adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...865....8C (fulltext)
line properties.) In our initial line list, we use the former model, adopting the tuned of −0.224 from DM12 . This blended feature is addressed more fully in the next section. We use this initial line list to generate synthetic spectra of the benchmark stars without any Be to compare to the observed spectra, as shown in Figure 3 . The solar spectrum clearly requires the inclusion of Be absorption to fit the observed spectrum at the location of both Be ii lines. In the modeled solar spectrum, the contentious Mn i line at λ 3131.037 Å is clearly visible in the blue wing of the Be line at λ 3131.065 Å. The Mn i line is stronger in the red giant spectra and is well fit with the larger adopted from DM12 . All three benchmark observations show excess absorption predicted by the models, especially below λ 3130.0 Å. Most concerning is that the Arcturus model is a poor fit to the spectrum in the contentious blue wing of the λ 3131.065 Å Be line. Some tuning of the gf -values is therefore required. 4.2. Tuning the Line List The poor fit seen at λ ≲ 3129.6 Å has little effect on the Be abundance analysis, but we did trace the problem to two Fe lines: λ 3129.102 Å (Peterson Kurucz 2015 ) and λ 3129.619 (VALD K14). Removing these lines (which we achieved by changing the by the arbitrarily large factor of −10) produces a much better fit to the spectrum. The focus of the remainder of astrophysical tuning is on a narrow wavelength region around the redder Be ii line, which is the more isolated of the two lines. Despite the relative isolation, there are still significant blends including the contentious Mn i noted earlier as well as a contribution in the red wing of the line by Zr i at λ 3131.109 Å. In the solar spectrum, this line is weak (∼1.5 mÅ equivalent width), but the equivalent width is at least an order of magnitude larger in red giant stars. Furthermore, Figure 3 shows that a nearby Cr i line is poorly fit in the solar spectrum and is nearby enough to potentially affect the neighboring Be blend. The synthesized Cr i line at λ 3131.212 Å is too weak in the spectra of all three benchmark stars. Its was increased by 0.60 dex in order to match the observed solar spectrum. Figure 3. Comparison of synthetic spectra generated with the newly compiled line list (solid lines) compared to observations (dotted lines), assuming no Be in the stellar atmospheres. The dashed vertical lines show the location of the Be ii lines, and the dotted–dashed vertical line marks the Mn i line at λ 3131.037 Å, using the tuned of −0.224 from DM12 . The solar spectrum (top, archival Subaru spectrum of Vesta) is clearly not well fit when Be is absent from the model. The spectrum of Pollux (middle) shows a generally good fit at wavelengths above ∼3130.5 Å, whereas the model for Arcturus (bottom) has conspicuous missing absorption just blue ward of the λ 3131.065 Å Be ii line. As we considered additional changes to the line list in the blended Be feature, we generated spectra
Paper: 2020MNRAS.499.1640M (fulltext)
Ly α power spectrum. Although the impact of inhomogeneous reionization in the Ly α forest is slightly larger than in our previous work, if one takes into account the statistical error in the eBOSS measurement (plotted in Fig. 2 ) and the systematic error in modelling reionization, it is currently challenging to use the 1D Ly α power spectrum to extract information from the reionization epoch via the impact of inhomogeneous reionization in the Ly α forest. However, in future work we will investigate this possibility. On the other hand, the expected effect of patchy reionization in the 3D Ly α forest power spectrum are significantly larger. We find that the percentage deviation is in the order of tens of per cent at z obs = 4.0 (e.g. for our model R2), and in the order of a few per cent at z obs = 2.0 (e.g. for our model T3). Our results for the survival of these thermal relics from reionization to low redshift might seem unconventional . However, the reason we obtain such strong ripples in the Ly α forest power spectra is because we are able to carefully track the small-scale structure prior to reionization. In particular, mini-voids at reionization get later compressed to mean-density while they simultaneously get reionized to high entropy. This HEMD gas gets usually heated to ∼3 × 10 4 K in our simulations and remains above the temperature–density relation even at z 4. As a result the temperature–density relation of the IGM becomes bimodal with the usual low-entropy phase and this high-entropy phase. The ultimate fate of the HEMD phase is to merge with the low-entropy mode; however, it takes cosmological time-scales for the merging to complete (see figs 4 and 5 of Hirata 2018 ). We stress here that it is really both the patchy nature of reionization , and the HEMD gas, i.e. ∂ψ/∂ z in equation ( 3 ) together that result in the memory of reionization in the Ly α forest computed in this work. To illustrate the former point, we turn-off the inhomogeneous recombination prescription of Sobacchi Mesinger ( 2014 ) in 21cmFAST , and consider the resulting, faster, reionization. Our results for the 3D Ly α power spectrum are reduced by a factor of 2 at z = 2.0 and ∼1.8 at z = 4.0. This decrease of deviations in the Ly α power spectrum is expected since a faster reionization model will have a narrower range of redshifts for the peak of that contribute the most to the integration in equation ( 3 ). Regarding the importance of the HEMD gas, on the other hand, we artificially limit the impact of the HEMD gas by only considering our transparency results for higher redshifts, say z re = {8.0, 9.0, 10.0, 11.0, 12.0}. Note that patches of the sky that reionize earlier will have the high-entropy gas merge with the low-entropy gas sooner, thus recovering the temperature–density relation of the IGM faster. In this case we obtain a decrease in the 3D Ly α results with a factor of 2.2 at z = 2.0 and 1.8 at z = 4.0. In Fig. 3 , we plot the k -dependence of both the
Paper: 2017ApJ...836...61M (fulltext)
a complete exploration of the parameter space, we first show a comparison between the model cumulative halo distributions and the observed number density of galaxies in the illustrative case of a sterile neutrino with (the candidate origin of the potential 3.5 keV line) in the limit of small Higgs portal coupling . In such a freeze-in limit (see Section 3 ), the scalar mass m S and the Yukawa coupling y will be related for any given value of , thus reducing the parameter space to be explored. In Figure 3 (left panel) we show the cumulative halo distributions in such a regime for a scalar mass of and for different values of the Yukawa coupling y , and we compare them to the observed number density of high-redshift HFF galaxies. The strong dependence of the maximum predicted number density on y is highlighted in the right-hand panel of Figure 3 , which shows that present data allow us to set a constraint at the 2- σ confidence level. Figure 3. Left panel: the cumulative mass functions computed at z = 6 for SD sterile neutrino models with and , for different values of the Yukawa coupling y (shown by the labels on the right) in the freeze-in limit. The shaded areas correspond to the observed number density of galaxies with within within the 1- σ , 2- σ , and 3- σ confidence levels. Right panel: the maximum value (including the theoretical uncertainties) of the predicted number density of DM halos at z = 6 for the case with as a function of the Yukawa coupling y . The upper shaded areas represent the observed number density of galaxies with within the 1- σ , 2- σ , and 3- σ confidence levels. We then extend our exploration to cover the whole parameter space of SD production models for sterile neutrinos. With this aim, we consider a grid of λ and y values for six different values of the scalar mass . The latter are chosen to cover the three different regimes where different production channels are dominant for the production of scalars from their coupling to the SM particles (see König et al. 2016 , and Section 3 ): the light scalar case , the intermediate case , and the heavy scalar case (in terms of the Higgs mass m h = 125 GeV). These different regimes have a rather strong impact on the distribution function of the scalar which directly translates into that of the sterile neutrino and, hence, on the resulting power spectrum. For each value of m S , we compute the power spectrum corresponding to each point in the plane as described in Section 3 . Then we represent in Figure 4 , left panel, the regions of the parameter space consistent with the galaxy number densities measured in the HFF (). These regions clearly split into a freeze-out (for ) and freeze-in (for ) family. For the freeze-out family, decreasing the scalar mass m S leads to a tighter bound on y , while yielding an approximate lower bound of for the Higgs portal coupling. For the freeze-in family, decreasing the scalar mass m S pushes the admitted values of λ to progressively
Paper: 2021MNRAS.505.1083G (fulltext)
with the wind. Fig. 15 shows the time evolution of the WIM for all simulations with self-gravity. We note that the main parameter that changes the total amount of the WIM is not the density of the cloud but mainly the magnetic field properties in the wind. All winds with a perpendicular field keep the clouds mostly intact due to the magnetic draping effect and reduce the mixing of warm atomic and hot ionized gas at the cloud–wind interface. The resulting mass in the WIM converges after about 10 Myr of evolution to give . For the non-magnetic wind, the masses converge after at a value of . The highest masses in the WIM are reached for with about . We highlight that this is 60 per cent of the initial mass of the low-density cloud. The parallel field configuration delays or completely suppresses the contraction perpendicular to the magnetic field and thereby maximizes the mixing that leads to the WIM. Figure 15. Time evolution of the warm ionized gas. The total mass mainly depends on the the wind properties rather than the cloud mass. We show only the low density up to the point of their destruction and their transport through the right-hand boundary. We find the lowest mass in , followed by the non-magnetic wind. For , the warm ionized mass is largest. 8 DISCUSSION AND CONCLUSIONS 8.1 Cloud survival For clouds exposed to a wind, the question arises whether the clouds are fed by condensation of wind material and grow, whether they are effectively unaltered, or whether the wind eventually disrupts the cloud. In absence of stabilizing mechanisms, the clouds are likely to be destroyed by the efficient growth of the Rayleigh–Taylor instability and the shear-driven Kelvin–Helmhotz instability (Cooper et al. 2009 ; Scannapieco Brüggen 2015 ; Schneider Robertson 2015 , 2017 ). Stabilizing mechanisms can be the cooling and a resulting contraction that reduces the effective interaction cross-section with the wind. It could also be the magnetic field configuration, which suppresses instabilities or – mostly for dense clouds – self-gravity, which leads to faster contraction and even collapse. Cooling can lead to fragmentation of the clouds (McCourt et al. 2018 ) but can also help to stabilize them by suppressing the mixing (Sparre et al. 2019 ). Stabilized condensations can grow in mass due to newly accreted gas from the wind (Marinacci et al. 2010 ; Armillotta, Fraternali Marinacci 2016 ; Armillotta et al. 2017 ; Gronke Oh 2018 , 2020 ). Magnetic fields help to keep the gas confined and increase the acceleration efficiency (McCourt et al. 2015 ; Banda-Barragán et al. 2016 , 2018 , 2019 ; Grønnow et al. 2018 ; Sparre, Pfrommer Ehlert 2020 ). All of the above-mentioned simulations have focused on overall lower densities and higher temperatures compared to this study. However, the surviving fragments develop strong density fluctuations, which are comparable to the initial configurations in our models. We evolve the systems for a time-scale of a few
Paper: 2019ApJ...877L..34E (fulltext)
[4.5] mean magnitudes of GM Aur taken in E4 to E8. There is a significant increase of emission in both Spitzer bands in E7. MIR emission shortward of ∼20 μ m has been attributed to optically thin dust within ∼1 au of GM Aur (Calvet et al. 2005 ; Espaillat et al. 2010 ). Espaillat et al. ( 2011 ) found that GM Aur’s Spitzer spectra varied by about 10% and attributed this to variability in the mass of dust in the inner disk. Using NASA IRTF SpeX spectra covering 1–5 μ m, Ingleby et al. ( 2015 ) found that the emission varied and also attributed this to dust-mass changes. The Spitzer spectra were not coordinated with indicators, but the SpeX data were taken within a day of HST UV data. With three epochs of HST data, Ingleby et al. ( 2015 ) found that and the IR emission are roughly the same between their first two epochs (taken about 1 week apart), but both and the IR emission decreased in the third epoch (taken about four months later). Their first and second epochs had of dust in the inner disk and the third epoch had of dust in the inner disk. Here we have only two MIR magnitudes, which is not ideal for constraining the optically thin dust model. However, we can estimate the amount of dust in the inner disk in E4 through E8 by scaling with the results of Ingleby et al. ( 2015 ) under the assumption that only the dust-mass changes (Espaillat et al. 2011 ; Ingleby et al. 2015 ). The SpeX spectra cover 3.6 μ m, but not 4.5 μ m where atmospheric telluric lines are strong. Convolving the SpeX spectra with the IRAC [3.6] bandpass, we determine that the SpeX flux in the first epoch of Ingleby et al. ( 2015 ) corresponds to an IRAC [3.6] magnitude of about 8.1, which is roughly consistent with our measurements in E4, E5, E6, and E8. Therefore, we assume that the mass of dust in the inner disk in these four epochs was about the same as that seen in the first epoch of the Ingleby et al. ( 2015 ) observations. For E7, if we scale the optically thin model from the flux seen in the first epoch of the Ingleby et al. ( 2015 ) observations to the flux seen in E7, we estimate a dust mass of , corresponding to a factor of ∼2.5 change. 3.2. Centimeter Continuum Emission In Figure 2 (left), we compare the spatial brightness distribution of our 2018 data to 2015 data from Macías et al. ( 2016 ). We combine the three epochs of 2018 VLA data in this figure to achieve a high enough sensitivity to use robust weighting ( r = −0.5) and a similar angular resolution to the 2015 data. There is a seeming change in the morphology of the jet emission between 2015 and 2018. The upper right emission that Macías et al. ( 2016 ) attributed to a jet appears to be present only in the 2015 data. The resolution and sensitivity of both maps are similar, suggesting that the difference in morphology is real. However, our resolution is limited, so higher spatial resolution observations are needed to confirm this. Figure 2. Left: superposition of 3 cm VLA observations of GM Aur from
Paper: 2018ApJ...868..131Y (fulltext)
and potentially applicable for an empirical fitting of particle distribution function in various physical situations, it turns out that such a model has a fundamental significance in the context of the self-consistent Langmuir turbulence theory as discussed by Yoon ( 2012 , 2014 ). The purpose of the present paper is to reformulate the modified κ -model of Scherer et al. ( 2017 ) in light of the quasi-equilibrium of turbulent Langmuir fluctuation and the related electron κ -like distribution. That is, the present paper aims to show that the modified κ -model of Scherer et al. ( 2017 ) generalizes the model solution discussed by Yoon ( 2012 , 2014 ), thereby resolving the undesired feature of “infrared catastrophe” associated with the steady-state Langmuir turbulence spectral intensity. The organization of the present paper is as follows: Section 2 discusses the generalized theory of turbulent quasi-equilibrium between the Langmuir turbulence and suprathermal electron distribution, which leads to the regularized κ -model of Scherer et al. ( 2017 ). In the last section we discuss the physical origin of the exponential cutoff factor and also discuss the possible application of the present finding. 2. Equilibrium State between Langmuir Fluctuations and Electrons The fundamental aspect and details of the theory of the quasi-equilibrium state between Langmuir fluctuations and suprathermal electrons are already discussed elsewhere (Yoon 2012 , 2014 ; Livadiotis 2017 ), so we will not repeat the derivations of the equations here. Essentially, the theory boils down to three equations. The first equation represents the formal solution to the steady-state particle (electron) kinetic equation,where f e ( v ) represents the isotropic electron velocity distribution function; m e is the electron rest mass; I L ( k ) corresponds to the steady-state Langmuir fluctuation intensity, k being the magnitude of the wave number, which is being replaced by by virtue of the resonance condition; and is the plasma frequency, n and e being the ambient plasma number density and unit charge, respectively. The second equation is the steady-state wave kinetic equation for Langmuir fluctuations that are in linear wave–particle resonant interaction with the particles (electrons),whereis the Langmuir wave dispersion relation,being the electron thermal spread. Here, T e represents the Maxwellian temperature for the electrons. That is, T e is a macroscopic parameter had the electron distribution function been given by the Maxwellian distribution function. Of course, when the underlying distribution function is non-Maxwellian, then T e is not the real temperature, which is well known, and has also recently been systematically discussed by Nicolaou Livadiotis ( 2016 ). Consequently, by T e , we mean the electron temperature in a loose sense. The third equation represents the steady-state turbulent interaction between Langmuir fluctuations and thermal ions (protons), Of the
Paper: 2021A&A...645A..95H (fulltext)
to 0.015 mag. Again, we used the Kron magnitudes for the subsequent analysis. An example of the excellent HEROES image quality, and also the various artefacts produced by bright objects in the field, is given in Fig. 3 showing the shell around the planetary nebula NGC 6543 (Cat’s Eye Nebula) that was ejected by its predecessor red giant star. The actual PN in the centre of the image is completely over-exposed. The high density of faint background objects shows the excellent sensitivity of the data. The NEP lies at relatively low Galactic latitudes, and therefore contains a rather large number of overexposed foreground stars showing up in this image (in cyan). In order to obtain reliable optical photometry for brighter objects, which are saturated in the HEROES HSC images, we also made use of the SDSS DR16 ( Ahumada et al. 2020 ), the Pan-STARRS DR1 DR2 ( Chambers et al. 2016 ), and Gaia DR2 ( Gaia Collaboration 2018 ) catalogues. We also cross-correlated our samples with the far-UV and near-UV photometry from the GALEX surveys ( Martin et al. 2005 ). Fig. 3. False colour HEROES image of the shell around the planetary nebula NGC 6543 (Cat’s Eye Nebula) close to the NEP. Blue represents the HSC g -band image and predominantly shows the green [OIII] line. Green represents the HSC r -band and predominantly shows H α . Red corresponds to the 4.5 μ m Spitzer image and mainly shows dust emission. 4.2. Mid-infrared observations of the HEROES field For photometric information in the 3−25 μ m band across the whole HEROES field we used the WISE all-sky survey catalogues. For the shorter wavelength W 1 (3.4 μ m) and W 2 (4.6 μ m) bands we used the CatWISE2020 all-sky catalogue ( Eisenhardt et al. 2020 ) (see also Marocco et al., in prep.), containing about 1.89 billion objects observed by the Wide-field Infrared Survey Explorer (WISE and NEOWISE), both of which have higher sensitivity and higher angular resolution than the original ALLWISE catalogue ( Cutri et al. 2013 ). The CatWISE2020 catalogue in the HEROES field contains 2.4 million objects and gives fluxes in the W 1 and W 2 band, which we converted to the AB magnitude system. For the longer wavelength W 3 (12 μ m) and W 4 (22 μ m) bands we used the original ALLWISE catalogue ( Cutri et al. 2013 ), again converting to AB magnitudes. At the faintest magnitudes the CatWISE2020 and ALLWISE catalogues are, however, severely confusion limited. In the centre of the HEROES field, we therefore also made use of the Spitzer Observations of the Euclid Deep Field North (Moneti et al., in prep.). The ESA Euclid mission is making great progress towards its launch, scheduled in 2022. Euclid ’s main goal is to survey a large fraction of the sky and image billions of galaxies to investigate dark energy and dark matter over the history of the Universe. Roughly 10% of the observing time will be dedicated to the Euclid Deep Fields, repeatedly observing three specific areas of the sky, covering a total of
Paper: 2021MNRAS.503.5425B (fulltext)
field, while higher density regions become dominated by an interplay between the action of gravity and magnetic fields. Similarly to the left-hand panel of Fig. 11 , the right-hand panel shows the HRO curves for two self-gravitating models, and . This was evaluated over the last output of these models, when t = 0.3 t ff (see the right-hand side of Fig. 1 which shows the filamentary 3D distributions for these models). Compared to the models that do not include self-gravity, there is an enhancement in the number of regions perpendicular to the magnetic field at higher densities. With the action of gravity, even the super-Alfvénic model shows a change in the number of counts of cos φ = ±1.0. The critical density (vertical green dashed line in Fig. 12 ) seems to be related to the densest bins, for models with sonic Mach numbers and 7.0. There is a jump in the values of density between the penultimate and last points in the diagrams that comes from the density range considered in the bins (as can be seen in Fig. 11 , the 20th bin has a wider range of densities compared to the 19th). In the super-Alfvénic case, the penultimate point is very close to ρ c for all times considered, while in the sub-Alfvénic case the penultimate point approaches ρ c as the system evolves. Tracing the critical density of a system using the PRS is an interesting possibility, and the exact relation between the two can be further explored following the evolution of the alignment between structures and the magnetic field at smaller scales and at latter times of the collapse. However due to the lack of an adaptive mesh with increasing resolution in the densest regions in our models, this is out of the scope of this work. Still, the results discussed up to this point are very similar to what was shown in the previous section for models without self-gravity. We note that self-gravity does increase the number of denser regions where is parallel to the gradient of density in the sub-Alfvénic models, but it has little or no effect when it comes to the simulations with . At smaller scales this is probably not true, since we expect that magnetic fields should be brought along with the fluid during collapse, at some point these cores must become sub-Alfvénic and once again would influence how the gas collapses. However, the simulations do not have enough resolution to follow the process up to this point. In order to compare the simulations with observations we need once again to integrate along a defined LOS. We will follow same directions used in Section 3.1.1 . Figs 13 and 14 show the PRS analysis ( Z 2D ) for the integrated density (column density) distribution along the three different LOSs. As in Figs 8 (for initial ) and 9 (for initial ), the left-hand column represents the LOS perpendicular to the initial magnetic field, the middle column shows Z 2D for a LOS making an angle of 45° with respect to the initial field, and the right-hand column is for a LOS parallel to the