adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...900...65B (fulltext)
the nonrotating case. Reconnection proceeds whenever the orientation of the BR is close to optimal and the continued disruption of the same section of overlying field eventually triggers a flare. The peak KE is higher for the corotating case because the flares are triggered at the same time unlike the counter-rotating case. The peak KE released for the simulations with two colliding rotating BRs is almost negligible. Looking at the simulation results more closely reveals that no flares are triggered in either simulation. This is likely because no section of the overlying field is disrupted for long enough to trigger a flare. Each rotating BR is in the optimal orientation only for a short time during rotation and never twice for the same section of overlying field. 4. Discussion The results from these simulations indicate that the motion of the emerging small-scale BRs that trigger flares can have a significant effect on the strength of those flares. In the most extreme cases the effect of motion alone caused over a threefold increase in the peak KE released by the flare. The proper motion of magnetic features such as these has been observed on the solar surface, for example, sunspots have been seen to drift at speeds of 0.14 km s −1 (Herdiwijaya et al. 1997 ) and moving magnetic features within sunspots move at ∼0.5 km s −1 (Zhang et al. 2007 ). Rotational motion of magnetic features has also been observed, the rotational velocity of sunspots has been measured at values of up to hr −1 during their emergence from the photosphere (Zhu et al. 2012 ). Photospheric motions are known to twist the coronal magnetic field into nonpotential configurations as discussed in Sundara Raman et al. ( 1998 ), Gerrard et al. ( 2002 ), Wang et al. ( 2018 ), and Yan Qu ( 2007 ). These magnetic fields can then be triggered resulting in a flare. In these simulations, however, which include a preexisting nonpotential field, we are only interested in how these motions effect the strength of the flare through the triggering process. In our first set of simulations we specifically considered linear oscillation of the emerging BR rather than simply drift; though, the results indicate that there may be little distinction. Interestingly the frequency at which the emerged BR oscillates does not seem to effect the strength of the flare. It is thought that this is because the frequency of the oscillation ultimately has no effect on how much of the overlying field is exposed to the disruptive trigger of the emerging small-scale BRs. The orientation of the oscillation on the other hand has been shown to be very important. Motion along the PIL generally increases the strength of the flare while motion across the PIL generally detracts from the strength of the flare. This is likely because the position of the emerging field on the PIL is the most disruptive to the overlying field and more of this field is disrupted if there is motion of the BR along the PIL. The most explosive
Paper: 2019MNRAS.490.3134L (acknowledgments)
ACKNOWLEDGEMENTS The authors acknowledge the National Computational Infrastructure (NCI), which is supported by the Australian Government, for providing services and computational resources on the supercomputer Raijin that have contributed to the research results reported within this paper. This research made use of the Argus Virtual Research Desktop environment funded by the University of Sydney. FL is supported by the University of Sydney International Scholarship (USydIS).
Paper: 2017ApJ...850...75S (fulltext)
et al. 1988 ) applied to IST EoS of quantum gases (see Bugaev et al. 2017 for details). This allows us not only to reproduce the properties of the normal nuclear matter but also to significantly extend the range of IST EoS applicability. The goal of this work is to study the constraints that can possibly be imposed on the IST EoS from the astrophysical observational data of NSs. This is necessary to find which set of parameters of this EoS simultaneously fits the experimental data from the A+A collisions and the astrophysical observations on NSs. The paper is organized as follows. In Section 2 we briefly review the novel IST EoS and in Section 3 we present the mass–radius relation, including a discussion of constraints coming from nuclear and high-energy nuclear physics experiments. The paper ends with a summary in Section 4 . 2. Equation of State: Model Description The novel thermodynamically consistent EoS is based on the virial expansion applied for the multicomponent mixtures with hard core repulsion (Sagun et al. 2014 ). In particular, proper consideration of the volumes of particles enables us to explicitly account for the surface tension that is induced by the interaction between constituents. In this formalism the IST EoS has the form of a system with two coupled equations, one for pressure p and another for the IST coefficient σ defined as follows,andwhere T , m i , , and R i are temperature, mass, chemical potential, and the hard core radius of the i -sort of particles. The eigenvolumes and surfaces are equal to and . The summations in Equations ( 1 ) and ( 2 ) are made over all types of particles and their antiparticles, with a corresponding partial pressure of point-like particles of type i . Here, denotes the shifted chemical potential due to the presence of the hard core repulsion. The dimensionless parameter α in Equation ( 2 ) was introduced due to the freedom of the Van der Waals extrapolation to high densities (Sagun et al. 2014 ) and accounts for the higher-order virial coefficients. As was shown in Sagun et al. ( 2014 ) to reproduce the correct phase-diagram properties of nuclear matter, such a parameter should obey the inequality . According to the conditions of wide causality range and compressibility factor, the value of the parameter α in the Statistical Multifragmentation Model was found to be equal to 1.25 (Sagun et al. 2014 ) . Further studies revealed the value α = 1.245 (Sagun et al. 2017b ) is best for reproducing the fourth and third virial coefficients. Another physical meaning of α is as a switcher between the excluded volume and the proper volume regimes (for details, see Sagun et al. 2017a ). As the value α = 1.245 was obtained for the Boltzmann statistics (Sagun et al. 2017a ) and could be different at zero-temperature, when quantum effects are significant, the baryon radius and α parameter are chosen to be free parameters in the model. On the other hand, when fitting the mass–radius curve to existing
Paper: 2021ApJ...916L...9A (fulltext)
. For the bifurcation point μ = 0, we derive from ( 6 ) the condition for the cycle-frequency ν to be given by equationThe solution with the lowest value, ν *, defines the critical dynamo number, at which the zero solution becomes unstable to be given byAt this point, Equation ( 4 ) has the harmonic solution B ( t ) = A sin ν * t . However, the amplitude A is unstable. It decays to zero, for dynamo numbers below , and would grow unbounded for higher values, if it were not for B max , which stabilizes the solution again. Simulations show that, at , the zero solution bifurcates to the harmonic solution B ( t ) ∝ sin ν * t , with amplitude close to B max . Further increasing leads to periodic and chaotic modulations of both the amplitude and the period. (Figure 3 ). This period-doubling route to chaos is a robust feature of BL-type dynamos, which has been observed also in simpler iterative map models as well as more complicated spatially resolved models (Charbonneau et al. 2005 ). The amplitude-modulations are superimposed to an increasing trend, starting at around B max . The period, on the other hand, is modulated around the linear solution ν *. For the sake of simplicity, we refer to this entire mode of oscillation as the weak mode. Figure 3. Field amplitudes B 2 as a function of the dynamo number , for q = 0.82, exhibiting the transition between the weak and the strong mode around . The inset shows the corresponding periods against (same units). Gray vertical lines indicate the dynamo number used for the realization in Figure 4 . If we introduce B min , this generic picture persists. However, the zero solution remains stable for dynamo numbers beyond . Hence, there is a range of dynamo numbers, roughly between and , for which the dynamo admits at least two stable modes (the weak mode and the zero mode). For even larger values of the dynamo number, a fundamentally different mode of oscillation emerges (the branch in Figure 3 above dynamo number ). This is a mode where B ( t ) is above B max most of the time and both the amplitude and the period are constant. We call it the strong mode. It is the stable limit cycle that appears when we approximate in Equation ( 4 ). This approximation is valid for large , where the cycle period is much larger than q . For smaller values of , the approximation fails and the system can no longer be approximated by a second-order ODE, hence the emergence of the chaotically modulated weak mode. The cycles of the strong mode have a characteristic shape, similar to the large solar cycles that were observed in the sunspot records (Figure 2 ): a steep rising limb, followed by a falling limb with a kink. This shape has been interpreted as an effect of the B -field exceeding B max before by Stix ( 1972 ), albeit with a non-BL-type dynamo model. The fit in Figure 2 has been achieved through optimizing parameters q and . Interestingly, the optimized value turned out to be quite close to the critical point where the strong
Paper: 2015ApJ...798...23L (acknowledgments)
This research is part of the project Cosmic-Lab ( http://www.cosmic-lab.eu ) funded by the European Research Council (under contract ERC-2010-AdG-267675). E.V. acknowledges ESO DGDF (SL) 14/51/E. NSO/Kitt Peak FTS data used here were produced by NSF/NOAO. We warmly thank the anonymous referee for suggestions that improved the paper.
Paper: 2019A&A...624A..10G (fulltext)
In addition, the HARPS archive contains plenty of observationsof solar-type stars, including a rich set of solar spectra taken by observing solar system bodies for many years. All these characteristics make HARPS the ideal instrument to investigate the precision of the normalization method that we have developed. The fact that the solar-sibling observations have been repeated for several years allows us to also investigate the stability of this instrument in time, and to determine to the extent to which the HARPS H α profile has remained constant in time. The test is performed with all solar spectra set out in Table 3 , for which ’s were derived. The plot in the top panel of Fig. 14 visually summarises the results displayed in the table. For each date, values are represented by plus symbols. Their weighted mean and corresponding spread values are drawn with bars. Next to them, the number of spectra used and their average S/N ratio are noted to show the precision reached when measurements from several spectra are combined. The weighted mean and spread of all measurements are represented by the horizontal line and the shade at 5744 ± 10 K. Evidently, there is no trend with time and the scatter is very low, which confirms the blaze stability of HARPS. This value is in perfect agreement with that of Coudé (see values in Table 2 ), which implies that not only is the blaze stable but it is also fully removed through the flat-field procedure. Table 3 Solar proxies. Notes. The list is ordered by date of observation along with the S/N of the spectra and the effective temperature derived from H α profiles. Fig. 14 Top panel : temperatures of the HARPS solar proxies in Table 3 vs. the date upon which spectra were acquired. Daily values are represented by plus symbols and weighted means and errors for each month are drawn in red. The weighted mean and error of all the measurements are represented by the continuous line and the shade on 5744 ± 10 K. Next to the bars, the number of spectra analysed and the mean S/N are noted. Bottom panel : errors of individual measurements in the top panel are plotted vs. S/N. The exponential curve given by the equation in the plot is the best fit to the points. In the bottom panel of Fig. 14 we plot the precision obtained from individual spectra as a function of S/N. It is observed that ~40 K can be obtained from spectra of S/N = 400–500. Finally, we compare the temperatures derived from HARPS with those derived from Coudé spectra for the other stars in common in our sample. The comparison is shown in Fig. 15 against the three main stellar parameters. It shows an excellent agreement with a negligible offset between the two samples of − 13 ± 34 K with no trends. The temperatures of all stars agree within 1 σ errors, with the exception of two ( δ Eri and HD 184985) that agree within 2 σ . 11 Summary and conclusions With the aim of better understanding and minimising the errors that affect T eff measurements from H α
Paper: 2017MNRAS.464.2234G (fulltext)
between the individual patterns. The middle panel of Fig. 9 shows the δ T b map at redshift 16. The number of sources drops quite drastically in this frequency channel as we are probing the initial stages of the cosmic dawn. The overlap between the individual patterns too is not that substantial. The decrease in the number of sources towards the lower frequency channels can be seen from the right-hand panel of Fig. 9 , where we show the light cone from our simulation box. The signal at the higher frequency end of the box is essentially the strong absorption signal arising from the significant overlap between the Ly α photons from the sources. Note that we have subtracted the mean from each frequency channel while making the image and thus the signal is a combination of emission and absorption regions. As the signals from the channels corresponding to redshifts 15 and 16 are dominated by the absorption regions, the mean of the expected signal is negative. The regions with positive values of the signal in the left-hand and middle panels of Fig. 9 arise because of subtracting the mean signal from the maps. In reality, these are the Ly α-deficient regions with almost no 21 cm signal. Figure 9. Left-hand panel: 24 × 24 map of the 21 cm signal (without smoothing) at the frequency channel ν c = 89 MHz generated from the simulation using the N -body simulation and a one-dimensional radiative transfer code. The ‘×’ marks represent the angular positions of the sources between a band ν c − 0.1 to ν c + 0.1 MHz. Middle panel: same as the left-hand panel, but at the frequency channel corresponding to ν = 84 MHz. Right-hand panel: the light-cone map of δ T b distribution. The arrows in the top label of the panel show the frequency channels corresponding to the maps in the left-hand and middle panels. The maps include the effect of redshift-space distortion and the light-cone effect. The top-left panel of Fig. 10 shows the smoothed image of the residual signal and noise at the central frequency channel after subtracting the foregrounds using the polynomial method. The noise in the panel corresponds to 200 h of observation, and the smoothing is performed using the Gaussian filter of size 30 arcmin. One can easily identify that even in the smoothed map, the signal is localized around the sources. The top-right panel of Fig. 10 shows the same but for 2000 h of observation. The SNRs of these top-left and top-right panels are 4.8 and 14.2, respectively. The bottom-left and bottom-right panels are the same as the top panels but at a different frequency channel corresponding to ν = 84 MHz ( z = 16). The corresponding SNRs in the bottom panels are 3.3 and 10.1, respectively. The Pearson cross-correlation coefficients χ, in this case, are given in Table 3 . We find that the foreground subtraction method works effectively in this case as well leading to reasonably high values of χ. Figure 10. Top-left panel: map of the residual signal and noise after foreground
Paper: 2019MNRAS.490.1470P (fulltext)
distribution of these galaxies in -space and compare it with the cumulative probability distribution function for a uniform distribution (i.e. a straight line). The Kologorov–Smirnov (KS) statistic for these distributions is D = 0.128 indicating consistency. Figure 2. The cumulative fraction of where α is the pitch angle is shown, using the central 90 per cent range of pitch angles from Yu Ho ( 2018 ). The dashed line shows a uniform distribution which is what would be expected if the spiral structure winds up at a constant rate (equation 5 ). The KS statistic for the comparison between these two distributions is D = 0.128, confirms a lack of statistical disagreement. As a consistency check, we also show the normalized distribution of pitch angles for different wavebands in Fig. 3 , again using data from Yu Ho ( 2018 ). As would be expected, the distribution of pitch angles are similar for the different wavebands. Within error bars (which we do not show for clarity), the pitch angles again exhibit a uniform distribution in the range for the B, V, R , and 3.6 μm bands. We leave any further studies across different wavelengths or other parameters to future observational studies. Figure 3. The distribution of pitch angles is shown for different wavebands, using the data from Yu Ho ( 2018 ). 4 DISCUSSION Recent work has attempted to correlate measurements of pitch angles with other galaxy properties as a means to distinguish between mechanisms for spiral arm generation. In these observational studies, the pitch angle is usually assumed to have a fixed value independent of radius or time. In particular, Yu et al. ( 2018 ) and Yu Ho (2018 , 2019 ) argue that measurements of pitch angles, and the correlations between these and other galaxy properties, are broadly consistent with the expectations of Lin–Shu modal density wave theory (Section 2.1 ). 3 We find nothing that contradicts this possibility. However, their arguments and analysis are predicated on the basic assumption of Lin–Shu theory, which is that each galaxy contains a spiral pattern whose pattern speed, Ω p , and pitch angle, α, are fixed in time (and space), being just dependent on the various properties of the galaxy, for example on morphology of the galactic rotation curve (fig. 11 in Yu Ho 2019 ) or the galactic shear rate (fig. 12 in Yu Ho 2019 ). Here, we investigate whether an alternative premise, i.e. that the pitch angles of galaxies evolve with time, is consistent with the observed data. Our finding that the distribution of pitch angles () is more or less uniform among their observed galaxies, opens up the possibility that the pitch angles are actually evolving in time, as is found in the simulations (e.g. Grand et al. 2013 ; Pettitt Wadsley 2018 ; Mata-Chávez et al. 2019 ). We note that in the modal density wave picture, this finding would need some other explanation. We argue here that the possibility of evolving pitch angles merits further investigation. Our simple
Paper: 2018ApJ...860...26R (acknowledgments)
We are thankful to WIND Spacecraft data providers ( wind.nasa.gov ) for making interplanetary data available. We are also thankful to the Department of Physics (Autonomous), University of Mumbai, for providing us with facilities for the fulfillment of this work. A.R. also thanks the Solar-TErrestrial Physics (STEP) group, USTC, China SCOSTEP visiting Scholar program. W.M. is supported by the Chinese Academy of Sciences (CAS) Presidents International Fellowship Initiative (PIFI) grant No. 2015PE015 and National Natural Science Foundation of China (NSFC) grant No. 41750110481. A.B. is supported by the NASA Living With a Star Jack Eddy Postdoctoral Fellowship Program, administered by UCARs Cooperative Programs for the Advancement of Earth System Science (CPAESS).”
Paper: 2016ApJ...826..156S (acknowledgments)
The authors wish to thank Bryce Croll and Saul Rappaport for their helpful suggested regarding observing, interpretation of the spectra, explanations of systematics, and a wider range of compositions to consider. Thanks to James P. Lloyd and Dong Lai for their advice on the interpretation of the transits. Based on observations made from the Infrared Telescope Facility, which is operated by the University of Hawaii under contract NNH14CK55B with the National Aeronautics and Space Administration. M.Z. acknowledges support from NASA OSS grant NNX14AD22G and the Center for Exoplanets and Habitable Worlds at the Pennsylvania State University. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.
Paper: 2017A&A...603A..82I (acknowledgments)
We thank all the members of the AKARI project. AKARI is JAXA project with the participation of ESA. The CME catalog is generated and maintained at CDAW Data Center by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a project of international cooperation between ESA and NASA. This research is financially supported by Grants-in-Aid for Young Scientists (A) No. 26707008, and Scientific Research (C) No. 25400220. T.K. is financially supported by Grant-in-Aid for JSPS Fellows No. 25002536, and the Nagoya University Program for Leading Graduate Schools, “Leadership Development Program for Space Exploration and Research,” from MEXT.