adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.472.3624P (fulltext)
estimated using the total luminosity derived from spectral energy distribution fitting. Traditionally, MYSOs have been found to have luminosities similar to those of OB stars. Spectral types have rarely been determined directly in MYSOs, and not at all in the case of RMS MYSOs. Previously, Kendall, de Wit Yun ( 2003 ) observed H -band spectra of three MYSOs, IRAS 171753544, IRAS 174412910 and IRAS 180791756 at intermediate resolution ( R 5000). They found photospheric absorption lines in IRAS 180791756 and IRAS 171753544, and assigned spectral types of B3V and O7-8V, respectively, to these two objects. Most of our MYSOs show strong, broad H i emission, due to a stellar wind (see also Section 4.3 ). However, two of the objects in this sample (G015.128800.6717 and G034.050000.2977_A) display absorption in the Br10, Br11 and Br12 lines. There are also hints of absorption in Br and Pa, alongside the dominant emission in these two objects. The RMS survey quotes G015.1288 as having L bol =13000 L . This corresponds to a spectral type of B0.5 for a zero-age MS star, using the values from Straizys Kuriliene ( 1981 ). The absorption profiles in the H band of G015.1288 were compared with spectra of normal stars from the surveys of Hanson etal. ( 2005 ) and Meyer etal. ( 1998 ). A spectral type of F or later would be hard to reconcile with the MYSO data, as F stars have a large number of metal lines. In addition, early O spectral types do not match either, as their Brackett lines are weaker and narrower than observed for G015. Late O and B1-5 stars show a He i line at 1.70 m, at comparable or higher strength than the hydrogen absorption lines, and is not present here. We note that the NIR spectral surveys of Hanson etal. ( 2005 ) and Meyer etal. ( 1998 ) do not have complete spectral and luminosity type coverage. The only B-type sample spectra available are those for B1, B5 and B8. Therefore, given the presence and absence of diagnostic absorption lines, a spectral type later than B5 and earlier than A9 would be expected for G015.1288. However, the Br10-12 absorption in the B5-A9 stars is much stronger than observed for G015.1288. It is possible that a significant amount of excess could veil the H i absorption to a comparable level to our observations. This is illustrated in Fig. 2 , which zooms in on the Brackett lines in the H -band spectra of a number of normal B8-A7 stars with added excess continuum emission. Overplotted on these are the GNIRS data for G015.1288. A B8 star with added excess does not match the observed H i profiles, as the lines in this star are considerably narrower than in G015.1288. In addition, the He i line is still strong enough to be observed. The A0Ib profiles match the observations in strength, but are narrower. An A0V star has broader wings in the Brackett lines than G015.1288. In the absence of more observational MS star spectra to compare our data with, we conclude that G015.1288 has the spectrum of an early A giant or
Paper: 2016ApJ...832....4G (fulltext)
by disk emission, even in the optical/near-IR, and changes at optical/near-IR wavelengths would reflect changes in the superheated disk rather than the central protostar. FU Ori ( D = 450–500 pc; Hartmann Kenyon 1996 ) is a wide (05) binary system consisting of a outbursting young stellar object modeled as a 0.3 M ⊙ with a 0.04 M ⊙ circumstellar disk (Zhu et al. 2007 ) at an A V of ∼1.8 (Zhu et al. 2010 ) and a more massive young companion ( M > 0.5 M ⊙ ; Reipurth Aspin 2004 ; Wang et al. 2004 ; Hales et al. 2015 ). The companion (FU Ori S) is at 227 au projected separation (Pueyo et al. 2012 ) at an A V of 10 ± 2 (Pueyo et al. 2012 ). In this work, we present new mid-IR photometry and spectroscopy of FU Ori obtained with the Stratospheric Observatory for Infrared Astronomy (SOFIA) in 2016, and we compare with previous observations with the Spitzer Space Telescope in 2004. We find that the continuum flux density at wavelengths shorter than 8 μ m (arising from the hot inner disk) has decreased by 12%–14%, dominating the change in bolometric luminosity. The continuum at wavelengths greater than 10 μ m has decreased only 7% within uncertainties. Additionally, we find that the H 2 O vapor absorption resulting from the superheated disk photosphere remains present. Finally, we note no significant changes in the 10 μ m silicate emission feature, which shows no evidence of crystallinity. Taken together, these observations suggest that the decrease in viscous dissipation energy over this period has occurred predominantly in the innermost section of the disk, which is changing rapidly. 2. OBSERVATIONS 2.1. SOFIA-FORCAST Spectroscopy We observed FU Ori with the Faint Object infraRed CAmera for the SOFIA Telescope (FORCAST; Herter et al. 2012 ) on board the SOFIA (Young et al. 2012 ) on two separate flights (on 2016 February 4 and 18, flights 272 and 279, respectively); see Table 1 for the Observing Log. The observations were performed in C2N (two-position chop with nod) mode, using the slit, and the NMC (nod-match-chop) pattern in which the chops and nod are in the same direction and have the same amplitude. In each case, an off-source calibrator was selected from an ensemble of observations from cycles 2–4, using the observation closest in zenith angle and altitude to the science target (A. Helton 2016, private communication). We did not use dithering. The FORCAST grism was used in three spectral bands, G063 (∼5–8 μ m), G111 (∼9–13 μ m), and G227 (∼18–25 μ m), from 5 to 25 μ m to achieve a spectral resolving power of R ∼ 200. The achieved signal-to-noise ratios (S/Ns) for the individual grism segments were 30 (G063, measured from 5.7 to 6.3 μ m), 46 (G111, measured from 11 to 12 μ m), and 22 (G227, measured from 21 to 26 μ m). The total observation time was 12,904 s (3.58 hr), including imaging, which totaled approximately 0.33 hr (see below). Table 1 Observing Log Notes. Observations were performed over two different flights, 272 (2016 February 04) and
Paper: 2019MNRAS.488..720B (fulltext)
et al. ( 2018 ) and Stiele Kong ( 2018 ), but due to sparser observations, the correlation is not as tight as seen in this work. The trend observed by Mereminskiy et al. ( 2018 ) resembles the correlation observed for GRS 1915+105 (Shaposhnikov Titarchuk 2007 ). Such correlation has been previously observed in several BHBs (Vignarca et al. 2003 ) and provides a tight link between the QPO and the hard-component emission. In that work, the correlation was found on time-scales of minutes in GRS 1915+105 and on scales of days-months for other transient BHBs. Here, we follow the QPO over 5 d. Fürst et al. ( 2016 ) also observe an extremely tight correlation of the QPO centroid frequency and power-law index in GX 339–4 in the frequency range of 0.6–1 Hz and Γ of 1.66–1.8. The authors estimate the mass of the BH using a scaling relation provided by Shaposhnikov Titarchuk ( 2007 ). The typical evolution of the outburst of a transient BHB is described best in the HID, where a counterclockwise path is followed. In the right ‘vertical’ branch, corresponding to the LHS, the flux increases, the power-law index is well correlated with it and the frequencies of the timing features, although no peaked QPO is usually observed, also increase (see Motta, Belloni Homan 2009 ; Motta et al. 2011 ). Since the spectrum gradually softens, this branch is not really vertical. In the top ‘horizontal’ branch, the spectrum softens further, with the power-law index Γ increasing in absolute value. The flux usually continues to increase, although not as fast as before, and the QPO frequency is correlated with both flux and Γ. Given the shape of the PDS and the values of the power-law index, our observations are clearly on the HIMS branch. As described before, usually both the LHS and HIMS branches see a correlation between frequencies of variability components, the flux, and the power-law index. In our AstroSat data, we see the absence of a correlation with flux, although in the NICER data following our observation this correlation is present. This clearly shows that the QPO frequency is related to the energy spectral shape and not to the flux in either of the two main components. In a thermal-Comptonization scenario, the index of the hard component steepens as the population of electrons cools due to the increase in soft photon input (see e.g. Motta et al. 2009 ). However, from Fig. 4 we can see that this is not the case here, as the secular increase in disc flux is not followed by Γ. Another parameter that determines Γ is the optical depth of the cloud τ 0 : for a spherical cloud and input photon energy much lower than the temperature of the electron cloud kT e , we have , where (Sunyaev Truemper 1979 ; Sunyaev Titarchuk 1980 ). Therefore in order to keep Γ from varying when the cloud temperature decreases is to increase τ 0 in a very specific way. Fluctuations in this relation could in principle give rise to the observed fluctuations in Γ, but it would be very ad hoc.
Paper: 2019ApJ...882..141M (fulltext)
well as for the PSF models, which are mandatory for the subsequent modeling. Excluding variance maps from our modeling process would lead to an underestimation of errors, since the flux contribution of the central point source is significantly higher than that of the sky. We create 2D two-component models of our objects utilizing the same PSFMC algorithm 8 8 See https://github.com/mmechtley/psfMC for additional information, examples, and documentation. (Mechtley 2014 ) as described and used in Mechtley et al. ( 2016 ). We use a point-source model and a Sérsic profile to simultaneously fit the central AGN component and underlying host galaxy respectively. Although the host galaxies may not be perfectly described by a Sérsic profile, it is a sufficient approximation, as we can determine potential asymmetries or tidal features. Furthermore, the advantage of using this two-component approach is twofold: First we ensure that the contributing flux of the central source is not overestimated and avoid an eventual PSF oversubtraction. Second, we also keep the computational time needed for the fits within reasonable limits. Contrary to most of the tools adopted to fit galaxies in 2D, our algorithm allows the user to input a prior probability distribution for the fitted model parameters of the different components, e.g., position in x and y , total magnitude, Sérsic index, position angle, etc. As a result, posterior probability distributions of all the fitted parameters with the corresponding covariances are produced. Additionally, the algorithm does not assume an error-free PSF, but implements the respective PSF variance maps during the convolution process. To that end, we deploy the HST focus model (Cox Niemi 2011 ) to determine for each source the time-dependent telescope focus and find the best matching PSF within our PSF library. For the actual fit we mask and exclude neighboring galaxies, whose central flux peaks do not coincide with the central source in the respective images. The resulting point-source component is then subtracted from the HST /WFC3 image to remove the AGN, and just display the host galaxy beneath. In Figure 2 we show from left to right for one of our 21 AGN sources the original, drizzled HST /WFC3 image, the convolved point-source model, and the point-source-subtracted model showing the host galaxy, once with the original sampling, and once smoothed by a σ = 2px Gaussian. All four images are shown in the same arcsinh scaling. Comparable plots for the remaining 20 AGNs are shown in Appendix A . Figure 2. Different steps of our AGN/host galaxy modeling process shown for one sample source: in the first column we show the original, processed HST /WFC3 image, the second column displays the PSF convolved point-source model, and the third and fourth columns show the host galaxy after point-source subtraction with the original sampling and smoothed by a σ = 2px Gaussian, respectively. This particular source is one of the five AGN host
Paper: 2017MNRAS.471.3347W (fulltext)
locations of the clusters on the sky are used to correct the individual distances for the inclination of the disc, thereby obtaining a more accurate absolute distance for the LMC. By doing so, we obtain an estimate of the distance to the centre of the LMC that is more accurate by accounting for the geometry of the galaxy. This process is outlined in van der Marel Cioni ( 2001 ) and Grocholski et al. ( 2007 ), and also presented in the equations ( 2 ) through ( 5 ) below. The distance to the LMC centre is denoted by D 0 . D is the distance to any location on the disc plane; in our case, this is the distance to each cluster. We assume the LMC disc to be inclined to the plane of the sky by the angle i around an axis at the position angle θ. θ is measured anticlockwise from the west; to align with usual astronomical convention, Θ is defined the position angle from the north (Θ = θ – 90). These values are adopted from van der Marel Cioni ( 2001 ); we note that Grocholski et al. ( 2007 ) explored a variety of geometries from other past works, finding differences in the resulting distance of ≲0.2 kpc for varying i and Θ. The right ascension and declination are referred to, respectively, by α and δ in the equations below, To calculate the distance to the LMC centre (D 0 above), it is necessary to calculate ρ and sin (φ − θ). The former is calculated as in equation ( 3 ): Via a typical trigonometric expansion, sin (φ − θ) may be determined as sin (φ − θ) = sin (φ) cos (θ) – cos (φ) sin (θ). These components are detailed in equations ( 4 ) and ( 5 ): Through these equations, we determine an average, error-weighted distance modulus to the LMC centre of D 0 = 18.20 ± 0.06, equivalent to 46.42 ± 0.45 kpc. However, NGC 1841 is a clear outlier in these data, lying more than 3σ from the mean D 0 distance to the LMC centre, leading to several possibilities. One interpretation of these results suggests that NGC 1841 may not be a member of the LMC disc or may have been pulled out of the LMC disc (Grocholski et al. 2007 ). Another view is that an uncertain estimate of the reddening could be affecting the distance measurement; if the reddening of NGC 1841 is doubled from E ( B − V ) = 0.18, it is brought into agreement with the LMC mean distance. Regardless of the reason(s) for the discrepancy, we remove NGC 1841 as an anomaly and continue determining the distance to the LMC centre. Leaving out the extreme outlier of NGC 1841, the error-weighted mean is 18.41 ± 0.04 (48.29 ± 0.38 kpc). This is within the range of the result from Section 4.1 from the HB analysis, as seen in Fig. 9 , where the subdwarf distances and the subdwarf distances adjusted for the LMC geometry are compared to those derived from the HB analysis. The subdwarf distances tend to be shorter by 0.14 mag on average than those of the HB analysis; the adjusted subdwarf distances are also shorter than the HB distances by 0.12 (leaving NGC 1841 out). The offset appears to be consistent regardless of
Paper: 2017MNRAS.469.2464A (fulltext)
of residuals on a cluster by cluster basis when compared to the grid of Kroupa IMF clusters. This increase in scatter is especially noticeable in mass, and originates from the M 3 degeneracy described above. As the mass of the cluster and its IMF slope are highly dependent on each other, the wide scatter in mass is further reflected in the inaccurate values inferred for 3 . Indeed, given the flat and broad posterior PDFs for the IMF slope, the medians of 3 have no physical meaning and simply reflect the median of the prior on 3 [in this case, a flat distribution between 3 (3.0, 1.5)]. Figure 12. Comparisons of the medians of the inferred posterior PDFs for all physical parameters to the true values adopted when simulating the mock clusters. Results for individual clusters are shown with crosses, while the median residuals and the corresponding 10th and 90th percentiles are shown with solid points. We plot analysis including broad-band photometry alone with (red, circles) and without (purple, diamonds) a variable IMF. We also show results of the variable IMF case when including H (blue, squares). Points are shifted along the x -axis for visualization purposes, with the leftmost point situated at the true x -axis value. Given this result, it is clear that obtaining refined constraints on the upper-end slope of the IMF requires a different approach. It is readily apparent that the ability to apply a prior to the cluster mass that is independent of the broad-band photometry may prove very useful in analysis such as this. Independent constraints on the masses of clusters that may then be used as priors may come, for instance, from dynamical measurements (e.g. Ho Filippenko 1996 ; Gerhard 2000 ). The era of 30m telescopes will make such measurements more feasible. To illustrate this point in more quantitative terms, we present in Fig. 13 a study of the accuracy with which 3 can be recovered as a function of the width of a prior on the mass. Results of three choices of IMF are presented, when leaving the mass unconstrained, and when applying a top-hat prior with width 0.5, 0.2 and 0.05 dex about the target cluster mass, for both broad-band filters only and broad-band filters combined with H. This analysis shows that, as expected, the medians of the PDFs on 3 converge to the true value as the precision with which the mass is constrained increases. Due to the difference between logarithmic intervals in mass and the linear scale in the IMF upper-end exponent, there is a weak scaling of 3 with the prior width, with the underlying values being recovered to within 3 0.2 when the mass is constrained to better than 10percent. However, more realistic constraints on the mass (to within a factor of 2), for instance via dynamical measurements, appear to be useful to rule out extreme variations in the IMF upper-end slope through multiwavelength broad-band photometry, given the assumptions of our model. The inclusion of H results in a wider scatter towards zero for
Paper: 2021ApJ...915...29J (fulltext)
of neutron-rich nuclei are required in r -process simulations, and most of these nuclei lie far beyond the experimentally known region. Theoretical predictions of the properties of these neutron-rich nuclei have large uncertainties, which can further interplay with the astrophysical uncertainties. This makes it even more difficult to address the issues of the r -process. Nuclear masses are extremely important for r -process simulations, as they are needed to extract the reaction energies that go into the calculations of all involved nuclear reaction rates. During recent decades, great achievements in nuclear mass measurements have been made thanks to the development of radioactive ion beam facilities, and about 2500 nuclear masses have been measured to date (Wang et al. 2012 , 2017 , 2021 ). Nevertheless, the masses of a large number of neutron-rich nuclei relevant to the r -process remain unmeasured and cannot be reached in the foreseeable future, due to the difficulties in production, separation, and detection. Therefore, reliable theoretical predictions for nuclear masses are imperative at present. However, an accurate prediction of nuclear masses is still a great challenge for nuclear theory due to the difficulties in understanding nuclear interactions and quantum many-body systems. Tremendous efforts have been made in improving the prediction of nuclear masses and, currently, the rms deviations between theoretical mass models (Pearson et al. 1996 ; Koura et al. 2005 ; Zuker 2008 ; Goriely et al. 2009 ; Wang et al. 2014 ; Möller et al. 2016 ) and the available experimental data reach about 0.5 MeV. The rms deviations are further reduced to about 0.2 MeV by employing machine learning approaches (e.g., Wang Liu 2011 ; Niu et al. 2013 ; Utama et al. 2016 ; Neufcourt et al. 2018 ; Niu Liang 2018 ; Wu Zhao 2020 ). However, different mass models predict very different masses when moving to the region of neutron-rich nuclei and, thus, lead to obviously different abundances of nuclei in the r -process nucleosynthesis simulations (e.g., Wanajo et al. 2004 ; Sun et al. 2008 ; Farouqi et al. 2010 ; Arcones Martínez-Pinedo 2011 ; Martin et al. 2016 ; Li et al. 2019 ; Zhao Zhang 2019 ). Therefore, it is important to address how the nuclear mass uncertainties limit our knowledge of the r -process (e.g., Sun et al. 2008 ; Farouqi et al. 2010 ; Arcones Martínez-Pinedo 2011 ; Brett et al. 2012 ; Xu et al. 2013 ; Aprahamian et al. 2014 ; Martin et al. 2016 ; Mendoza-Temis et al. 2016 ; Mumpower et al. 2016 ; Li et al. 2019 ; Sprouse et al. 2020b ). Addressing the impact of mass uncertainties on r -process abundances is useful to deepen our understanding of the r -process in the present stage. For instance, can we identify the dominant r -process site with current uncertainty (e.g., Mumpower et al. 2015c ; Martin et al. 2016 )? Can we understand the origin of the rare-earth peak (e.g., Surman et al. 1997 ; Goriely et al. 2013 )? To what extent can we
Paper: 2016MNRAS.458.1581A (fulltext)
= 6.50 a 0 and along R and φ for θ = 75° is given. These cuts show that the R stretch coordinate is strongly coupled with θ and φ . Finally, it is worth noting that this interaction potential is strongly anisotropic especially along the R , θ and φ coordinates. This strong anisotropy is expected to lead to significant values for the collisional rates of the HNS rotational (de)-excitation by He. Figure 2. Two-dimensional contour plots of the 3D HNS–He PES along the R and θ coordinates for fixed φ = 0°, 90° and 180°. We give also the cuts along the angular coordinates and along R and φ for θ = 75°. Energies are in cm −1 . Black (red) contour lines represent repulsive (attractive) interaction energies. Table 1. Equilibrium geometries and energies of the minimal structures of HNS–He 3D-PES for φ = 0°, φ = 90° and φ = 180°. 3 DYNAMICAL CALCULATION DETAILS We consider the collision between an asymmetric top molecule (HNS) with He atom. The rotational Hamiltonian of the HNS molecule is given by where I α (α = x , y , z ) are the principal moments of inertia with respect to the principal axes of inertia (Gx), (Gy) and (Gz) of HNS. (G,x,y,z) is the body-fixed reference related to the space-fixed reference (G,X,Y,Z) by the Euler angles. I x , I y and I z are related to the rotational constants according to , and . The total angular momentum of the molecule is such that . D J , D JK and D K are first-order centrifugal distortion corrections. The rotational wave function | j τ m 〉 of an asymmetric top can be defined by three quantum numbers j , τ , m and can be expressed as a linear combination of the rotational wave functions | j τ m 〉 of a symmetric top molecule such as (Townes Schawlow 1955 ): where, j, k and m denote the rotational quantum number, its projection along the molecular z -axis and its projection along the space-fixed Z -axis, respectively. τ is the pseudo-quantum number that varies between − j and j . The rotational levels of an asymmetric top molecule are conventionally labelled (adopted hereafter) where K a and K c are the projection of the rotational angular momentum along the axis of symmetry in case of prolate and oblate symmetric tops limits being related to τ through τ = K a − K c . The rotational energy levels, of HNS, are deduced from the ab initio rotational and distortional constants calculated by Ben Yaghlane et al. ( 2014 ) at the CCSD(T)-F12/cc-pVQZ-F12 level of theory which are A e = 0.63583, B e = 0.61585, C e = 19.59885, D J = 9.11 × 10 −7 , D K = 4.68 × 10 −5 , D JK = 5.30 × 10 −3 (all values are in cm −1 ). The precision of these rotational parameters should be better than ∼0.3% according to theoretical benchmarks and their comparison to experimental data dealing with sulphur compounds (see for instance Cazzoli, Puzzarini Gauss 2014 ). All data represent predictions that can help for the identification of this molecule. For scattering calculations, only the first 29 levels of HNS molecule are considered. This corresponds