adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...594A..88D (fulltext)
Chandraobserved 4C +74.26for 70 ks in total using the HETGS in combination with the ACIS detector. The total exposure time was split into two observations that were taken two days apart. For both Obs-ID 4000 and 5195 we retrieved the Medium (MEG) and High Energy Grating (HEG) spectra and their respective response matrices from the tgcat 1 archive. We further treated these spectral products with the CIAO tools (version 4.6). For each observation and for both HEG and MEG, we combined the first positive and negative spectral order using the CIAO script add_spectral_orders. Hence, we fitted jointly HEG and MEG (allowing a free intercalibration factor) with a simple phenomenological power law to check for variability between the two observations. The fitted slopes (, ) and normalizations (Norm, Norm) were very consistent with each other (see below for a physically motivated fit). Therefore, we were able to sum the spectra of individual observations into a single spectrum to improve the signal-to-noise ratio. We did this using the CIAO script add_grating_spectra. We reduced the raw XMM-NewtonObservation Data Files (ODF), available at the ESA archive 2 , using the Science Analysis Software (SAS, version 13) and the HEASARC FTOOLS. We created calibrated EPIC-pn event files selecting only the unflagged single events. To check the time stability of the background we used the light curve in the hard 10–12 keV band, which is background dominated. A high level of background light due to soft proton contamination is evident towards the end of the observation. Thus, we cleaned the event file using a time filter, following the same procedure explained in Di Gesu et al. (2013) . For RGS-1 and RGS-2 we created calibrated event files and background light curves taking the background from CCD 9. The RGS background light curve was quiescent. Next, for all the instruments, we extracted the source and background spectra and we created the spectral response matrices. Finally, we extracted the source count rate in all the available OM filters, namely ( Å), ( Å) and ( Å). Using the interactive SAS tool omsource, we computed the source count in a circular region centered on the source coordinates and with a radius of 6 pixels. For the background we used another circular region of 12 pixels, free from other sources and instrumental contamination. We converted the count rates to fluxes using the standard conversion factors provided in the SAS watchout web page 3 . Hence, assuming , we corrected all the fluxes for the Galactic reddening ( =0.39, Schlafly Finkbeiner 2011 ). For the correction, we used the IDL routine ccm_unred, which dereddens a user-defined vector of fluxes using the Galactic extinction curve of Cardelli et al. (1989) . Fig. 1 Spectral energy distributions for 4C +74.26. Filled circles: OM fluxes corrected for the Galactic extinction. Open squares: X-ray intrinsic continuum obtained from a phenomenological fit of the EPIC-pn spectrum. Fig. 2
Paper: 2018ApJ...867..127O (fulltext)
suppression of gas inflow into a massive core (Ikoma Hori 2012 ). For example, high opacities of dust grains in a disk reduce accumulation of a disk gas by a massive core (Lee et al. 2014 ; Lambrechts Lega 2017 ). Gas dynamics around a planet embedded in a disk would be another adjustment knob for the gas flow. Ormel et al. ( 2015 ) found that an isothermal gas flow from high altitude might escape from the gravitational sphere of a planet before accreting onto it. A rapid recycling of a disk gas regulates gas accretion onto a massive core, whereas in a non-isothermal case this recycling process would be limited by an entropy gradient between the ambient disk gas and the atmosphere (Cimerman et al. 2017 ; Kurokawa Tanigawa 2018 ). According to recent magnetohydrodynamic (MHD) simulations, the gas flow structure in a disk has two layers, which is similar to the layered accretion model (e.g., Gammie 1996 ). There can be a rapid gas flow driven by a disk wind near the disk surface due to the effect of magnetic braking (e.g., Bai Stone 2013 ; Suzuki et al. 2016 ), while the accretion flow would be weaker at the midplane of the disk. In fact, global ideal MHD simulations observed a supersonic accretion flow in the upper layer of a disk (Zhu Stone 2018 ). A rapid gas flow near the disk surface may put a brake on runaway gas accretion onto a planetary core. In this paper, we have investigated how a time-dependent layered accretion in a disk affects formation of super-Earths having H 2 /He atmospheres. We have performed N -body simulations for formation of close-in super-Earths, coupled with disk evolution based on MHD simulations and gas accretion onto a planetary core. Note that hydrogen can be generated from secondary processes such as degassing via oxidation of metallic iron by water (Abe et al. 2000 ) and H 2 O photolysis and escape from a planet via stellar X-ray and extreme ultraviolet (XUV) irradiations (e.g., Lammer et al. 2013 ) and interactions between the stellar plasma and a hydrogen atmosphere (e.g., Kislyakova et al. 2013 ). However, a long-term evolution of a super-Earth’s atmosphere after the formation stage (≳10 Myr) is beyond the scope of this paper. The paper is structured as follows: We present detailed descriptions of models in Section 2 . Results of gas accretion rates onto planetary cores and their orbital evolution are shown in Section 3 . The radial dependence of the occurrence rate of gas giants and the validity of our model are discussed in Section 4 . We summarize this study in the last section. 2. Model 2.1. Disk Evolution For the evolution of a protoplanetary disk including effects of magnetically driven disk winds, we adopt the model of Suzuki et al. ( 2016 ), in which the diffusion equation is given bywhere Σ g , Ω, c s , and H are the gas surface density, the Keplerian angular velocity, the sound speed, and the disk scale height, respectively. The first two terms on the right-hand side induce radial mass accretion
Paper: 2019A&A...624A..85V (fulltext)
atmospheres with the classical 1D approach we can calculate abundance corrections and abundance errors. Given a measured LTE abundance, we define the abundance correction as the abundance difference compared to LTE, such that the non-LTE equivalent width matches the LTE equivalent width. 3 Results 3.1 Departure coefficients In order to understand the behaviour of the abundance corrections and , we can look at the variation of the departure coefficients, b ( τ R ) = n NLTE ∕ n LTE , for the 1D hydrostatic models and 2D models. For the first, b 1D values were calculated for the lower 3s 5 S and upper 3p 5 P levels of the O I 777 nm triplet in the 4D parameter space τ R , T eff , log g eff , ξ t . For each combination a cubic interpolation that splits the multi-dimensional case into a sequence of 1D interpolations was performed on the grid of 1D models to obtain departures from LTE as functions of the Rosseland optical depth. In the 2D models – due to the horizontal inhomogeneities produced by convection – departures from LTE b ( τ R ) are different for in- and outflows (e.g. Amarsi et al. 2016 ). In order to compare departures from LTE for 2D models with the 1D case, b 2D values for each individual 2D snapshot were horizontally averaged over surfaces of constant optical depth. Both 2D models predict a similar dependence of the abundance corrections on effective temperature. Since they are similar we focused on the nine-day model, and calculated median 2D departures from LTE. We plot the departure coefficients in Fig. 3 . Namely, the median 2D departures from LTE as a function of τ R and photometricphase for the lower and upper levels are shown by solid blue and red lines, respectively. One-dimensional interpolated b 1D departures from LTE are shown by dashed lines using the same colours for the levels. Qualitatively, the departures from LTE of 1D and 2D models show a similar dependence on the photometric phase. The lower and upper levels of the O I 777 nm triplet, namely 3s 5 S and 3p 5 P, are almost always overpopulated during the pulsating cycle. At photometric phases φ ph ≈ 0.3...0.8 the lower and upper levels are less underpopulated compared to the rest of the phases, due to the minimum on the light curve and effective temperature. Quantitatively, for this range of phases 1D and 2D models predict very similar optical depth dependences of the departures from LTE. As a result, the abundance correction is small. Moreover, the 2D non-LTE versus 1D LTE abundance correction turns out to be smallest, as shown in Sect. 3.5 . 3.2 O I 777 nm triplet profiles We illustrate the temporal evolution (over a pulsation cycle) of the normalised emergent LTE and non-LTE O I 777 nm triplet flux profiles in Fig. 4 for M3 and M9. The line profiles are shown without adding instrumental broadening or noise. This allows us to identify the occasional multi-component structure of the line profiles, for example at photometric phases φ ph = 0.299 and 0.318 for M3 and
Paper: 2021MNRAS.502.1623M (fulltext)
8 of Savino et al. 2019 ) is about 0.5–0.6 × 10 −4 M ⊙ yr −1 , in good agreement with the SFR of about 0.4 × 10 −4 M ⊙ yr −1 measured in our work. The both measurements show the comparable total stellar mass 3.13 × 10 6 M ⊙ (4.2 × 10 6 M ⊙ ). The bottom panel of Fig. 4 shows the stellar mass formed in the last 2 Gyr versus the galaxy absolute magnitudes. The percentage of the mass formed is small for all the objects. This plot could demonstrate how intensive is the residual SF in dSph galaxies depending on the degree of isolation of the object. Highly isolated galaxies clearly show recent residual SF (>10 per cent of the total stellar mass), while within the virial radius it is practically zero. And XVIII and Tucana are situated beyond the virial radius (but within the LG zero-velocity sphere). Following the logic of the above plot, they should be located between those two extreme cases. None the less, Tucana is again out of the trend, located among the galaxies inside the virial radius. It also could be an argument in favour that in the past this dwarf was among the closer satellites of M 31. Simpson et al. ( 2018 ) predicted quenching of SF in satellite galaxies of the LG from a suite of 30 cosmological zoom simulations of MW-like host galaxies. The authors found that systems within host galaxy R 200 are comprised of two populations: first infall and ‘backsplash’ galaxies that have had a much more extended interaction with the host. Backsplash galaxies that do not returned to the host by z = 0, exhibit quenching properties similar to galaxies within R 200 and are distinct from other external systems. The last scenario are well match with the SF properties of Tucana dSph. It should be noted that the results are affected by the well-known problem of age–metallicity degeneration in the process of SFH measurement, and some dependence of the results on the isochrone set used (Williams et al. 2017 ). Besides, the obtained CMDs are somewhat not deep enough, in the sense that the photometric limit is brighter than MSTO. At the same time, uniform data set and its homogeneous processing should reduce the influence of these factors on the results of our comparative analysis to a minimum. When analysing SFHs of dwarf satellite galaxies in the nearby galaxy groups (first of all, the LG), one should take into account, that even in a particular mass range and fixed environment, there is a large degree of scatter in the SFHs, frequently noted in various researches (see e.g. Garrison-Kimmel et al. 2019 ). The mentioned study has used a set of about 500 dwarf galaxies taken from FIRE-2 (The Feedback In Realistic Environments 2) simulations to explore how their SFHs vary with environment. They conclude, that the satellites ( d ≤ 300 kpc) tend to form their stars earlier than equivalent 300 d 1000 kpc dwarfs, consistent with the picture, where interactions with MW-mass hosts inhibit SF. A galaxy could loss its gas due to ram pressure effects and tidal
Paper: 2018MNRAS.478.4575L (fulltext)
of SNe Iax, which are in the range 12+log(O/H)=∼8.16–8.87 (Magee et al. 2017 ; Lyman et al. 2018 ), somehow lower than the solar abundance (Caffau et al. 2008 ). This indicates that the site of SNe Iax may have relatively lower metallicity compared with other subtypes of SNe Ia, but comparable to that of SNe II or SNe Ibc. Note that Lyman et al. ( 2018 ) recently carried out an analysis of metallicity for the host galaxies and explosion sites of a large sample of SNe Iax through both integral-field and long-slit spectroscopy and they also found that the SN Iax explosion site metallicity distribution is similar to that of core-collapse SNe. We also measure the fractional flux of the SN site, which is the fraction for which the total host light in a pixel is fainter than or equal to the light in the pixel at the location of the SN. This parameter can be used to trace the difference/similarity between luminosity at SN locations and the light of the hosts (Fruchter et al. 2006 ). As the fractional flux is independent of galaxy morphology and size, it is thus a better parameter to quantify the correlation between SN progenitor environments and the light of their hosts (i.e. Wang et al. 2013 ). Such an analysis can be performed for 21 SNe Iax of our sample by using the host-galaxy images from SDSS DR12 (Alam et al. 2015 ) and Panstarrs (Chambers et al. 2017 ); the results are also tabulated in columns (9)–(11) of Table 5 . The distribution of the fractional fluxes of our SN Iax sample in the ugr bands is shown in Fig. 14 . The fractional flux distributions of SNe Ia, SNe Ibc and SNe II are overplotted for comparison and the measurements of these samples are taken from Wang et al. ( 2013 ). One can see that the progenitor populations of SNe Iax belong to the brighter regions of the host galaxies, similar to the case for SNe Ibc (especially in the g and r bands), suggesting that SNe Iax may arise in larger star-forming regions that produce more massive stars. This is also evidenced by the Kolmogorov–Smirnov (KS) test results, as tabulated in Table 6 . One can see that SNe Iax and SNe Ia trace the host-galaxy light differently, with a very low probability (i.e. P = 0.04 in r ) that they come from the same stellar populations. SNe Iax also show some differences from SNe II in the light distribution of progenitor populations, with KS test results of P = 0.13 in g and P = 0.17 in r , respectively. In the u band, however, we notice that SNe Iax seem to show large differences from all kinds of SNe in the fractional flux distribution. This is likely related to the relatively larger uncertainty in the SDSS u -band photometry. As for the comparison between SNe Iax and SNe Ibc, P values of 0.33 obtained in the g band and 0.37 in the r band indicate that the hypothesis that they share a similar fractional flux (and hence environment) distribution cannot be rejected. Figure 14. Histogram distribution of the fractional flux of the host-galaxy light at the location
Paper: 2018MNRAS.481.3382N (fulltext)
× 5 pixel 2 raster pixd (after neighbour subtraction) centred on the target stars, and PSF( i, j ) is the value of the local PSF-model expected in the pixel ( i, j ). Introduced by Bedin et al. ( 2008 , see also Bedin et al. 2009 , 2010 ; Simioni et al. 2018 ), the parameter RADXS is a shape parameter that allows us to distinguish sources that deviate from a PSF shape. It is a comparison between the measured flux of the source outside the core (in an annulus 1.0 r 2.5 pixels) and the flux expected from the PSF-model. For RADXS >0, the source is broader than that expected from the model (i.e. galaxies), while for negative values of RADXS the source is sharper than the PSF (i.e. cosmic rays and artefacts). Finally, the KS2 routine gives the number of images in which a star is found ( N ) and the number of consistent measurements of the star used to compute its average position and flux ( N ). 3 COLOUR–MAGNITUDE DIAGRAMS In Fig. 2 , we show an example of selection of well-measured stars for the case of NGC 6752 and the photometric method 1. Similar plots can be made for the methods 2 and 3. The top and middle panels of Fig. 2 show the selection of the stars based on the distribution of the photometric errors ( RMS , left-hand panels), the quality of fit ( QFIT , centre panels), and the shape of the sources ( RADXS , right-hand panels), in the case of F275W (top panels) and F336W (middle panels) filters. The selection based on RMS and QFIT are performed as done by Milone et al. ( 2012 ): we divided the distributions in 12 magnitude bins and, in each bin, we calculated the 3.5σ-clipped average of the magnitude and of the parameter, where σ is the standard deviation associated with the average value in the given bin. We added to the mean parameter of each bin 3.5 × σ, and we linearly interpolated these points (green line). We excluded all the points above (in the case of the RMS ) or below (in the case of the QFIT ) the green line (azure points). For the sharp parameter, we selected all the stars that satisfy the condition: −0.15 RADXS +0.15. Bottom panels show the m F275W versus ( m F275W − m F336W ) colour-magnitude diagram (CMD) for the stars that pass the selection criteria in both filters (left-hand panel) and for the stars that were rejected in at least one filter (right-hand panel). In red, the stars that are saturated in at least one filter and that have been recovered from the ‘first-pass’ photometry. From the CMDs, it is clear that many stars with poor photometric quality are rejected. Figure 2. Procedure adopted for selecting well-measured stars in the cluster NGC 6752. Top and middle panels show the selection of the stars based on RMS (left-hand panels), QFIT (central panels), and RADXS (right-hand panels) parameters: in azure the stars that are rejected. Bottom panels show the m F275W versus ( m F275W − m F336W ) CMD cleaned by rejected stars in both filters (left-hand panel) and the CMD of the stars rejected in at least one filter. Red
Paper: 2020ApJ...903...11J (fulltext)
solves the MHD process of an isolated, twisted magnetic flux tube, initially placed in the convection zone, buoyantly rising into the atmosphere. The full set of basic resistive, adiabatic MHD equations in conservative form are solved by a finite difference scheme with the spatial derivatives by the fourth-order central differences and the temporal derivatives by the four-step Runge–Kutta scheme. Periodic boundary conditions are applied for the x -direction and symmetric boundaries for both the y - and z -directions. The initial background atmosphere of the simulation is gravitationally stratified, which consists of an adiabatically temperature-gradient convection zone (, where km is the length unit of the FES model), the cool isothermal photosphere/chromosphere (), and the hot isothermal corona (). The flux tube is initially placed at with the form of and , where r is the radial distance from the tube’s axis, the radius, kG the axial field strength, and the twist intensity (the negative sign indicates a left-handed twist). The middle of the tube is made buoyant by a small density deficiency and the tube started to emerge due to its own buoyancy. As the time-dependent bottom boundary data for the data-driven model, we extracted the time sequence of 2D data at fixed heights from the FES model. To test the behavior of the data-driven model with different inputs, data sets at three different slices from the FES model are used, including (referred to Z00 , representing the photosphere), ( Z10 , i.e., the chromospheric layer), and ( Z20 , i.e., the coronal base). The slices were sampled at every (where s is the time unit of the FES model) from 0 to 500. The slices spanned over , resolved by the uniform grid spacing of 200 × 200, thus the resolution is . The data sets include three components of magnetic and velocity field, i.e., , and v z . As we have mentioned in Section 1 , the Lorentz force in the boundary data is an important factor affecting the output of the data-driven model. Thus we quantitatively assess the Lorentz force as well as torque in the data of different layers. The sum of Lorentz force (and torque ) in a volume can be expressed by surface integral (Aly 1989 ; Sakurai 1989 ), and by neglecting contribution from the side and top boundaries, they can be written as integrals of the 2D data sets:andFollowing Duan et al. ( 2020 ), to assess the data with respect to the force-free condition, the forces can be normalized by the integrated magnetic pressure force F p that is given byand the normalized forces are ratios defined as , , and , respectively. Similarly, the normalized torques are defined as , , and , where T p is magnitude of the net torque induced by only the magnetic pressure force, whereFor a field being close to force-free, it must have ratios much less than unity, i.e., and . Metcalf et al. ( 1995 ) suggested that the magnetic field can be considered as force-free if the normalized forces are all less than or equal to
Paper: 2019ApJ...876...84X (fulltext)
consistent with previous studies that show an anticorrelated relationship between the magnetic field strength and the ionopause altitude (Elphic et al. 1980 ; Russell Vaisberg 1983 ; Phillips et al. 1985 ; Angsmann et al. 2011 ). The ionospheric thermal pressure balances the magnetic pressure difference at the ionopause. To sustain stronger magnetic pressure, it needs higher thermal pressure, and thus higher plasma density in the ionosphere. Therefore, the ionopause would be pushed down to a lower altitude with higher plasma density. However, it is important to point out that this process is based on an assumption that the ionization of the ionosphere is not significantly changed. Figure 4. Left to right: the magnetic field strength near the periapsis prior to, during, and after the passage of the ICME on November 4, November 5, and November 6, respectively. The IMA measurements of heavy ion density are overplotted (red dots). The heavy ion density on November 6 is not available. Nevertheless, the altitude of the ionopause during the strong ICME (Figure 4 (b)) did not decrease at all considering that the magnetic field strength increased a lot. The magnetic pressure differences () across the ionopause on November 4 and 6 are 0.7, and 1.1 × 10 −8 dyn cm −2 , respectively. But on November 5, the magnetic pressure difference reaches 2 × 10 −7 dyn cm −2 ( from 250 to 116 nT) or 9.0 × 10 −8 dyn cm −2 even if we use 200 nT as the magnetic strength in the ionosphere. To balance such atypically higher magnetic pressure, the plasma density of the ionosphere must be correspondingly much greater. The heavy ion densities measured by the IMA instrument are also shown in Figure 4 . Although the resolution of IMA data is low, we can still easily see that the ionospheric plasma density was over 1000 cm −3 during the strong ICME with respect to that less than 400 cm −3 during quiet solar wind. The much higher ionospheric plasma shows that the ionosphere had been greatly excited and perhaps entirely expanded under the impact of the strong ICME. The strengthened ionosphere during the passage of ICME has been evidenced by the fact that the magnetized ionosphere can be demagnetized by some ICMEs during solar minimum (Zhang et al. 2008a ). Due to the limitation of measurements, we have no ideas about the reason why the ionosphere was greatly enhanced by the ICME. However, previous study of the Venus and Mars ionosphere has determined that the solar energetic particles (SEPs) could cause extra ionization of the neutral particles in exosphere through impact ionization and charge-exchange reactions (Futaana et al. 2008 ). Simulations also show that the SEPs can effectively increase the ionization rate of the Venusian atmosphere (Plainaki et al. 2016 ). 4. Discussions and Conclusions We have presented very detailed observations of the induced magnetosphere and ionosphere during the passage of the extremely strong ICME on 2011 November 5. From our observations, a clear