adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...585A..24M (fulltext)
to bounce against the surface to gain velocity in the direction perpendicular to the surface. The simplest way to treat this process is to assume an elastic collision, Here is the fraction of kinetic energy retained by the product colliding with the surface, which has an effective mass . For the specific case of hyperthermal scattering of O on graphite surface, Hayes et al. (2012) have proposed an equivalent mass of 130 a.m.u. The effective mass is much higher than one single C atom or even a carbon ring because the rigidity of the surface induces a collective behavior of the atom and a higher effective mass. The chemical energy is certainly split in many different ro-vibronic states, not at all in thermal equilibrium. We assumed that an equal share of energy is spread throughout all the degrees of freedom, so that the total chemical energy available for the kinetic energy perpendicular to the surface is . We assumed that the only velocity perpendicular to the surface corresponds to a distribution of speed and temperature. In this sense, . The probability for the product to have an energy (or liberation velocity) higher than the binding energy becomes In Fig. 3 we compare the results obtained experimentally (blue triangles) to our predictions. We first used a constant of 0.4, which implies that we assumed that 40% of the total energy is retained by the product (red hexagons). In this case, our predictions do not reproduce the experimental results well. In a second step, we considered a fraction that depends on the mass of the products, as described in Eq. (1). These predictions are represented as green squares and do match the chemical desorption efficiencies observed experimentally very well. This clearly indicates that the chemical desorption process is sensitive to the binding energy, the energy distributed in the products (depending on their mass), and the degrees of freedom. For some of the reactions such as OH+H, O+H, our predictions slightly underestimate the experimental results. Table 2 Binding energies on bare and icy surfaces. Notes. For HCO, CHO, and CHOH we assume . (a) Cazaux Tielens (2004) ; (b) Bergeron et al. (2008) ; (c) Al-Halabi van Dishoeck (2007) , Amiaud et al. (2007) ; (d) Pirronello et al. (1997) ; (e) Amiaud et al. (2006) for low coverage; (f) Minissale et al. (2015) ; (g) Dulieu et al. (2013) ; (h) Speedy et al. (1996) , Fraser et al. (2001) energy derived of 5800 K with pre-factor of 10 s here corrected to have a pre-factor of 10 s; (i) Noble et al. (2012a) ; (j) Borget et al. (2001) , Minissale (2014) ; (k) Karssemeijer Cuppen (2014) ; (l) Garrod Herbst (2006) ; (m) Noble et al. (2012b) ; (n) Collings et al. (2004) ; (o) Fuchs et al. (2006) ; (p) extrapolated from Kimmel et al. (2001) . The formula (Eq. (2)) can be translated to the case of silicates surfaces. The efficiency is slightly lower, and thus reducing the effective mass from 130 to 110 matches the most constrained reaction (O+O) very well. Taking the
Paper: 2018MNRAS.478L..95K (acknowledgments)
ACKNOWLEDGEMENTS I thank B. Hansen and B. Zuckerman for useful discussions.
Paper: 2019ApJ...878..120S (acknowledgments)
We thank the anonymous referee for providing constructive comments that have improved the contents of this paper. We thank Ronin Wu for sharing PACS observations. STO2 is a multi-institutional effort funded by the National Aeronautics and Space Administration (NASA) through the ROSES-2012 program under grant NNX14AD58G. This research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Y.S. acknowledges support from the NASA postdoctoral program. M.G.W. was supported in part by NSF grant AST-1411827. C.K. was supported through NSF grant AST-1410896. Kristina Davis is currently supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1801983.
Paper: 2016A&A...586A.144V (fulltext)
measurements in -band for the two prototypes are presented in Fig. 3 . Fig. 3 Azimuthal average of the amplitude transmission in band for the chromium oxide (green) and aluminum (blue) SLLC prototypes as a function of the normalized distance from the apodizer center (). The increase in transmission close to zero corresponds to the pattern introduced in the design to facilitate the opto-mechanical centering of the apodizer. The design specifications are represented by a gray envelope. The transmission of the microdots map designed by Aktiwave is also plotted in orange. This profile appears noisier because the microdots map is binarized. The top plot shows the normalized transmission measurement, and the bottom plot shows the error with respect to the nominal transmission profile. The vertical dotted lines correspond to the radii where we plot the transmission as a function of wavelength in the left panel of Fig. 4 . The transmission map of the aluminum prototype in band is shown as an inset in the top left corner of the plot. Fig. 4 Chromaticity of the amplitude transmission of the aluminum prototype. Left : normalized amplitude transmission as a function of wavelength at different radii in the pupil (dotted vertical lines in Fig. 3 ), for which the value is indicated on the right side of each curve. Right : measured amplitude transmission at the four wavelengths as a function of the original specification. The dashed and dash-dotted lines show the 1 decade/decade and 0.5 decade/decade trends. From the very first measurements it appeared that the chromium oxide prototype did not meet the specifications in the low-transmission part (20%) of the apodizer. Because only one free position was available in the SPHERE apodizer wheel and because limited time was available for extensive tests during the reintegration period, we chose to keep the aluminum prototype inside the instrument, which is closer to the specifications. For this reason, we do not discuss the results from the chromium oxide prototype in more detail, and we refer to Vigan et al. (2014) for some additional information and measurements. From now on, we refer only to the aluminum prototype. For the aluminum prototype, the transmission is closer to the specifications, but there are still some problems in the low-transmission parts of the apodizer. The minimum transmission obtained in the central part and at the edge of the pupil is 2.5%. This measurement illustrates the difficulty of manufacturing apodizers for the near-IR with very high optical densities, as was already identified by previous studies in the context of the GPI instrument ( Sivaramakrishnan et al. 2009 ). A microdots apodizer relies on the absorption and diffraction of light by small (20 m in our case) metallic dots deposited on a substrate whose distribution is such that the density of dots produces the required transmission profile. As detailed by Dorrer Zuegel (2007) , the spatial filtering produced by the
Paper: 2021A&A...645A.105G (fulltext)
. This correction therefore also accounts for the average offset induced from the PSF residual ellipticities which is of similar amplitude to the c -terms 15 . We note that we choose not to implement an additional empirical PSF-dependent correction for the PSF contamination, as our α measurements are noisy. Furthermore, if α is not a constant, and is instead correlated with galaxy properties, atmospheric seeing, or image depth, for example, applying an average correction would artificially imprint a PSF-correlation across our full data set that could potentially be more problematic than the low-level average bias that we currently detect. 3.5.2. Accuracy requirements and validation of the first-order systematics model In Fig. 7 we compare two measured star-galaxy cross-correlation functions with the amplitudes predicted by the linear systematics model from Eq. ( 12 ). The standard star-galaxy cross-correlation function, , is related to the linear systematic model parameters as 16 Fig. 7. Comparison of two star-galaxy cross-correlation functions with their corresponding linear systematics model predictions (Eqs. ( 15 ) and ( 16 )) for the five tomographic bins. The star-galaxy cross correlation function (dark blue) is in reasonable agreement with the model (blue bar), where the width of the model reflects the 2 σ uncertainty. The residual star-galaxy cross correlation function (shown pink), and corresponding model (grey), are scaled by a factor of ten in order to display the measurements on the same scale. We also scale the correlation functions by to aid visualisation of the large-scale signal. We can also construct a residual star-galaxy cross correlation function , where As with previous sections, we focus on the ξ + ( θ ) term only, as the ξ − ( θ ) term is consistent with zero for the OmegaCAM PSF. Figure 7 shows reasonable agreement between the measured star-galaxy correlation function (shown dark blue), with the model prediction (blue band, Eq. ( 15 )) which we calculate by taking α from Fig. 6 , β = −1, δ ε PSF from the detector level model in Sect. 3.4 , and the other terms measured directly from KiDS-1000. We note that the band encompasses the 2 σ uncertainty from the measurement of α . All other error terms are sub-dominant. In contrast we find little agreement between the measured residual star-galaxy correlation function (shown pink), with the model prediction (grey dotted line, Eq. ( 16 )). This suggests that our faint magnitude linear extrapolation of the residual PSF ellipticity, δ ε PSF , is not representative of the detector level bias that the galaxies have experienced. We find that the average residual star-galaxy correlation is significantly lower than the expectation from the extrapolated chip-dependent residual PSF ellipticity model. We therefore conclude that whilst we find significant flux dependence in the PSF residual ellipticity for 3/32 OmegaCAM chips (see Fig. 4 ), there is no evidence in the shear catalogue
Paper: 2017ApJ...840..104S (fulltext)
0.7 dex), which seems unlikely to be consistent with our assumed scatter of , given the small size of that sample. Figure 3. Halo-to-stellar mass ratio (the inverse of the SHMR) as a function of stellar mass. The red and blue dots denote the measurements for the low- and high-redshift samples, respectively. The red solid curve and cyan-shaded area are the best-fit SHMR relation and its 1 σ uncertainty derived by fitting our low-redshift sample combined with the results of H15. The blue solid curve is the best-fit SHMR relation of our high-redshift sample combined with H15. The red and blue dashed curves are the relations fitted by Leauthaud et al. ( 2012 ), using COSMOS measurements. We also compare our measurements with other galaxy–galaxy lensing measurements, including SDSS (Mandelbaum et al. 2006 ), CFHTLenS (H15), COSMOS X-ray clusters (Leauthaud et al. 2010 , 2012 ), and GAMA (Han et al. 2015 ). This scatter for individual systems suggests that selection effects may still be a concern, in our and other published determinations of the SHMR. For instance, Leauthaud et al. ( 2015 ) found that the CMASS sample is significantly affected by mass incompleteness, and is ∼80% complete at only in the narrow redshift range z = [0.51, 0.61]. The LOWZ sample, on the other hand, is ∼80% complete at for z = [0.15, 0.43]. If completeness is correlated with high or low stellar mass for the primary galaxy, it could mean that the true scatter is larger than what we have assumed. Follow-up studies of the individual objects in our samples, as well as data from larger forthcoming lensing surveys, will be required to determine the exact effect of these biases on the mean relation. A Chabrier ( 2003 ) IMF was assumed by the measurements of H15 , Leauthaud et al. ( 2012 ), and Han et al. ( 2015 ). Ilbert et al. ( 2010 ) investigated the possible sources of uncertainty and bias by comparing stellar mass estimates between methods. The expected difference between a Kroupa IMF and a Chabrier IMF is not significant. 5. Conclusions Combining samples of massive galaxies and clusters from the redMaPPer and LOWZ/CMASS catalogs, respectively, with the shear catalog of the CS82 survey, we have measured the projected density profiles around central galaxies at two different redshifts. We fit the lensing shear signal around these galaxies as a sum of contributions from the main DM halo, the central galaxy, centering errors and physical offsets between the galaxy and the halo, and neighboring halos. From the resulting fits, we determine the mean halo concentration as a function of mass, and also the mean relation between (central) stellar mass and halo mass (the SHMR). Our main results are as follows. 1. We find that halo concentration decreases weakly with mass, c = A ( M / M 0 ) B , with the amplitude A ∼ 5–6 and slope B ∼ −0.14. These parameter values are in good agreement with the predictions of simulations, e.g., Duffy et al. 2008 or Klypin et al. 2016 , given a
Paper: 2015ApJ...815..123D (acknowledgments)
The authors thank the anonymous referee for comments and suggestions that improved the paper. The authors also thank J. Martin-Pintado for allowing us to utilize his 2001 7 mm observations of Sgr B2 North. C.D.P. and M.M.M.L. were supported by NSF grant AST-1211460. M.M.M.L. was additionally supported by NSF grant AST-1109395 and the Humboldt Foundation. T.P. acknowledges financial support through a Forschungskredit of the University of Zürich, grant No. FK-13-112, and from the DFG priority program 1537 “Physics of the interstellar medium.” R.S.K. acknowledges support from the Deutsche Forschungsgemeinschaft (DFG) via the SFB 881 The Milky Way System (sub-projects B1, B2, B5, and B8), and the SPP (priority program) 1573, Physics of the ISM. He also thanks the European Research Council for significant funding under the European Community’s Seventh Framework Programme (FP7/2007-2013) via the ERC Advanced Grant STARLIGHT (project number 339177). R.G.M. acknowledges support from program UNAM-DGAPA-PAPIIT IA101715.
Paper: 2020A&A...642L...7P (fulltext)
lines is explained by (i) the very similar upper level energy and critical densities ( E up = 33 K, and 35 K, and n cr ∼ 7−5 × 10 5 cm −3 , and 17−9.5 × 10 5 cm −3 , at 20−100 K, for the H 2 CO and the CS line, respectively, Shirley 2015 ) and (ii) the similar distribution of the H 2 CO and CS abundance in the disk suggested by thermo-chemical models (e.g., Fedele Favre 2020 ). Both molecules are released from grains and/or formed in the gas phase in the molecular layer, following the photodissociation of CO, which makes available C for the formation of small hydrocarbons, together with atomic O. The CS is formed from reactions of small hydrocarbons with either ionized or atomic S (e.g., Le Gal et al. 2019 ), while the main formation route of H 2 CO is via the reaction CH 3 +O (e.g., Loomis et al. 2015 ). Additionally, CO 2−1 and CS 5−4 emissions strongly decrease in the outer disk-midplane, where molecules freeze-out onto dust grains (freeze-out layer). By contrast, H 2 CO is only a factor of 2 less intense, which indicates that the H 2 CO molecules trapped on the icy grains in the cold midplane are partially released by nonthermal processes (UV, X-ray, cosmic-rays-induced, and/or reactive desorption). We tentatively detected CH 3 OH emission at 3 σ in the disk region where H 2 CO and CS emissions are bright, that is to say the warm molecular layer where molecules should be efficiently released from grains, while it remains undetected in the outer disk midplane. In the right panel of Fig. 3 , the [CH 3 OH]/[H 2 CO] abundance ratio inferred for the Class I disk of IRAS 04302 is compared with the estimates obtained in hot corinos around Class 0 protostars, in the other Class I disks observed by ALMA-DOT, in the Class II disks of TW Hya and HD 163296, and in comets. The abundance ratio in the disk of IRAS 04302 (∼0.5−0.6 for T ex ∼ 20−100 K) is consistent with the upper limits derived for the other Class I and early Class II disks observed by ALMA-DOT (between 0.7 and 3.2, Podio et al. 2019 ; Garufi et al. 2020 , and in prep.). On the other hand, it is lower by a factor of ∼2.5 than that inferred for the Class II disk of TW Hya (∼1.27−1.73 for T ex ∼ 25−75 K, Walsh et al. 2016 ; Carney et al. 2019 ), while it is larger than the upper limit obtained for HD 163296 ( 0.24, Carney et al. 2019 ). As methanol forms on the dust grains due to CO freeze-out and subsequent hydrogenation, the lower [CH 3 OH]/[H 2 CO] in HD 163296 may be due to a smaller degree of CO ice chemistry in the warmer disks around Herbig stars (e.g., Pegues et al. 2020 ). The abundance ratios in Class I and Class II disks are from a factor of 2 to 2 orders of magnitude lower than the values inferred in the hot corinos around the Class 0 protostars IRAS 16293-2422 A and B in Taurus ( Jørgensen et al. 2018 ; Persson et al. 2018 ; Manigand et al. 2020 ), and NGC 1333-IRAS 4A and IRAS 2A in Perseus ( Taquet et al. 2015 ). This difference may be due to an evolution of the methanol and
Paper: 2018A&A...615A.113M (acknowledgments)
Acknowledgements I am very grateful to the anonymous referee for his/her helpful comments and suggestions that have led to a significant improvement in the presentation of this manuscript. I am also grateful to Amherst College for its support through a John Woodruff Simpson Lectureship, and to Purple Mountain Observatory in Nanjing, China, for its hospitality while part of this work was being carried out.
Paper: 2017MNRAS.465.2198D (fulltext)
distribution. 3 In reality, the whole structure moves on the same short dynamical time-scale of equation ( 1 ) and hence the effective surface density is much reduced if the system as a whole begins to re-expand. We suspected that the choice of fixed initial density may be the reason for the undetectable shift of the peak on Fig. 8 . Our choice of initial conditions implies that the system size R o M 1/3 ; had we chose instead to use an empirical relation such as R o M 1/5 for stellar clusters (Larsen 2004 ), we would have found a scaling of E s / E s a 3 N 7/5 . The same rise in N as before would have produced a shift from 40 to 6.5au in the peak of the distribution, and this is still too large to go undetected. 5 EXTREME TIGHT AND WIDE BINARIES As mentioned in Section 3.2 , Bate ( 2012 ) has found several examples of binaries reducing their separation over time through stellar encounters, from 10au down to 0.5au. Tighter systems were hindered by numerical resolution issues. The regularized treatment of close encounters (which allows us to integrate up to machine precision) of the code nbody 6 means that the same collisional process will be at play in the calculations that we have performed. Since no binaries with semimajor axis a shorter than 1au were inserted in the initial conditions, we focus first on the statistics of binaries that evolved to a 1au. In the second part of this section, we will explore the formation and the evolution of very wide ( a 10 4 au) binaries, many of which end up loosely bound to the stellar cluster as a whole. 5.1 Tight binaries Several binaries with a 1au were detected at the end of the low-density simulations. By comparison, almost none developed in the high-density runs. Their properties are summarized in the left-hand panel of Fig. 10 . The data are taken from N 20k runs but similar statistics were obtained for the other setups. The top row shows the binarys mass as a function of semimajor axis, while the bottom row on that figure graphs the distance of their barycentre to the centre of the cluster. Open circles denote binaries that already existed at t = 0, and became tighter over time, while filled circles are new binaries that formed in the course of the simulation. Inspection of these new, tight binaries shows that both of their components were part of binaries originally inserted in the system. Thus all new tight binaries shown on Fig. 10 are the results of strong binarybinary interactions leading to splitting or exchange. (None of the original binaries that survived to the end of the simulation had a 1/2au.) Figure 10. Left-hand panels: total binary mass (top) and distance to centre of the cluster (bottom) versus the semimajor axis for binaries tighter than 1au at the end of the simulation. Right-hand panels: same layout for binaries wider than 10 4 au. The greyed area in the top panels shows the 90percent probability range of a binary total mass from random pairing given the present IMF. The red
Paper: 2017ApJ...851....8L (fulltext)
lag between the formation of the jet and the outflows. Because our analysis depends on the long-slit data with a specific P.A., the estimated size of the NLR based on [O iii ] may be underestimated if the slit is off from the outflow direction. Figure 8. Photometric size–luminosity relations for AGNs. Black and magenta dots show the measured sizes from Husemann et al. ( 2013 , 2014 ). Green dots present the results from Karouzos et al. ( 2016a ). The measured values from Bae et al. ( 2017 ) are denoted by blue dots. Our measured [O iii ] sizes are shown by red dots. In this plot, the [O iii ] luminosity is the extinction-uncorrected values. Dashed and solid lines present the slope values from linear regression of Husemann et al. ( 2014 ) and Bae et al. ( 2017 ). The flux distribution of [O iii ] is often used to define the size of the NLR. However, the extent of the photoionization region is different from that of gas outflows, as gas can be ionized by the central source while the kinematics of the ionized gas can simply follow the gravitational potential of the host galaxy without showing any outflows. Therefore, Karouzos et al. ( 2016a ) and Bae et al. ( 2017 ) measured the size of the outflows based on the [O iii ] kinematics compared to stellar kinematics instead of measuring the extent of the photoionization region. A detailed comparison between the photoionization and outflow sizes will be presented in the future (D. Kang et al. 2017, in preparation). While the outflow size measured based on the spatially resolved kinematics provides much better representation of the outflow energetics, it requires high quality data with a good spatial resolution, which are missing in our MMT observations. Future IFU observations with high S/N may overcome the limitations of our analysis based on MMT long-slit spectra. 5. Discussion and Conclusions Various studies in the literature showed the connection between gas kinematics and radio emissions. For example, Mullaney et al. ( 2013 ) reported that in AGNs with moderate radio luminosity, the radio luminosity has strong effects on the [O iii ] profile. The morphology and position angle of the radio emission and the ionized-gas emission have strong connections. In the case of the targets in which the jet and ionized-gas emissions are aligned, the radio jets may be responsible for driving outflows and shock ionization, hence driving the correlations between radio emissions and gas kinematics (e.g., Veilleux 1991 ; Bower et al. 1995 ; Zakamska Greene 2014 ). In a radio-loud AGN, 4C12.50 fast outflows in HI gas are detected with an extremely blueshifted velocity, ∼1000 km s −1 relative to the systematic velocity at the location of the southern jet. These outflows are considered as a result of the interaction between radio jet and the ISM (Morganti et al. 2013 ). By using near-infrared data, Tadhunter et al. ( 2014 ) reported the highly distorted kinematic profiles in the H 2 1-0 S(1) λ 2.128 μ m line, showing