adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...850...46T (fulltext)
(Hindmarsh 1983 ). We regularly reconverge the PT structure with the radiative/convective code during the time integration until we reach a steady-state for both the chemistry and energy conservation/hydrostatic equilibrium. We then get a coherent atmospheric structure with out-of-equilibrium chemical abundances. As in Tremblin et al. ( 2016 ), we propose that a process similar to fingering convection is responsible for this turbulent mixing and leads to the out-of-equilibrium abundances. Fingering convection can be triggered in the atmospheres of brown dwarfs and exoplanets because of the gradient of mean molecular weight induced by the chemical transitions CO/CH 4 and N 2 /NH 3 at chemical equilibrium. Similar to convective motions that lead toadiabatic PT structure in the deep atmosphere, fingering convection could impact the PT structure of the atmosphere. As in Tremblin et al. ( 2016 ), we propose that fingering-convective motions can induce a temperature gradient reduction, leading to a reddening in the modeled spectrum. This temperature gradient reduction in the atmosphere is modeled using an artificially reduced adiabatic index γ in between two pressure levels on top of the convective zone. In Figure 1 , we show the influence of various model parameters at effective temperatures of 1700 and 1300 K, representative of the objects studied in this study. At 1700 K we can see a degeneracy between gravity and metallicity on the spectral shape of the H and K bands. Decreasing gravity or increasing metallicity tends to form a triangular H band and a strong and flatter K band. Decreasing the temperature gradient in the atmosphere by decreasing the effective adiabatic index γ increases the reddening in J − K . Increasing the surface gravity at an effective temperature of 1300 K has a strong effect on the out-of-equilibrium chemistry and the quenching of CO in the deep atmosphere. At higher gravities, the CO/CH 4 transition is deeper in the atmosphere, hence it is more difficult to prevent the formation of CH 4 (strongly appearing at log g = 4.5) for a given mixing coefficient K zz . Thus, varying K zz would approximately lead to the same effect as varying gravity for T eff = 1300 K. For the fitting of the observations we have probed a similar parameter space, log g in [3.5, 4.5], [M/H] in [0, 0.5], T eff in [1200, 1800], and γ in [1.01, 1.1]. Figure 1. Effects of varying different parameters on the spectral models obtained with the ATMO code. 2.1. DENIS J1425279-365023 DENIS J1425279-365023 was first identified as an L5 dwarf (Kendall et al. 2004 ) with a trigonometric distance of ∼11.57 ± 0.11 pc (Dieterich et al. 2014 ). A SpeX Prism obtained by Gagné et al. ( 2015a ) indicates that the object is a L4 INT-G object based on the index-based classification of Allers Liu ( 2013 ). Based on the galactic position and space velocity of the object, Gagné et al. ( 2015a , 2015b ) also concluded that DENIS J1425-36 is a bona fide member of the
Paper: 2019A&A...621A.138K (fulltext)
constraints and verification of the TeV emission mechanism in 30 Dor C. Hadronic and leptonic mechanisms predict different flux for the (10 GeV–1 TeV) band, both currently below Fermi -LAT ( Atwood et al. 2009 ; Ackermann et al. 2011 ) sensitivity and between Fermi and H.E.S.S. covered energy ranges, but within the capabilities of C.T.A. An example of the prospects of C.T.A. in this regard is shown by Acero et al. (2017b ) as applied to the SNR RX J1713.7–3946. The recent H.E.S.S. paper on RX J1713-3946 ( H.E.S.S. Collaboration 2018 ) demonstrates the possibilities for C.T.A. observations of LMC objects. H.E.S.S. revealed the TeV shell of RX J1713.7–3946 in unprecedented detail, and was found to extend further than the X-ray shell. This allowed the probing of particle escape, while the GeV and TeV spectra were covered to a level of accuracy that allowed a very detailed comparison of leptonic and hadronic emission mechanisms, including a magnetic field map for the leptonic case. 5 Conclusion We have presented an analysis of new Chandra observations of 30 Dor C in the Large Magellanic Cloud, the first superbubble detected in TeV γ -rays. These observations provided the sharpest view of the synchrotron X-ray shell of 30 Dor C, allowing us to perform a detailed morphological study and estimate the B -field in the superbubble, a key discriminator in assessing the dominant TeV γ -ray emission mechanism with a low B -field ~ 15 μ G required for a purely leptonic origin, a B -field of ≳ 50 μ G for a completely hadronic-dominated origin, or B -field of ~ 20 μ G for a 50–50 contribution of the leptonic and hadronic mechanisms to the TeV γ -rays. Using the new Chandra data, MCELS2 H α , and 6 cm radio continuum images we found an anti-correlation between the synchrotron X-ray and H α /6 cm shells. In addition, we discussed how long-slit spectroscopy of various regions of the 30 Dor C shell has shown no evidence of the high velocities necessary to explain the synchrotron X-rays (≳ 3000 km s −1 ). Rather the H α expansion velocities are more typical of an expanding superbubble ( 100 km s −1 ). We suggested that the SNR responsible for the synchrotron X-rays has reached the 30 Dor C supershell and has stalled in some regions, but continues through gaps in the shell in others. This is similar to the observed anti-correlation seen in RCW 86 ( Vink et al. 2006 ; Helder et al. 2013 ), which is attributed to the SNR evolving into a wind-blown cavity and encountering density gradients, though the velocity differences between synchrotron X-ray and H α shells are not as extreme. This may be a result of more pronounced density gradients and/or the fact that the shock radius is so much larger in 30 Dor C, meaning the shock energy has been distributed over a large area, making it more sensitive to these density gradients. We estimated the downstream B -field from the synchrotron X-ray shell. This was achieved by fitting the observed radial profile in sectors around 30
Paper: 2016MNRAS.460..844M (fulltext)
map in configuration space with a specific measurement error given. As possible noise sources, we take into account background fluctuations due to undetected clusters and the instrumental noise of ALMA itself. Furthermore, we include an estimate of the fluctuations introduced by primary CMB anisotropies. Since the different noise contributions are not correlated with each other, the total uncertainty is then given by Gaussian error propagation. 4.1 Background fluctuations due to undetected clusters Galaxy clusters unresolved by the telescope beam contribute a background noise level y bg that needs to be taken into account in all following calculations. Clusters are unresolved if they appear (much) smaller than the beam size. The background signal is thus dominated by low-mass clusters (Bartelmann 2001 ). Since an ideally homogeneous background could be removed from the data, we only have to consider the average background fluctuation level Δ y bg . The mean background level contributed by unresolved clusters with the differential mass function n ( M , z ) is where the mass- and redshift-integrations have to be carried out over that area in the mass–redshift plane where clusters are unresolved. The integrated Compton- y parameter Y ( M , z ) of a cluster with mass M at redshift z is where the angular diameter distance D d to the cluster appears and the total number of electrons N e . We choose the cluster mass function n ( M , z ) described by Sheth Tormen ( 1999 ), The number of background sources follows a continuous probability distribution. If one neglects any spatial cluster correlations, the rms due to background fluctuation is 4.2 Instrument noise Another relevant source of noise is the measurement noise of the telescope and the detector. We assume that the measurement error of the telescope has a Gaussian distribution with standard deviation σ Tel around zero, with σ Tel depending on the telescope and detector configuration and the setup of the specific observation. Since we need a resolution for our simulation that is comparable to other observations of massive galaxy clusters, we choose the characteristics of the Atacama Large Millimeter/submillimeter Array ( ALMA ) as an example telescope for achieving sufficiently precise thermal SZ observations. For all simulations presented in the remainder of this work, we use the following configuration of ALMA : All N ant = 32 antennas are assumed to be available with a baseline to be set. This configuration has then a given angular resolution in a frequency band chosen to be centred on ν = 116 GHz. Its bandwidth is assumed to be Δν = 7.5 GHz. According to the ALMA user manual (Lundgren 2012 ) the point-source sensitivity σ Tel of ALMA is where T sys is the temperature of the system, 1 η q the quantum efficiency (η q = 0.96), η c the correlator efficiency (η c = 0.88), A eff the effective area of an individual antenna, n p the number of polarization states ( n p = 2), and t int is the
Paper: 2020ApJ...904..136E (fulltext)
et al. 2014 ). When the collision velocity is assumed to be the velocity difference between the stem and bowl region (∼1.5 km s −1 ), the kinetic energy released by the collision is estimated to be erg cm −3 , where the H 2 density of 10 3 cm −3 is arbitrarily assumed. This can be balanced by the cooling rate by Goldsmith Langer ( 1978 ), and the equilibrium temperature is roughly estimated to be 100 K. Here, we assume the cooling time of 10 4 yr according to Inoue Inutsuka ( 2012 ). This temperature is comparable with that observed in the interacting region. Thus, this rough estimate implies that the slight temperature enhancement is possible in the interacting region. Warm gas with a temperature of 74 K, heated by the filament–filament collision, is cooled down mainly by rotational line emission of CO for an H 2 density of 10 3 cm −3 (Inoue Inutsuka 2012 ). As mentioned above, the cooling time () is estimated to be around 10 4 yr according to Koyama Inutsuka ( 2000 ) with a cooling rate of 10 −26 erg cm −3 s −1 (Inoue Inutsuka 2012 ). Assuming that this warm gas expands with a constant velocity of 1.5 km s −1 , which corresponds to the typical velocity difference between the blueshifted and redshifted components of the Pipe nebula of 3.0 km s −1 and 4–5 km s −1 , respectively, the gas can extend in space by about 0.015 pc within the cooling time. This length corresponds to a typical spatial scale of a shocked warm region, and hence we hereafter denote it as (=0.015 pc). On the other hand, the extent of the cloud along the line of sight () can roughly be estimated from the H 2 density ( n (H 2 )), the OH column density ( N (OH)), and the OH fractional abundance ( X (OH)) relative to H 2 with the following equation:We employ an H 2 density of 10 3 cm −3 and an OH column density of 5.3 × 10 13 cm −2 , the latter of which is determined for the (0′, 32′) position (Table 5 ). Assuming an OH abundance of 10 −7 , which is a typical value in diffuse clouds (Wiesemeyer et al. 2012 ), is estimated to be ∼0.2 pc. If the OH fractional abundance is one order of magnitude higher ( X (OH) = 10 −6 ), which could be possible in a shocked gas according to Draine Katz ( 1986 ), could be 0.02 pc. The latter value ( ∼ 0.02 pc) for the shocked gas case ( X (OH) = 10 −6 ) is comparable to the of 0.015 pc. This result suggests the presence of shock-induced compression at the interface of the two filaments, and supports the filament–filament collisions picture in the Pipe nebula. 7. Summary We have observed the four hfs components of the OH 18 cm transitions toward the Pipe nebula along the two strip lines and determined the gas kinetic temperatures accurately. Along strip-1, the gas kinetic temperature is found to have no relation to the apparent distance to the nearby star, θ -Oph. This result suggests that the heating effect from θ -Oph does not significantly contribute to the heating of the Pipe nebula, although a possibility that θ -Oph illuminates the
Paper: 2020A&A...634L...6Y (fulltext)
on the impurity, physical structure, and the temperature of ice grains. In turn, we may be able to set constraints on the processes experienced by 2I and its formation conditions. There are three objectives of our study: (1) to search for diagnostic absorption features of water ice and other compounds (such as hydrated minerals or organic materials) in the coma or on its surface; (2) to compare water ice features in 2I to other Solar System icy objects (e.g., comets, Centaurs, and Kuiper Belt objects) and set constraints on its birth conditions; (3) to search for the ultra-red organic materials that may be present on the surface of 2I or in the coma as a result of long-term exposure to Galactic cosmic rays or UV bombardment. 2. Observations and data reduction We observed 2I on three separate occasions. The first two observations were made using director discretionary time allocations. The first observation was carried out in the morning twilight on 2019 September 19 UT using the SpeX spectrograph at the 3m IRTF telescope. The second observation was made on September 24 UT with the Gemini Near-InfraRed Spectrograph (GNIRS) spectrograph on the 8m GEMINI telescope. The last observation was made in service mode, again with the SpeX/IRTF, on October 9. The observing geometry is shown in Table 1 . Table 1. Journal of the 2I observations. Notes. (†) r h and Δ are the heliocentric and geocentric distances, respectively; (‡) α is the phase angle; (§) χ is the airmass. The SpeX observations were made with the high-throughput prism mode (0.8–2.5 μ m) and a 0.8′′ × 15′′ slit that provides a spectral resolving power of R ∼ 150 ( Rayner et al. 2003 ). The GNIRS observations were taken with the short camera (plate scale: 0.15′′ pix −1 ). The SXD cross-dispersed mode, 32 l mm −1 grating and 1.0′′ slit, provides an averaged resolving power of R ∼ 1700 over 0.9–2.5 μ m ( Elias et al. 2006 ). At least one nearby G-type star was observed together with the comet during each run, which was used both as the telluric correction standard star and the solar analog. The SpeX data were reduced using the reduction pipeline SpeXtool ( Cushing et al. 2004 ). The GNIRS data were reduced using the Image Reduction and Analysis Facility (IRAF) software and the Gemini IRAF package. Taking advantage of the similarity between the SpeX data and GNIRS data, we used the SpeXtool xtellcor routine for telluric correction and the xmergeorders routine for merging the GNIRS SXD spectra. 3. Results The NIR observations of 2I are presented in Fig. 1 . Although the three spectra were taken on different dates and with different instruments, they consistently appear featureless and show moderately red spectral slopes. We did not detect the diagnostic absorption bands of water ice at 1.5 and 2.0 μ m. Among the three NIR observations, the Gemini dataset has the highest signal-to-noise ratio (S/N). Thus, we use the Gemini spectra for further analysis. Fig. 1. Near-IR spectra of 2I/Borisov,
Paper: 2018MNRAS.474..597T (fulltext)
of least massive black holes (shown as green points) in order to compare the properties of the descendants of the haloes with massive quasars with those of haloes whose black hole mass is much lower, around that of the seed black hole mass. While matching haloes in the BT hydrodynamic and BTMassTracer simulation, we note that the halo mass is different in the matched haloes due to baryonic effects and there can be differences, which are mostly within 20 per cent. Figure 4. Two-dimensional histogram of the logarithm of halo mass (log 10 ( M halo [M ⊙ ])) and the black hole mass (log 10 ( M BH [M ⊙ ])) in the BT Hydrodynamic simulation at z = 8. The 23 most massive black holes are shown by scatter points in purple, while the least massive blackholes are shown in green. We also show as cyan points, the blackholes in the progenitor haloes of the most massive haloes at z = 0. Note that, for the cyan points, the halo masses shown correspond to that of the progenitors at z = 8. In the left-hand panel of Fig. 5 , we plot the halo mass function of the dark matter haloes at z = 8, 5, 2.5, 1.2 and 0. We also plot as points on the halo mass function at z = 8 and 0 the hosts of the most massive black hole tracer particles (corresponding to the purple points in Fig. 4 ). We find that at redshift z = 0, the halo masses are distributed over a wide range, compared to that at z = 8. From the figure, we can therefore clearly see that the descendants of the most massive black holes at z = 8 are not all the most massive haloes at z = 0. However, the descendants are still relatively massive haloes with mass above M * = 5.0 × 10 12 h −1 M ⊙ at z = 0. The individual tracks for the halo masses of the four most massive black hole tracers are also shown in the figure. We can see that these objects approximately keep their rank order over all redshifts. Figure 5. Left-hand panel: halo Mass function at z = 8, 5, 2.5, 1.2 and 0, and the location of the tracked hosts of the four most massive BH's on the mass function. Right-hand panel: normalized histogram of the halo masses at z = 0 of the descendants of the most massive black holes at z = 8 compared with the histogram of most massive haloes at z = 0. To further understand the distribution of the halo masses, we plot the normalized histogram of the host halo masses of the descendants of the z = 8 massive black holes, compared with the 23 most massive haloes at z = 0 in the right-hand panel of Fig. 5 . While the most massive haloes are distributed above 3.2 × 10 14 h −1 M ⊙ with a peak in the distribution at ∼1.8 × 10 15 h −1 M ⊙ , the quasar descendants have a peak distribution in the mass at ∼2.24 × 10 14 h −1 M ⊙ . These 23 most massive haloes and 23 quasar descendants have a space density of 4 × 10 −7 h 3 Mpc −3 . The massive haloes are all Coma-like objects, whereas the quasar descendants have masses an order of magnitude smaller, comparable to galaxy groups with a space density ∼ 10 − 5 h 3 Mpc − 3 . We can
Paper: 2016ApJ...820..141M (fulltext)
molecules can be due to their reaction to form NH 4 SH in the deposited ice. The most intense band of NH 4 SH and other ammonium salts that may be present is the transition located at 1470 cm −1 . It is Unfortunate that this band overlaps with the methanol band in the same region and complicates the estimation of the column density of . Figure 3. IR spectra of H 2 S–CH 3 OH–NH 3 –H 2 O (3:3:3:1) deposited on a cold substrate (50 K) before (blue) and after (red) electron irradiation with a 10 keV electron beam (fluence = 2 × 10 21 eV cm −2 ). Figure 4. Left: Gaussian deconvolution for the 2556 cm −1 absorption corresponding to H 2 S. Band A corresponds to CH 3 OH, bands B and C correspond to H 2 S (Jiménez-Escobar et al. 2014 ), and band D corresponds to H 2 O. Right: Gaussian deconvolution for the 3367 cm −1 absorption corresponding to NH 3 . The red curve in Figure 3 shows the post-irradiation spectrum of the “with H 2 S” mixture. We note a significant decrease in band intensity of the initial constituents, and particularly a total disappearance of the H 2 S band. This is interpreted as complete destruction of the H 2 S molecules by electron irradiation. Among products of irradiation we observe the same molecules produced after irradiation of the “without H 2 S” mixture. Additionally, in the “with H 2 S” mixture alone, after irradiation an intense band appears at 2040 cm −1 . This band is assigned to the C = O stretching mode of OCS, which is observed at 2019 cm −1 in pure OCS ice and shifted to 2046 cm −1 in OCS-H 2 O and OCS-CO 2 mixtures (Ferrante et al. 2008 ). Previous experimental studies reported the formation of OCS after irradiation of ices containing CO or CO 2 as the carbon source and H 2 S or SO 2 as sulfur source (Ferrante et al. 2008 ; Chen et al. 2015 ). Our assignment of the 2040 cm −1 band to OCS is supported by the absence of this band in the infrared spectrum of the irradiated “without H 2 S” mixture, where no sulfur was present. OCS can be a product of reactions between a CO (which is produced from the dissociation of methanol as explained above) and an S atom or HS radical, as illustrated here: The column density of OCS after irradiation at 50 K (before heating) is 1.7 × 10 16 molecules cm −2 . This corresponds to a ratio of N (OCS)/ N (H 2 S) = 8.5%. In Figure 3 it is noteworthy that the absorption band at 1477 cm −1 assigned to methanol is in the same position as the strong absorption band of () (Howett et al. 2007 ). This makes it difficult to detect any production of the ammonium salt during the irradiation and heating of the sample. Thus, to assess the chemistry, we have performed an irradiation experiment using 15 N labeled ammonia. Figure 5 compares the infrared spectra of labeled and unlabeled mixtures after irradiation. We observe a shift of 16 cm −1 of the OCN − band in agreement with the isotopic shift 17 cm −1 measured in the matrix-isolated molecule (Maki Decius 1959 ). This confirms our assignment of this
Paper: 2018MNRAS.475.1829S (fulltext)
probability distributions are shown in the right-hand panel of Fig. 2 , and lead to a strong bias towards Solar and super-Solar metallicity systems as potential terrestrial planet hosts, while substantial tails of potentially habitable terrestrial planet systems remain down to low metallicities. Given the flat evolution in the mean gas phase metallicity of the Universe over cosmic time, this probability distribution has relatively little effect on the evolution of habitabilities at any lookback time 10 Gyr, but instead acts primarily as a scaling factor, truncating the maximum habitable fraction at any redshift at ∼50 per cent. It will, however, tend to suppress the habitable fraction in low-mass galaxies (log(M/M ⊙ ) 8.5), since these have lower typical metallicities as shown in Fig. 3 . We note that since Lineweaver ( 2001 ) defined this probability distribution, the number of exoplanetary systems known has grown exponentially (see e.g. Winn Fabrycky 2015 , for a recent review). None the less, the metallicity dependence of planet hosting remains a complex subject, with relatively poor observational constraints, particularly at the low end of the planetary and stellar-mass functions where observational selection effects are significant. None the less, the distribution above remains broadly consistent with current observations. Wang Fischer ( 2015 ) examined the distribution of planets selected by the Kepler telescope. They lacked the sample size to determine the detailed metallicity distribution of the planet hosts, but found that the median metallicity for terrestrial planet hosts is log( Z /Z ⊙ ) = 0.04 (with Z ⊙ = 0.018), which matches the peak of the Lineweaver ( 2001 ) distribution in P HE ( Z ). By contrast, other studies such as that of Adams Kane ( 2016 ) and Buchhave et al. ( 2012 ) have found no statistically significant difference in metallicity between stars hosting terrestrial planet candidates and the whole Kepler sample, even when only planets in their habitable zones are considered. Hobson et al. ( 2017 ) also suggest that, while M-dwarf stars hosting massive planets are biased towards high metallicities, no such bias is seen for the hosts of low-mass planets. At the lowest metallicities, Zackrisson et al. ( 2016 ) have studied the history of metallicity enhancement in the Universe and its effect on terrestrial planet formation. They apply a gradual iron enrichment cut-off between 10 per cent and 1 per cent of Solar metallicity, extending well below the Lineweaver ( 2001 ) cut-off we apply, although, as Fig. 2 demonstrates, the vast bulk of galaxies have a mean gas phase metallicity above 10 per cent Solar for the entire lookback interval being considered. It is likely that future exoplanet telescope missions such as TESS and PLATO , together with extensive ground-based follow-up, will be required to clarify this issue. However, as noted above, given the relatively slow evolution of mean metallicities in cosmic time, we