adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...650A..47P (fulltext)
first order in δ n The general solution of Eq. ( A.29 ) then reads where α n ( T 1 ) and β n ( T 1 ) are two functions to be determined with the first-order equation. Using Eq. ( A.31 ) in Eq. ( A.30 ), it is straightforward to show (e.g., via the method of variation of parameters) that, if non-null, the terms in brackets will induce terms proportional to τe ± iτ in the expression of , up to a factor on the order of α n and β n . This will make on the order of up to a timescale on the order of O(1/ δ n ) and, therefore, would break the perturbation ansatz in Eq. ( A.28 ). Therefore, to obtain a uniformly valid expression up to such timescales, the terms in brackets have to vanish. This condition leads to with A n and B n two arbitrary constants. As a consequence, the amplitude is ruled only by the coupling term on the right-hand side of Eq. ( A.30 ), which depends on the leading-order amplitudes with n ′ ≠ n . For n ′ ≠ n , it is obvious that the amplitudes follow the same zeroth-order and first-order equations as in Eqs. ( A.29 ) and ( A.30 ); however, because of the considered scaling, T k for k = 0 or 1 in these equations are replaced by with π = ( ω n ′ / ω n ). As a result, we get where A n ′ and B n ′ are two arbitrary constants. According to Eqs. ( A.30 )–( A.32 ) we thus find that is ruled at leading order by Using again the method of variation of parameters, we can show that the amplitude is equal to a series of terms as a function of n ′ that are ). At this point, we assume that the magnitude of slowly varies with n ′, so that it is for n ′∼ n ; this will be checked a posteriori (see the resulting velocity amplitudes in Fig. 2 ). Provided for n ∼ n ′ and otherwise, as justified in Appendix A.2.3 , the main contribution to results from the terms of the series associated with n ′ ∼ n ; as a result, with Δ ω n = | ω n − ω n − 1 |. Within the asymptotic limit, ω n /Δ ω n ∼ n ≫ 1 and thus in order of magnitude. This highlights afterward that, instead of δ n , it is more appropriate to scale the non-adiabatic perturbation of the mode amplitude by in such a way that the perturbation ansatz becomes and the new first-order perturbation . As a result, the relative error when considering up to a timescale on the order of T damp is on the order of . We check a posteriori at the end of Sect. 3.1 that in the considered asymptotic frequency range. Therefore, all the previous perturbation developments, fortunately, hold true and the coupling terms do not stand for secular terms. These terms thus have no effect on the two-timing solution provided by , and are just responsible for the relative error on the order of . In summary, provided that the mode amplitude slowly varies with the radial order, the solution of the homogeneous equation as a function of the variable t , which is uniformly valid up to a timescale on the order of O( T damp ), takes the form, according to Eqs. ( A.31 ) and ( A.32 ), of the adiabatic solution modulated by an exponential
Paper: 2019ApJ...877..142S (acknowledgments)
This work has been supported by the Croatian Science Foundation under the project 7549 “Millimeter and submillimeter observations of the solar chromosphere with ALMA” and also supported by NASA Heliophysics LWS grant NNX16AB79G (SHS). Facility: SDO (AIA) . Software: astropy (Astropy Collaboration et al. 2013 , 2018 ), Period04 (Lenz Breger 2005 ), SunPy (SunPy Community et al. 2015 ).
Paper: 2017ApJ...837..139C (fulltext)
computed by Madau Dickinson ( 2014 ), after we converted it to a Kroupa IMF, and the red curve shows it multiplied by 0.4 to match roughly the SFR density history of the SCUBA-2 sources. However, once we also take into account the different FIR calibrations that were used in this work vs. in Madau Dickinson, we find that the SCUBA-2 data are only 0.29 times the Madau Dickinson curve. The red shaded region shows the 68% confidence range when allowance is made for the systematic uncertainties in the SFR calibrations. (b) Number density per unit comoving volume per unit SFR vs. SFR in five redshift intervals (see legend). The error bars in Figure 25 (a) are purely statistical. However, systematic errors in the SFR conversion and in the redshift estimates may also be important. In order to estimate these potential errors, we recomputed the SFR density history randomly, reassigning the SFRs and redshifts within the potential error ranges. Based on these Monte Carlo calculations, we computed the 68% error range produced by the systematics, which we show as the red shaded region. For the highest and lowest redshifts, where there are fewer objects, the systematic and statistical errors are similar, but for redshifts between 2 and 4, the systematic error dominates. In Figure 25 (b), we show the SCUBA-2 SFR density distribution functions computed for the same redshift intervals versus log SFR. Although the normalizations of the distribution functions are changing—they rise to reach a peak at before dropping at higher redshifts—the shapes over are remarkably similar; that is, the number density of high SFR galaxies relative to that of lower SFR galaxies is about the same in each redshift interval. However, at some point this similarity in shape must break down, as there cannot be large numbers of powerfully star-forming galaxies at very high redshifts. The present data show that this must occur at redshifts higher than . In Figure 26 , we show the SCUBA-2 distribution functions of Figure 25 (b) recomputed for the coarser redshift intervals , and 4–6 (colored circles; note that in the interval there are not enough sources to plot the two highest SFR bins). We compare these functions with those computed at lower SFRs from the extinction-corrected UV samples of Reddy Steidel ( 2009 ) and van der Burg et al. ( 2010 ) (small colored symbols and curves), assuming a Kennicutt ( 1998 ) conversion of UV luminosity to SFR for a Kroupa ( 2001 ) IMF. This emphasizes the disjoint nature of the SFRs of galaxies selected by UV and submillimeter observations (e.g., Barger et al. 2014 ): only submillimeter observations can find the most powerfully star-forming galaxies at redshifts . Both the UV and SCUBA-2 distribution functions have similar shapes in the interval. Thus, the number density of high SFR galaxies measured from the SCUBA-2 sample relative to that of lower SFR galaxies from UV-selected samples is about the same in this redshift interval. However, the
Paper: 2021MNRAS.502.1605B (fulltext)
fields to search for weak diffuse emission from the targets (e.g. Cook et al. 2020 ). For this work, we have used data from the 150 MHz TIFR GMRT Sky Survey (TGSS). The first alternative data release (TGSS-ADR1) of this survey (Intema et al. 2017 ) provides the continuum Stokes I images of 99.5 per cent of the sky area north of declination δ = −53° (i.e. ∼90 per cent of the full sky), at a resolution of 25 × 25 arcsec 2 for fields that are north of 19° declination, and 25 × 25 arcsec 2 /cos (δ − 19°) for fields south of 19°. The median value of RMS noise is 3.5 mJy beam −1 for the survey. Out of around 65 dSph galaxies of Milky way and Local Group (McConnachie 2012 ; Drlica-Wagner et al. 2020 ), 23 are found to be within the TGSS footprint. There were five more targets observed in the TGSS, but are not included in this analysis due to presence of image artifacts in those fields. The target coordinates, their half-light radius and distance (McConnachie 2012 ; Koposov et al. 2015 ; Laevens et al. 2015 ; Martin et al. 2015 ; Martínez-Vázquez et al. 2019 ; Cook et al. 2020 ) are tabulated in Table 1 , and the half-light radius distribution is shown in Fig. 1 . Note that the unique hybrid array configuration of the GMRT implies that the telescope has sensitivity to both small-scale compact structures as well as large-scale diffuse emission, with the largest detectable structure (corresponding to the shortest baselines) being 68 arcmin. If the diffuse emission from these targets have angular extent ∼few × r h , there will not be any significant missing flux issue in this analysis. Figure 1. Histogram of the half-light radii, r h of the sample of 23 dSph galaxies used in this study. Table 1. List of the dSph galaxies used in this analysis, with their J2000.0 coordinates, half-light radius ( r h ), and distance ( D ). 3 DATA ANALYSIS For the 23 dSph galaxies, we have used the TGSS-ADR image archive to extract the image of a region centred around the optical position of each of the galaxies in FITS format combining data from multiple overlapping TGSS pointing. For the first part of our analysis, we have extracted 1° × 1° region for each of the target dSph galaxies. Further analysis was done using the casa (Common Astronomy Software Applications) package. We have visually inspected each field to identify and blank all the compact sources (unresolved and marginally resolved) using a 5 σ cut off. These compact sources are unrelated to the expected faint diffuse emission (originating from processes directly involving the DM annihilation/decay) at the galaxy-wide scale, that is of our interest. This will also filter out any plausible unidentified imaging artefact present in the field. We then shift the central coordinate of all the fields, regrid and convolve the data to exactly same pixel size and same synthesized beam before combining to create a stacked image from all 23 dSph target field. We have also selected 23 ‘control’ regions, located close to
Paper: 2016A&A...587A.152M (fulltext)
such quartet at 173.2 GHz that corresponds to the transitions gave us an opportunity to assign the whole series of this kind of transitions and get an initial approximation for the rotational constants of CHNH (at this stage the tunneling parameters were kept fixed at the values of the main methylamine isotopologue, Motiyenko et al. 2014 ). Obtained in this way, the initial model allowed us to continue assigning the spectra in a usual iterative manner by adding new assigned lines to the fit, refining the Hamiltonian model, and producing new predictions. The Doppler-limited resolution of our spectrometers provided an opportunity to resolve nuclear quadrupole hyperfine splittings for 898 rotational transitions. Typically, as for the main methylamine isotopologue, a resolved pattern of the hyperfine structure was observed as a doublet with an approximately 2-to-1 ratio in intensities. The stronger doublet component contains unresolved hyperfine transitions with selection rules and , whereas the weaker doublet component corresponds to the transition. At the same time, a number of hyperfine components with selection rules were also assigned. To provide the hyperfine-free frequencies we used in our analysis of the rotation-torsion-wagging spectrum of the ground state of C methylamine, we fitted the frequencies of the individual hyperfine components using the model described above. The quadrupole hyperfine parameters , and that we obtained from the fitting of the hyperfine patterns of the rotational transitions are presented in Table 1 . Table 2 Main rotational and torsion-wagging splitting parameters (all in MHz except of which is unitless) of the ground torsional state of C and C methylamines. Notes. (a) . Fig. 1 Predicted (in red) and observed (in blue) rotational spectrum of C methylamine between 882 and 908 GHz dominated by -type series of transitions with . A slight inconsistency between predicted and observed spectrum, which may be visible for some strong lines, is due to source power and detector sensitivity variations. Weak unassigned lines are due to higher excited vibrational states. In total, the dataset for the rotational spectra of CHNH in the ground vibrational state includes 2721 rotational transitions with an upper value of . This set of rotational transitions was fitted to the effective Hamiltonian of the group-theoretical high-barrier tunneling formalism that was developed for methylamine by Ohashi Hougen (1987) . The fit adopted in the present study as the “best” achieved the root-mean-square (rms) deviation of 0.039 MHz that corresponds to the weighted rms deviation of 0.73. The Hamiltonian model includes 75 parameters. The values of the molecular parameters obtained from the final fit are presented in Table 1 . In Table 2 , the main rotational and torsion-wagging splitting parameters for CHNH and CHNH ( Motiyenko et al. 2014 ) are compared. From the comparison we see that the values of the main tunneling splitting parameters in
Paper: 2017ApJ...836...22H (fulltext)
et al. 2009 )). This provides a framework on which to interpret the line fluxes in a quantitative manner in Section 4 . The EXES and TEXES spectra contain contributions from the stellar photosphere, inner CSE outflow, and large scale optically thin wind dust emission. Allowing for image quality (SOFIA) and diffraction (IRTF) and the angular size of the emission, the relatively compact line and extended dust emission experience different throughput losses at the slit. Model differential corrections combined with ISO fluxes provide EXES and TEXES flux spectra, which we expect to be accurate to ±30%. Line profile shifts and distortions can arise from features in the underlying photospheric continuum, so we simulated the photospheric component using MARCS (Gustafsson et al. 2008 ) model atmospheres ( K, , solar abundances, 15 macroturbulence) using the BSYN code in a similar manner to Ryde et al. ( 2002 ; see references therein). The synthetic spectrum for the K is rather featureless, except for weak absorption features 5% below continuum level. However, there is not a good understanding of the mid-IR photospheric spectra of luminous cool evolved stars (see, for example, Sloan et al. 2015 and Ryde et al. 2015 ). Inspired by Ryde et al. ( 2006a , 2006b ), we examined a model for K, and this spectra showed deeper absorption features at the 20% level. Taking into account the accuracy of the Barber et al. ( 2006 ) H 2 O line list used in the synthetic spectra, we added Gaussian emission profiles at different wavelengths to the synthetic spectra; convolved them with the instrumental Line Spread Function (LSF), which is assumed to be Gaussian; and then performed a Gaussian fit to the resulting profiles. We found that at 25.99 μ m, centroid velocity shifts only occurred at the percent of a level, while near 17.4 μ m we found that the underlying H 2 O features induced a redshift of up to ∼0.4 to the emission. Close to the [Fe ii ] lines, photospheric water lines are the strongest features. The observed line profiles of Betelgeuse from SOFIA-EXES and IRTF-TEXES are shown in Figures 3 and 4 , and a TEXES observation of α Sco is shown in Figure 5 . The line profile measurements are summarized in Table 3 . We discuss the [Fe ii ] 25.99 and 17.94 μ m lines first, and the non-detections of 22.9 μ m last. Figure 3. SOFIA-EXES [Fe ii ] 25.99 μ m spectrum from α Ori. The blueshifted emission that is well separated from the telluric contamination near . The Callisto spectrum is shown below. Table 3 Observed [Fe ii ] Line Properties Note. a Residual contamination after division by telluric calibrator, leading to additional uncertainties at the 1 level. 3.1. SOFIA-EXES: <inline-formula> <tex-math> </tex-math> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjaa566fieqn153.gif"></inline-graphic> </inline-formula> Figure 3 shows the 25.99 μ m spectrum for α Ori, which is cleanly separated from the telluric feature, as confirmed by the
Paper: 2018MNRAS.480..402D (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank the anonymous referee for his/her very useful comments that have allowed us to improve our paper. We thank Yi-Jung Yang for useful discussions. This work is supported by the National Natural Science Foundation of China (Grant No. 11703098, Grant No. 11275247, and Grant No. 11335012) and a 985 grant at Sun Yat-Sen University. YMH gratefully acknowledges the support of the National Natural Science Foundation of China (1170030038). FKP acknowledges support from the Doctoral Starting up Foundation of Guizhou Normal University 2017 (GZNUD[2017] 33).
Paper: 2017MNRAS.468.4000C (fulltext)
) suggest that these events, and hence IMBHs, are not equally distributed amongst galaxies but rather they find that the Milky Way is expected to underproduce these events by a factor of 10 2 compared to compact blue galaxies. This means the number of lower mass IMBHs in the Milky Way could be as low as 10 2 . As these young clusters evaporate, if they evaporate, their IMBHs would be left with at most only the few stars within the IMBHs sphere of influence. If there are such low-mass IMBHs threading their way through the Milky Way then Gaia may provide a unique opportunity to spot them. As Gaia (de Bruijne 2012 ) observes a billion or so stars in the Milky Way, making very high-precision measurements of the peculiar motion of the stars, it should observe some systems for which their motions can only be explained with the addition of an IMBH. However, if there are in fact only 10 2 such IMBHs in the Milky Way, and Gaia is only sampling 1 out of every 10 2 stars in the Milky Way, we would expect to detect on order of 1 of these objects. Of course, as with the rest of this letter, caution must be taken when considering such a low number of statistics. The spatial distribution of these systems should not deviate much from their initial distribution, at least not as a result of dynamical friction since for the 10 3 M case dynamical friction should only result in an inward migration of the black hole of 40pc, and only 0.4kpc even for a 10 5 M black hole in 13Gyr (assuming a value of 6 for the Coulomb logarithm). Additionally, the all-sky, X-ray survey provided by eROSITA (Merloni etal. 2016 ), with an anticipated launch data in 2016, will likely provide many more IMBH candidates with its sensitivity and long expected exposure time. 4.3 IMBHs in relation to other black holes Finally, in Table 1 we compile the number and total mass of different types of massive objects [stars, stellar mass black holes (StMBHs), IMBHs and SMBHs] in both our Galaxy and within a sphere with a radius of 100Mpc. Here, we outline how we arrived at these values: in the Milky Way there is one observed SMBH, Sgr A*, with a mass of 4.110 6 M (Ghez etal. 2008 ). Caramete Biermann ( 2010 ) estimate a total number of SMBHs within 100Mpc with masses 10 7 M to be about 2.410 4 and from the mass density provided therein, we estimate a total mass of 2.810 11 M within the same volume. We must be careful to note that while the number of SMBHs within 100Mpc may be larger due to excluding the SMBHs with masses 10 7 , Caramete Biermann ( 2010 ) claim that the total mass is barely dependent on the cutoff due to a flattening of the integral mass function at lower mass. Table 1. The estimated number and total mass of SMBHs, IMBHs, StMBHs and Stars within the Milky Way (left two columns) and within 100Mpc (right two columns). In this work, we have calculated the values for the number of IMBHs within 100Mpc and within the Milky Way. In order to estimate the total mass of the IMBHs, we used
Paper: 2018ApJ...869..135S (acknowledgments)
F.S. was supported by the Max Planck Society grant “Science Projects in Preparation for the PLATO Mission.” S.B. acknowledges support from NSF grant AST-1514676 and NASA grant NNX16AI09G.
Paper: 2018MNRAS.475.5501M (acknowledgments)
Acknowledgements Based on observations carried out as part of the Large Program ASAI (project number 012-12) with the IRAM 30-m telescope. IRAM is supported by INSU/CNRS (France), MPG (Germany) and IGN (Spain). This work was supported by the CNRS program”Physique et Chimie du Milieu Interstellaire” (PCMI) and by a grant from LabeX Osug@2020 (Investissements d'avenir - ANR10LABX56). E. M. acknowledges support from the Brazilian agency FAPESP (grant 2014/22095-6 and 2015/22254-0). I.J.-S. acknowledges the financial support received from the STFC through an Ernest Rutherford Fellowship (proposal number ST/L004801/1).
Paper: 2015ApJ...806..270Z (acknowledgments)
K.Z. is supported by Leverhulme Research Project Grant RPG-2015-096 and the HKRGC grant (Project 14306814), D.K. is supported by the NSFC under grant 11473014, and G.S. is supported by the National Science Foundation under grant NSF AST-0909206. The computation made use of the high-performance computing resources in the Core Facility for Advanced Research Computing at Shanghai Astronomical Observatory, Chinese Academy of Sciences.
Paper: 2019MNRAS.485.5073D (fulltext)
it rises rapidly to a peak at . Bottom: The metallicity distribution function over the entire galaxy. The bottom panel of Fig. 3 shows the metallicity distribution function (MDF) across the model. The model’s MDF peaks at nearly Solar metallicity and has a long tail to low . The MDF is similar to some extent to the MDF of the bulge (e.g. Zoccali et al. 2008 ; Gonzalez et al. 2015 ). 3 KINEMATIC FRACTIONATION Fig. 4 shows the evolution over the last (i.e. since redshift z = 0.4), of the bar amplitude, A 2 , defined as the usual m = 2 amplitude of the Fourier moment measured overall stars (e.g. Debattista Sellwood 2000 ). The bar forms quite late, starting from ( z ≃ 0.19). It reaches a peak amplitude at , and weakens somewhat in the next Gyr, as is often seen in simulations of isolated galaxies. In isolated simulations, bars generally experience renewed growth past this point (e.g. Combes Sanders 1981 ; Sellwood Moore 1999 ; Debattista Sellwood 2000 ; Athanassoula 2002 ; Bournaud Combes 2002 ; O’Neill Dubinski 2003 ; Martinez-Valpuesta et al. 2006 ), but the late bar formation in this simulation does not give the bar time to strengthen again. Figure 4. Bar amplitude evolution in m12m since redshift z = 0.4 (corresponding to of evolution). 3.1 Density separation Fig. 5 shows the mass density distribution at z = 0 in face-on and edge-on projections for the model separated by different stellar populations. As in Debattista et al. ( 2017 ), younger populations exhibit a stronger bar and a more prominent box or peanut (B/P) shape than the older ones. The difference in the B/P strength as a function of age is a signature of kinematic fractionation as discussed in Debattista et al. ( 2017 ). Figure 5. Density distributions in the face-on (top panels) and edge-on (bottom panels) projections. The bottom left panels show the full edge-on projection, while the bottom right panels show a cross-section with particles chosen within . In the top row of each set of panels we show the accreted stars (left) and all the stars (right). The rest of the panels show populations separated by time of formation of the stars, age. Fig. 5 also shows the density distribution of the stellar population that was accreted, which we define as stars that formed at radius . The accreted stars, which account for of all the stars, formed primarily () by redshift z = 1.27 (). They have a density distribution similar to that of the oldest (age now) in situ stars, i.e. those formed at (see also El-Badry et al. 2018b ). Like the oldest bin, no bar or X-shape is present in the accreted population. 3.2 Age and metallicity dependence of bar amplitude Fig. 6 shows the dependence of the usual m = 2 Fourier global bar amplitude on the age and metallicity. As also apparent from the face-on maps in Fig. 5 , the bar is stronger in younger populations than in the older ones. The very oldest populations have quite a weak bar overall, while the youngest populations have a 3.5 times stronger bar.