adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJS..254...37F (fulltext)
human beings in near-Earth space, and provides a certain reference value in designing spacecraft materials in future deep-space exploration missions. Figure 8. GCR radiation dose rates on the lunar surface measured by the LRO/CRaTER. The horizontal dashed–dotted line marks the peak value of does rate in the solar minimum P 24/25 . 4. Conclusions The Sun was extraordinarily quiet during the recently complete solar cycle 24, with very low sunspot numbers, an extremely flat HCS, a reduced CME eruption rate, and a weak interplanetary magnetic field and turbulence level. The observed GCR intensities (both in interplanetary space and at the ground level) are sensitive to the heliospheric conditions, the so-called solar modulation. In this work, we focus on the variations of the GCR intensities for the period from 1997 through 2020 with measurements from the ACE/CRIS instrument, and investigate the influence of inner heliospheric environments on the GCR intensities. In addition, the long-term variations of the ground-based NM count rates are also studied. The conclusions are summarized as follows. 1. The GCR intensities observed at 1 au reach the highest levels in the solar minimum P 24/25 since the launch of the ACE spacecraft, which is ∼25% higher than that in the solar minimum P 22/23 and ∼6% higher than that in the solar minimum P 23/24 . With the extension of IMP-8 measurements and BON2020 numerical simulation results, we find that the GCR intensities during the recent solar minimum are at the highest levels since the start of the space age, having particle energies from tens to several hundred MeV nuc −1 . The record-breaking GCR intensities at 1 au increase the radiation dose rates near the lunar surface by ∼5% in comparison to those of the solar minimum P 23/24 , from the measurements of LRO/CRaTER instrument. 2. The peak value of NM count rates in the solar minimum P 24/25 is lower than that in late 2009. The difference between GCR intensities in interplanetary space and NM count rates seems to be relevant to the different modulation processes of the high- and low-energy particles. We think that the high-energy GCR particles are not as heavily modulated as the low-energy ones, which means the low-energy GCR particles are more likely to be influenced by varying solar modulation. An alternate or additional possibility is that the NM count rates are sensitive to the conditions of the Earth’s magnetosphere and atmosphere. It remains to be further studied in a future work. 3. We find that during the solar minimum P 24/25 , the mean solar polar field strength remains weak and is close to the value in the 2009 solar minimum; the HCS tilt angle reaches a minimum value of ∼2.1° in 2020 April, which is ∼22% lower than that in the solar minimum P 22/23 and ∼53% lower than that in the solar minimum P 23/24 ; the CME eruption rate is very small, and is less than half of the CME rate during the solar minima P 22/23 and P 23/24 ; the strength and
Paper: 2021ApJ...909...71R (fulltext)
the WISE images, which are likely the shock fronts. At the angular resolution of the H i data, this appears to be the case. Figure 5. Position–velocity slice for clouds G228 and G230. The left panel is the unWISE mid-infrared image, with a dashed green line illustrating the path along which the slice was made. The right panel shows the brightness temperature of the 21 cm line, with the horizontal axis being position along the slice (starting from 0 at the top of the green line in the left panel), and the vertical axis being the velocity (0.8 km s −1 pixels, spanning 50 km s −1 ). 4.2. Atomic and Molecular Content Comparing the Planck and GASS surveys highlights further the drastic differences between the G228 and G230. Figure 6 shows that G230 has a far higher amount of dust per unit atomic gas than G228. We showed earlier that both G228 and G230 contain CO in their cores, with central velocity and width similar to those of the narrow H i component, and CO core brightness comparable between the two clouds (Reach et al. 1994 ). Figure 6. Comparison of dust optical depth and H i column density for G228 and G230. The purple scale bar is 1° long (1.75 pc if the cloud is at a distance of 100 pc). The left panel shows the dust image, and the right panel shows the scatter-plot. Subsets of the data are shaded in color on the left image and have colored symbols on the right. In red are the points with the highest excess of dust optical depth compared to H i column density; these locations are concentrated in cloud G230, where CO was also detected. In green are the points selected to have the highest H i column density. The H i peak is in G228, and not coincident with the dust peak, which is in G230. In blue are the points selected in the southern “tail” of the image, south of G228 and G230. This region has roughly the same dust-versus-gas trend as the rest of the image and is normal, diffuse atomic gas. This figure, and similar ones in this paper, was made using the package glue (Beaumont et al. 2015 ). The Planck and GASS surveys show that the amount of dust per unit atomic gas is radically different between G228 and G230. If we use the gas column density from the H i 21 cm line and the dust optical depth from far-infrared emission (see Equation (4) of Reach et al. 2015 ), the atomic gas-to-dust ratio is [G/D] = 84 in the regions surrounding the clouds, [G/D] = 55 in G228, and [G/D] = 12 in G230. If the total gas-to-dust ratio is 84 everywhere, then there is significant non-atomic gas in both clouds. The amount of this “dark” gas per unit atomic gas (Equation (7) of Reach et al. 2015 ) is f dark = 1.5 in G228 and f dark = 7 in G230. There is no large-scale CO emission from the main bodies of the clouds in the Planck CO survey, which sets a limit W (CO) 7 K km s −1 on widespread CO. But we did detect CO from the cloud cores using ground-based telescopes (Reach et al. 1994 ); the CO brightness was W (CO) = 2.1 K km s −1 . Mapping observations showed
Paper: 2019MNRAS.485..770L (acknowledgments)
ACKNOWLEDGEMENTS This work was supported by the Russian Foundation of Basic Research (grant 17-52-80139 BRICS-a). SST acknowledges the support of the Academy of Finland grants 309228, 316932 and 317552. VD thanks the Deutsches Zentrum for Luft- und Raumfahrt (DLR) and Deutsche Forschungsgemeinschaft (DFG) for the support. Authors thank to Prof. Frederick Walter for comments and suggestions that allowed to improve the manuscript. The research has made use of Chandra data and software provided by the Chandra X-ray Centre. We used also data supplied by the UK Swift Science Data Centre at the University of Leicester. Results are also based on data products from VVV Survey observations made with the VISTA telescope at the ESO Paranal Observatory.
Paper: 2019MNRAS.482.1485P (fulltext)
exploration of the entire multidimensional space of atmospheric parameters. We thereby assume no a priori values for the H 2 O abundances and cloud/haze properties. There are also several differences between our study and that of Barstow et al. ( 2017 ). The differences are of three kinds: model parametrizations, statistical sampling algorithms, and reported estimates. We here discuss each of these in turn. There are several important differences in the model parametrizations. The main aspect of the Barstow et al. ( 2017 ) study is that it is effectively a grid-based retrieval, in the sense that a separate retrieval is conducted for each assumption of a cloud prescription. Importantly, the study treats two kinds of cloud models separately: a model with Rayleigh-like slopes (i.e. our hazes) and one with grey opacity (i.e. our clouds). On the other hand, our cloud/haze model simultaneously accounts for small and large particle sizes (see equation 4 ). An important distinction also lies in the aerosol parametrization. Our study includes a grey cloud overlain by a haze such that both types of aerosols are retrieved for a planet, although the retrieved parameters could indicate no hazes (i.e. a = 1 and γ = −4) and/or no grey clouds (i.e. P cloud ≳ 10 bar). On the other hand, Barstow et al. ( 2017 ) allows for the presence of a vertically finite cloud or haze deck at variable locations in the atmosphere. In some cases (e.g. HD 189733b) where our study retrieves a deep cloud layer with an extensive overlying haze layer, Barstow et al. ( 2017 ) retrieve a decade-confined haze layer confined to low pressures. These two different interpretations, however, produce similar fits to the spectrum. An additional crucial difference in the aerosol models is that this study accounts for inhomogeneous clouds and hazes along the planetary limb. The inclusion of patchy clouds is important since the shape of the H 2 O feature in the WFC3 bandpass is sensitive to the degree of cloud contribution (e.g. see Line Parmentier 2016 ). These differences in modelling are likely another principal element responsible for discrepancies in the cloud and haze properties between this work and that of Barstow et al. ( 2017 ). Secondly, Barstow et al. ( 2017 ) assumes an isotherm for the observable region of the atmosphere (pressures below 0.1 bar) whereas we allow a fully general temperature profile that allows for any temperature gradient. Thirdly, there is a significant difference in the treatment of opacity in the radiative transfer. Whereas the previous study assumes the correlated- k distribution for opacities, our study uses opacity sampling from very high-resolution cross-sections for the different molecules. This difference in opacity treatments could potentially influence the retrievals. However, generally similar water abundance estimates between the two methods is encouraging. Fourthly, the cloud properties and temperature profiles in Barstow et al. ( 2017 ) were
Paper: 2020A&A...633L...6K (fulltext)
cool prominence plasma being ejected away from the reconnection region. The bulk of the ejected material consists of two main components denoted as S1 and S2 in Fig. 2 . Component S1 moves upwards freely and undergoes helical motion believed to be the result of the reconnection of a twisted bundle of prominence threads with overlying field lines. Helical motion of the ejected surge material suggests that the reconnection lead to the release of the magnetic twist accumulated in the flux rope supporting the prominence. The ejection of component S2 is triggered by the reconnection of a prominence thread with a closed coronal loop. The confined material then moves along a closed loop-like trajectory and subsequently falls towards the solar surface. Fig. 2. Evolution of the rotating flux tube. Top : superimposed snapshots in IRIS FUV SJI data during three different phases of the torsional oscillation of the surge material. White dotted lines outline the helical trajectories of the plasma threads. The arrow shows the direction of the rotation. The white vertical line corresponds to the fraction of the slit intersecting the surge plasma. The freely moving and confined component of the ejected surge material are denoted S1 and S2, respectively. Bottom : IRIS Si IV and Mg II K spectra of the surge material corresponding to the fraction of the slit marked by the white line. We show spectral snapshots from three different phases of the motion of the plasma at the slit position. In each panel, the vertical dashed line indicates the mean Doppler velocity of the plasma. The alternating tilt of the emission in the different phases, denoted in each panel by the tilted dashed line (obtained by visual examination) corresponds to the torsional motion of the surge material. Animation of this figure is available online . The direction of the helical motion of the surge material alternates, suggesting a large-scale torsional wave is set up in the flux tube following the reconnection. The helical motion of the individual threads is observable both in the imaging and spectral data. Rotation in a clockwise direction viewed edge-on is revealed in spectral data as opposite red and blue-shifted emission on the top and bottom edges of the flux tube, relative to the mean line-of-sight velocity. The reverse is true for a counter-clockwise rotation. The IRIS SG slit is in sit-and-stare mode and crosses the surge material at an angle. As a result, it is possible to determine the presence and direction of rotation from variations of the Doppler shift as a function of distance along the slit. Figure 2 shows SJI snapshots at various phases of the torsional motion of the flux tube as well as the Si IV and Mg II K line profiles along the slit between y = −254′′ and y = −244′′, where the slit intersects with the surge material. We note that the imaging and spectral snapshots are not concurrent because by the time the third phase of the propagating oscillation is visible in the
Paper: 2018ApJ...853...21G (acknowledgments)
S.G. and P.H. acknowledge the support from grant 16-17586S of the Czech Science Foundation (GAČR). S.G. and P.H. acknowledge the support from project RVO:67985815 of the Astronomical Institute of the Czech Academy of Sciences. S.G. and P.H. thank for the support from the MPA Garching. U.A. thanks for the support from the Astronomical Institute of the Czech Academy of Sciences. D.H.M. acknowledges financial support from the STFC, the Leverhulme Trust, and NASA. ALMA is an international partnership of the European Southern Observatory (ESO) representing its member states, the U.S. National Science Foundation (NSF) and the National Institutes of Natural Sciences (NINS) of 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. S.G. is an expert member of the Solar Simulations for the Atacama Large Millimeter Observatory (SSALMON) network.
Paper: 2017ApJ...843...40A (acknowledgments)
We acknowledge the support from: the US National Science Foundation (NSF); the US Department of Energy Office of High-Energy Physics; the Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory; Consejo Nacional de Ciencia y Tecnología (CONACyT), México (grants 271051, 232656, 260378, 179588, 239762, 254964, 271737, 258865, 243290, 132197), Laboratorio Nacional HAWC de rayos gamma; L’OREAL Fellowship for Women in Science 2014; Red HAWC, México; DGAPA-UNAM (grants IG100317, IN111315, IN111716-3, IA102715, 109916, IA102917); VIEP-BUAP; PIFI 2012, 2013, PROFOCIE 2014, 2015; the University of Wisconsin Alumni Research Foundation; the Institute of Geophysics, Planetary Physics, and Signatures at Los Alamos National Laboratory; Polish Science Centre grant DEC-2014/13/B/ST9/945; Coordinación de la Investigación Científica de la Universidad Michoacana. Thanks to Luciano Díaz and Eduardo Murrieta for technical support.
Paper: 2016ApJ...825...17Z (fulltext)
between the data and the model. The four panels correspond to the four respective sub-samples: N = 6 BAO data, N = 30 DA data, the N = 35 combined BAO+DA sample with the H ( z = 2.34) data point excluded, and the full N = 36 combined BAO+DA data. Figure 8. Histograms of the Om ( z i , z j ) two-point diagnostic residuals R Om ( z i , z j ) calculated with different samples: N = 6 BAO data, N = 30 DA data, the N = 35 combined BAO+DA sample with the H ( z = 2.34) data point excluded, and the full N = 36 combined BAO+DA data. In each panel the results for three different cosmological models are shown. Figure 9. The Omh 2 ( z i , z j ) two-point diagnostic residuals displayed as the weighted mean (left panels) and as the median value (right panels) indicated by dashed lines surrounded by color bands denoting 68% confidence regions. In each panel the results for three different cosmological models are shown. The long solid line shows the level expected for perfect agreement between the data and the model. The four figures correspond to the four respective sub-samples: N = 6 BAO data, N = 30 DA data, the N = 35 combined BAO+DA sample with the H ( z = 2.34) data point excluded, and the full N = 36 combined BAO+DA data. Figure 10. Histograms of the Omh 2 ( z i , z j ) two-point diagnostic residuals calculated with different samples: N = 6 BAO data, N = 30 DA data, the N = 35 combined BAO+DA sample with the H ( z = 2.34) data point excluded, and the full N = 36 combined BAO+DA data. In each panel the results for three different cosmological models are shown. One can see from Figure 7 that the residuals R Om ( z i , z j ) for the ΛCDM model are closer to zero than for the wCDM or CPL models, irrespective of the sample. From Table 4 one can see that the residuals of the wCDM or CPL models summarized in the weighted mean scheme are more than 15 σ away from the expected value of zero (for the full sample). This deviation in terms of median statistics is even bigger. One can also see this clearly in Figure 8 where the histograms of R Om ( z i , z j ) are shown. In the case of the Omh 2 ( z i , z j ) diagnostics the performance of the ΛCDM and wCDM models is similar: using the full sample, the weighted mean of residuals is at 7 σ away from zero. As shown in Figure 10 , the bulk of the distributions for the ΛCDM or wCDM models contain zero in their tails, while the distribution for the CPL model is considerably away from zero (it corresponds to 26 σ for the weighted mean). It seems that despite the presence of some tension between the ΛCDM model and the two-point diagnostics evaluated on the most recent H ( z ) data, as noted, e.g., in Sahni et al. ( 2014 ) and Ding et al. ( 2015 ), this model still performs better than its immediate extensions, wCDM or CPL, in particular the latter. It should be stressed that the above mentioned performance of different models refers only to the two-point diagnostics considered. Therefore, this cannot be treated as a
Paper: 2020MNRAS.494.2798H (fulltext)
a small fraction of the light to the photon counter of the profiling subsystem. In an AO system, typically about 97 per cent of the light of the LGS is directed towards the WFS by a beam splitter. The remaining 3 per cent of the light from the LGS follows the path to the science camera. This fraction could be notch-filtered and redirected to a high-speed photon counting detector, used to measure sodium density profiles. While the retrieved profiles taken at the LZT demonstrate that the CW lidar profiles are consistent with the ‘standard’ lidar profiles, our results obtained with a large area APD sensor used in linear mode were affected by a robust radio-frequency interference. In a photon-noise limited case the CW lidar method should have provided profiles with an accuracy of 20–40 m RMS centroid offset. Instead, the spread found in our experimental data is of the order of 150–200 m. We believe that this discrepancy is due to electromagnetic interference in the linear APD signal detected. Our theoretical analysis and the numerical simulations show the potential of the method. The method could yield sodium centroid profiles with high accuracy. Different scenarios could be realized, either one LGS modulated with a higher modulation strength (>60 per cent) or multiple LGS modulated with low modulation strength (≈30–40 per cent) could be used. We note that laser amplitude modulation strengths of 30–40 per cent result in 15–20 per cent decrease in the LGS return flux, which can be acceptable already with current 22W CW sodium lasers. Recent experiments done at the ESO WLGSU in La Palma (Haguenauer, Boanccini Calia Pfrommer 2019 ) have demonstrated the feasibility of splitting the uplink laser beam in two, to create two LGS angularly separated. The subsequent reduction of 50 per cent of the LGS flux corresponds to the loss of power due to a modulation of 100 per cent (ε = 1), in our CW lidar. The multiconjugate AO system at Gemini South (GeMS) splits one 20W CW laser into five LGS which reduces the return flux from one LGS to 20 per cent. For the new AO system under development for Gemini North, it is planned to split one 20W CW laser into two LGS. Calculations in the frame of the ESO Mavis project Phase A study indicate that using the 20W CW lasers in the VLT Galacsi LGS-AO system, with 20 × 20 cm sub-apertures Shack-Hartmann wavefront sensor, will still be able to support MCAO systems at visible wavelengths. Considering also that RD work is in place at ESO to both, enhance the LGS return flux by frequency chirping, and by increasing the laser power beyond 50 W at 589 nm, there is a good outlook for the CW lidar method to be used in operations, turned on when necessary. As this is the time of designing the extremely large telescope AO systems, we solicit the system experts to take provisions for the CW lidar implementation into account. Besides providing sodium laser profiles and abundances on time-scales of several seconds, of general use
Paper: 2019ApJ...886...35D (fulltext)
for its mass. Changing any parameter in a reasonable range does not lead to a consistent solution. As shown by Lorenz et al. ( 2005 ), the secondary of V1331 Aql reaches about 96% of the Roche radius. Thus, V1331 Aql exhibits a detached configuration, but the secondary is about to fill its Roche lobe. It was not possible to obtain a consistent age for the binary V413 Ser because of a disagreement on the HR diagram for all searched values of Z and α ov . V413 Ser is located in the Serpens star-forming region (Chavarria-K et al. 1988 ). The Serpens dark cloud contains an extremely young star cluster and most probably both components of V413 Ser are pre-main-sequence objects (Çakırlı et al. 2008 ) and not the main-sequence ones as we assumed in our age determination. Results for IM Mon, Ori V1388, and V380 Cyg will be presented below. IM Monocerotis : Till now, IM Mon was studied in detail only in two papers, i.e., Bakiş et al. ( 2010 ) and Bakis et al. ( 2011 ). In Bakis et al. ( 2011 ), the analysis of all available photometric and spectroscopic observations allowed them to determine the absolute values of masses: M A = 5.35(24) M and M B = 3.32(16) M , and radii: R A = 3.15(4) R and R B = 2.36(3) R . Moreover, it has been shown that IM Mon is a member of Ori OB1a association and the age of the system is about 11.5 Myr. The age was estimated from a comparison of the location of stars on the diagram with the isochrones of Girardi et al. ( 2000 ). A comparison of the observed high-resolution spectrum with Kurucz atmosphere models gave atmospheric parameters and metallicity [Fe/H] = +0.2. The age of IM Mon determined by Bakis et al. ( 2011 ) is in agreement with earlier determinations for Ori OB1a association by Blaauw ( 1991 ) and Brown et al. ( 1999 ). Using the masses and radii, we tried to re-determine the age of the system, as illustrated in Figure 9 . We found that metallicity of at least Z = 0.025 is needed to find a common age of the components that is in line with the values of [Fe/H] obtained by Bakis et al. ( 2011 ). Then, the age of the system is about 9.7(2.9) Myr, which is in agreement with the previous determinations. However, when we put the stars on the HR diagram (the bottom panel of Figure 9 ), it turned out that the secondary component is overluminous for its mass. The evolutionary tracks for the metallicity typical for galactic B-type stars, Z = 0.014, would be consistent, although marginal; but in this case, there is no common age for both components even if the large overshooting ( α ov = 0.5) is assumed for the primary star. One reason for that could be an effective temperature that is too high. The lower value of T eff would move the star to the right and down in the HR diagram. The second explanation could be a more complicated configuration of this binary, i.e., maybe the system is not fully detached or there is a third light. Figure 9. The similar plots as in Figure 6 but for components of IM Mon. V1388
Paper: 2015A&A...578A..13K (acknowledgments)
We would like to thank the referee, Albert Bosma, for comments that improved the quality of the paper.