adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.493..106I (fulltext)
distance between the points on a zenith angle circle being ∼ R / N μ ) and equally spaced points on the dayside from terminator to disc edge (Fig. 1 ) with the same approximate separation. Our computed disc-averaged radiance was thus calculated as where μ i are the Gaussian quadrature points, Δμ i are the Gaussian weights, φ ij are the azimuth angles (which are different for different μ i ), and ij are the azimuth angle trapezium-rule integration weights. To ensure correct normalization, ∑ j ij = 1.0 and (i.e. ). In addition, we assumed that the exoplanet had a transit with low impact parameter such that northern and southern hemispheres were observed at the same emission angle and we also assumed north/south symmetry such that we only had to integrate over half the disc. Figure 1. Example of disc-averaging quadrature points chosen for N μ = 2, 3, 4, and 5 and phase angle ζ = 45° (where 0° is nightside and 180° is dayside). The daylight side of the planet is coloured light grey, while the nightside is dark grey. The zenith angles of the chosen Gauss–Lobatto quadrature scheme are shown by the dotted white lines. The azimuth points along each zenith angle line (indicated by the asterisk symbols) are chosen to coincide with the terminator for zenith angles that intersect the terminator and be roughly equally spaced with a fractional disc distance of 1.0/ N μ . As we assume north–south symmetry, only points on or above the equator are needed for disc-integration. An example of the chosen quadrature points for our scheme using N μ = 2, 3, 4, or 5 zenith angles is shown in Fig. 1 for a phase of 0.125 (i.e. a phase angle ζ = 45°). Here we can see that as we add more zenith angles, the number of sampled points on the disc is greatly increased. In our scheme if a given zenith angle circle intersects the day–night terminator, one azimuth quadrature point is placed on that terminator and the remaining azimuth points are chosen to be roughly equally spaced on the day and nightside, as noted above. The reason for doing this is that if we were to observe a planet where the reflected starlight is strong, we would expect that contribution to fall rapidly towards the terminator. By making sure we place a quadrature point at the terminator, we can thus ensure a better estimate of the azimuthal average of the radiance at a given zenith angle. Although in this study we have not included reflected starlight in our models, the disc-integration scheme we have defined means that we well be able to seamlessly and accurately incorporate this in future. 2.1 Computation of contribution of reflected starlight Assuming a Lambertian reflecting surface, the scattered radiance at any point on the disc is equal to α F star /π, where α is the reflectivity and F star is the local stellar irradiance (i.e. W m −2 ), calculated as F star = P cos θ/(4π d 2 ), where P is the total power of the star, d is the distance of the planet from the star, and θ is the local stellar zenith
Paper: 2016A&A...590A..25M (acknowledgments)
This paper makes use of the following ALMA data: ADS/JAO.ALMA#2012.1.00453.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. S.A. acknowledges support from the Swedish National Science Council grant 621-2011-4143. K.S. was supported by MOST grant 102-2119-M-001-011-MY3. E.G.-A. is a Research Associate at the Harvard-Smithsonian Center for Astrophysics, and thanks the Spanish Ministerio de Economía y Competitividad for support under project FIS2012-39162-C06-01 and NASA grant ADAP NNX15AE56G. F.C. acknowledges support from the Swedish National Science Council grant 637-2013-7261. J.M.-P. acknowledges partial support by the MINECO under grants AYA2010-2169-C04-01, FIS2012-39162-C06-01, ESP2013-47809-C03-01 and ESP2015-65597-C4-1.
Paper: 2020MNRAS.493.2472G (fulltext)
selected from an ensemble of similar objects. Light curves were constructed by taking one of the observable parameters from the formal solutions and computing its response to the driving in depth from equation ( 8 ). In detail, this was carried out by fitting a cubic natural spline through the parameter values at the known depths. 4.4 Variable background radiation The operations for variation of the background radiation follow a broadly similar approach to those used in Section 4.3 . However, there are important differences. First, we apply only the default sinusoid and function D 6 as driving functions, since only D 6 is based on a model where the background, rather than the pump, is varied. The process is easier in the sense that the parameter that is varying in time is simply a parameter in equation ( 4 ), and no complicated justification is required for its use. A significant complication in the variable background case, however, is that the driver in the background analogue of equation ( 8 ) now demands a response from a number of different models, each computed with a different value of i BG . Therefore, modelling flares from background variation is computationally considerably more expensive. 5 VARIABLE PUMPING RESULTS We plot, in Fig. 3 , the flare light curves, over one period, that result from driving with pump functions D 0 – D 5 for a prolate cloud with Γ = 0.6, with the observer placed in the optimum position (viewing the cloud along the z -axis close to the long axis of the cloud). The cloud style is uniform in unsaturated inversion. The parameters of equation ( 8 ) in this case are τ min = 10.0 and Δτ = 5.0, corresponding to a regime that is already characteristic of quite strong saturation. Throughout Section 5 , the y -axes of graphs are shown in flux density, based on a background specific intensity that is scaled to the saturation intensity of the maser transition. Flux density is likely the best parameter to plot in view of the prominent role of single-dish monitoring in repetitive flare studies. An explanation of the flux-density scale appears in Appendix A . Owing to the nature of the saturation intensity, and particularly the fact that this quantity may itself depend on the energy density of pumping radiation, a conversion formula to units of Jy at a typical source distance, and for a particular maser transition, for example, is not trivial. Such a conversion is also discussed in detail in Appendix A . The variability index of the maser light curve, defined as in the Introduction as F pk / F qui is 1.74 in Fig. 3 . The same parameter for the driving functions, replacing flux densities with values of the depth multiplier, is 1.5. Although the variability index of the maser is significantly larger here, the light curve in Fig. 3 demonstrates the smoothing effect of saturation: as exponential growth gives way to a functional form that is closer to linear at high flux density, the maser responds more weakly to the driving
Paper: 2017ApJ...836...32K (fulltext)
to study the reactions of carbon atoms with anthracene and coronene. In our computational study, we were mainly interested in the investigation of the pathways found previously for the reaction of carbon with benzene (Krasnokutski Huisken 2014c ) and naphthalene. Hence, we focused on the formation of seven-membered carbon rings. Our computations predict the barrierless reaction of carbon atoms with anthracene molecules and the possibility of a ring opening for all three aromatic cycles, i.e., the formation of seven-membered carbon rings in the center as well as on the sides of the molecule. Thus, the calculations reveal the possibility of the same reaction pathways as observed for benzene and naphthalene. This is in line with the experimental results obtained for benzene, naphthalene, and anthracene molecules. However, similar to the case of naphthalene, the carbon atom cannot be inserted into the CC bond shared by two aromatic rings. Figure 8 shows the molecular structures of the initial associative complexes between coronene and the carbon atom. It can be seen from this figure that, similar to the cases of the other PAHs studied, the carbon atom lands on the top of the CC bonds. The binding of the carbon atom to the bonds of the central ring is the weakest, and increases when the carbon atom moves to the side of the coronene molecule. Similar to the previously studied PAH molecules, the carbon atom cannot be inserted into the CC bond shared by the two aromatic rings. Therefore, the formation of a seven-membered ring in the center of coronene is not possible. Such a structure was found to be a transition state, with negative binding energy of the carbon atom. The binding of the carbon atom to the coronene molecule was enhanced going from the initial triplet state to the singlet state of the system. The highest binding energy (186.2 kJ mol −1 ) was found for the carbon atom on top of the peripheral CC bonds in the singlet state. With a relatively small energy barrier, the carbon atom can be inserted into the peripheral CC bonds of the coronene molecule. In this case, the reaction pathway is the same as for the other molecules studied. However, in the case of coronene, there is a considerable increase in the number of CC bonds, which cannot be opened. Therefore, landing of carbon atoms on these sites would not lead to the formation of a seven-membered ring. Two of the four complexes shown in Figure 8 , namely complexes 1 and 2, have rather low binding energy. Moreover, the energy barrier for the formation of a seven-membered carbon ring was computed to be a little higher compared to the case of benzene and naphthalene molecules. Therefore, there is a higher chance that the initial associative complexes 3 and 4 could also be frozen inside the liquid helium droplets. This explains the results of the calorimetric measurements showing only low energy released in the reaction of carbon atoms with coronene molecules. Figure 8. Molecular
Paper: 2019ApJ...873..119B (fulltext)
of higher frequency as well as others due to nonlinear interactions of the flows. Our present simulations indeed show complex timing properties of the radiation as well as the flow dynamics. For this, we chose the flow to have an accretion rate () below the Eddington limit for all of the cases we studied. The exponent α in the cooling rate was varied from 0.5 (bremsstrahlung) to 0.6 to observe the effects of the strength of cooling on the oscillation of shocks. The specific angular momentum λ was chosen based on the recent study by Deb et al. ( 2017 ), where an onset of vertical oscillation was seen between the two values chosen here. The variation of the radius of the neutron star had a more complex effect as it controlled the effects of radiation pressure on the hydrodynamics through the parameter when a self-consistent variation is chosen. Note also that for the timescales of simulations C1 to C5, the mass and momentum deposited on the star were negligible as compared to the star’s mass and spin. This accumulation of mass and momentum would become significant for timescales of the order of years or more, which is outside the purview of the current paper. However, the energy release at the surface was found to be significant, resulting in a measurable change of T NS . We show, among other things, that the simulations produce both low- and high-frequency QPOs, and the oscillations last during the whole simulation period (more than 200 dynamical timescales measured at the injected flow radius, i.e., 30 r S ). This suggests that the QPOs are formed from a part of the flow dynamics and are not a transient effect, as inferred by others (e.g., Barret et al. 2005b ). We measure the QPO frequencies and find that both the centroid frequencies and Q factors match well with the observed results of neutron stars such as GX17+2, 4U 1728-34, and Cir X-1. We believe that the advective flow suggested in the literature, while explaining the behavior of the source Cir X-1 (Boutloukos et al. 2006 ), may be the same as the dynamic transonic flow solution we discuss here. We showed that the presence of angular momentum itself can generate multiple modes of oscillation in CENBOL and NBOL, manifesting as QPOs in the PDS, in the presence of cooling. In Figures 1 (b), 2 (c)–(d), and 3 (a), we see different types of shocks are being formed. The outer shock was found to be vertical near the equatorial plane and oblique away from the plane, very similar to what was seen for simulations around black holes ( MLC94 ). The bending instabilities reported in M01a are also found here and correspond to the hectohertz oscillations found in the PDS. So far, in the literature, a model that appears to be capable of phenomenologically addressing both timing and spectral properties is the TL model of TLM98 , who assumed the disk to be Keplerian to begin with. The viscosity was also assumed to be high enough to maintain a Keplerian distribution. The QPOs are then explained as the
Paper: 2018A&A...611A..53J (fulltext)
g is a binary loss function and s j the scorefor learner j . The predicted class for x 0 is associated with the index k for which the vector Δ is minimal. Appendix C Description of classification algorithms: SVM – Ensemble methods Support-machine vectors The SVM method classifies the data by finding the best hyperplane separating the datapoints of one class from those of another category. Given a training set of M datapoints , where x i refers to the P-dimensional feature vector and y i the associated label that indicates whether the datapoint belongs to the positive class ( y i = +1) or the negative class ( y i = −1), the objective of SVM is to separate the data into distinct classes using a separating rule in form of a parametrized function f ( x ). For linearly separable data, the equation of the hyperplane is: where the scalar product w . x is equivalentto w ⊤ x . An infinity of hyperplanes verify Eq. ( C.1 ), but only one hyperplane maximizing the margins between the observationsand the hyperplane exists. This optimal hyperplane verify: To find the“best” linear hyperplane minimizing the margins , the SVM algorithm consists in solving a quadratic problem: For non-linearly separable data, the use of a kernel φ trick enables to map the distribution of the datapoints x into a projected space where φ ( x ) can be linearly separable, and defines, in the same approach as in Eq. ( C.2 ), a quadratic problem: where f ( x ) = f W , B ( x ) = W . φ ( x ) + B = 0. The selection of an adequate kernel K is determined by a list of criteria. By definition, a kernel must be symmetric, definite positive, square integrable and satisfy: Among commonly used kernels: Linear: K ( x i , x j ) = x i . x j Power: Gaussian (rbf): Further details about the SVMs are available in Vapnik (2000) and Cristianini Shawe-Taylor (2000) . C.2: Ensemble classifiers The principle of ensemble methodology is to combine a set of predictions from different learners in order to improve the accuracy of a single learner. Among the ensemble methods, two distinct approaches are identified: 1. Averaging methods: Bagging/ Random forests... 2. Boosting methods: AdaBoost/GentleBoost/RSBoost/... – AdaBoost AdaBoost, known also as “Adaptive Boosting” refers to a specific algorithm of boosted classifier defined as the sum of individual predictions from T weak learners. The algorithm aims to minimize the (weighted) classification error at each iteration: where: x i is the feature vector of the i th observation. y i is the true label of the i th observation. h t is the prediction of learner t . 1 is the indicator function. is the weight of the i th observation at step t . t the iteration step from 1 to T . At the first iteration, the weights are initialized (e.g., ) and the weak learner h t is obtained by minimizing the error ε t . For the next iteration, the weights of the learner ( t + 1) are adjusted according to the performance of the previous one ( t ): whether increase for misclassified
Paper: 2020A&A...644A..92Z (fulltext)
of C 6+ . However, the Ecker–Kröll model ( Ecker Kröll 1963 ) predicts a much higher population fraction of C 6+ compared with that obtained using our model. This differs from the conclusion drawn using the solid-density Mg, Al, and Si plasma ( Ciricosta et al. 2012 , 2016 ; Zeng et al. 2020 ), where the IPDs determined by the Ecker–Kröll model were in much better agreement with the experimental results than those of the SP model. Fig. 4. Charge-state distribution of C plasma as a function of the plasma temperature at mass densities of 2.25 g cm −3 ( a ), 3.75 g cm −3 ( b ), and 5.0 g cm −3 ( c ). Our results are compared with the Debye–Hückel (DH; Debye Hückel 1923 ), Stewart–Pyatt (SP; Stewart Pyatt 1966 ), ion-sphere (IS; Rozsnyai 1972 ), and Ecker–Kröll (EK; Ecker Kröll 1963 ) analytical models. We obtained the best agreement with the Ecker–Kröll model ( Ecker Kröll 1963 ) for the CSDs at a mass density of 3.75 g cm −3 . The SP ( Stewart Pyatt 1966 ), IS ( Rozsnyai 1972 ), and Debye-Hückel ( Debye Hückel 1923 ) models predicted a higher population fraction for C 5+ and a lower fraction for C 6+ than those predicted by our model and the Ecker–Kröll model. With the increasing mass density up to 5.0 g cm −3 , our theoretical IPDs and those of the IS and Ecker–Kröll models exhibited good agreement. The SP and Debye–Hückel models predicted a higher population fraction for C 5+ and a lower fraction for C 6+ . The aforementioned results can be seen in Fig. 5 , which shows the average ionization degree of C plasma as a function of the plasma temperature (100–500 eV) at mass densities of 2.25, 3.75, and 5.0 g cm −3 . Our predicted average ionization degree is consistent with those of the Ecker–Kröll model ( Ecker Kröll 1963 ) at a density of 3.75 g cm −3 and the IS and Ecker–Kröll models at a density of 5.0 g cm −3 , which is completely in accordance with the features observed with respect to CSDs (Fig. 4 ). Fig. 5. Average ionization degree of C plasma as a function of plasma temperature at mass densities of 2.25 g cm −3 ( a ), 3.75 g cm −3 ( b ), and 5.0 g cm −3 ( c ). Our results are compared with the Debye–Hückel ( Debye Hückel 1923 ), Stewart–Pyatt ( Stewart Pyatt 1966 ), ion-sphere ( Rozsnyai 1972 ), and Ecker–Kröll ( Ecker Kröll 1963 ) analytical models. 3.3. Analysis and explanation of a recent experiment Here, we verify the validity of our proposed model of screening potential by comparing its predictions with the results of a recent experimental measurement of the average ionization degree of C in a CH-mixture plasma ( Kraus et al. 2016 ). In this experiment, a highly compressed CH-mixture plasma (polystyrene) was created via spherically convergent shocks at the National Ignition Facility. Then, the spectrally resolved X-ray scattering of line radiation at 9.0 keV was recorded. The experiment measured an average ionization degree of 4.92 ± 0.15 for C at an average temperature of 86 ± 20 eV (radiation–hydrodynamics simulations show that
Paper: 2020A&A...633A..77A (fulltext)
to first order with b – which is degenerate with the function of σ n e – and the only way to break the degeneracy is to have enough variation in the secant of the zenith angle. In a sparse and noisy observation of ΔTEC, the secant variation is poor and it is expected that this degeneracy exists. Therefore we also show the product b σ n e , and we see that this compound value discrepancy is smaller by approximately 35%. Table 2. Average and standard deviation, over all experimental conditions, of the difference between the learned physical hyperparameters and the true hyperparameters. In Table 3 we summarise the performance of the physical kernel against each competitor kernel. We display the mean of ΔLPO C ( η ), and ΔLPH C ( η ) over all experimental conditions, as well as their values at the nominal experimental conditions of 30 directions per 12.6 deg 2 , and ΔTEC noise of 1 mTECU, which is indicated with η nom . We use bold font in Table 3 to indicate the best competitor model. Table 3. Probability ratio FOMs (see text) averaged over experimental conditions and at nominal conditions. Notes. Larger values indicate that the physical model is more probable. Bold face indicates the best performing competitor model (lower number). We first consider the ability of each model to represent the observed data. For the dawn ionosphere, the M52 competitor kernel has the best (lowest) ⟨ΔLPO C ⟩ η = 1.55 and , implying that the M52 kernel model is 55% and 46% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. We note that the M32 kernel produced similar results. For the dusk ionosphere, the EQ kernel model is likewise the best among all competitors, being only 73% and 54% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. In all experimental conditions, the physical model provides a significantly more probable explanation of the observed data. We now consider the ability of each model to infer the held-out data. For the dawn ionosphere, the M52 competitor kernel has the best (lowest) ⟨ΔLPH C ⟩ η = 1.49 and = 1.31, implying that the M52 kernel prediction is 49% and 31% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. We note that the M32 kernel produced similar results. For the dusk ionosphere, the EQ kernel model is likewise the best among all competitors, with predictions only 16% and 12% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. In the case of the rougher dawn ionosphere, the physical model provides a significantly more probable prediction of the held-out data in all experimental conditions. However, for the smoother dusk ionosphere at nominal conditions, the physical model is only 12% more probable than the EQ kernel model,