adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...877L...8J (fulltext)
peak temperature that we have previously reported (Jennings et al. 2011 , 2016 ) continued through late northern spring to the solstice. The latitude of maximum temperature (center of north–south symmetry) closely tracked the subsolar point from 13 S in 2005 to 24 N in 2017. We find a non-zero phase offset that corresponds to a small seasonal lag at equinox, about 0.1 Titan months. Figure 1 shows that a small lag is consistent with the non-uniform GCM case, although it is not as clear that there is a lag in the uniform case (see also Tokano 2019 , Figure 3). Our seasonal lag agrees with that reported by Janssen et al. ( 2016 ) and is also in line with the observations of Voyager 1 IRIS near the previous equinox in 1980 November (Flasar et al. 1981 ; Courtin Kim 2002 ). A small lag is consistent with the low thermal inertia required of the surface for diurnal variations (Cottini et al. 2012 ). Our equatorial temperature of 93.5 ± 0.4 K at equinox ( L s = 0°) agrees with the Voyager IRIS measurement, 93 ± 1 K (Courtin Kim 2002 ). From early to late in the mission the maximum amplitude of the cosine dropped by about 1 K, from 93.9 to 92.8 K. The time-dependent width in formula (1) signifies a “flattening” of the temperature distribution as the peak moved north, i.e., a more gradual fall-off toward the poles. An examination of the measurements and of the fitted cosines suggests that, although the peak temperature decreased, the average of north and south pole temperatures in each period remained approximately the same throughout all seven periods. Our formula can be used to inspect the change in surface temperature at any particular latitude over the two-season mission. For example, at the Huygens landing site, 10 S, the temperature ranged from 93.9 to 92.5 K between 2005 and 2017. Because the peak temperature decreased as it shifted northward, the semi-annual temperature variation was strongly dependent on latitude. The temperature variation was greatest at 40 S (93.6–91.7 K), where the surface was relatively dry, and was smallest at 40 N (92.6–92.7 K), where surface temperatures were moderated by the wetter north (Lora et al. 2015 ; Tokano 2019 ). Changes in surface condensation must be much less at mid-northern than at mid-southern latitudes. At 40 S between winter and summer, vapor pressures would have increased by a factor of 1.26 for methane and 1.61 for ethane while at 40 N they would have varied by only factors of 1.01 and 1.03. Any northward migration of volatiles while the south is warmer will not be fully undone later in the yearly cycle because the northern temperatures never get as warm as in the south. The temperature ranges at the poles were virtually identical: 91.9–89.8 K at 80 S and 89.9–91.9 K at 80 N. Both poles, at their coldest, reached the triple points for methane (90.7 K) and ethane (90.3 K). The poles may at times harbor both liquid and solid phases. Overall our results indicate that the northern hemisphere presents a
Paper: 2016ApJ...818..161N (fulltext)
Figures 2 (a)–(f) show compressed spectra from 85 to 101 GHz toward CO Peak 1, NQC2, N79, N44C, N11B, N113, and N159W, respectively. In the preparation of these data, we corrected slight intensity variations among the subscans for 1.5 hr by using the intensity of the strongest line of the observing band. For the compressed spectra, we subtracted the baseline by using the fifth-order polynomial over the 2.2 GHz region. Although the standing waves caused by the telescope system are still visible, we were readily able to identify emission lines, because the typical line width observed toward the target sources (5 km s −1 ) is narrower than the period of the standing waves. Lines are identified with the aid of the spectral line database CDMS (Müller et al. 2001 , 2005 ). We used the 13 CO line velocity ( V LSR ) of 229, 266, 285, 282, 232, 235, and 238 km s −1 for CO Peak 1, NQC2, N11B, N44C, N79, N113, and N159W, respectively, to identify the molecular lines. We detected eight molecules (CO, 13 CO, CS, SO, CCH, HCO + , HCN, and HNC) in the seven sources, c -C 3 H 2 in N79, N44C, N11B, N113, and N159W, and CN in N79, N44C, and N113 (Table 4 ). N 2 H + is marginally detected in N113 and N159W. The detection criterion is that the line is detected at the expected velocity with the significance of 5 σ or higher in the integrated intensity. Note that the c -C 3 H 2 line was not covered with the frequency setting for CO Peak 1. Since line intensities in CO Peak 1 and NQC2 are typically weaker by a factor of 2 than the other sources, fewer lines were detected in these sources. The signal-to-noise ratio is rather poor for N113 and N159W in comparison with the previous studies (Chin et al. 1997 ; Heikkilä et al. 1999 ; Wang et al. 2009 ), and hence, only strong lines were detected in this study. Figure 2. Compressed spectra observed toward CO Peak 1 (a), NQC2 (b), N79 (c), N44C (d), N11B (e), N113 (f), N159W (g). The spectrum of the spiral arm of M51 is taken from Watanabe et al. ( 2014 ) (h). The composite spectrum of a translucent cloud is prepared by averaging the intensities of HCN, HCO + , HNC, CS, and SO observed toward 38 translucent clouds by Turner ( 1995a , 1995b , 1996 ), and Turner et al. ( 1997 ) (i). The spectrum of Orion KL (j) and NGC 2264 CMM3 (k) are taken from Watanabe et al. ( 2015 ). Note that the vertical scale is different from source to source. Table 4 Observed Line Parameters Notes. The errors are 1 σ . The upper limits are 3 σ . a Assumed. The spectral profile of each molecular line is shown in Figures 3 and 4 . For Figures 3 and 4 and Table 4 , the baselines are subtracted in a narrow range (typically 100 MHz) by using the fifth-order polynomials to remove the base line ripples. We conducted Gaussian fitting to each base-line-subtracted spectral line to obtain the V LSR , Δ v , and the peak T MB , where the observed frequencies are converted to the LSR velocities based on the rest frequency of each transition given in CDMS.
Paper: 2021MNRAS.505.5340M (fulltext)
confidently identify these periodic variations. Price-Whelan et al. ( 2020b ) identified 19 635 candidate binaries in the APOGEE survey based on radial velocity variations between multiple observations. Of these, 53 are in our giant star data set, including 15 of our RC stars – one of which is Li-rich. It has only three observations with APOGEE, indicating an RV range of . In terms of photometric signatures of binarity in the form of transits, assuming random orbital inclinations and observed orbital period distributions (e.g. Raghavan et al. 2010 ), 2.5 ± 0.5 per cent of our Li-rich RC stars should be in eclipsing binary systems. The secondary star will block about 1 per cent of the disc of the primary with an eclipse period of ∼3 d. This does not require a very high cadence in photometric monitoring, but none of our Li-rich RC stars have so far had light curves in ASAS-SN (Kochanek et al. 2017 ) or TESS (Ricker et al. 2014 ) that show evidence for eclipses. The astrometry from Gaia is precise enough to measure the motion of the photocentre of some binary systems (depending on heliocentric distance and the binary properties). The model assumption underlying the data processing for Gaia DR2 is that sources are single objects, and if there are photocentre shifts in binary systems these are interpreted as larger than expected astrometric errors. Work by Penoyre et al. ( 2020 ) and Belokurov et al. ( 2020 ) have used large values of the Renormalized Unit Weight Error ( RUWE ), an astrometric error metric reported by Gaia , to identify possible binaries. Our data set excludes stars with large RUWE by construction (it is part of flag_sp ), because parallax is an important prior in our stellar parameter determination. 3.5 Infrared excess A fraction of Li-rich RGB stars have been reported to have excess flux in their spectral energy distribution in the IR, which has been postulated as a sign of a physical connection between lithium production and mass-loss. This can be seen in photometry from the IRAS satellite (e.g. Fekel Watson 1998 ) and from WISE , although the latter’s wavelength coverage does not reach as far into the IR. A close investigation by Rebull et al. ( 2015 ) found that the majority of lithium-rich giants in the literature with reported IR excesses were artefacts in the WISE catalogue or cases of source confusion. However, they do confirm some as real cases of IR excess, and they do find that the stars with the largest IR excess are lithium-rich K giants. Within our data set, there are only 1862 giants with clean WISE detections; that is, they have the cc_flags confusion flag set to 0000 and the ph_qual photometric quality flag set to A for W 1 and W 4 . Fig. 9 shows an IR colour–magnitude diagram for these stars, divided in the same way as Fig. 7 with RC stars in the upper panels, RGB stars in the lower panels, all stars in the left-hand panels, and the lithium-rich stars highlighted in the right-hand panels. There are 549 RC stars
Paper: 2021MNRAS.503.2077B (fulltext)
a very different architecture, PCA-Davies-nominal performs very similarly with scatter just below : . The increased sample size of the training sample, better purity, and improved intrinsic continuum recovery compared to PCA-Davies-smaller make a clear difference, as the latter has . Finally, using only wavelengths that would be observed in the optical at 5.5 z 6.2, PCA-optical only predictably performs worse than new PCAs and stacks of neighbours with access to longer wavelengths, but still better than both older PCAs and power-law reconstructions, with . Over the Ly β forest [972 λ(Å) 1000], the distinction in performance between methods is similar, with the Power-Law method doing the worst with at 1σ (2σ). Again, both larger-sample PCAs are more accurate with for PCA-Davies-smaller . The improved training sample improved the bias and reduced scatter below to for PCA-Davies-nominal , overtaking Neighbours stacking that has an accuracy of . PCANN-QSANNdRA performs very similarly to PCA-Davies-nominal , with an accuracy of . Finally, the optical wavelengths-only PCA performs significantly worse than IR PCAs but still better than power-law extrapolation, with . Beyond mean biases, the residuals from the different methods show very different wavelength dependences (Fig. 4 ). Residuals at the location of broad emission lines can be seen in the Power-Law, PCA-Pâris-10 , and PCA-Pâris-5 methods, while the Neighbours technique and PCA methods trained on larger samples ( PCA-Davies-smaller, PCA-Davies-nominal ) do not show residuals at the locations of the N ii 1085 Å, Fe iii 1125 Å, and C iii 1175 Å broad emission lines. This indicates that correlations between blue-side emission lines and red-side properties are, unsurprisingly, adequately captured by stacks of quasars with similar red-side properties. The more recent PCA decompositions also capture broad-line correlations. An important addition in the PCA-Davies-smaller and PCA-Davies-nominal was an independent fitting parameter |δ z | 0.05 to account for redshift uncertainties in the testing sample. Indeed, small residuals visible around the O vi line in PCANN-QSANNdRA are likely due to the lack of redshift correction as a fitting parameter, which is mitigated by the larger number of components (53/42 for the red/blue sides compared to 10/6). The absence of a redshift shift parameter in the other models might disadvantage them if the quasar redshifts in eBOSS are more inaccurate than in z > 5.5 quasars. Indeed, the systemic redshifts of high- z quasars are often known to be within |δ z | 0.0005 from detections of sub-millimetre emission lines in quasar hosts (Willott, Bergeron Omont 2015 ; Decarli et al. 2017 ; Eilers et al. 2020 ). The n > 5 components of the Pâris-10 PCA introduce shape perturbations at the edges of broad emission lines that account for some of these redshift uncertainties. We find that these high-order components give continua unphysical shapes (see e.g. Kakiichi et al. 2018
Paper: 2017MNRAS.467.4647K (fulltext)
points also remain almost unchanged. Further investigation of stabilizing effects due to increasing magnetic field strength will be discussed in Section 6. Let us compare the stability of relativistic magnetized jets with their non-relativistic counterparts. Fig. 8 shows the non-relativistic stability analysis of the strongly magnetized jet. Fig. 8 of this paper is borrowed from Kim etal. (2015) , where it was labelled Fig. 5 . We can directly compare Fig. 6 with Fig. 8 (a). We see that the fundamental pinch mode in Fig. 6 (a) for the mildly relativistic ( 0 2) case is slightly more stable than the fundamental pinch mode in Fig. 8 (a). The growth rate of the fundamental pinch mode even for the mildly relativistic jet is now below the stability criterion. For the first reflection pinch instability mode, the growth rate at resonance (the wavenumber where its growth rate is a maximum) has a slightly lower value. However, the difference is not significant. Figure 8. The dispersion relation of the non-relativistic magnetized jets with 0.1 and M 4. The velocity profile in the jet is top-hat. The plasma of the jet is 1/2. This is obtained in the non-relativistic linear stability analysis that appears in Kim etal. (2015) . Panel (a) shows the pinch mode ( m 0) instabilities, while panel (b) shows the kink mode ( m 1) instabilities. The red and blue lines represent the fundamental and the first reflection mode. The real and imaginary part of are shown as the solid and dashed lines. The dotted curves refer to the stability criterion for each of the unstable modes. Similarly, let us also compare our relativistic results with non-relativistic results for the kink ( m 1) modes. Please compare Fig. 7 with Fig. 8 (b). Actually there are only minor improvements in the stability for the fundamental kink mode of the mildly relativistic jet. For the first reflection mode, there is only a very tiny range of unstable wavelengths in the non-relativistic jet. However, the range is relatively wider in the relativistic jet. 5.2 Scaling property of the strongly magnetized jets Analogously to Section 4.3 , we study the stability of magnetized jets with increasing Lorentz factors. In this section, we use the strongly magnetized jets ( 1/2) to verify that there still exists a scaling law even in the magnetized case. Fig. 9 is analogous to Fig. 5 but for the strongly magnetized jet case. Fig. 9 (a) shows the pinch mode ( m 0) instabilities. In Fig. 9 (a), we can see the same trend that is shown in Fig. 5 (a). The temporal growth rate of the fundamental pinch instability mode is inversely proportional to the square of the Lorentz factor ( I 1/ 0 2 ). The growth rate of the first reflection pinch instability mode, however, is just proportional to 1/ 0 . Figure 9. The scaling property of the strongly magnetized jets ( 1/2) with increasing Lorentz factor. Panel (a) shows the pinch mode ( m 0) instabilities, while panel (b) shows the kink mode ( m 1) instabilities. The red
Paper: 2015MNRAS.448.3242M (acknowledgments)
The authors are grateful to ISRO-RESPOND Programme for providing the financial assistance under grant no. ISRO/RES/2/370 for carrying out this work. Three of us (BM, NI and NA) are also highly thankful to IUCAA, Pune for providing the necessary facilities in completing this work.
Paper: 2019ApJ...878..150R (acknowledgments)
We thank Paul Duffell, Chris Matzner, and Frank Timmes for useful discussions. This research was funded by the Gordon and Betty Moore Foundation through grant GBMF5076. E.R.C. acknowledges support from NASA through the Einstein Fellowship Program, grant PF6-170170. E.Q. thanks the theoretical astrophysics group and the Moore Distinguished Scholar program at Caltech for their hospitality and support. This work was supported in part by a Simons Investigator Award from the Simons Foundation. The software used in this work was developed in part by the DOE NNSA-ASC OASCR Flash Center at the University of Chicago. Software: FLASH (Fryxell et al. 2000 ).
Paper: 2016MNRAS.455L..26X (acknowledgments)
We thank our referee for valuable comments. JL is supported by NSFC grant 11203032, and LG is supported by the Strategic Priority Research Program ‘The Emergence of Cosmological Structures’ of CAS grant XDB09000000, NSFC grant 11333005, and NAOC grant Y234031001. This research is based on data obtained from the Chandra Data Archive and uses data from NuSTAR mission.
Paper: 2016A&A...593A.109D (acknowledgments)
G.D. would like to warmly thank Alessandro Romeo, Kirsten Knudsen, and Clive Tadhunter for the useful discussions that contributed to improve this paper. The authors also thank the referee for detailed suggestions and a thorough report that helped to clarify this paper. G.D. also thanks Nina Hatch for providing HST fluxes for the Spiderweb galaxy and A. Galametz for providing images for part of the sample. G.D. thanks Philip Best for providing the 3C 368 and 3C 470 data. N.S. is the recipient of an ARC Future Fellowship. The work of DS was carried out at Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Based on observations made with the NASA/ESA Hubble Space Telescope, and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA) and the Canadian Astronomy Data Centre (CADC/NRC/CSA). GD acknowledges the support from the ESO scientific visitor programme.
Paper: 2017MNRAS.470.3095R (acknowledgments)
Acknowledgments I thank the anonymous referee for useful comments and guides on how to improve this work. This work was supported by Sharif University of Technology's Office of Vice President for Research under Grant No. G950214.
Paper: 2018ApJ...869...46K (acknowledgments)
The authors would like to thank Sonny Harman and Yuka Fujii for useful discussions regarding this work. E.V.Q. and M.J.W. are grateful for support from GSFC Sellers Exoplanet Environments Collaboration (SEEC). This research has made use of the Habitable Zone Gallery at hzgallery.org . The results reported herein benefited from collaborations and/or information exchange within NASA’s Nexus for Exoplanet System Science (NExSS) research coordination network sponsored by NASA’s Science Mission Directorate.
Paper: 2018MNRAS.473L..79B (acknowledgments)
Acknowledgements MB, GPS and SLM acknowledge support from the Science and Technology Facilities Council through grant number ST/N000633/1. CPH acknowledges financial support from PRIN INAF 2014.
Paper: 2019MNRAS.488.3482M (acknowledgments)
ACKNOWLEDGEMENTS I acknowledge funding from the Swiss National Science Foundation. I am very grateful to Dan Fabrycky for thoughtful comments on drafts of this manuscript, and to Moe Maxwell for insight regarding the surveys of binary mass ratios. I also appreciate discussions with Anastasios Fragkos, Georges Meynets, Diego Muñoz, Marc Pinsonneault, Lisa Prato, Mads Sørensen, and Amaury Triaud which helped motivate some ideas. Finally, I acknowledge the referee, Hagai Perets, who provided a thorough review, which undoubtedly significantly improved the quality of the paper.
Paper: 2017MNRAS.470.1321B (fulltext)
their rotation profiles did not commence until later. A sample of ETGs were observed to be elongated (Sandage, Freeman Stokes 1970 ), which was expected to be due to rotation (Larson 1975 ). Early measurements of rotation profiles in lenticular (S0) galaxies confirmed the expectation of strong rotation (Williams 1975 ). Astronomers were then surprised by data suggesting that there was little rotation in some elliptical (E) galaxies (e.g. Bertola Capaccioli 1975 , who measured significantly less rotation in the flattened elliptical NGC 4697 than present in spiral galaxies). This unexpected finding led to much research regarding the nature of rotation versus anisotropic velocity dispersion (e.g. Illingworth 1977 ; Binney 1978 ; Schechter Gunn 1979 ; Davies 1981 ; Kormendy Illingworth 1982 ; Davies et al. 1983 ). One of the early manners in which information about rotational support was condensed was the so-called Binney ( 1978 ) diagram. This diagram presented the ratio of the maximum rotational velocity to the central velocity dispersion v /σ against the ellipticity ε for individual galaxies. In particular, this diagram was used by Binney ( 1978 ) to address the question of how galaxies that are not rotationally supported can be elongated by anisotropy. Davies et al. ( 1983 ) later used this diagram and noted that fainter ellipticals ( M B > −20.5 mag) tended to be rotationally supported, whereas brighter ellipticals ( M B −20.5 mag) were pressure supported by near-isotropic velocity dispersion. The use of this diagram was ideal for longslit data, in which a maximum rotation value along the major axis and a central velocity dispersion could be measured. With the increasing use of two-dimensional spectroscopy, in particular via integral field units (IFUs), much more information was available with which to characterize the kinematics of galaxies. In order to utilize 2D measurements of both velocity and velocity dispersion within a specified aperture, an observationally measureable, luminosity-weighted spin parameter, denoted by λ R , was developed by the SAURON team (Emsellem et al. 2007 ). The use of spatial weighting allowed this parameter to better indicate the rotation of galaxies with non-conventional kinematics (whose properties could not simply be summarized by major-axis measurements, such as galaxies with kinematic twists and misalignments as was later discussed by Krajnović et al. 2011 ). The main outcome of the use of the λ R parameter by the SAURON (de Zeeuw et al. 2002 ) and ATLAS 3D (Cappellari et al. 2011a ) teams was to plot this parameter in conjunction with the ellipticity ε in a spin-ellipticity diagram to identify two classes of early-type galaxies according to their kinematics: fast and slow central rotators. Despite the use of high-quality 2D kinematic data, the final results are still typically reduced to a single measurement for each galaxy. The breadth of information which can be gained from this diagram is further