adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.493.2854N (fulltext)
modes are combined. Had that study examined the case of ω = 90°, it would have found smaller widths showing that the dynamics is more complex than just summing up single mode widths. This fact was later recognized in a more recent article (Gallardo 2020 ). Secondly, our analytical disturbing function as well as our multiple-argument pendulum models not only give precise resonance widths for each mode, their combinations, and their variations as functions of ω, they also agree with the chaos indicator. So our approach is not just to compare the analytical widths to the integration of the full equations of motion like it is done in the ‘dynamical map’. Instead, we seek the numerical separatrices obtained from measuring precisely how two nearby orbits diverge. In this sense, it is worth reminding the reader that the megno chaos indicator is one of the most precise diagnostic tools in resonance dynamics. In conclusion, the fact that the disturbing function is an expansion with respect to eccentricity (as in this work we used the second interpretation of the disturbing function without expanding with respect to inclination like in section 5.3 of Paper I) does not mean that it cannot probe the dynamics accurately. For instance, we could even explain for the first time the width of the chaotic separatrix layer of the inner 2:1 resonance as well as predict accurately the extent of the asymmetric libration domains for outer resonances. Therefore, the conclusion of Gallardo ( 2019 ) regarding the importance of the disturbing function at arbitrary inclinations and the analytical pendulum models is unjustified. While in the process of developing our multi-argument two-harmonic pendulum model to derive resonance widths for outer as well as inner resonances, we learned of the work of Lei ( 2019 ). The author tested the conclusion of Gallardo ( 2019 ) that the expansion of the disturbing function cannot model the 3:1 resonance accurately and developed what they termed a ‘multi-harmonic pendulum model’ and applied it to the disturbing function of Paper I. The author says that the disturbing function they use is somewhat different but it is not as they choose from the outset not to expand the perturbing potential with respect to inclination. That possibility is already present in Paper I and was termed the second interpretation of the disturbing function in section 4.3 of Paper I. It was already used to estimate resonance widths in section 5.3 of Paper I and was reprised in this paper. What Lei ( 2019 ) does differently is to rewrite the fully derived disturbing function (3) by trigonometrically splitting the resonant argument with respect to as follows: The details of the indices in the above sums are found in Lei’s paper and are not essential to our discussion. The author then selects from this new expression the ‘harmonics’ of in order to model the 3:1 resonance with the disturbing terms: We use the quotes to refer to harmonics in Lei’s formulation in
Paper: 2019ApJ...886...50G (acknowledgments)
The authors thank Prof Subir Sarkar (University of Oxford) for stimulating discussions related to this work. This research was supported in part by the Engineering and Physical Sciences Research Council, UK (EP/M022331/1, EP/N014472/1, EP/N002644/1, EP/P010059/1). All data that support the findings of this study are available from the authors upon request.
Paper: 2020ApJ...893...76K (fulltext)
19:03 to 19:39 UT. We plot in panels (e) and (f) of Figure 11 the maps of Alfvénic Mach number (with and without accounting for magnetic mirroring) covering the interval from 19:03 to 19:43 UT. During this time interval, the magnetic connections from the shock are on open field lines connected to the AR and to polar coronal holes. Significant magnetic turbulence must prevail in the AR in order for the modeled location to agree with the observations. 5. Connecting the Shock Evolution with the Time History of the Relativistic Protons at Earth In this section, we investigate whether particles accelerated by shocks crossing field lines reaching Earth can explain the time history of relativistic protons observed in GLE72. Kurt et al. ( 2019 ) detail observations made by several neutron monitors. They show that the proton angular distribution was strongly anisotropic early in the event and only became isotropic at about 18:00 UT. The Fort Smith (FSMT) neutron monitor located in the Northwest Territories (60 north, 112 west) was relatively closely aligned with solar magnetic field lines at 16:00 UT. Therefore, it provides an excellent monitor of early particle acceleration at the Sun. We plot the pressure-corrected FSMT 1 and 5 minute count rates from 15:30 to 17:00 UT in Figure 12 . The rates rise in response to the event at about 16:08:30 UT. Figure 12. Comparison between the evolution of the and the time history of the GLE72 count rates from FSMT neutron monitor (see text for details). In determining the evolution of the shock wave parameters at regions well connected to Earth, we use the methods presented in Kouloumvakos et al. ( 2019 ) and also in our discussions in Sections 3 and 4 . The shock wave intersected magnetic field lines that were well connected to Earth at 15:57 UT, and it became supercritical around 15:59 UTl. 7 7 This assumes that the shock accelerating the particles was quasi-perpendicular requiring a critical Mach number of ∼2.7, which was reached at 15:59 UT. The mean M A and M fm parameters continued to increase, reaching a maxima near 18:00. The mini-neutron monitor DOMC (Usoskin et al. 2015 ) NM station situated at the South Pole recorded the most intense flux with a peak at 18:50 UT (see Mishev et al. 2018 ). The density compression ratio saturated within 15 minutes and reached an asymptotic value of 4. Using a similar model to the one used in Section 4.3 to predict the time profile of the γ -ray emission, we examine the shock origin of the particles that access field lines reaching Earth. We consider an function that includes only the term sh discussed in Section 4.3 , which approximates the effectiveness of shock acceleration. In order to directly compare estimated proton flux with the FSMT NM count rate, we need to shift to account for the time it took the protons to reach Earth. A common approach is to assume that the particles propagate along IP magnetic field lines following a Parker spiral whose shape is defined by
Paper: 2017ApJ...851...58L (fulltext)
G. The blue and red solid circles denote the current positions of two example binaries with initial parameters days and , and day and , respectively. Figure 4 shows the distribution of the binaries at end of the evolution with f = 0, 0.02, 0.1, and 0.3, corresponding to either the donor star having evolved to be a WD or the age of the binary exceeding Hubble time. The binaries with orbital periods at the beginning of the second RLOF above the so-called bifurcation period (Pylyser Savonije 1988 , 1989 ) evolve into low-mass binary pulsars (LMBPs), with a helium WD companion (depicted with circles), while those with orbital periods below the bifurcation period evolve into compact LMXBs with substellar companion stars (depicted with triangles). To see how the bifurcation period changes with mass transfer and evaporation, we present the orbital period evolution for five NS binaries with different f values in Figure 5 . It seems that the bifurcation periods are quite close to each other (∼1 day) and increase slightly with increasing f . Figure 4. Distribution of the final orbital period and donor mass under different values of f . The blue and red symbols have the same meanings as in Figure 2 . The solid line is the theoretical – relation of LMBPs given by Tauris Savonije ( 1999 ). Figure 5. Post-AIC evolution of the orbital period under different values of f for five NS binaries. Here denotes the time at AIC. From top to bottom, the initial parameters are: days and , days and , days and , days and , and days and . The solid line in the four panels in Figure 4 is the theoretical relation for LMBPs given by Tauris Savonije ( 1999 ). We can see that if there is no evaporation in the post-AIC evolution, the resulting distribution is well-consistent with the theoretical relation, which is the same as in the LMXB evolution. However, once evaporation is included, the distribution deviate from the theoretical prediction, especially in the case of f = 0.3: for a given , the orbital period is longer than expected. The reason is that in the late stage of the evolution, the orbital evolution and mass loss are mainly driven by evaporation rather than mass transfer via RLOF (see also Jia Li 2016 ). To demonstrate the detailed evolutionary processes, in Figures 6 and 7 , we present two representative examples for the post-AIC binaries, which are marked in Figures 3 and 4 . Figures 6 and 7 correspond to the blue circle with days and , and the red triangle with day and , respectively. Figure 8 compares the rates of AML caused by different mechanisms in the evolutionary paths presented in Figures 6 (left panel) and 7 (right panel). In Figures 6 and 7 , the left panel displays the evolution of the mass transfer rate (black solid line), the evaporative wind loss rate (red line), and the donor mass (blue line). The black dashed line represents the critical mass transfer rate for disk instability. The right panel depicts the evolution of the NS mass (solid line) and
Paper: 2017MNRAS.466.4239G (fulltext)
Evolution of 21-cm Structure (EOS; Mesinger etal. 2016 ) 9 2016 data release. These semi-numerical reionization simulations are 1.6Gpc on a side with a 1024 3 grid and include state-of-the-art subgrid prescriptions for inhomogeneous recombinations and photo-heating suppression of star formation. The 2016 EOS data release corresponds to two simulation runs, with the efficiency of supernovae feedback adjusted to approximately bracket the expected EoR contribution from faint galaxies. The two runs are: Faint galaxies the EoR is driven by galaxies residing in haloes with masses of 10 8 M h / M 10 9 , and is characterized by numerous small cosmic H ii regions. Hereafter we will refer to the EoR morphologies resulting from this simulation as S mall H ii . Bright galaxies the EoR is instead driven by galaxies residing in haloes with masses of M h 10 10 M , and is characterized by spatially more extended H ii structures. We refer to these simulated EoR morphologies as L arge H ii . We note that the L arge H ii ionization fields have a factor of few10times more power on large scales during the EoR, compared to the S mall H ii ionization fields. Although these are two opposite extremes in terms of H ii region sizes, the S mall H ii EoR morphology is likely more realistic (see the discussion in Mesinger etal. 2016 ), and so we use this simulation as our fiducial model. Nevertheless, we include both extremes to explore the dependence of our results on EoR morphology. We construct samples of sightlines through our simulations which are terminated on one end at a halo, and then extended in a random direction through the simulation volume for a distance of 200 comoving Mpc. Along each sightline, we sum the contributions from all encountered H i patches to construct a composite optical depth for the damping wing (e.g. Miralda-Escud 1998 ). We purposefully exclude the H i contribution from pixels 16 comoving Mpc (2 physical Mpc) from the QSO host halo.This corresponds to the minimum possible radius of the H ii region around ULAS1120 inferred from measurements of its near zone (Mortlock etal. 2011 ). We do not explicitly include the flux from the QSO in our reionization maps. Doing so would require assumptions about the QSO lifetime and ionizing luminosity. Neglecting the QSO ionizing contribution implies that the surrounding H ii region could be larger than predicted by our EoR models. For a given a larger surrounding H ii region implies a smaller integrated damping wing optical depth. Thus if the QSO contributes in growing the surrounding H ii region, our modelled optical depths should be associated with even higher values of , shifting the probability distribution functions (PDFs) we present below towards larger . At z 7.1, we select 10 4 identified haloes in the mass range 6 10 11 M h /M 3 10 12 , consistent with the inferred dynamical mass of the host halo of ULASJ1120 (Venemans etal. 2012 ). Importantly, these higher host halo masses, made possible
Paper: 2020A&A...633A..27O (acknowledgments)
Acknowledgements We thank the anonymous referee for her/his fruitful comments. The ASTE project is led by Nobeyama Radio Observatory (NRO), a branch of National Astronomical Observatory of Japan (NAOJ), in collaboration with University of Chile, and Japanese institutes including University of Tokyo, Nagoya University, Osaka Prefecture University, Ibaraki University, Hokkaido University, and the Joetsu University of Education. M.O. and S.P. are members of the Carrera del investigador científico of CONICET, Argentina. M.B.A. is a doctoral fellow of CONICET, Argentina. This work was partially supported by grants awarded by ANPCYT and UBA (UBACyT) from Argentina. M.R. wishes to acknowledge support from FONDECYT(CHILE) grant No 1140839. M.O. is grateful to Dr. Natsuko Izumi for the support received during the ASTE observations.
Paper: 2015MNRAS.449.2219M (acknowledgments)
We are grateful for the extraordinary contributions of our CTIO colleagues and the DES Camera, Commissioning and Science Verification teams in achieving the excellent instrument and telescope conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the DES Data Management organization. Funding for the DES Projects has been provided by the US Department of Energy, the US National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência e Tecnologia, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratories, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Edinburgh, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l'Espai (IEEC/CSIC), the Institut de Fisica d'Altes Energies, the Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas AM University. PM, ES, EH, KP, KH are supported by the US Department of Energy under Contract No. DE- FG02-91ER40690. SB, TK, MH, JZ, BR acknowledge support from European Research Council in the form of a Starting Grant with number 240672. DG was supported by SFB-Transregio 33 ‘The Dark Universe’ by the Deutsche Forschungsgemeinschaft (DFG) and the DFG cluster of excellence ‘Origin and Structure of the Universe’. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2009-13936, AYA2012-39559, AYA2012-39620, and FPA2012-39684, which include FEDER funds from the European Union. This paper has gone through internal review by the DES collaboration.
Paper: 2016MNRAS.460.3975H (fulltext)
are for inclined (left) versus face-on (right) orientation. While both Figs 11 and 12 relate to the simulation without cooling, virtual infrared views on the run with cooling are shown in Figs 13 and 14 . Fig. 13 contrasts Keck (top row) with ALMA (bottom row) images at three times in the evolution ( t = 4.5, 5, 5.5). This demonstrates that resolution is once more key to clearly distinguish the very fragmented mixing zone where dust now gets concentrated in a more spherical (rather than clearly spiral) fashion, dominated by local dust clumps and filaments (as shown in Fig. 6 ). Fig. 13 shows in each panel how a linear colour scale (shown with contours) no longer yields a clearly detectable rotating spiral pattern, as outward moving large localized clumps may seem unaligned with the underlying orbital motion. The colour scale in Fig. 13 plots the same data in a logarithmic scale (as was also adopted for all previous figures), where only in the ALMA views, one can still find evidence for a rotating spiral pattern. Again, this longer wavelength view shows grain emission from both the large and small grains. Note that the possible failure to detect a clear spiral pattern in infrared views may have significant consequences for interpreting actual infrared evolutions, where binarity is easily linked with clear rotating spirals: our findings suggest that more erratically varying dusty WR environments could be binary systems where radiative cooling effects induce the variability. Disentangling episodic dust makers from binary configurations using only infrared variability is thus poorly constrained. Figure 13. Synthetic images for the run with cooling. Top row for Keck at 2.45 μm, bottom for ALMA at 400 μm, for time 4.5, 5 and 5.5. Convolution is done with 20 versus 2 pixels. We show linearly distributed contours, on top of a logarithmic colour image. Figure 14. Synthetic images for ALMA on the run with cooling. We compare images for two times t = 5.4 (left) and 5.9 (right), for face-on (top) versus inclined (bottom) orientation. Finally, Fig. 14 contrasts virtual ALMA views at times 5.4 (left column) and time 5.9 (right column) for the case with cooling (using the same representation as for the adiabatic case in Fig. 11 ). The difference between top and bottom row is purely in orientation, where the top view is for the face-on line of sight, while the bottom has an inclination of 35°. Unlike the adiabatic case from Fig. 12 , where the overall orientation with respect to the spiral obviously mattered, the case with cooling shows little variation with viewing angle. This is in line with the more volume-filling, spherical redistribution of the mixing zone, as well as with the fact that the views are always dominated by the densest clumps and filaments. This effect will return in virtual photometric observations, which we discuss in the next paragraph. 3.2.3 Virtual photometric observations The fact that we obtained virtual infrared views over areas
Paper: 2020ApJ...895...86G (fulltext)
considered at the highest density ( ρ max ), i.e., at t = t coll . From this stage onward, we have assumed that the temperature of the dust and the gas is the same. Thus, we have considered a negative slope for T gas for the collapsing phase. Throughout the first phase, the visual extinction parameter is considered to be constantly increasing from to finally at in t = t coll . Second phase: the second phase of the simulation corresponds to a warm-up phase. Since G10 is a high-mass star-forming region, we consider a moderate warm-up timescale ( t w ) 5 × 10 4 yr (Garrod 2013 ). Therefore, during this short period, the temperature of the cloud from T ice can reach the highest hot-core temperature T max . The density, temperature, and visual extinction parameter remain constant at ρ max , T max , and , respectively. Third phase: this phase belongs to the post-warm-up time. Here, we have considered a post-warm-up timescale ( t pw ) of 10 5 yr, so the total simulation time is . The parameters such as density and visual extinction are assumed to be the same as they were in the warm-up phase. The temperature of the cloud is kept at T max throughout the last phase. 4.2. Binding Energies and Reaction Pathways To study the desorption energy (BE) and reaction pathways of three peptide-bond-like species HNCO, NH 2 CHO, and CH 3 NCO and their isomers/precursors, we use the Gaussian 09 suite of programs (Frisch et al. 2013 ). Recently, Das et al. ( 2018 ) made an extensive effort to estimate the BE of 100 interstellar species on water-ice surfaces by applying a quantum chemical approach and compared their values with the available experimental results. They found that on average, the computed BE shows a larger deviation from experiments when they considered a single water molecule as a substrate. The deviation is minimal when they used pentamer or hexamer configurations of water clusters. They provided a scaling factor for the extrapolation as the computation was performed with smaller water structures. We carry out a quantum chemical study to find out the BEs of the three peptide-bond-related species considered in this work along with their potentially observable isomers. To estimate the BEs of these species, we have used a similar method and basis set (MP2/aug-cc-pVDZ) as mentioned in Sil et al. ( 2017 ) and Das et al. ( 2018 ). Our calculated BE values are given in Figure 7 . For some cases, we have found multiple probable sites for the adsorption and thus obtained multiple BE values. In that case, we take the average of the multiple BEs. Calculated BEs with a single water molecule are then scaled up by a factor of 1.416 (Das et al. 2018 ) to have a realistic estimation. Additionally, in Figure 7 we present the BE values for some of these species with a hexamer configuration of water clusters. Since the BEs with the pentamer/hexamer configurations show minimum deviation (Das et al. 2018 ), one can use these BE values in the model without scaling. We are