in Fig. 6 (right), where each Chandra observation is represented by a data point. These data points indicate net count rates, which are background-subtracted. As expected, the transient observation generally has a count rate much higher than other observations. However, unlike the CDF-S XT1 and XT2 events, most of the other transient candidates have detectable signals in some of the non-transient observations. Also, CDF-S XT1 and XT2 tend to have higher hardness ratios than the rest of the selected transient candidates (Fig. 5 ). These differences indicate that most of the new transient candidates are physically distinct from CDF-S XT1 and XT2 (see Section 3.3 ). 3.3 Optical/NIR counterparts We have compiled the likelihood counterpart matching results from the survey catalogues (Table 1 ). All the transient candidates have optical/NIR counterparts. The counterpart properties are presented in Table 3 . We also match the counterparts with the Gaia catalogue (Gaia Collaboration 2018 ) using a 1 arcsec matching radius, and mark the sources with non-zero parallax and/or proper motion as ‘star’ in Table
Paper: 2018MNRAS.474.3700K (acknowledgments)
Acknowledgements This work was supported by the University of Zurich Candocreseach grant (Forschungskredit Candoc; FK-14-081 and FK-15-087).
Paper: 2020MNRAS.495L..92M (fulltext)
distance and shows a greater emission of gas and dust. Here, solar gravity and solar radiation pressure are shaping the morphology of the dust coma, and its tail is in fact obvious and oriented in nearly antisolar direction. Dust emission in comet 2I/Borisov seems to be less collimated than C/2014 B1, with the dust particles widely scattered in antisolar direction, possibly due to the combination of a lower ejection speed and the effect of a greater radiation pressure. Our estimate of the size of dust particles in the range of 30–100 μ m radius is consistent with that recently reported by Jewitt Luu ( 2019 ). However, it should be considered that in both cases the estimates apply to particles displaced to the end of the visible tail in order to reproduce its apparent length. In fact, using higher β values in the Finson–Probstein diagram, in the region close to the nucleus the simulation approaches the wide scattering of the emitted dust observed in the HST images, which might be suggestive of the presence also of dust particles with a smaller size. 4.2 An emissive rotating structure Assuming that the nucleus of comet 2I/Borisov is rotating and that the geometric position of the spin axis is such to favour a prolonged insolation, the dust emission from a single source located on the sunlit hemisphere should be modulated by the insolation and the resulting features of the inner coma should provide indications on the orientation of the spin axis. In the case of two emissive structures with similar morphology that are observed in approximately opposite directions, it could be supposed that both originated from the same active source on a nucleus that is rotating with a spin axis coinciding with the axis of the angle between the two structures and, if so, that they would be more evident when their position corresponds to the projection on the plane of the sky for the greater condensation of material on the perpendicular to the view (Sekanina 1987 ). The finding of a jet at PA 180 ○ and of its counterpart at PA 10 ○ suggests that the active area originating the structures is located in a near-equatorial position, and that the nucleus is probably rotating with a spin axis lying near the plane of the sky and geometrically oriented at PA ∼100 ○ or PA ∼100 ○ + 180 ○ (Fig. 3 ). Unfortunately, from the HST images it is not possible to determine the direction of rotation of the nucleus. To further verify our findings, we run a computer model of the inner coma using a proprietary software (P. Pellissier, 2009) specifically designed to reproduce Earth-based observations of the dust coma structures and of the development of the tail. The parameters needed to run the model are the properties of the dust particles, those of the nucleus, and the geometric conditions of the Sun–Earth–comet system. The following physical parameters of the dust particles, compatible with the observations of Rosetta on comet 67P (Fulle et al. 2015 ; Rotundi et al. 2015 ;
Paper: 2021MNRAS.500.3462M (fulltext)
and the profiles of total velocity dispersion, we finally derive the reconstructed profiles of pseudo-entropy to be compared with the true profiles. Furthermore, in order to verify the accuracy of this procedure based on the Jeans equation to recover pseudo-entropy profiles as a function of the dynamical state of a cluster, we divided our set of simulated haloes into relaxed and disturbed. To carry out this classification, we followed the prescription in Biffi et al. ( 2016 ) and described here in Section 2 . In Fig. B1 , we compare pseudo-entropy profiles recovered from the Jeans equation procedure with the true intrinsic profiles for both relaxed and disturbed systems from the Hydro-1x simulation. In the right-hand panels, we show their density maps in logarithmic scale traced by the DM particles in two clusters within the 0.5 r vir spheres (in white). In each of the two panels on the left, we compare recovered and intrinsic profiles of S ( r ) for both the selected cluster (solid lines), whose density map is reported on the right, and the entire cluster sample (dashed lines). Upper panels display the profiles for relaxed clusters, while lower panels are for disturbed systems. The solid lines are specific to the single cluster, being in cyan the true profile and in orange the one produced by the Jeans equation procedure while the dotted lines refer to the median values of all 29 clusters in the simulation set. In the upper panel, we see that true and Jeans equation profiles overlap at almost all radii, showing that the Jeans equation procedure has correctly reproduced the true profile, which is also in line with the median result. In the lower panel, we show the same profiles but when obtained for a cluster labelled as ‘disturbed’, since a major merger is occurring as shown by its density map. In this cluster, the true profile consistently deviates from the median and the Jeans equation profiles. The latter, in turn, suffers a shift in normalization but not in the slope with respect to the median profiles. Figure B1. On the left: pseudo-entropy profiles (DM particles) for a relaxed and a disturbed cluster in the Hydro-1x simulation at z = 0. The dashed lines are obtained by stacking the true profiles (in cyan) or those obtained by the Jeans equation procedure (in orange), while the solid lines are for the profiles of the specific cluster. On the right: density maps in logarithmic scale traced by the DM particles in the same objects. The brightest colours indicate the areas at higher density. The white circles identify the 0.5 r vir spherical region centred on the cluster centre as computed by SubFind. To quantitatively describe the differences between recovered and true pseudo-entropy profiles, we follow the same procedure described in Section 3 to fit a power-law profile, which depends on two parameters, and concentrate our interest on the accuracy in recovering the slope α. In the first two lines of Table B1 , we report the true and the
Paper: 2019MNRAS.483..985B (acknowledgments)
ACKNOWLEDGEMENTS We are grateful to the anonymous referee for improving the quality of this work. We thank Madusha Gunawardhana for helpful discussions and Themiya Nanayakkara for providing data from ZFIRE. This work used the Distributed Research utilizing Advanced Computing (DiRAC) Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the Science Technology Facilities Council (STFC) DiRAC High Performance Computing Facility ( www.dirac.ac.uk ). This equipment was funded by the Department for Business Innovation Skills (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. RAC is a Royal Society University Research Fellows. We also gratefully acknowledge the Partnership for Advanced Computing in Europe (PRACE) for awarding us access to the resource Curie based in France at Trs Grand Centre de Calcul. This work was sponsored by the Dutch National Computing Facilities Foundation (NCF) for the use of supercomputer facilities, with financial support from the Netherlands Organization for Scientific Research (NWO). This research made use of astropy , a community-developed core python package for Astronomy (Astropy Collaboration 2013 ). This work has benefited from the use of Py-SPHViewer (Benítez-Llambay 2017 ).
Paper: 2015ApJ...810...71F (acknowledgments)
We thank Kristian Finlator, Brian Siana, Rychard Bouwens, Pascal Oesch, Dan Jaffe, and Jon Trump for useful conversations. S.L.F. acknowledges support from the University of Texas at Austin College of Natural Sciences. M.S. was supported by a NASA Astrophysics and Data Analysis Program award issued by JPL/Caltech. R.J.M. acknowledges ERC funding via the award of a consolidator grant. This work is based on observations made with the NASA/ESA Hubble Space Telescope , obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program #12060. This work is also based in part on observations made with the Spitzer Space Telescope , which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Support for this work was provided by NASA through an award issued by JPL/Caltech.
Paper: 2016MNRAS.463.4301H (acknowledgments)
We thank Matthias Gritschneder and the whole CAST group for helpful comments and discussions. We also thank the anonymous referee for improving the structure of the paper. SH wants to thank Alexander Beck for useful comments that clarified the presentation of the paper. AB and SH are supported by the priority programme 1573 ‘ Physics of the Interstellar Medium ’ of the German Science Foundation and the Cluster of Excellence ‘ Origin and Structure of the Universe ’.
Paper: 2018ApJ...859..111H (acknowledgments)
We thank the anonymous referee for their insightful review. We thank E. Fomalont and J. Williams for useful discussions and suggestions during the analysis of the data. This paper makes use of the following ALMA data: ADS/JAO.ALMA.2015.1.00200.S . ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), 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. This research made use of astropy , a community-developed core python package for astronomy (Astropy Collaboration et al. 2013 ). This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. S.P. and L.C. acknowledge support from the Millennium Science Initiative (Chilean Ministry of Economy) through grant Nucleus RC130007. S.P. acknowledges CONICYT-Gemini grant 32130007. L.C. acknowledges support from CONICYT-FONDECYT grant 1171246. This paper used the Brelka cluster, financed by Fondequip project EQM140101. C.P. acknowledges funding from the Australian Research Council (ARC) under the Future Fellowship no. FT170100040. This work was undertaken with the SOLA (Soul of Lupus with ALMA) collaboration centered at the Joint ALMA Observatory. Facility: Atacama Large Millimeter/Submillimeter Array. Software: Common Astronomy Software Applications (McMullin et al. 2007 ), radmc-3d (Dullemond et al. 2012 ), MCFOST (Pinte et al. 2006 , 2009 ), Astropy (Astropy Collaboration et al. 2013 ).
Paper: 2015A&A...583A.142N (acknowledgments)
J.N. is funded by PUT246 grant from the Estonian Research Council. We acknowledge funding from the ESF grants IUT26-2, IUT40-2 and TK120 (the European Structural Funds grant for the Centre of Excellence “Dark Matter in (Astro) particle Physics and Cosmology”). Thanks to F. Pace for providing mapping routines. Thanks to P. Teenjes and M. Einasto for help. E.B. acknowledges the financial support provided by MIUR PRIN 2011 “The dark Universe and the cosmic evolution of baryons: from current surveys to Euclid” and Agenzia Spaziale Italiana for financial support from the agreement ASI/INAF/I/023/12/0. We thank S. Borgani and G. Murante for giving us access to the simulations analysed in this paper. C.G. thanks CNES for financial support.
Paper: 2015A&A...580A..23P (acknowledgments)
This project is financed by the SoMoPro II programme (3SGA5916). The research leading to these results has acquired a financial grant from the People Programme (Marie Curie action) of the Seventh Framework Programme of the EU according to the REA Grant Agreement No. 291782. The research is further co-financed by the South-Moravian Region. This work reflects only the authors’ views, and the European Union is not liable for any use that may be made of the information contained therein. It was also supported by the grant 7AMB14AT015 and the financial contributions of the Austrian Agency for International Cooperation in Education and Research (BG-03/2013 and CZ-09/2014). This research has made use of the WEBDA database, operated at the Department of Theoretical Physics and Astrophysics of the Masaryk University. This paper is dedicated to Roswitha Müller who died during its preparation.
Paper: 2020ApJ...895...35W (fulltext)
et al. 2011 ; Ramos Almeida et al. 2011 ; Ichikawa et al. 2015 ; Audibert et al. 2017 ), i.e., N c ≃ 3–15, we find that at 86 GHz becomes ≃0.2–1.2, which is of the order of unity and agrees with observational properties of N1. To constrain the number density of the clump, getting the size of the clump ( r c ) should be required. The size of the self-gravitating clump in the context of AGNs has been estimated in the literature (e.g., Krolik Begelman 1988 ; Hönig Beckert 2007 ; Kawaguchi Mori 2011 ). According to the authors of these works, we have r c ≤ 0.05 pc at 1 pc from the central SMBH with M BH = 8 × 10 8 M ⊙ (Scharwächter et al. 2013 ). On the other hand, the size of clumps can be constrained by the observations of transient X-ray absorption events in nearby AGNs, i.e., the typical size is 0.002 pc (e.g., Markowitz et al. 2014 ; Tanimoto et al. 2019 ). By adopting N c and r c , taking into account their uncertainties of 3 ≤ N c ≤ 15 and , the lower limit of number density of each ionized cloud can be given byIn the case of ionized gas clumps, the typical Thomson scattering opacity of each cloud can be estimated as since the optical depth is given by , where σ T is the Thomson cross section. If we adopt the opacity ratio of the dusty gas and the ionized gas is ≃10 3 at the UV band for the AGN radiation (e.g., Umemura et al. 1998 ; Ohsuga Umemura 2001 ; Wada 2012 ), the optical depth of each cloud is , which is consistent with the lower value for nearby Seyfert galaxies (e.g., Table 10 in Ramos Almeida et al. 2011 ). This might indicate that the ionized gas is also clumpy within the 1 pc region of 3C 84. Wada et al. ( 2018 ) examine properties of the ionized gas irradiated by less luminous AGN such as Seyfert galaxies based on their “radiation-driven fountain” model (Wada 2012 ). They found that the ionized region show nonuniform internal structures, corresponding to the clumpy fountain flows caused by the radiation pressure on dusty gas, although the typical density () is smaller than our estimate. In addition, by the optical/NIR observations, the existence of dense clumps with cm −3 has been reported from the detection of coronal lines within NLR (e.g., Murayama Taniguchi 1998 ). These high density clouds in NLRs might contribute the absorption feature of N1. Alternatively, since the density is comparable to number density n c ≃ 10 3–5 cm −3 , based on the momentum balance between the jet thrust and the ram pressure from the clump (Nagai et al. 2017 ; Kino et al. 2018 ), these ionized clumps may contribute not only the absorption of counter jet but also the feedback on jets. Since the dust sublimation radius of 3C 84 is (e.g., Kino et al. 2018 ), the dust components in these dense clumps could be survived. If this is the case, the clumpy clouds might be related to the polar elongation in MIR continuum emission distributions revealed by high-resolution observations in nearby Seyfert galaxies (e.g., Tristram et al. 2014 ; Asmus et
Paper: 2017ApJS..232...22D (fulltext)
yielding mutually consistent rotation speeds seem to have all but invalidated those suggestions; comprehensive and up-to-date Galactic rotation curves no longer show any dip near the Sun’s Galactocentric distance, at least not those based on the main mass tracers (see, e.g., Bland-Hawthorn Gerhard 2016 ). It is still possible that the appearance of such a dip could be limited to the young(est) populations located in the Perseus spiral arm (e.g., Sofue 2013 ; Genovali et al. 2014 ). Genovali et al. ( 2014 ) also suggested that the clumpy distribution of classical Cepheids across the Galactic thin disk and their possible association with giant molecular clouds may hamper their use for Galactic rotation measurements. Note that the H i , CO, and field population tracers also imply larger values for both (at kpc) and the ratio than the canonical IAU recommendation, although the widths of their full distributions encompass the IAU values within their 1 σ ranges. In view of these distributions, and considering that both the field populations and the H i or CO gas kinematics reflect the underlying Galactic mass distribution most closely, we contend that the post-1985 combination of these measurements may indeed yield the most appropriate value for the Galactic rotation speed for . The 40 post-1985 measurements of based on field star samples, H i or CO observations, and Galactic mass modeling yield . Note that, while dominated by a statistical component, the uncertainty does include a contribution from a systematic component, given that the contributing values include such uncertainties. Similarly, the resulting ratio based on these values is Θ 0 / R 0 = 27.12 ± 0.39 km s −1 kpc −1 ( σ = 2.45 kms −1 kpc −1 ). At this point, it would be wise to perform a final check of the Galactic rotation speed at the solar circle by considering the Galaxy’s rotation curve with respect to the external reference frame provided by the Milky Way’s satellite galaxies and other Local Group members, as well as the Milky Way’s GD-1 giant stream. Fortunately, a number of studies have attempted to do just this. Lynden-Bell Lin’s ( 1977 ) and Einasto et al.'s ( 1979 ) original attempts yielded rotation speeds that bracketed the expected range. Lynden-Bell Lin ( 1977 ) determined km s −1 for kpc (with a reduced uncertainty of 13 km s −1 resulting from the application of more a priori constraints), while Einasto et al. ( 1979 ) found km s −1 , again for kpc. More recent derivations of in external reference frames, all recalibrated for , have resulted in (30) km s −1 (Kochanek et al. 1996 ) and (216 ± 18) km s −1 (Koposov et al. 2010 ). This thus implies that there is no compelling evidence suggesting that the IAU-recommended km s −1 is no longer valid, although it would be more appropriate to increase the recommendation to km s −1 , provided that the recommended Galactocentric distance is reduced from to kpc (Paper IV ). A comparison of the Galactic rotation speeds implied by