adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018A&A...619A.120K (fulltext)
et al. 2016 ) and can potentially suppress clumping instabilities ( Vishniac 1983 ; Ntormousi et al. 2017 ). This may well set the actual size scales of the resulting clumps in our simulation that includes the cloud. We would also expect that the actual star formation sites are filamentary, like the dense regions in the simulations of Inoue Inutsuka (2012) , and like those seen in recent observations of Pokhrel et al. (2018) . We focus here, however, on the connection to the larger scale, and refer to these other studies for details of the evolution onsmaller scales. While magnetic fields have in principle the potential to suppress the clumping instability in supershells, this is not required to explain the HI observations of the USco supershell, where the filamentary appearance of the shell suggests that the Vishniac instability is acting. Magnetic fields might, however, explain the absence of molecular gas in the HI shell around USco. We stress that the Vishniac instability is not crucial for our scenario. Suppression of instabilities at the shell surface of our global simulation would reduce the extent of the mixing zone, which is responsible for most of the radiative cooling in the bubble. This would therefore increase the size of the simulated bubble, such that a smaller number of massive stars would be required to reach the observed extent. The blow-out of the HI supershell around USco suggests that Sco–Cen formed from a large sheet of dense gas, similar to the dense sheets in large-scale simulations of the ISM (e.g. de Avillez Breitschwerdt 2004 , 2005 ; Seifried et al. 2017 ). In these simulations, these dense sheets are “walls” of superbubbles several hundred parsecs in size. Seifried et al. (2017) show that molecular gas builds up in such sheets over millions of years. Dawson et al. (2015) may have observed this process at the interface of two HI supershells in the Carina arm of the Milky Way. Giant molecular clouds formed in this way are less violently formed than in typical colliding flow simulations of molecular cloud formation (e.g. Inoue Inutsuka 2008 ; Körtgen Banerjee 2015 ), because the pressure in the surrounding superbubbles is modulated by the actual energy production of the massive stars. This might make the clouds comparatively tenuous and delay the formation of stars ( Dawson et al. 2015 ). A more gradual buildup of molecular clouds is also seen in more sophisticated, multi-phase colliding flow simulations ( Inoue Inutsuka 2012 ). Inutsuka et al. (2015) detail a scenario of magnetised molecular cloud and star formation along these lines. In general, molecular cloud formation at the interface of HI supershells is frequent ( Dawson et al. 2013 ). Near the solar circle, molecular cloud formation is only weakly coupled to spiral arm passages (e.g. Koda et al. 2016 ). How could the actively star-forming Lupus I cloud have formed in this scenario? Our analysis of multi-wavelength observations of the Lupus I molecular
Paper: 2018ApJ...854..134S (fulltext)
that auroral emission excited by energetic particles is likely to dominate over airglow excited by EUV and X-rays. Nevertheless, if high-energy photons are incident at ∼3 × 10 30 erg s −1 (10 34 erg over 1 hr), the incident energy is ∼135 TW for a planet of Earth’s radius at 0.05 au. The fraction converted to airglow depends completely on the photochemistry of the upper atmosphere. If there is significant ozone present, essentially all the light shortward of 320 nm will be absorbed in the ozone Hartley band, and will potentially be available for atmospheric chemistry and excitation processes, such as excitation of the rotational–vibrational Meinel series of hydroxyl (OH) emission lines in the near infrared. Leinert et al. ( 1998 ) tabulated the strongest terrestrial airglow lines, and the OH line series is by far the strongest, at 4.5 MR (mega-Rayleighs, where R is the Rayleigh unit of surface brightness), compared to, e.g., the green oxygen line at 558 nm of 250 R, or 10 kR auroral 558 nm emission for typical zenith airglow brightnesses. With the high-energy photon flux, and the timescales for the stellar wind and CMEs being comparable, high-resolution spectroscopic searches for optical/NIR line emission are likely to benefit from filtering according to this timescale—the optimal times to seek an atmospheric response are during and for the few hours following strong flares. An instrument such as the CRyogenic high-resolution InfraRed Echelle Spectrograph (CRIRES) on the European Southern Observatory’s Very Large Telescope (VLT) would seem well suited to a search of this nature, or the High Accuracy Radial velocity Planet Searcher (HARPS) spectrograph analyzed in detail by Luger et al. ( 2017 ). Lovis et al. ( 2017 ) discuss coupling the high-resolution spectrograph ESPRESSO to the adaptive optics instrument SPHERE at the VLT in order to measure both a spatial offset and a Doppler shift for Proxima Cen b, and conclude that such a measurement is feasible. If additional filtering on the flares were introduced, the signal-to-noise ratio could potentially be improved further. 5.4. Complexities and Opportunities The geometric distribution of flares on the surface of the star is unknown. Hunt-Walker et al. ( 2012 ) found no correlation with rotational phase for AD Leo, similar to Hawley et al. ( 2014 ), indicating a distribution of many flare sources at different longitudes and a relatively uniform flare distribution, stochastic in both outburst frequency and location. A non-uniform flare distribution could influence the illumination pattern of the space local to the star, and could work either in favor if flares are concentrated to the equatorial regions, or against if to the poles (presuming the planets to share the same equatorial plane). The planet may be illuminated by flares that we do not see, because they are both “around the back” of the star, and conversely, we may see flares that the planets do not. The degree to which flares are
Paper: 2021ApJ...916...56R (fulltext)
low-mass particles for the v200R1 model. The upper, middle, and bottom panels are the projected position of the each particles for φ = 30°, 45°, and 60°, respectively. The blue and red arrows show the radial velocity direction, moving toward or away from us, respectively. The size of the arrows indicates the tangential velocity. However, the number of particles approaching or moving away from us is dependent on the projected angle, but the explosion is not at all isotropic in the x -axis, which is the axis of movement of the massive particle. But the morphology of the explosion is similar to the one observed in Orion BN/KL. It is important to note that the massive particle in the Orion BN/KL explosion should be a runaway massive star with a velocity of at least 150 km s −1 , but the dynamical interaction of the massive star with clumps with a total mass comparable to the star’s mass could substantially decrease the velocity of the star at the end of the interaction. However, it is not the goal of this work to study the dynamic interaction of low-mass particles. 5. Conclusion We presented analytical and numerical solutions of the dispersion of particles due to dynamical interaction with a single massive particle. We have considered that the particles gather into a cluster and that they have a negligible mass with respect to the massive particle. The dynamical interaction with the massive particle produces a quasi-isotropic ejection of the particles. Then, we carried out a set of numerical simulations of spherical distribution of massless particles ( N -body simulations) for verifying our analytical solutions and obtaining an observational result. We have obtained a very good agreement between the numerical and analytical results. The main conclusions are that: 1. The gravitational ejection mechanism is able to accelerate small clumps or cloudlets (i.e., low-mass gas fragments) to jetlike velocities and, therefore, it should be thoroughly explored in future work. 2. The terminal velocity v ∞ of each particle is function of its own impact parameter. 3. The maximum terminal velocity is given by the limit when the particle has an impact parameter equal to zero and it is twice the velocity of the massive particle (. The minimal terminal velocity is related to the cluster radius. 4. The ejection angle of each of the particles is linearly related to the terminal velocity, and therefore is related to the impact parameter. Compact distributions of particles that are dynamically disturbed by a massive particle, which passes through the center of the distribution, produce more collimated ejections than in the case of more scattered clusters. 5. The resulting dispersion has an explosive signature, such as: (a) a small-scale common origin, (b) an isotropic distribution, and (c) velocities proportional to the distance from the common origin. This is the result of a short time interaction, which could be the mechanism that produced the explosive outflows.
Paper: 2021ApJ...909L...7H (fulltext)
3 (c)) accompanied by large fluctuations in the proton density and temperature (Figures 1 (d)–(e)). The coexistence of kinetic Alfvén waves and Alfvén ion cyclotron waves was demonstrated in this slow solar wind during 2018 November 6 (Huang et al. 2020a ), similarly to the observations in the fast solar wind at 1 au (e.g., He et al. 2011 , 2012 , 2015 : Podesta 2013 ; Bruno Telloni 2015 ). The plasma β varies from 0.1 to ∼10, but mostly less than 1 (Figure 1 (f)). Figure 1. Overview observations from 2018 November 4 to 6. (a) The magnitude of magnetic field, (b) the three components of magnetic field (c) the three components of the proton velocity in RTN coordinates, (d) the proton number density, (e) the proton temperature, (f) the proton β , and (g) the distance away from the Sun. To be able to study the magnetic energy spectra at the sub-ion scales for 15 minute windows, the Fourier transform was performed for the merged FGM and SCM measurements (Bowen et al. 2020a ) to obtain the magnetic energy spectra for each 15 minutes. The 15 minute duration ensures having at least one correlation time of the turbulent fluctuations (Parashar et al. 2020 ). Furthermore, to avoid possible “contamination” of the background turbulent spectra from intermittent but intense waves (e.g., ion cyclotron waves, whistlers), we excluded the magnetic spectra that showed clear spectral peaks from MHD to electron scales. One should point out that the electron measurements are not available during this time interval. But we can estimate the Doppler-shifted frequency of the electron inertial length f de ∼ 180 Hz and the Doppler-shifted frequency of the electron gyroradius f ρe ∼ 320 Hz based on the assumptions of N e = N i and T i / T e = 5. This indicates that the electron characteristic frequencies are larger than the Nyquist frequency of the magnetic field data provided by PSP, thus preventing us from investigating electron-scale turbulence. Figure 2 (a) presents an example of the resulting magnetic spectra. Three distinct frequency bands can be clearly evidenced: (i) the first one is an inertial range with the slope of −1.71 below 1 Hz, (ii) the second one is a steep ion transition range ( f −3.63 ) at the scales between the proton cyclotron frequency and the Doppler-shifted frequencies of the proton inertial length d i and gyroradius ρ i (i.e., f ∼[2, 9] Hz), (iii) the third one has a scaling f −2.68 between the ion and electron scales (∼[10, 60] Hz). These three distinct spectral features are also confirmed by all 219 selected spectra shown in Figure 2 (b). However, the spectral indices have large variations in the ion transition range. Figure 2. Power spectral densities of the magnetic field. (a) One example of magnetic spectrum during the time interval from 11:13:11 UT to 11:28:11 UT on 2018 November 6; (b) all selected spectra. The three vertical arrows from left to right correspond to the proton cyclotron frequency f ci , the Doppler-shifted frequency of the
Paper: 2019MNRAS.482.5441O (fulltext)
the stable nature of the inclination oscillations, the incompleteness of detecting high-eccentricity and high-inclination objects, and the greater likelihood of low-inclination objects impacting the Earth (as mentioned in Section 1 ) suggest that extensive observation of high-inclination locations of vertically unstable quasi-satellite orbits may be helpful in detecting populations of potentially hazardous asteroids in advance before they reduce inclination. The long-term stable inclination oscillations associated with quasi-satellite orbits obtained in the simplified model in this study may appear to be contradictory to the shorter lifetime of , of only tens of thousands years, on its quasi-satellite motion. We do not expect long lifetimes of such objects in the real world experiencing close approaches to planets at low-inclination states (Wajer Królikowska 2012 ), but the stable nature of the inclination oscillations in the simplified model implies an ability to capture objects even in the real world. Certainly, was trapped in the quasi-satellite motion exhibiting inclination oscillations from a chaotic orbit (see fig. 5 in Kinoshita Nakai 2007 ). The stable nature is significant in our conjecture, not because of the longevity after experiencing low-inclination states, but because of the ability to capture objects in the inclination oscillations at high inclinations before reducing their inclinations. This paper focuses on the fundamental dynamics of vertical instability and inclination excitations of co-orbital orbits in the circular restricted three-body problem, but limitations exist due to the simplified assumptions of the model and additions of various perturbations, such as the eccentricity of Jupiter, and the gravitational effects of other planets could be promising directions. In such non-autonomous systems, computations of invariant manifolds associated with resonant periodic orbits (Voyatzis Antoniadou 2018 ) and quasi-periodic invariant tori (Olikara Scheeres 2012 ) would be natural extensions of the present work. 5 CONCLUSIONS This paper explored the effects of vertical instability on inclination excitations of co-orbital orbits in the Sun--Jupiter circular restricted three-body problem. Computations of invariant manifolds associated with vertical instability of the planar co-orbital periodic orbits enabled quantitative analyses of inclination excitations. Quasi-satellite orbits exhibited long-term stable, highly eccentric, substantial inclination oscillations between near zero and tens of degrees without escaping from quasi-satellite states. We pointed out the possibility that populations of undetected potentially hazardous asteroids exist at high-inclination locations of Jupiter’s vertically unstable quasi-satellite orbits. Analyses of bifurcation and phase-space structures revealed that stable, three-dimensional families of periodic quasi-satellite orbits host inclination oscillations in a stable manner. However, planar
Paper: 2019A&A...629A..15R (fulltext)
reduced mass of the collision system. The collisional coefficients for N 2 H + are taken from Lique et al. (2015) , who computed them for the N 2 H + /p-H 2 system. The rates for the DCO + /p-H 2 system were computed by Pagani et al. (2012) , who take into account the hyperfine structure given by the deuterium nucleus. Finally, the collisions of HCO + with p-H 2 were published by Flower (1999) , from which we scaled the coefficients for HC 18 O + . In all cases, the o-H 2 is neglected,which is a reasonable assumption given that the ortho-to-para ratio (OPR) in L1544 is found to be as low as OPR = 10 −3 ( Kong et al. 2015 ). Fig. 4 Profiles of dust temperature (green), gas temperature (red), H 2 volume density (black, in logarithmic scale), and velocity (blue, in units of 0.1 km s −1 ) for the L1544 model developed in Keto et al. (2015) . The velocity in the model is negative (it represents contraction motions), but it is shown here as positive for improved readability. We tested all the 15 models described in Sect. 3.1 . The abundance profiles produced by the chemical model often systematically overestimate or underestimate the observed fluxes, and need to be multiplied by a numerical factor to find a good agreement with the observations. However, MOLLIE requires too much computational time to allow a full sampling of the parameter space. For each molecule, we therefore proceeded as follows: first, we test all the original models, varying the evolutionarystage and the thickness of the surrounding cloud, and we select the one that best reproduces the trend of the different transitions (e.g. the ratio between the peak temperatures of the brightest component of each line). We then multiply the whole profile by a numerical factor, testing approximately five values until the observed fluxes are matched. This is considered the best fit to the observations. We want to highlight that our goal is to obtain a simultaneous fit for all molecules that is roughly consistent with the observations, and a full parameter-space search for the best possible fit within the model uncertainties is beyond the scope of the present paper. In the following text, we focus on the abundance profiles that provide the best-fit solution for each molecule. As an example, wepresent in Sect. 3.2.2 the resulting spectra from several of the 15 abundance profiles for N 2 D + . 3.2.1 Best-fit solution for the observed molecules at the dust peak The best-fit solutions for each molecule are shown in red in Figs. 1 and 2 overlaying the observations. We also include here the analysis of a single-pointing observation of HCO + (1–0) performed at the dust peak (see Sect. 2 for details). Despite the fact that a map for this transition is not available, it is important to check the behaviour of the chemical model for HCO + , the main isotopologue of its family. The observed spectrum of HCO + , together with the best fit from MOLLIE, is shown in the bottom-right panel of Fig. 2 .
Paper: 2020ApJ...898...42C (fulltext)
between the spectral and temporal index in various regimes (cooling, density regimes, or an injection from the central engine). These relations present tests without delving into details of the models (e.g., Zhang Mészáros 2004 ; Gao et al. 2013 ). Using the conventional notation, F ν ∝ t − α ν − β , we obtained α X , β X for GRB 190829A afterglow in the X-ray bands. In Table 3 , we present the indices of the flux and flux density (@ 10 keV) for the segments of the light curve. We particularly analyze segments III (flare) and IV (break) regions in detail, starting with phase IV. Table 3 The Best-fit Values of Spectral and Temporal Indices for Episodes Marked in Figure 4 Notes. We use a power-law or a smooth-broken-power-law a function to model the XRT light curves for different segments using the python module emcee (Foreman-Mackey et al. 2013 ). All quoted errors on spectral parameters correspond to the 16th and 84th percentiles. a s : smoothness parameter, α 1 : index before break t b , α 2 : index after break (Wang et al. 2018 ), log( L ): log-likelihood, n : number of data points, and k : number of the free parameters in the fit. b Excluding 3 × 10 5 –2 × 10 6 s. The segment in the flux light curve shows a shallow change (0.3 ± 0.3) in the temporal decay index. Contrary to this, in the flux density (@10 keV), α X changes by 1.1 ± 0.2. Naively, one may tend to recognize the break with a jet break, however, upon closer examination the photon index softens during ∼3 × 10 5 –2 × 10 6 s (vertical dashed lines in Figure 4 (b)). This is also reflected in the softening of the hardness ration (vertical dashed lines in Figure 4 (c)). Since after this period, the photon index settles down to its previous value before the spectral change, it suggests a rebrightening within the low-energy Swift-XRT band. The passage of some spectral break frequency is also less likely due to the same reason, because any frequency crossover will cause an irreversible change in the spectral index. Phases IV and V (excluding the region between the black dashed lines) is consistent with a typical decay with α X ∼ 1.22 ± 0.03. The UVOT data in this phase is possibly dominated by the contribution of the host. The observation in the i -band shows the rising part of a supernova (Perley Cockeram 2019c ) contemporaneous with the break t b . Consider adiabatic cooling without energy injection from the central engine. The inferred value of α X (1.31 ± 0.15) from observed β X (1.21 ± 0.1) matches with the observed value (1.22 ± 0.03) within error bars for the spectral regime ν > ν c and the interstellar or Wind mediums (Table 3 ). We estimate the value of the electron distribution index p = 2.42 ± 0.2 from p = 2 β . We calculate the kinetic energy in the jet E K,iso = 1.7 × 10 52 erg according to Equation (17) of Wang et al. ( 2015 ), which is valid for the case ν X > ( ν m , ν c ). We took a prebreak segment from phase IV (0.27–1.9 days) with a mean photon arrival time ∼0.7 day.
Paper: 2021ApJ...913..116J (fulltext)
); and r is the distance from one specific location in the silicate's interior to its surface. 3. Results 3.1. Boundary Conditions In the standard case, we assumed spherical orthopyroxene minerals with radii of 50 μ m (Figures 2 (a)–(c)) and 500 μ m (Figures 2 (d)–(f)) condense in a region with a heliocentric distance of 1 au from the proto-Sun. The minerals are implanted by the protons in the solar nebula for 1 day, 1 month (30 days), 1 year (365 days), and 10 years (3650 days). For both silicates, the simulations were run with retention coefficients ( N R ) of 0.01, 0.1, and 0.5. Other critical boundary conditions applied for our standard case are listed in Table 1 . Figure 2. Simulated concentration profiles of water in the interior of the silicates. Spherical silicates with radii of 50 μ m (a)–(c) and 500 μ m (d)–(f) are assumed to be implanted with nebular plasma hydrogen for 1 day, 1 month (30 days), 1 year (365 days), and 10 years (3650 days). Three retention coefficients ( N R ), i.e., 0.01, 0.1, and 0.5, are applied for the implantation. With the same retention coefficient (e.g., N R = 0.01), it takes a longer time for 500 μ m sized silicate (d) to evenly distribute the projectile ions than the 50 μ m sized silicate (a). The higher retention coefficient leads to the larger production of water. For instance, implantation with a retention coefficient of 0.5 can produce more than 1000 ppm water in one year in the 50 μ m sized silicate (c), whereas when the retention coefficient is 0.01, the production is less than 200 ppm (b). The gray shaded area indicates the range of previously reported water contents (200–1000 ppm) of the ordinary chondritic minerals, which can be well produced by the implantation–diffusion processes with 0.1 ((b), (c), (e), (f)). Boundary conditions for the simulations are listed in Table 1 . Table 1 Boundary Conditions Applied for the Standard Case References. (1) Wu Judge ( 1979 ), (2) Lewis ( 1972 ), (3) Fromang et al. ( 2002 ), (4) Öberg et al. ( 2011 ), (5) Stalder Skogby ( 2003 ), (6) Dürr Höfer ( 2006 ). We first adopt a high nebular gas pressure ( P = 10 −3 bar) for the chondrule formation (Galy et al. 2000 ). The ambient temperature when chondrule minerals formed was characterized by a range of temperatures, from 600 K to >900 K (Rubin 2000 ). The presence of a primary trolite in type 3 ordinary chondrites indicates an ambient nebular temperature of 650 K (Rubin et al. 1999 ). Accordingly, we select this value as the background temperature for the implantation and diffusion. In previous studies, the ionization fraction ( f ion ) of protoplanetary disks have been reported to be with orders between 10 −12 and 10 −10 (Fromang et al. 2002 ; Öberg et al. 2011 ) and we adopt a median value of 10 −11 . Due to the lack of the parameters applicable to the low-temperature (650 ± 100 K) diffusion of hydrogen in nominally anhydrous silicate minerals, here we use the diffusion coefficient ( D 0 ) and activation energy ()