adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...865...72E (fulltext)
ones (Figure 8 ). Overall, Figure 11 does not suggest any significant trends in the mass error with respect to merger history, percentage of incomplete data, or percentage of unbound particles. Thus, except in extreme cases, these quantities may not play a role in underestimating the mass. In the following section, we explore the possibility that the inner GC analogs are influencing the model fit, as suggested in Section 5.1.3 . 5.3. Sensitivity to Inner Tracers Our studies of the MW, and in particular the sensitivity analyses in Papers 2 and 3 , found an increased mass estimate when inner GCs were not included in the analysis. We repeat this kind of analysis here by removing the inner GC analogs from the MUGS2 galaxies, and recalculating the mass estimate M 200 . Each galaxy in the MUGS2 simulation is unique in shape and spatial distribution of GC analogs. In order to remove inner GC analogs in a consistent way across all galaxies, we find the scale radius r s of each galactic disk via a simple exponential fit, and then remove GC analogs within 7.5 r s . The MW’s scale radius is roughly 2 kpc, so in our own Galaxy this cut is akin to removing GCs within r gc = 15 kpc of the Galactic center. It should be noted that the galaxies already had GC analogs removed within 3 r s in the original analysis. The new mass estimates after removing the inner GC analogs are shown in Figure 12 . In general, the estimates are better but at the cost of wider Bayesian c.r. The true M 200 values lie within the Bayesian c.r. in 13 out of 18 cases—a slight improvement from 8 out of 18 (Figure 9 ). Three regulated galaxy masses that were previously underestimated are now captured within the uncertainties. The mass estimate for g 15807, previously a large outlier, is also notably improved. Figure 12. Results from the sensitivity test: mass estimates after selecting tracers outside of r = 7.5 r s ). True values are shown as blue diamonds, estimates are dark purple circles, and the 95% c.r. are shown as errorbars. In light of this result, and evidence presented in Section 5.1.3 , we suggest that the combination of the GC analog number density profile, the percentage of complete data at small r , and the incomplete data at large r are the culprits of the mismatch between the predicted and true mass profiles. To test this hypothesis, one could rerun the analysis in a future sensitivity test, gradually increasing the number of proper motion measurements at large distances. We leave this to a future work. 5.4. Discussion The underestimation of the total mass appears to be a systematic bias in our method, as it applies to these MUGS2 galaxies—especially the regulated variety. Although it may be tempting to extrapolate results from these blind tests to our previous MW results using GCs in Paper 3 , it is important to make this kind of inference cautiously, for a few reasons: (1) The GC analog populations are dissimilar to the MW, and the MUGS2 galaxies, in the end,
Paper: 2017MNRAS.472.3914A (fulltext)
is no longer achieved and no solutions are found. (For more details on the Grad–Shafranov solver, refer to Paper I .) 3 RESULTS We next present results from various runs for the magnetosphere, as it adapts to the internal evolution. In the leftmost panel of Fig. 1 , we show the 2D projection of the magnetic field lines (on the r θ plane) at several snapshots throughout the evolution of the sample field defined as model A in Section 2.2 . In this case, the initial field is dipolar, with a toroidal component of relative strength , confined within the critical field line defined by P c = 0.5 P o . Crucially, a value of P c P o allows for a toroidal field to be present in the magnetosphere at the start of our simulation. Figure 1. Left-hand panel: snapshots of the projection of field lines in the meridional plane for model A at three different times: the initial field configuration at t = 0 is shown with dotted lines; the field at t = 1500 yr is shown with dot–dashed lines; and the field at t = 1579 yr (the last time-step before the magnetosphere saturates) is shown in solid lines. The star is shown in grey, and the distances are rescaled by the stellar radius. The critical field line at t = 1579 yr (confining the magnetospheric currents) is shown thicker. Note the accelerating expansion of the field lines over the last two snapshots. Middle and right-hand panels: 3D field lines for the initial magnetic field (at t = 0) and for the last snapshot before reaching the critical point (at t = 1579 yr), respectively. We find that, starting with an initial toroidal field occupying part of the magnetosphere, it gradually increases in amplitude over the course of the evolution. Consequently, the strength of magnetospheric currents, total energy, helicity and twist in the magnetosphere steadily increase over time, and the field lines stretch outwards as the simulation progresses. This continues until the toroidal field reaches a maximal amplitude, beyond which simple field configurations with lines connected to the interior are no longer feasible solutions of the Grad–Shafranov equation (see Paper I ), and thus the magnetosphere reaches what we refer to as a critical point . For the particular model presented here, this happens at approximately t max ≈ 1579 yr. It is important to notice that t max is not very sensitive to the exact twist at which the reconnection event occurs because, once the twist is significant (∼1 rad), the evolution proceeds very rapidly. At this moment, we expect the magnetosphere to undergo a violent rearrangement on the Alfvén time-scale, resulting in the expulsion of some or all of the excess energy stored in the toroidal component. After this rearrangement, the evolution will continue, starting from the new initial state, and the process is repeated. Thus, one would expect recurrent outbursts or flares throughout the evolution, each resulting in the expulsion of a fraction of the available energy. The amount of energy
Paper: 2016MNRAS.455.3042C (fulltext)
turn depends on much of the physics of the cloud. Section 2 reviews briefly the details of the particle heating model grid and discusses the physical and chemical properties of the grid. Section 3.1 explores the effect of a large quantity of cold clouds on the composite model spectrum for BCGs. Section 3.2 discusses the effects of optical depth on the predictions for the BCG line ratios and Sections 3.3 and 3.4 explore the effect of turbulence and magnetic fields. Some words of caution about the models are given in Section 3.5 . A discussion and predictions for future observations are presented in Section 4 . 2 MODEL The particle heating model used in this paper is essentially that described in F2009 for optically thin emission from warm ionized gas adapted to include physics pertinent to very cold clouds. The model is motivated by spectra from extended emission-line regions in BCGs which show spatially coincident emission from molecular, atomic and ionized gas, suggesting that the total emission in the filaments is from clouds with a range of densities and temperatures. F2009 parametrize the contribution of the different phases to the total volume as a weighted sum of ‘cloudlets’ of different densities but constant pressure. The weighting function, α, is taken to be a power law in the cloud density, and hence the cloud density function will be this power law. The line emissivity ( in erg cm −3 s −1 ) summed over the distribution of clouds is where n i are the specific densities and we simulate the grid in density bins of 0.1 dex from log 10 ( n ) 0 to 6. The pressure in the ionized filaments has been measured by Heckman et al. ( 1989 ) and is similar to the X-ray derived pressure of the ICM. Throughout the paper we will refer to ‘hot gas’ as X-ray emitting ( T ≳ 10 6 K) gas, ‘warm gas’ as ionized, optical emitting clouds ( T ∼10 000 K) which emit strongly in Hα, ‘cool gas’ as T ∼ 1000 K gas which emits strongly in H 2 12 μm and ‘cold gas’ as gas with temperatures T ≲ 100 K, which emit in the strong fine structure lines of [C ii ] λ157 μm , and many molecular emission lines. F2009 use the Hα and H 2 12 μm emission-line ratios to determine α for the Perseus cluster (a constant pressure of ∼10 6.5 is used for the Perseus cluster). They find α = −0.35 can reproduce all emission lines from the warm ionized ( T ∼ 10 000 K) gas to within a factor of 2 (but see Johnstone et al. 2012 for a discussion of the Hα flux in the Perseus cluster). This value of α implies the majority of the volume is filled by hot ionized gas with only a minor contribution from the more massive, cold clouds. Hα emission is predominantly from ∼10 4 K gas while the H 2 12 μm emission line is from gas ∼10 3 K. The authors point out that the data available at the time are not able to constrain a large reservoir of very low temperature clouds. However, extrapolation of α to low-temperature clouds gives good agreement with mass estimates from CO emission lines though these may be
Paper: 2018ApJ...852L..28A (fulltext)
applied to the data of the Apatity Neutron Monitor ( http://www.nmdb.eu/nest/search.php/ ); the results, reported in the third panel of Figure 3 , show a periodicity that seems to coincide with the observed one by PAMELA. To try to explore the origin of this quasi-periodicity, a raw periodgram (95% Kolmogorov–Smirnov confidence level) of the first two rigidity spectra, 0.4–0.65 GV and 0.65–15 GV, has been carried out. The results are presented in the fifth panel of Figure 3 . Peaks appear around 580, 450, 370, 320, and 230 days. The 580-day periodicity is possibly linked to an ∼600-day periodicity reported in Valdés-Galicia et al. ( 1996 ), which could be associated with fluctuations in the southern coronal hole area and in large active regions. The seasonal ∼370-day cosmic-ray variation is caused by the Earth’s rotation (Forbush 1954 ). The 230-day peak was also found in El-Borie et al. ( 2011 ) as a 0.7-year periodicity during the A > 0 solar cycle (1992–2000). The last periodicity, ∼450 days, was already described in Valdés-Galicia et al. ( 1996 ) as a possible 1.2-year periodicity but its origin is not reported. The nature of the ∼450-day periodicity could be related to quasi biennial oscillations or QBOs (Vecchio et al. 2012 ; Laurenza et al. 2012 ). QBOs have been detected as a prominent scale of variability in GCRs, but they could just be more an effect of superposition of other periodic/quasi-periodic processes and not stochastic perturbations. Higher QBOs have been observed during solar maxima with respect to solar minima (Bazilevskaya et al. 2015 ); this could explain a higher signal in the descending phase of PAMELA proton data (see Figure 1 ) and could be related to different drift effects in different polarities of the HMF. In the past, numerous periodicities between 0.5 and 4 years have been correlated to QBOs (Kato et al. 2003 ; Rybák et al. 2001 ; Kudela et al. 2002 , 2010 ; Benevolenskaya ( 1998 ) and it is very difficult to disentangle their effects from others. In this work, a different hypothesis is proposed for the 450-day periodicity as a study case: a Jovian origin, more exotic but largely proposed in the past. It is well known that the planet Jupiter possesses a very intense magnetosphere due to the combination between its strong magnetic field, about 10 4 times larger than that of the Earth, and the weakness of the solar wind at 5 au. Evidence that Jupiter could generate high-energy particles has been shown for electrons (Teegarden et al. 1974 ; Simpson et al. 1974 ), and more recent studies revealed that the impulsive and quasi-periodic bursts observed in Jupiter’s duskside magnetosphere also contain protons and helium nuclei in the range 0.7–10 MeV/nucleon (Zhang et al. 1995 ). Moreover, an indication that acceleration mechanisms can operate in magnetospheric environments has been found on Earth, where trapped particles are shown to be accelerated to relativistic energies by local acceleration acting in the heart
Paper: 2017MNRAS.466.1894M (fulltext)
developed in our group, is available online in our web page ( http://www.iem.csic.es/fismol/fmap/descarga.htm ). An iterative method is implemented in the MIR with an algorithm described in detail by Zanchet et al. ( 2013 ). Briefly, two multidimensional functions, defined as differences between experimental and calculated spectra, are minimized iteratively. To calculate the transmission spectra through a three-layer system like our experimental one, an analytical model based on Fresnel coefficients was employed. Several variables are fitted: k and n at each wavelength, ice thickness, visible refractive index, and the ratio of coherent/incoherent transmitted IR radiation. To carry out this fitting, several experimental spectra of different thickness are necessary, as well as accurate measurements of the film thickness and of the refractive index n 0 of the ice. After the iterative procedure, the final fitted thickness and visible refractive indices differ from the measured values by less than the experimental error. This condition is used as a measure of the consistency of the fitting. For the NIR region, this procedure cannot be used due to problems in the baseline simulation, which is affected by scattering and double IR reflections. These effects are of the same order of magnitude than the NIR absorptions. Hence, in this frequency range, after a proper subtraction of a baseline in the absorbance spectrum, Lamberts law is applied to obtain the imaginary part of the refractive index: where L is the ice thickness, the absorbance spectrum and the imaginary part of the refractive index. The real part of the refractive index is subsequently obtained using the KramersKronig relation. Optical constants of the mixtures are presented in Fig. 6 . The corresponding n 0 values are the ones given in Table 1 . Optical constants for the mixtures are presented in Fig. 7 . Figure 7. Optical constants in the more relevant NIR and MIR spectral regions of CH 4 :C 2 H 4 ices with proportions (3:1), (1:1) and (1:3) at 30K. Files with the optical constants determined in this work for pure species and mixtures are given in the supplementary material . We have calculated also the optical constants of pure ethylene ice at 30K. Fig. 8 shows a comparison of absorption coefficients, in the NIR and values in the MIR obtained from the present data with previous results. Although the absorption coefficients given by Quirico Schmitt ( 1997 ) and those of Hudson et al. ( 2014 ) do not correspond exactly to the same phase of ethylene ice, the agreement in the NIR region is quite good (Fig. 8 , top panel). The comparison of in the MIR region with the data of Hudson et al. ( 2014 ) manifests the different phases of the ices that were already commented on in Fig. 3 (Fig. 8 , bottom panel). Figure 8. Absorption coefficients (a) and imaginary part of the refractive index (b) of pure ethylene ice at 30K determined in this work compared with previous literature data. 3.4
Paper: 2016A&A...588A..65K (fulltext)
polarized emission at intermediate and high Galactic latitudes. In extrapolating the foreground contribution at the various frequencies, we take the Planck 353 and 30 GHz and WMAP--band color corrections into account to properly include the real frequency response of the instruments ( Planck Collaboration IX 2014 ; Planck Collaboration V 2016 ). We recall that synchrotron and dust maps of Fig. 3 report the actual value of the spectra in the bandpower in those pixels corresponding to regions where we have a signal detection above 3, and the upper limit on it in all the other pixels. Nevertheless, we treat detections and upper limits in the same way by rigidly extrapolating the complete maps in frequency. To distinguish among different cases, we divide the sky regions into four classes, depending on whether we reach detections or upper limits for synchrotron and dust, and in the following figures, we use different colors to distinguish them. Classes are defined in Table 2 . Figure 4 shows the minimum foreground amplitude (sum of synchrotron and dust contributions) recovered in each sky patch after frequency extrapolation, together with the associated error. Amplitudes are expressed in terms of , computed by dividing the total minimum amplitude of foreground emission in K by the value of the CMB primordial modes power spectrum with tensor-to-scalar ratio in the bandpower, which is equal to K. Therefore, a value of , for example, means a foreground contribution at the level of a CMB GWs signal with . For regions belonging to class (1), maps in Fig. 4 use colored pixels to report the estimated values of and the associated errors. For each map we also report the histogram of the retrieved values. Uncertainties are calculated as the quadratic sum of the statistical errors coming from the white noise in the sky maps and the errors coming from the uncertainties on the frequency scaling parameters. For regions in classes (2), (3), and (4), we are able to put only upper limits on , which are shown in Fig. 4 . We also report the errors on these upper limits, which as before, are the quadratic sum of the statistical errors (included only if we have a detection of at least one of the two kinds of signal) and the frequency scaling ones. Figure 5 shows the map reporting, for each region, the frequency at which we find the minimum of foreground emission, together with the associated histogram. In this map, we again distinguish the four classes of regions using different color scales. For class (1), since we are dealing with detections, the value of each pixel represents the actual frequency where the minimum of foreground emission is reached. For classes (2) and (3), the reported value represents the frequency at which we set the upper limit on in the corresponding region. This frequency should be seen as an upper (for class (2)) or a lower limit (for class (3)) on the real frequency at which the foreground emission reaches its actual minimum. For class number
Paper: 2020ApJ...905L..22S (fulltext)
strong negative gas-phase metallicities from the center toward the stripped tails, consistent with an inside-out formation in isolation, followed by a outside-in removal of gas upon infall into the cluster (e.g., Bellhouse et al. 2019 ; Franchetto et al. 2020 ). Our galaxies show offset H α -flux peaks, extended tails of nebular emission, and bow-like and asymmetric velocity maps reminiscent of those seen in jellyfish galaxies. However, the tantalizing alignment of these features with the merger axis is broadly incompatible with galaxies infalling into the cluster radially and, unlike jellyfish galaxies, we actually find consistent evidence against radial metallicity gradients. We can draw comparisons between detailed studies of infalling galaxies in local clusters. For example, Chemin et al. ( 2006 ) conducted a detailed kinematic analysis of 30 typical spiral galaxies in the 1.2 × 10 15 M ⊙ Virgo cluster, located at a distance of just ∼16.5 Mpc. The bulk of their sample is located outside the core of the cluster, specifically at relative velocities and radii larger than our sample (see the phase–space diagram in Figure 5 ). Chemin et al. ( 2006 ) found evidence for disturbed H α kinematics, but the offsets between the peak of the stellar light and the H α are of the order of 0.2–0.4 kpc, which is smaller than what we observe in our galaxies (0.6–1.9 kpc). Outside of its core, the Coma cluster (7 × 10 14 M ⊙ , located at ∼100 Mpc) is dominated by disturbed galaxies with tails of H α emission (Gavazzi et al. 2018 ). Three Coma galaxies are located to the cluster center as close as our galaxies Sausage 6–9 (Figure 5 ). However, these three galaxies are undergoing extreme ram pressure. With only one galaxy powered by SF and two by AGN, there is little to no H α within the stellar disk and the bulk of H α is found outside the galaxies, streaming out in long tails (Yagi et al. 2010 ). Observations of both Coma and Virgo indicate that infalling galaxies can present tails of H α emission and perturbed kinematics, but the H α gas is removed from the outside in, while the peak of the emission, close to the nucleus of the galaxies does not get displaced. Figure 5. Phase–space diagram for the cores of the Sausage, Virgo, and Coma clusters, highlighting kinematically disturbed H α galaxies. We show our five galaxies with respect to the properties of the closest subcluster (as derived from weak lensing and dynamics in Jee et al. 2015 ), together with data from Chemin et al. ( 2006 ) on the Virgo cluster and Gavazzi et al. ( 2018 ) for the Coma cluster. Our galaxies are embedded deep within the ICM. Unlike galaxies at similar cluster-centric radii and relative velocities in Coma and Virgo, which are almost completely devoid of H α within the disk, the Sausage galaxies show H α throughout the stellar disk, with large offsets between the peak of H α and the stellar light. Considering that our galaxies are deeply embedded in the hottest parts of the ICM (see
Paper: 2019MNRAS.482..240K (fulltext)
role of the hydrodynamic effects, we compare a 3D hydrodynamic simulation, published in Nawaz et al. ( 2016 ), to a ballistic jet model (Gower et al. 1982 ) for the precessing jet source Hydra A in Fig. A1 . Model parameters are given in Table A1 . It is clear that even for this comparatively low-power jet, the curvature induced by precession dominates the observational appearance for most of the region where the jet is observed. It is therefore reasonable to take curvature as signature for precessing jets. Figure A1. Different methods of modelling the precessing jet in Hydra A. (a) VLA image of Hydra A. (b) Hydrodynamic simulation of a jet with parameters empirically matched to Hydra A. (c) Ballistic jet model (Gower et al. 1982 ) for Hydra A. Even though Hydra A has a weaker jet than the powerful jet sources discussed elsewhere in this article, the jet path is over most of its length well modelled by the hydrodynamic simulation as well as the ballistic model. This suggests that precession effects are indeed observable in real radio sources. Panels (a) and (b) were adopted from Nawaz et al. ( 2016 ). Table A1. Parameters for the jet models compared in Fig. A1 . a Ω t ref in Gower et al. ( 1982 ). APPENDIX B: RADIO MAPS FOR THE COMPLETE SAMPLE We provide here all the radio maps of our complete sample. Shown is Stokes I in Jy beam −1 . The mapping of the flux levels onto the colour map is non-linear and has been adjusted carefully for each map to ensure lobe and jet structures are well visible. Equatorial coordinates are given on the maps. Observing frequency, resolution, redshift, and precession indicators are given in the individual figure captions. The features of precessing jets are indicated on each map if applicable. Figure B1. 3C 20, 8.41 GHz, resolution: 11, redshift: 0.174. C: Curved jet structure on the eastern side. E: Lobe brightness asymmetry/jet at edge of lobe on the western side. R: Multiple terminal hotspots on the eastern side; wide terminal hotspot on the western side. S: S-symmetry. Additionally, interaction with ambient medium. Figure B2. 3C 22, 8.56 GHz, resolution: 075, redshift: 0.938. C: Curved jet. E: Misaligned lobe axis on the eastern side. Lobe brightness asymmetry on the western side. S: S symmetric jets. Figure B3. 3C 33.1, 4.85 GHz, resolution: 2948 × 2472, redshift: 0.18. C: Curved eastern jet. E: Misaligned lobe axis on the eastern side. S: S-symmetric jets. Jet enters lobe on southern side. Jet bends with brightening, possibly related to jet–cloud interaction. Southern tip-of-lobe hotspot lies along the continuation of inner southern jet. The innermost part of the jets is aligned with the lobe axis [solid black line, position angle (jet side) indicated]. Figure B4. 3C 41, 8.46 GHz, resolution: 02, redshift: 0.795. C: Curved southern jet. E: Misaligned lobe. R: Wide terminal hotspots on both sides. S: S-symmetric jet–hotspots. Southern jet (yellow dotted line) misaligned with lobe axis (solid white line) by