adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...627A..80O (fulltext)
mean water abundance is 5 × 10 −5 n H . The accreting D/H ratio is inferred from the observed CH 2 DOH/CH 3 OH ratio (see Table 2 ) as α m = 1.1 × 10 −3 , assuming that the initial (statistical) deuteration of CH 2 DOH in the ice is conserved during the hot core phase. The post-evaporative gas-phase chemistry is also entirely neglected (full details can be found in Faure et al. 2015a ). The results of the model are plotted in Fig. 5 : it is found that the observed CH 2 DOH/CH 3 OD ratio of 3.2 ± 1.5 can be reproduced for a solid s-HDO/s-H 2 O ratio in the range ~8 × 10 −4 −2 × 10 −3 . Therefore, while the CH 2 DOH/CH 3 OD ratio of 3.2 ± 1.5 is consistent with a statistical deuteration, the occurrence of H/D exchanges cannot be excluded. In particular, it should be noted that H/D exchanges can explain the range of values depicted in Fig. 4 , that is CH 2 DOH/CH 3 OD ratios in the range ~0.1−10. To our knowledge, this is currently the only mechanism able to explain the nonstatistical deuteration of methanol in both low- and high-mass protostars. Fig. 5 Gas-phase abundance ratio of the deuterated isotopologs, CH 2 DOH/CH 3 OD, as function of the initial (cold) water ice deuteration. The dotted line corresponds to the analytic solution CH 2 DOH/CH 3 OD = 0.004/(HDO/H 2 O), see Faure et al. (2015a ) for details.The ratio observed toward NGC 7538-IRS1 is represented by the orange hatched zone. 5 Conclusions To summarize, we investigated the deuteration of methanol in the high-mass star-forming regions NGC 7538-IRS1 using IRAM-30m observations. Our study shows that the fractionation degree of deuterated methanol is low in this source. From the numerous (≥10) detected lines, we estimate a CH 2 DOH/CH 3 OD relative abundance ratio of 3.2 ± 1.5. Although our findings are commensurate with statistical deuteration at the icy surface of grain mantles, we cannot exclude H/D exchanges between water and methanol at the present time. Further observations of water and HDO are required to address this point. Appendix A Spectroscopic and observational line parameters Tables A.1 – A.4 list the spectroscopic and observational line parameters for the observed CH 3 OH-A, CH 3 OH-E, 13 CH 3 OH, CH 2 DOH, CH 3 OD-A and CH 3 OD-E transitions. Table A.1 Methanol. Table A.2 Methanol. Table A.3 13 C methanol. Table A.4 Deuterated methanol. Appendix B Partitionfunction of CH<sub>3</sub>OD Table B.1 gives the respective CH 3 OD-A and CH 3 OD-E rotation partition functions we use in the present study. Table B.1 Rotational partition function for CH 3 OD. Appendix C IRAM-30m observations overlaid with our LTE modeling for <sup>13</sup> CH<sub>3</sub>OH, CH<sub>3</sub>OD-A and CH<sub>3</sub>OD-E Figures C.1 – C.3 show a montage of the detected 13 CH 3 OH, CH 2 DOH, CH 3 OD-A and CH 3 OD-E transitions (see Tables A.3 and A.4 for the observational line parameters) along with our LTE modeling. Fig. C.1 Montage of detected transitions associated with the methanol 13 C isotopolog
Paper: 2019A&A...626A..86S (fulltext)
J03473-019 and J22518+317 ( Fig. 3 ) can be compared to, for example, those of fully convective J19169+051S ( Fig. 4 ) and J19511+464 ( Fig. 3 ). The only noticable feature is that fully convective stars tend to have stronger average magnetic fields represented by magnetic components of stronger strength. For instance, in the hottest partly convective star J05365+113( Fig. 3 ) we derive a very weak magnetic field with only two magnetic components (0 and 2 kG, respectively). Fig. 3 Distribution of filling factors as derived from Ti lines. The stars are sorted according to the S/N of their spectra (S/N decreases from left to the right and top to bottom). Fig. 3 continued. In our sample, we find several objects with remarkably very similar properties of their magnetic fields. For the two stars with largest υ sin i values, J01352-072 ( Fig. 3 ) and J04472+206 ( Fig. 3 ), we recover the same average magnetic fields with identical filling factors. The only difference between these stars is that J04472+206 is one spectral type cooler. Next, we find a twin of J07446+035 (very well studied M dwarf YZ CMi, Fig. 4 ), which is J23548+385 (not studiedat all, Fig. 3 ). Both stars have very similar filling factors, average magnetic field, projected rotational velocities, but slightly different spectral types. In addition, another famous M dwarf J22468+443 (EV Lac) has filling factors similar to J07446+035 and J23548+385, although its average magnetic field is noticeably weaker. We next looked for a connection between our filling factors and the geometry of large scale magnetic fields. In our sample, we have nine stars for which the geometry of their surface magnetic fields was previously derived from polarimetric measurements. In particular, Morin et al. (2010) showed that partly convective M dwarfs tend to have complex multipole fields with strong toroidal contribution, while stars that are fully convective generate dipole-dominant predominantly poloidal magnetic fields. Following this classification, in Fig. 4 we grouped M dwarfs in columns depending on whether the star has dipole-dominant magnetic field (left column) or more complex multipole-dominant field (right column). One can see that there is no obvious difference in filling factors between these two groups. A smooth distribution is found for dipole-dominant J01033+623 on one side, and multipole-dominant J08298+267 and J19169+051S on the other side. Likewise, J07446+035 and J22468+443 have almost identical filling factors but their magnetic field geometries were found to be very different. Nevertheless, there seems to be one common feature that is intrinsic to complex multipole fields as observed from filling factors, which is the appearance of strong zero-field components. This zero-field component is strongest in four out of five stars with multipole-donimant fields with an exception of J22468+443. 4.4 Magnetic field and rotation The detection of very strong magnetic fields in M dwarfs needs to be
Paper: 2020A&A...642A.157M (fulltext)
is injected (brown) is different from the false positive when no planet is injected (in red, completely to the black curves on the left-hand side panels) for supergranulation (especially SGlow) alone, but it is similar when granulation and supergranulation are superposed. For GRA, ALL GRAhigh,SGmed , and ALL GRAhigh,SGlow , the red and brown curves are similar, that is, the percentage of false positives is the same whether a planet (of 1 M Earth ) is injected or not. However, the situation is different for SGmed and SGlow because when no planet is injected, the percentage of false positives is the same, even though they have very different rms RV. This is due to the fact that here the comparison of the power is made with the FAP. Because the shape of the power spectrum is the same between SGmed and SGlow, and because the FAP values are scaled with the rms, both power and FAP increases from SGlow to SGmed in a similar manner and the percentage of false positives is similar. In the case of ALL GRAhigh,SGlow , the signal is dominated by GRAhigh, hence, a level that is similar between GRAhigh and ALL GRAhigh,SGlow , while the situation is intermediate for ALL GRAhigh,SGmed . For GRAlow, the rate of false positives is very small in all cases. However, when added to supergranulation (either SGmed or SGlow), the latter dominates, and rates are very similar to those obtained when combining with GRAhigh, only slightly lower. The level of false positives here may be large because our analysis is too simplistic. When a peak is detected above the FAP, we should test the robustness of the detection to determine whether the peak is stable or not for example. More sophisticated methods will have to applied in this area in the future (see Sect. 6 ). Another representation of these results is shown in Fig. 18 , showing the percentage of false positives (sum of the two contributions described above) versus the detection rate (computed on the cases with an injected planet), which is similar to a ROC curve (but where each point corresponds to a spectral type). Each curve corresponds to one of the OGS configurations. Ideally, we would like points to be in the lower right corner. Points at the top have a high false positive level and points on the left correspond to poor detection rates. If we compare the global level of false positive here and the rms for each type of OGS configuration, we see that there is not a direct correspondence, because a granulation-like signal provides better performance due to their more suitable power spectrum (for a given rms). High-mass stars are to the left of each curve and lead to high rates of false positives and low detection rates except for granulation alone (for GRA low all points are in the lower right corner), and to a lesser extent ALL GRAhigh,SGlow . We also note that the highest level of false positives is obtained for SG alone. However, when granulation is added to supergranulation, the rms increases, but the level
Paper: 2018ApJS..235...37A (fulltext)
We calculated a simple reduced-chi-square statistic for each DM model, finding for the YMW16 model and ∼10.2 for the NE2001 model. (The χ 2 statistic accounted only for uncertainties in the parallax distances, not in the electron-density models.) The similarity in these χ 2 values suggests the two models are comparable in their ability to predict distances to pulsars based on DM values, at least in the distance regime probed by millisecond pulsar timing parallax measurements (within ∼2.5 kpc of the Sun). Figure 4. Distances from timing parallax measurements (67% confidence) vs. distances from DM models for pulsars with significant timing parallax detections. The blue points show the NE2001 DM model distances, while the red points show the YMW16 model distances. The black dashed line shows a one-to-one relation. 4.2. Parallax Upper Limits The remaining 25 pulsars had timing parallax measurements with significances of less than 3 σ . Of these, 22 had positive parallaxes and only 3 had negative parallaxes in the formal timing fits. This is strong evidence that most of the non-detections have a true low-level parallax signal; otherwise, there would be comparable numbers of negative and positive parallax measurements. None of the negative parallaxes had large significance. We used 95%-confidence upper limits on parallax to compute 95%-confidence lower limits on distance for these pulsars. The details of this calculation are outlined in M16 . We have not attempted to correct for Lutz–Kelker bias (Verbiest et al. 2010 ), as we do not believe we have enough prior information on the spatial and luminosity distribution of the millisecond pulsar population to accurately correct for this bias (see M16 ). For the lower limits on distance, our 95% limit remains a conservative approach, in the sense that compensation for Lutz–Kelker bias would only push these limits higher. Among the pulsars with parallax upper limits, a few have significant discrepancies with previous measurements (PSRs J0636+5128, J1455−3330, J1640+1224, B1937+21). As with the pulsars discussed in Section 4.1 , the source of these discrepancies are not clear, but may result from differences in DM variation models or red-noise models. 4.3. Distance Constraints from Rotation and Orbital Period Derivatives Observed pulsar rotation period derivatives are a combination of intrinsic pulsar spin-down and kinematic terms due to the acceleration and transverse motion of the pulsar relative to the Sun (Shklovskii 1970 ; Nice Taylor 1995 ). By assuming that the pulsar is losing rotational energy, so that it has a positive rotation period derivative, an upper limit can be placed on the pulsar distance. See M16 Section 4.3 for details. Table 5 lists such “ distance” constraints, which we calculated following the procedures of M16 . In the table, we omitted distance limits greater than 10 kpc as physically uninteresting, and we did not calculate a constraint for PSR J1024−0719, as its observed rotation
Paper: 2021MNRAS.504.1407R (fulltext)
timing solution in NGC 6624 (pulsars D, E, F, and H), as well as to validate the timing position derived for the new eccentric binary MSP (pulsar G). With the observing setup used for the two TRAPUM observations of this cluster (obs. ids 10L-trapum and 12L-trapum) reported in Section 2 , the individual beams making up the tiling have elliptic shapes with semi-axes in the range of ∼8–11 arcsec at half power, with the exact values depending on the elevation and other factors. We assume these values as the maximum uncertainty for the positions that we derive for these pulsars. The results are summarized in Table 5 , where we list the beams in which each pulsar was detected with highest S/N, and their boresight coordinates. The same is shown graphically in Fig. 9 , where the beams with localized pulsars are higlighted in grey. As can be seen, pulsars D and H are found to be the closest to the cluster centre. The eclipsing redback F and the isolated pulsar E are found at the much larger distances of ∼0.42 and ∼0.79 arcmin from the cluster centre, respectively, putting the former near the edge of, and the latter well outside, the half-power of the tied-array beam of the MeerTime observations (which used only the 1-km core antennas) of NGC 6624. This explains why NGC 6624F and E are detected with significantly lower S/N than expected in the MeerTime data as compared to the TRAPUM data, even after accounting for the different number of antennas used. Finally, the eccentric binary pulsar NGC 6624G showed up with highest S/N in beam #002. This had boresight coordinates of α = 18 h 23 m 41150 and δ = −30°21 ′ 3850. This is just ∼6.2 arcsec away from, and therefore consistent with, the corresponding timing-derived position of G. Figure 9. Tiling of the first TRAPUM observation (obs. id 10L-trapum) of NGC 6624. 60 dishes were used to synthesize 288 beams, with an overlap fraction of 80 per cent, around the nominal centre position of the cluster. The light-grey ellipses show the contours of the TRAPUM beams at their 84 per cent level of the power of their boresights, at 1284 MHz. We highlight in solid grey those beams where some of the pulsars show up with the highest S/N: the name of such pulsars are shown next to the beam number. The red ellipses show, for comparison, the L -band beam (at half power for top, centre, and bottom of the band) of one of the MeerTime observations of NGC 6624, which used only 42 antennas of the 1-km core of the array. Table 5. List of the NGC 6624 pulsars without a known position and corresponding beams where they were detected with highest S/N in the first TRAPUM observation of the cluster (id 10L-trapum). θ ⊥ is the angular distance between the beam boresight and the nominal cluster centre. 4.2.3 L-band detection of the transitional pulsar M28I M28I (also known as PSR J1824−2452I) is a redback system in M28 beloging to the rare class of transitional MSPs (see Jaodand, Hessels Archibald 2018 ; Papitto de Martino 2020 for
Paper: 2020MNRAS.495.4570M (fulltext)
law may be more appropriate in these cases. Figure 9. A two parameter estimate of accreted mass fractions . The distribution of stellar mass fractions and 2D power-law slopes for the mass-matched sample of TNG100 galaxies, colour coded by the fraction of stellar mass beyond 2 half-mass radii. The full TNG100 parent sample is shown in grey points. Smaller symbols indicate galaxies for which a single power law was not an acceptable assumption, included here only for completeness (approximately ∼37 per cent of the samples require a more complex fit; see Section 6.2 for details). In general, shallower slopes correspond to higher accreted fractions, albeit with significant scatter. At fixed power-law slope, galaxies with higher stellar mass fractions in the outskirts have higher total accreted fractions. Figs 8 and 9 also raise the question of why these observations seem to be ‘missing’ stellar mass beyond 20 kpc (or, phrased differently, why TNG100 galaxies appear to be more massive and extended than DNGS galaxies at fixed stellar mass). No galaxy in DNGS has a stellar halo mass fraction above 10 per cent (when measured outside of 20 kpc), whereas the TNG100 galaxies in Fig. 9 can have up to 40 per cent of their stellar mass beyond this radius. One possible explanation is that DNGS galaxies may have low total accreted mass fractions relative to their mass-matched counterparts in TNG100. It is worth pausing to point out, however, that our capacity to ask (and answer) this question depends heavily on the validity of three assumptions: The parameters we chose to observationally estimate the accreted mass fraction (stellar halo mass fractions and power-law slopes) are able to provide an appropriate description of the outer profiles of galaxies – and, further, that these profiles contain sufficient information to characterize the assembly histories of galaxies. TNG100 reproduces observed properties of galaxies accurately enough that we should expect agreement in stellar halo properties. DNGS represents an unbiased sample of Milky Way-mass spiral galaxies. We will delve more deeply into these assumptions in Section 8 . First, however, we will proceed in Section 7 as though each of these are true, and attempt to carve out a more complete physical understanding of galaxies with low accreted mass fractions. 7 INSIGHTS FROM STELLAR PARTICLES We have seen from Figs 6 – 9 that DNGS galaxies appear to have low accretion fractions relative to their mass-matched TNG100 galaxies. The next logical question, then, is: ‘What are the assembly histories of (TNG100) galaxies with low accretion fractions?’ If, for example, this subset of galaxies shares any additional aspects of their assembly histories, we can potentially gain physical insight into our observations. In this section, we explore several different properties of our mass-matched TNG100 galaxies, combining information from the density profiles and individual stellar particles to characterize their past
Paper: 2021ApJ...910L...8Z (fulltext)
location of the continent, especially for the fast-rotating case; for P = 5 days, this transport can increase from 7 gigatons per second (Gt s −1 ) in an aquaplanet to 14 Gt s −1 when the continent is located to both the northeast and northwest of the substellar point (not shown). This transport brings along with it the ozone and water vapor; it is unclear how the change of this transport will affect the ozone of the dayside. Therefore, AOGCMs are required to fully understand the ozone distribution on these exoplanets when a continent is present. 5. Conclusion We investigate the climate impact of a relatively small terrestrial continent on a synchronously rotating exoplanet by using an AOGCM. It is found that the climate is highly sensitive to the location of the continent, mainly because of the existence of a strong equatorial westerly jet in the ocean on such exoplanets. This jet is responsible for ∼90% of the heat transport from dayside to nightside, and the strength of this jet largely determines the global climate. When a continent blocks the equatorial westerly at the equator, the climate cools. The strongest cooling effect is obtained when it lies slightly to the west of the substellar point. On the other hand, the westerly jet strengthens and the climate warms when a continent obstructs the poleward branch of the oceanic gyre circulation. Enhanced oceanic heat transport to the nightside reduces the sea ice coverage there, and thereby reduces the sea-ice input from nightside to dayside. This reduces the planetary albedo and raises the GMST. The GMST can vary by more than 20° C simply by varying the location of a small continent. The outer edge of the HZ around an M-dwarf defined by stellar constant can change by 15% when a small continent is located at different places. Because the atmospheric circulation, atmospheric water vapor, and ocean upwelling all change with the location of the continent, the atmospheric chemistry and biosignatures may also change. Although major topographic features tend to align with the star-planet axis for synchronously rotating exoplanets (Wieczorek 2007 ; Turbet et al. 2016 ; Salazar et al. 2020 ), small continents or island arc chains may exist everywhere. Our results demonstrate that these small features can still change the global climate of a synchronously rotating exoplanet by as much as that of a round continent. We also demonstrate that it is possible to engineer the climate on such exoplanets by building a dam at appropriate locations. Appendix Model and Experimental Design A.1. Climate Model The simulations presented in this work were done with the fully coupled AOGCM CCSM3 (Collins et al. 2006 ). The model is the same as that used by Yang et al. ( 2019 ). The atmosphere and land share the same Gaussian grid, and the ocean and sea ice share the same grid. The resolution of the CCSM3 model is set to “T31_gx3v5.” In this setting, the horizontal grid spacing is approximately 3.75° by 3.75° for the
Paper: 2018MNRAS.475L..20G (fulltext)
described via perturbations. Although isotropy has been directly tested (Blake Wall 2002 ; Bernui, Oliveira Pereira 2014 ; Bengaly 2016 ; Schwarz et al. 2016 ; Tiwari Nusser 2016 ; Bengaly et al. 2017 ; Bernal, Cardenas Motta 2017 ; Javanmardi Kroupa 2017 ), homogeneity is much harder to probe by observations (see e.g. Clarkson Maartens 2010 ; Maartens 2011 ; Clarkson 2012 ). As is well known, the smaller the scale we observe, the clumpier the universe appears. However, non-uniformities, such as groups and clusters of galaxies, voids, walls, and filaments, are expected in a Friedmann–Lemaître–Robertson–Walker (FLRW) universe according to cosmological simulations. In such a background, a transition scale is also expected, above which the patterns composed by these structures become smoother, eventually becoming indistinguishable from a random distribution of sources. This homogeneity scale r h has been identified and estimated at 70–150 Mpc h −1 , using data from several galaxy and quasar surveys (Hogg et al. 2005 ; Scrimgeour et al. 2012 ; Nadathur 2013 ; Alonso et al. 2015 ; Pandey Sarkar 2015 ; Laurent et al. 2016 ; Sarkar Pandey 2016 ; Ntelis 2017 ), although other authors have claimed no evidence for it (Sylos Labini, Montuori Pietronero 1998 ; Sylos Labini 2011 ; Park et al. 2017 ). In the context of the ΛCDM paradigm, an upper limit for the homogeneity scale was estimated by Yadav, Bagla Khandai ( 2010 ) to be r h ∼ 260 Mpc h −1 . Tests of homogeneity of the matter distribution by counting sources in spheres or spherical caps are not direct tests of geometric homogeneity, i.e. of the Cosmological Principle. Source counts on spatial hypersurfaces inside the past lightcone cannot be accessed by this method, since the counts are restricted to the intersection of the past lightcone with the spatial hypersurfaces. Instead, source counts provide consistency tests: if the count data show that the matter distribution does not approach homogeneity on large scales, then this can falsify the Cosmological Principle. Alternatively, if observations confirm an approach to count homogeneity, then this strengthens the evidence for geometric homogeneity – but cannot prove it. A test of homogeneity of the galaxy distribution that does probe inside the past lightcone has been developed by Heavens, Jimenez Maartens ( 2011 ) and Hoyle et al. ( 2012 ) – but this test is unable to determine a homogeneity scale. When a length-scale r h is used to probe homogeneity, a further assumption is made – a fiducial FLRW model is assumed a priori, in order to convert redshifts and angles to distances. In order to circumvent this model dependence, one can use an angular homogeneity scale θ h (Alonso et al. 2014 ). It was shown by Alonso et al. ( 2015 ) that the θ h determined from the 2MASS photometric catalogue is consistent with ΛCDM-based mock samples within 90 per cent confidence level. In this letter , we make tomographic measurements of θ h in the Luminous Red