adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.479..768P (fulltext)
of single vibrational transitions. Row 1 of Fig. 6 shows the temperature measured by the internal sensor (solid line) and determined by our algorithm (data points). There is a linear offset to the sensor temperature, the molecular temperature being systematically higher. This discrepancy can be corrected by a linear function A least-squares fit yields a = 1.414, T off,1 = 292.2 K, and T off,2 = 290.45 K. The dashed line in row 1 of Fig. 6 shows the corrected sensor temperature. The residuals between corrected sensor temperature and molecular temperature (σ = T measured − T alg ) displayed in row 2 show minor systematics during the unheated phases. The real cell temperature can then be determined from the algorithm output by inverting equation ( 24 ). We consider three cases for the origin of the temperature offset and discuss them in the following sections. Figure 6. Determined parameters of the measurements as a function of time. Shaded grey areas mark the time intervals during which the heating was switched on. Row 1: Data points with error bars represent the temperature of each spectrum as derived by our algorithm. The solid line shows the temperature determined with the gas cell sensor and the dashed line is the linearly corrected sensor temperature. Residuals between the corrected sensor temperature and the molecular temperature are given in row 2. Row 3 shows the determined effective RV/wavelength shift of each spectrum. 5.3.1 Erroneous model We assume the energy levels to include two terms, the vibrational and rotational energy (equation 12 ). In reality, there are several other contributors to the energy levels, such as a translational and an electronic term. We do, however, argue that the omission of these terms cannot lead to a bias in temperature determination, since the temperature determination is only dependent on the difference between energy levels Δ E ; hence, any term independent of the quantum numbers ν and J is cancelled out in equation ( 18 ). 5.3.2 Heating by the source lamp The source lamp in our setup is located relatively close to the iodine cell, so heating of the gas cannot be dismissed outright. As can be seen in the lower panel of Fig. 3 , the absorption is in fact temperature dependent. The lamp therefore heats the gas and causes a temperature gradient between the cell centre and the cell wall, where the temperature sensor is located. But if this effect were large enough to be present in the data, the temperature offset would not drop to zero at lower temperatures (see upper panel of Fig. 6 and equation ( 24 )), which is why we can rule it out as the underlying cause. 5.3.3 Heat sensor Equation ( 24 ) shows that the difference between the sensor and algorithm temperatures drops to zero at a temperature of 290.45 K, which was in fact the room temperature inside the laboratory at the time of the measurements. Furthermore, the residuals between corrected sensor temperature and the one determined via our algorithm show
Paper: 2020MNRAS.498.1786Y (fulltext)
constraint is nearly same. So, we find that, different choices of the group prior p 0 ( z , Ω) cannot significantly affect the constraint of the Hubble constant. On the other hand, for LHVIK network with SBBH mergers, we find the error bars of H 0 are quite large in both cases. Figure 8. The posterior distributions of Hubble constant H 0 . The upper, middle, and lower panels show the results of for the LHVIK, LHVIKCA, CE2ETD network, respectively. The left-hand panels represent the cases of uniform window function w i = 1, and the right-hand panels represent the cases of w i proportion to the stellar mass of groups. In each panel, there are four lines. The green line is the result in the case of A and the blue one is that of B (see the main text for the details). Similarly, the red line is the result in the case of A, and the black line is that of B. Note that, green line overlaps with blue one, and red line overlaps with black one. For LHVIKCA network with SBBH mergers, we find that 44 events satisfy the criteria and are used for the parameter constraining. The posterior distributions of H 0 derived from this network are given in the middle left-hand panel of Fig. 8 , which are for both case of A and case of B. While for SBBH mergers, we find 21 events can be used for the parameter constraining, and the corresponding result is in the case of A and H 0 = 69.97 ± 0.86 km s −1 Mpc −1 in the case of B. Same to the cases of LHVIK network, we find that the effect of group prior p 0 ( z , Ω) is not significant. In comparison with the corresponding cases with LHVIK network, we find the uncertainties of H 0 are nearly five times smaller. Due to the contribution of the two 8-km detectors in the network, the localization ability for the individual event is significantly improved, which leads to two effects on the H 0 constraining: First, the uncertainties of the redshift distribution of the GW event is reduced. Secondly, the number of the GW events satisfying the criteria is increased. So, we conclude that, if the proposals of 8-km detectors can be realized in the near future, the capabilities of GW cosmological, based on the SBBHs as the standard sirens, will be greatly improved in the 2G detector era. Now, we turn to the 3G detector network CE2ETD. As anticipated, we find that the accuracies of H 0 are much better than that of the 2G detector networks. For mergers, 66 events can be used for the parameter constraining, which follow the result H 0 = 70.000 ± 0.008 km s −1 Mpc −1 for both cases with different prior choices. For SBBH mergers, the constraint of H 0 is 70.000 ± 0.018 km s −1 Mpc −1 , derived from 65 events, which satisfy the selection criteria. The posterior distributions in these cases are presented in the lower left-hand panel of Fig. 8 . We find that, even comparing with the results of 2G LHVIKCA detector network, the uncertainty of H 0 is reduced by a factor of 30. Nair, Bose Siani ( 2018 ) discussed a single ET’s accuracy of H 0
Paper: 2020ApJ...893..122D (fulltext)
law (in semimajor axis): as the semimajor axis increases, the number of massive planets increases beyond the limit in which they can dynamically coexist. In the case of RV1 and RV2, the choice of “optimistic,” “nominal,” and “pessimistic” does not directly affect the Jovian planets; it changes the distribution of the Neptune and Terran planets which are drawn from SAG13. However, there may be some differences even in the RV1 or RV2 Jovian population between the “optimistic,” “nominal,” and “pessimistic,” due to random chance or due to secondary effects imposed by the implementation of a “trusted” region, which we discuss further in Section 3.4 . Imposing Hill-stability criteria by iteratively injecting planets and assessing their resulting orbital architectures based on mass and spacing regulates the number of planets per star. The SAG13 distribution (black lines in Figure 4 ) is suppressed much more strongly than the RV1 (blue lines) and RV2 (orange lines) distributions. As an example, the “optimistic” SAG13 distribution results in 71% of the predicted Jupiters to be unstable. Table 2 quantifies the percentage of predicted planets that are stable for each of the various models. Table 2 Percentage of Jupiters Predicted by Each Power-law Set which are Found to be Dynamically Stable Both the RV1 and RV2 models include power-law breaks that effectively reduce the number density of Jovian planets at larger orbital radii. As a result of a lower frequency and strength of dynamic interactions, the relative suppression of the RV1 and RV2 distributions is more modest. For RV1 and RV2, respectively 79% and 93% of Jupiters over the parameter space are found to be dynamically stable. Table 3 Percentage of Neptunes Predicted by Each Power-law Set which are Found to be Dynamically Stable Table 4 Percentage of Terrans Predicted by Each Power-law Set which are Found to be Dynamically Stable Because we assume that the largest planet in each system is stable and retain the larger planet in any unstable pair, the distribution of Jovians has an impact on the stability of all other planet types as we explore in Sections 3.1.2 and 3.1.3 . 3.1.2. Neptune Planets (2.04 ≤ <italic>M</italic><sub><italic>p</italic></sub>/<italic>M</italic><sub>⊕</sub> ≤ 75.51) Interestingly, the influence on the semimajor axis distribution of the imposing stability criteria becomes even more pronounced for lower-mass planets. Cold Neptunes, which are concentrated near the SAG13 radius critical point of R = 3.4 R Earth , have an unrealistically high occurrence rate. These cold Neptunes are unstable. Of all planet types, cold Neptunes are being removed in the greatest number (although not in the largest percentage). Table 3 quantifies the percentage of predicted Neptunes that are stable for each of the various models. All three demographics sets we implement—SAG13, RV1, and RV2—use SAG13 power-law indices for Neptune- and Terran-mass planets. Each Neptune population begins the same,
Paper: 2019MNRAS.485...88P (fulltext)
values using least squares. Phase residuals of the cross-correlation spectrum are analysed and additive corrections to the a priori weights are computed on the basis of this analysis. The uncertainties of estimates of group delays are derived from uncertainties of fringe phases and additive weights corrections. The uncertainties of fringe phases depend on fringe amplitudes. The explicit expression can be found on page 233 of Thompson, Moran Swenson ( 2017 ), equation (6.63). Computation of total group delays and phase delay rates. The group delays and phase delay rates derived at the previous step are corrections to the a priori delays and phase delay rates used during correlation. The mathematical model of the a priori group delay and phase delay rate used by the correlator is expanded over polynomials of the fifth order at 2-min long intervals that cover the time range of a VLBI experiment. Using these coefficients, the a priori group delays and phase delay rates are computed to a common epoch within a scan for the event of arriving the wavefront at a reference station of a baseline. Using these a priori group delays and phase delay rates, the total group delays for that epoch are formed. 3.2 Astrometric analysis Total group delay is the main observable for astrometric analysis. During further analysis, the a priori model of group delay, more sophisticated than that used for correlation, is computed, and the differences between observed and theoretical path delays are formed. The partial derivatives of this model over source coordinates, station positions, the Earth ordination parameters, atmosphere path delay in zenith direction, and clock function are also computed. Then, corrections to those parameters are adjusted using least squares. The frequency set-up used for this campaign, selected due to hardware limitation (see as an example the set-up for v271i segment in Fig. 2 ) posed a challenge in data analysis. The Fourier transform over frequency over baselines with atca, ceduna, mopra in this example that uses LBDAR DAS has strong secondary maxima (see Fig. 6 ). The amplitude of the second maximum is 0.98, the third maximum 0.93, and the fourth maximum 0.83 with respect of the global maximum. Due to the noise in data and remaining instrumental phase distortion, the fringe-fitting process cannot reliably distinguish the primary and the secondary maxima, and as a result, group delay is determined with the ambiguity of N × 1/2.56 × 10 8 ≈ 3.9 ns, where N is a random integer number, typically in a range [−2,2]. Figure 6. The normalized fringe amplitude as a function of group delay between stations that had LBDAR recording system. The fringe amplitude is divided by the amplitude at the global maximum. At the first stage of the astrometric data analysis, we processed the so-called narrow-band group delays derived as an arithmetic mean of group delays computed over each IF independently and estimated source positions using least squares. The
Paper: 2019MNRAS.485.3569H (fulltext)
0.18, respectively, for the 80 per cent/20 per cent train/test split (right-hand panels in Figs 4 and 5 ). Given more training data for the final model, these hyperparameters should deliver an appropriate model complexity that does not overfit the data. As before, I train 100 models with different random states to avoid being biased by the random components in the fitting process. Henceforth, I will report the median value of the predictions from the 100 models. In Fig. 7 , I compare input T e metallicities with those predicted by the MLP model from strong lines. The error bars show the 1σ standard deviations of the 100 predictions. In general, the spread in metallicity due to the random components in the fitting process is much smaller than the dispersion of the calibration. Fig. 7 suggests that the best MLP model performs reasonably well over a wide range of metallicity. Figure 7. Comparison between oxygen abundances from the best MLP model (Section 2.3.4) and the T e method. The data points are the working sample described in Section 2.1 . The error bars are the standard deviations of the 100 predictions from the 100 model realizations. The dashed line is the one-to-one line. 2.3.5 Feature sensitivity Extracting physical interpretation from neuron weights and biases is known to be difficult. However, it is worth investigating how sensitive different features are to the output to understand their importance to the network. The sensitivity analysis can be achieved by computing the partial derivative of the output with respect to the input features. Using the best MLP model, I perturb one feature at a time while keeping the other features unchanged and compute the changes in the output metallicity. The small perturbation is set to 3 per cent of the standard deviation of the input feature of the working sample. The response of the model to the perturbations is calculated for the entire working sample. Fig. 8 presents histograms of the resulting changes in the output metallicities in the form of a ‘violin’ plot. The top three features that the network output is most sensitive to are [S ii ]/[N ii ], [N ii ]/[O ii ], and [N ii ] (deviate most from zero). Interestingly, photoionization models have demonstrated that the [N ii ]/[O ii ] ratio is a good metallicity diagnostic (at above 0.2 solar) due to its insensitivity to ionization parameter (Kewley Dopita 2002 ; Dopita et al. 2013 ). The combination of [N ii ]/[S ii ] and [N ii ]/H α is also suggested by Dopita et al. ( 2016 ) to be an excellent diagnostic that is insensitive to ISM pressure and ionization parameter. The neural network appears to rely more heavily on features that are also important in diagnostics developed from photoionization models, despite that this information is not given a priori. This demonstrates the ability of the neural network to directly ‘learn’ from the data. Figure 8. Feature sensitivity of the best MLP model, as measured by perturbing one feature at a time and
Paper: 2019ApJ...881L...5V (fulltext)
gradually distorted until a singular point appears, where the derivative is discontinuous. Further increasing y , the singular point moves at different x positions, thus generating a discontinuity that is also in the derivative . Starting from the position y = 0 where the solution is regular, the singularity develops over a distance ( y ∼ 6), which is of the order of the typical wavelength. This singularity is found also for other values of z , indicating that a singular surface is present in the 3D structure of A z . At the singular surface the components B x and B y are discontinuous, therefore the surface represent a current sheet. In contrast, the component // is regular in the whole domain. In Figure 1 (bottom panel) the profiles of the magnetic field components and of are plotted as functions of x , for y = 6.13 and z = 0.78. The presence of the discontinuity is clearly visible, mainly in the B y profile. The profile of is remarkably constant, except for a dip localized at the singular point. In fact, the constancy of is guaranteed by Equation ( 1 ), while the smoothing procedure, which modifies the “ideal” solution mostly around the singular point, represents an additional element that does not allow the solution A z to exactly satisfy Equation ( 1 ). Figure 1. Case A. Top panel: profiles of as functions of x , for various values of y , and at z = 0.78. Bottom panel: profiles of B x , B y , B z , and as functions of x , at y = 6.13 and z = 0.78. To study a situation that is closer to the solar wind turbulence, in Case B we considered more complex forms for A x ( x , y , z ), A y ( x , y , z ), and a z ( x , z ) that have 3D power-law spectra (2D for a z ). For the evaluation of these quantities, we used a synthetic turbulence model (STM) introduced by Malara et al. ( 2016 ) and already employed in modeling magnetic turbulence (Pucci et al. 2016 ; Perri et al. 2017 ). The STM builds up a turbulent field as a superposition of localized fluctuations with different amplitudes at different logarithmically distributed spatial scales. The model can reproduce an anisotropic spectrum (Malara et al. 2016 ) that follows the critical-balance scaling law (Goldreich Sridhar 1995 ), where and l ⊥ are the perturbation scale lengths parallel and perpendicular to the mean magnetic field , respectively. Using the STM, we generated A x , A y and the boundary condition a z in the scale ranges π ≤ l ⊥ ≤ π /32, . On average, the amplitude of a fluctuation in the spectrum of the vector potential i th component scales as . For h = 1/3 the spectrum of the spatial derivatives of has a Kolmogorov index () in the perpendicular directions, while in the parallel direction. We choose the parallel direction along x , which is approximately the direction of (see below). The STM can also reproduce intermittency through a p -model technique (Meneveau Sreenivasan 1987 ); in the present case we used p = 0.5, corresponding to no intermittency. A more detailed description of
Paper: 2017MNRAS.467L..91C (fulltext)
of Sgr in that area of the sky with mean velocities v r 130 and115kms 1 . The position of these peaks are compatible with the ones derived from the velocity distribution of the target stars. According to the classification provided by these models, the bulk of Sgr stars around v r 130kms 1 might belong to the trailing arm of that halo substructure while the less significant peak might correspond to the leading arm. The mean heliocentric distance predicted by PR10 for both wraps in this area of the sky is d 30kpc. On the other hand, LM10 predicts mean distances of d 21 and 28kpc for the leading and trailing arms, respectively. The expected two sections of the Sgr tidal stream along the line of sight to Whiting1 are evident in our spectroscopic results but not in the colourmagnitude diagram (CMD) shown in Fig. 1 . We analyse the distribution of target stars belonging to the Sgr components as a function of g by counting stars associated with the leading [160 v r (kms 1 )100] and trailing [90 v r (kms 1 )150] arms with a bin size of g 0.1. Fig. 3 shows the normalized distribution obtained for both populations and we are not able to determine a shift in the g band between the subsamples of target stars. This might indicate that the leading and trailing arms of Sgr are spatially coincident or have slightly different distances, making it difficult to distinguish the populations in the CMD. Therefore, our results are in good agreement with the predictions made by the PR10 model both for the position and the kinematics of the leading and trailing components of the Sgr tidal stream around Whiting1. Figure 3. Left-hand panel: CMD for stars beyond 10arcmin from the centre of Whiting1. The positions in the diagram of stars belonging to the Sgr trailing and leading wraps are indicated as orange stars and green triangles, respectively. The blue isochrone corresponds to a t 10Gyr and [Fe/H] 1.5 population. Right-hand panel: normalized distribution of stars belonging to both arms as a function of g in the range 19 g 23.5 and following the same colour code as in the left-hand panel. According to the PR10 classification, target stars with radial velocities around v r 120kms 1 might be associated with an old component of the leading arm of Sagittarius, accreted a long time ago (2Gyr). While the northern and southern trailing arms have been well characterized using different tracers such as RRLyrae, blue horizontal branch, sub-giant branch and red clump stars (e.g. Koposov etal. 2012 ; Belokurov etal. 2014 ), the southern leading arm predicted both by PR10 and LM10 , has remained undetected. This might be due to the coincidence of its projected path with that of the trailing arm or to its lower surface brightness. Only tentative detections have been reported using alternative tracers such as carbon stars, with distances and velocities consistent with our results along that line of sight (Huxor Grebel 2015 ). Alternatively, the group of stars with positive radial
Paper: 2016MNRAS.459.1082C (fulltext)
the models relax during 200 T cr (about a Hubble time); the final snapshot was again rotated using the moments of the inertia tensor of the 80 per cent most tightly bound particles. These last systems (one for each model) were evolved during an additional interval of 300 T cr in order to verify their stability. For each of the two models, the snapshot corresponding to 100 T cr of this last evolution was the one used to study and classify the respective orbital contents; we call them E2af and E5af, respectively, for brevity. Fig. 1 shows the kinematic and structural characteristics of the E2af model. In this figure, the abscisae q stands for the ellipsoidal radius where a , b and c are the semi axes of the model, computed following the method of Dubinski Carlberg ( 1991 ), as adapted by Holley-Bockelmann et al. ( 2001 ); Q , on the other hand, is the same as q but projected on to the coordinate planes. The abscisae M ( r )/ M is the mass interior to radius r in units of the total mass of the model. The upper-left panel shows the density profile of the model, computed using ellipsoidal shells with constant axial ratios taken at the half-mass radius. As can be seen, the rotation has not substantially affected the cusp of the model (γ = −1.023 ± 0.020, computed from the densities of the 10 000 innermost particles binned in 100 intervals of distance to the centre). The upper-right panel shows the axial ratios, computed in shells each containing 3 per cent of the particles, previously sorted by energy. The ratios of the semi axes of the shell containing 81 per cent of the mass in its interior are b / a = 0.939 and c / a = 0.791, in good agreement with the values that correspond to the 80 per cent most tightly bound bodies ( b / a = 0.925, c / a = 0.823, T = 0.447), i.e. the parameters used in our previous works. Comparing these values with those of the original E2a model, we can see that the b / a ratio has increased, whereas the c / a ratio and the triaxiality have decreased. The bottom panels show the kinematical profiles, in which , where i is a Cartesian direction x , y or z in the left-hand panel, and r (radial) or t (tangential) direction in the right-hand panel. The anisotropy parameter . Fig. 2 shows the same characteristics but for the E5af model. Again, the cusp is well preserved (γ = −0.956 ± 0.023). In this case, for the same shell as before, b / a = 0.916, c / a = 0.602, while for the 80 per cent most tightly bound particles, b / a = 0.896, c / a = 0.641 and T = 0.335, i.e. both axial ratios have increased and the triaxiality decreased with respect to the E5a model. It might seem odd that rotation has not made the model flatter, but it should be recalled that the process that led from the non-rotating to the rotating model implied considerable changes in structure of the former. For example, about 30 per cent of the original particles were eliminated because they increased their energies beyond the established limits, and the a semi