adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.491.4345R (fulltext)
ID34) or (ID02, ID29), where one star is a Sct pulsator, but the other one is not pulsating (ID29) or pulsating only in the low-frequency domain (ID34). All the Sct stars in our sample are Sct/Dor hybrids, confirming the already known result that over 98percent of Sct stars are hybrids (Balona 2018c ). For all stars, most peaks in the low-frequency region are independent modes. In a number of stars, some of the peaks in the low-frequency region are among those of highest amplitude. Hence, the definition of a Dor star is simply a Sct star where no significant frequencies are present above 5d 1 . There appears to be no real difference between the two classes of stars. Dor, Sct, and constant stars are to be found within the same small region at the cool end of the instability strip (Balona 2018c ). Many of the Sct stars discussed above seem to exhibit rotational modulation (Table 1 ), as shown by the presence of a low-frequency peak and its harmonic, both of which tend to be variable in amplitude. This is an indication on the existence of stellar activity, either through star-spots or any other corotating surface inhomogeneity. These rotational peaks are, in many cases, among the peaks with the largest amplitudes. Moreover, rotational modulation dominates the photometric variability of some Dor stars in our sample. We detect rotation in nine out of 16 Sct stars, i.e. 56percent of the sample. In six of them the rotational peaks are among the 20 largest amplitude peaks. Moreover, we detect rotation peaks in 26 out of 34 main-sequence stars (76percent) or 16 out of 24 main-sequence stars falling within the general location of the Kepler Sct instability region (Fig. 1 ). In some cases, it seems that more than one spot could be present on the stellar surface (e.g. ID11). In other cases, like ID14, rotation seems to be almost synchronized with the orbital period in a binary system, similar to the cool rotating variable ID06. One star, ID28, seems to be a ROTD-type variable similar to those detected by Balona ( 2013 ): most likely, the sharp peak is the rotational frequency, and the hump is due to Rossby modes (Saio etal. 2018 ). There is a clear indication of activity and star-spots on the surface of the A-type stars, as previously found by Balona ( 2011 , 2013 ). This suggests the presence of magnetic fields on the stellar surfaces that are not expected in such hot stars with very thin convective envelopes. The dynamo mechanism, which is thought to be responsible for magnetic activity in cool stars, cannot operate in stars with predominantly radiative envelopes. Star-spots seem to be also common among the much hotter B stars (Balona 2016 ; Balona etal. 2019 ). This is not surprising since there is no difference in the envelope structure between the A and B stars. Amplitude variations are found among the frequencies of highest amplitude in all four stars belonging to GroupIII (see last column of Table 1 ). They show a diversity of behaviours, including
Paper: 2018MNRAS.473.4077P (fulltext)
different resolution elements (gas, DM, stars and BHs); the solution of the (magneto)hydrodynamical equations for the gaseous component; a treatment of the most important radiative cooling and heating processes; a mechanism for the conversion of gas into stars; stellar evolution and subsequent chemical enrichment of the interstellar, circumgalactic and intergalactic medium; outflows of gas from the star-forming regions of galaxies; and the formation, growth and energetic feedback of supermassive BHs in distinct low- and high-accretion rate states. In developing the TNG model, we have pursued three simultaneous goals: to bring together under a consistent framework the improvements implemented in arepo to the accuracy and robustness of the underlying numerical methods; to introduce new, rich physics; and to address the key deficiencies of the previous model. The main numerical updates are summarized in Section 2.2 and include an improved estimator for spatial gradients across gas cells, a more efficient gravity solver and time integration scheme, and a more consistent advection of the passive scalars. Together, these changes give a more accurate and better convergent hydrodynamical scheme, and will allow us to pursue even more ambitious simulations in the future. The principal new physics in the TNG model are the inclusion of MHD (see Section 2.2.1 and references therein) and a dual-mode, thermal and kinetic BH feedback model (see the companion paper by Weinberger et al. 2017 ). The new kinetic BH feedback has been shown to alleviate some of the discrepancies identified in Illustris in comparison to observations at the high end of the halo mass function ( ≳ 10 12 –10 14 M ⊙ ), in particular regulating the stellar content of massive galaxies while preserving realistic halo gas fractions. In fact, in confronting the problems identified in Illustris (see Introduction section), we aim to open new physical regimes that were impossible to robustly study in previous simulations. These include not only massive haloes emitting at X-ray wavelengths, but also the galaxies occupying the transitional green valley, and the properties of low-mass dwarfs, including cluster satellite populations. In this paper, we have focused on the intermediate- and low-mass end of the galaxy population (halo masses of a few 10 10 –10 13 M ⊙ ) and have presented an updated model for the galactic winds feedback by modifying their directionality, thermal content, velocity and energy scalings (Section 2.3.2 ). We have also updated our reference yield tables (Table 2 and Fig. 1 ) and adjusted some stellar evolution choices (Section 2.3.3 ). The TNG galactic winds (whose implementation and scalings largely follow those of Springel Hernquist 2003 ; Oppenheimer Davé 2006 ; Oppenheimer Davé 2008 ; Vogelsberger et al. 2013 ) are now launched isotropically from the star-forming regions of galaxies, bringing a simplification to the modelling that by itself does not significantly affect
Paper: 2019ApJ...883..165H (fulltext)
1,20−19 (252,789.82 MHz), U253020, U267402, U268464, U268485, and U268833. 5. Comparison to single-dish results. Eight light curves were studied in detail in our single-dish work (He et al. 2017 ), but only six of them can be compared to the ACA results, because the other two lines (of C 4 H) suffer from the interferometric baseline problems in the ACA data, showing negative artifacts, as mentioned earlier. Two of the six line features (SiS 14−13 at 254,103 MHz and the blended lines of C 3 N+HCN+C 4 H at 267,120 MHz) follow the NIR light curve well in both the single-dish and ACA data. However, for the four line features that were anticorrelated with the NIR light in the single-dish results, none of them shows the anticorrelation in the ACA data: two of them (HCN, v 2 = 1, 3−2, l = 1 f at 267,199 MHz and the blended lines of 29 SiS+HCN, v 2 =2 0 at 267,243 MHz) show strong variations in phase with the NIR light and continuum; 30 SiO,6−5 at 254,216 MHz varies similarly but with a smaller amplitude; and the blended line feature of Na 37 Cl+CH 2 NH+HC 3 N at 254,700 MHz shows modest variation. Why are the anticorrelation cases in the single-dish data missing in the ACA results, while the correlated cases remain unchanged? The ALMA monitoring started about 7 yr (∼four stellar pulsation cycles) later than our single-dish work, and thus secular changes of stellar pulsations could have occurred. However, the different phase behaviors of the different lines cannot be explained solely by this; some unknown mechanisms operating in the circumstellar envelope (CSE) are necessary to create differences among them. The mechanisms are possibly related to the missing flux in the ACA data, as discussed earlier, and the different telescope beam sizes involved in the two works, 29′′ for the single-dish versus 4′′ × 8′′ (or 6′′) for ACA. However, a radial phase delay due to radiation transportation is unlikely, because the difference in beam radii only corresponds to ∼1552 au at a source distance of 135 pc, which can produce a phase lag of only ∼9 days with the speed of light. This is insufficient to explain the observed change from anticorrelations to correlations with the NIR or millimeter continuum light. The only known candidate mechanism is that proposed by Cernicharo et al. ( 2014 ) from their simulations in which the evacuation of molecular populations in low- J energy levels by IR excitation is different for different molecular lines at different radii of the CSE. We will analyze the time series of ALMA ACA maps and do simulations to further explore this idea in coming papers. 6. Remarks on special species. A special case is Na 37 Cl. Its J = 20−19 (254,663.465 MHz) and 21−20 (267,365.833 MHz) transitions have similarly strong broad line profiles, with peak fluxes of about 1.5 Jy beam –1 . However, while the former shows little difference between its maximum- and minimum-phase line profiles in Figure 4 , the latter has its line profile definitely varying
Paper: 2020ApJ...890....2A (fulltext)
while the other segments have been fading throughout the observations. This supports and quantifies the apparent asymmetries in the images in Figure 4 . Figure 6. The 3.6 μ m light curves of the four ER segments and the 3 surrounding stars, derived from the decomposition of the 04 mosaics at three epochs. Formal uncertainties on the flux densities are ≲2%. The mapping and decomposition procedures were also applied to the individual epochs of targeted observation without averaging into broader intervals. In this case, ∼25% of the 04 pixels in the images contain no data, and the decomposition is applied giving no weight to these pixels. The results are, as expected, much noisier than those illustrated above, but the same trends do emerge when similar averaging is applied after, rather than before, the decomposition. 5. Discussion Across all IR wavelengths, the light curves are reasonably well fitted by the model of Equation ( 1 ) with similar parameters (Figure 2 ). This empirical model is similar in form to those developed by Lundqvist Fransson ( 1991 ) and Dwek Felten ( 1992 ) to explain the prompt line emission from the ER. In those models, the light travel time across the ER (326 d for an ER with radius R = 239 lt-days and inclination i = 43°; Dwek Felten 1992 ) is explicitly modeled. Here, the light travel delays can be assumed to be a contributing factor to the width of the Gaussian component of our model. Irregularities in the radius and density distribution of the ER and asymmetries in the radius (or velocity) of the blast wave would be other contributing factors. The X-ray light curve is also well fitted by this model, although the date, d 0 , of the peak of the Gaussian function is somewhat later, and the exponential timescale, τ d , is significantly longer than at the IR wavelengths. Both these differences act to shift the peak of the X-ray light curve to a later date. In a very simple interpretation, and assuming that the IR emission is from dust that is collisionally heated by the X-ray emitting gas (Dwek 1986 ; Bocchio et al. 2013 ), the shorter exponential timescale for the IR emission compared to that of the X-ray emission, may suggest that dust is being destroyed faster than the gas is cooling. However, additional factors such as the development of a reverse shock, and variations in the density (and consequent post-shock temperature) of the CSM may also contribute to the differences in d 0 and τ d between the IR and X-ray light curves. Systematic differences in the IR and X-ray trends that may be indicative of dust destruction had been much more difficult to identify prior to about Day 8000 while both light curves were still rising (e.g., Dwek et al. 2008 , 2010 ). As shown in Figure 2 , we also find that the model of Equation ( 1 ) provides a good fit to the evolution of the B, R, and F502N band optical emission reported by Larsson et al. ( 2019 ) if restricted to dates after Day 5500 when the interaction with the ER is
Paper: 2020ApJS..248....2F (fulltext)
magnetogram data in weak-field regions. Figure 1. Input distribution of the test configuration, displayed in longitude–latitude order. This case has m = 200, n = 400, a = π /2 − 0.1, b = π /2 + 0.1, c = 0, and d = 0.4. The input distribution is a field of random numbers distributed between −0.5 and 0.5. Figure 2. Recovered field of plotted vs. the input field of for the centered grid case shown in Figure 1 . By changing the definition of how finite-difference approximations to spatial derivatives are defined, and where different variables are located within the grid, we can improve this behavior dramatically. In the centered grid case, all variables are colocated at the same grid points. By defining the radial magnetic field and its time derivative to lie at the centers of cells, with the electric field components lying on the edges (or “rails”) surrounding the cells, the finite-difference approximations to the derivatives can be made to obey Faraday’s law to floating-point roundoff error. Figure 3 shows the analogous scatterplot shown in Figure 2 , but using the staggered grid definition described above. The relationship is a straight line. Figure 4 shows the difference between the recovered and original values of . Note that the amplitude of the error is multiplied by 1 × 10 12 , so that the error term is visible in the scatterplot. These two plots clearly show that solutions for the electric fields in a staggered grid formulation can do a far better job of representing the observed data than can the centered grid formulation. Figure 3. Recovered field of plotted vs. the input field of for the staggered grid test case. Figure 4. Difference between recovered and input as a function of the input for the staggered grid case. These figures motivate the development of our more detailed staggered grid formalism, which is described in Section 3.4 . 3.4. The Staggered Grid Formulation for PDFI_SS In three dimensions, Yee ( 1966 ) worked out a second-order-accurate finite-difference formulation for Maxwell’s equations, pointing out that if one places different variables into different locations within the grid, the governing continuum equations (the curls in Maxwell’s equations) become conservative when written down in a finite-difference form. In recent years, “mimetic methods” have been developed, which are higher-order analogs to the Yee grid, in that different variables are defined at different locations within a voxel (such as at interiors, faces, or edges), and with some internal structure in these voxel subdomains allowed. The locations of the variables depend on which integral conservation law is being applied (see, e.g., Candelaresi et al. 2014 and references therein). For our work, the second-order-accurate Yee grid is sufficient. The Yee grid is the basis for the numerical implementation of the MF code described by Cheung DeRosa ( 2012 ). In PDFI_SS, we have a slightly different situation, where most of the calculations are defined on a
Paper: 2016MNRAS.462.2099H (fulltext)
in the NIR images and represents the red, old stellar population of the disc. The K s band isophotes trace the stellar density. In most of the cases, the LSB galaxy discs are found to host big, bright bulges; these have been discussed in earlier studies of bulges in LSB galaxies (Galaz et al. 2002 ). The diffuse stellar disc is barely visible in NIR for most of the sample. This can be due to two reasons: (i) they have a predominantly younger population of stars in the disc which cannot be traced very well in the NIR bands. However, most LSB discs show very little star formation. (ii) Instead, it is more likely that the surface mass density of the stellar discs (Σ * ) are very low and hence show very little structure in either NIR or optical images. Isophotal contours in K s do not show any significant twisting in the bar region for the sample galaxies. This suggests that the stellar orbits are aligned along the bar major axes (x 1 orbits) (Das, Anantharamaiah Yun 2001 ; Sormani, Binney Magorrian 2015 ). If there are stellar orbits perpendicular to the bar length (x 2 orbits), they will be apparent in the K s contours, but most of our sample show simple, nested, contours aligned along the bar major axis. Just from visual inspection, about half the sample of 29 galaxies in Fig. 1 have bright, classical bulges. The remaining have either boxy bulges or are bulgeless. Several galaxies have short oval bars and boxy bulges as well. These systems may represent bars that are undergoing secular evolution and may finally evolve into large, boxy bulges. (Kormendy Kennicutt 2004 ). The galaxies are individually described below. CGCG 381−048: it has a long thin bar which is clearly visible in all J , H , and K s bands. Bulge is small but brighter. Disc is very diffuse and hardly visible for short exposures. UGC 1920: it has an oval bar with a bright, classical bulge. Disc features are very faint. UGC 1455: the galaxy has a roundish, disky bar. Classical bulge. Very bright and covers a large fraction of the bar. The disc is faint. NGC 5905: long thin bar and a bright, classical bulge. Traces of spiral arms and a corotation ring are visible in the NIR images. UM 163: long thin bar and a large, classical bulge. But the disc is very faint. UGC 11754: it has an oval shaped, short bar. No clear bulge. Disc features are visible. PGC 68495: disky, oval bar. Small bulge. Galactic disc is diffuse and faint. UGC 2936: highly inclined galaxy that has a short, oval bar, and distinct spiral arms. The disc spiral arms are surprisingly clear. UGC 10405: very short bar with a bright bulge. Disc is poorly seen. 0223−0033: bright bulge with a small, oval bar followed by the spiral arms. The galactic disc is clearly visible in all the three bands. UGC 5035: small, oval bar and a large, classical bulge. UGC 9087: long bar with bright classical bulge. Disc is very faint. LSBC F568−08: short, oval bar. No clear bulge. Maybe a bar evolving into a bulge. LSBC F568−09: short bar with
Paper: 2019ApJ...874...35S (fulltext)
an effective exit channel for molecules to desorb to the surface, in line with the conclusion from Yuan Yates ( 2014 ). In contrast, this study demonstrates that the photodesorption yield of CO 2 is not ice-structure dependent, which is incompatible with the preceding works based on IR spectroscopy for quantification (Öberg et al. 2009 ; Yuan Yates 2014 ). As mentioned in Muñoz Caro et al. ( 2016 ), the photodesorption yield of CO ice is independent of ice structure, and the linearly decreasing photodesorption yield for increasing deposition temperature might be due to orientational disorder. Therefore, whether they are polar CO or non-polar CO 2 , their structures are not directly related to the photodesorption yield. This work has implications for the interpretation of gas-phase observations toward cold interstellar and circumstellar environments where ice mantles were submitted to UV irradiation. The ice analog component studied in this paper, pure CO 2 ice, is believed to form in ice mantles by two processes. One is the CO 2 segregation out of the CO 2 –H 2 O mixture, leading to CO 2 inclusions in water ice. The other is a distillation process, in which CO evaporates from a CO 2 –CO ice mixture, leaving pure CO 2 behind (Pontoppidan et al. 2008 ; Kim et al. 2012 ). In the former process, UV irradiation of CO 2 inclusions in water ice produces CO, O 2 and CO 3 molecules that will be mainly retained in the ice with a negligible desorption. The latter evolutionary phase of the ice is favorable for the photodesorption of CO 2 , because CO 2 ice is exposed to the surface of ice mantles, and our experimental results are more directly applicable to this scenario. Only CO 3 is not expected to photodesorb in this case, according to the experiments. In particular, the photodesorption yields of CO 2 , the CO and O 2 photoproducts, in molecules photon −1 , should be valid to estimate the density of these species in the gas of cold environments. In dense clouds, the secondary UV field originated by excitation of molecular hydrogen is about 10 3 –10 4 photons cm −2 s −1 , and should be dominated by the molecular emission lines with a smaller contribution of the more energetic Ly α photons produced by atomic hydrogen. This UV emission spectrum, along with the total value of the UV flux, dictates the values of the photodesorption yields in our experiments. In Section 3.3 of this paper, we report the linear dependence of the photodesorption yields with the UV lamp emission and ice absorption spectra in the UV. Pure CO 2 ice is expected to be in the crystalline form in ice mantles, since the band around 4.30 μ m (2328 cm −1 ) of amorphous CO 2 ice is absent in the observed spectra toward dense clouds and protostars; this suggests that CO 2 ice was formed on the dust at temperatures above 25 K or was submitted later onto these temperatures (Escribano et al. 2013 ). We confirmed that this ice structure reflects the temperature of ice deposition in the
Paper: 2016ApJ...821....7T (fulltext)
Red velocity vectors are km s −1 and white ones are km s −1 . The filament is forming just left of center where the two bubbles are merging. Figure 12. Same as Figure 11 , for model M5_27T1. Velocity vectors are color-coded, ranging from 20 to 1500 km s −1 . The strength of the GW determines how filaments evolve. We note two interesting cases outlined below. 5.2. Mass Anchors Model M5_34T (Figure 11 ) has sufficient energy to form a GW, but the wind does not entirely disrupt all filaments. As shown in Figure 11 two distinct bubbles emerge from the central starburst. Their boundaries merge to form a dense filament that stretches pc back to anchor on the starburst reservoir. The 540 km s −1 wind ablates mass off the reservoir, pushes it into the filament, and by 1.5 Myr has extended the filament pc above the disk plane where it is drifting along at only 50−100 km s −1 . Due to continual mass loading at the base of the filament it stays anchored, allowing it to persist and grow. At some point the filament should disrupt entirely due to either Kelvin–Helmholtz instabilities or heating and evaporation. But our resolution is insufficient to maximize filament survival time (see Cooper et al. 2009 ). 5.3. Filament Lift Off In model M5_27T1 (Figure 12 ) the filament again forms along the bubble contact. But now, after 1 Myr it detaches from the disk reservoir and lofts into the free-flowing wind of the now merged bubbles. This filament differs from its slow counterpart model M5_34T1; it has a larger cross section to the impinging wind, so it fragments more due to Kelvin–Helmholtz instabilities. The surrounding wind flows at 1420 km s −1 while the filament moves at 0−50 km s −1 before lift off but attains 200−500 km s −1 thereafter. This filament would be analogous to the disrupted clouds studied by Cooper et al. ( 2009 ). 6. SYNTHETIC ABSORPTION LINES We showed in Section 4 that cold gas emission entrained in the GW is 4–5 dex fainter than the galactic disk. A more sensitive probe of this gas may be its absorption of background starburst continuum. We synthesize absorption lines for three temperature regimes, denoted “molecular,” “warm,” and “soft X-ray,” with temperature ranges given in Table 1 . A trivial line source function suffices for kinematical signatures of the three temperature regimes. Absorption spectra are derived by integrating optical depth in N cells along the column viewed perpendicular to the diskThe velocity channels have a resolution of 10 km s −1 and range from −1800 to 200 km s −1 . Absorption profiles are shown in Figure 13 for models M5_27T1 (top panel) and M5_34T1 (bottom). The “soft X-ray” line shows the structure of the hot free-wind inside the expanding bubble. The velocity at maximum absorption is the average speed of the free wind. The long tail of the profile back toward Galaxy systemic velocity, especially prominent in model M5_27T1, reveals gas flowing radially at the average speed of the free wind but not entirely