adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...597A.105F (fulltext)
components, respectively. The solid red curve is the sum of these two models. Fig. 8 Schematic representation of the spherically symmetric model for the molecular components of Arp 299A showing approximate spatial scales. Most of the molecular absorption is formed in the outer layers of the dense, most compact C component while the HO emission in the submillimeter seems to originate in the more optically thin C component. A tentative inflowing C component is included to account for the redshifted ground state OH doublet at m. Fig. 9 Reduced for the observed high-lying HO lines in our grid of models with . Fig. 10 for the OH at m with different values of applied to the best fit model of the inner component found using the HO lines. 3.3. Outer component Even in the best fit model for the inner component there is flux missing in the low-lying OH and and HO lines detected with PACS and, especially, the low-lying HO lines detected with SPIRE. To account for this missing flux in the models, a less excited and more extended component is required. To constrain the parameters of this outer component we have followed the same general procedure as for the inner component, but now for the low-lying lines normalized to the flux of the HO m line. In this process we have also taken the best fit model for the inner component into account. The observed line ratios are best reproduced with a dust opacity in the range . In this component collisional excitation becomes non-negligible for cm, but in these models the undetected HO m line goes into emission, favoring models with lower where collisional excitation is negligible. The reduced for our grid of models at the preferred dust opacity is presented in Fig. 11 . The line ratios are best reproduced with dust temperatures between and K, and HO columns between and cm. To also account for the absolute fluxes, the component should have a radius of pc. The best fit is achieved with , K, cm and pc. Fig. 11 Reduced for the observed low-lying HO lines in our grid of models with for the outer component, taking the contribution from the inner component into account. In Fig. 12 we present the reduced for the OH doublets in the outer component for different values of applied to the best fit model found for HO, now taking the OH contribution from the inner component into account. The OH m doublet which is redshifted by km s is not included here. The best fit is achieved with a column density of OH in the range to cm, corresponding to an OH/HO ratio in the range to . Applying a similar process to the upper limits of the OH doublets we get an upper limit to the OH column density of cm. Fig. 12 Reduced for the OH doublets, except for OH m , with different values of applied to the best fit model of the outer component found using the HO lines. 3.4. Extended component Most of the flux in both the HO and the OH lines is reproduced with the two components described in Sects. 3.2 and 3.3 . The ground state OH doublet at m is,
Paper: 2015ApJ...803...34B (acknowledgments)
We thank Peter Capak, Mark Dickinson, Jim Dunlop, Richard Ellis, Steve Finkelstein, Kristian Finlator, Jason Jaacks, Ross McLure, Ken Nagamine, Masami Ouchi, Rogier Windhorst, and Haojing Yan for valuable conversations. Feedback from our referee substantially improved this paper. We acknowledge the support of NASA grant NAG5-7697, NASA grant HST -GO-11563, ERC grant HIGHZ #227749, and a NWO vrij competitie grant 600.065.140.11N211. This research has benefitted from the SpeX Prism Spectral Libraries, maintained by Adam Burgasser at http://pono.ucsd.edu/~adam/browndwarfs/spexprism .
Paper: 2021MNRAS.500.1279F (fulltext)
to the GCs in M20, we derive a dynamical mass enclosed within the outermost GC (i.e. 2.2 kpc) of 3.48 (± 0.84) × 10 8 M ⊙ , with the uncertainty given by the range in potential and orbital anisotropy described above. Our TME dynamical mass is larger than the GC-based half mass, but agrees within the uncertainties. Allowing for a reasonable range in γ would increase the uncertainty to around ±50 per cent; where assuming a lower (higher) value of γ would lead to a reduced (increased) TME mass. The resulting mass-to-light ratio from our TME analysis is M 1/2 /L 1/2 = 12.7 ± 3.7. Forbes et al. ( 2019 ) detected 20 GC candidates associated with NGC 5846_UDG1. If we assume the same mean GC luminosity function turnover magnitude (i.e. M V = –7.3) and width as found by Miller Lotz ( 2007 ) for dE galaxies in the Virgo cluster, then the inferred total GC population is around 45. For this count, the Burkert Forbes ( 2020 ) relation between the number of GCs and the halo mass implies a total halo mass of ∼2 × 10 11 M ⊙ . Given that there are several unknowns in translating the detection of GC candidates into an estimate of the total GC system, it is useful to note that M20 confirmed at least a dozen GCs associated with NGC 5846_UDG1. This gives a lower limit on the total halo mass with this relation, of 0.6 × 10 11 M ⊙ . Both of these total halo mass estimates far exceed the total stellar mass of 1.1 × 10 8 M ⊙ . For comparison, M33 has the same inferred total halo mass of 2 × 10 11 M ⊙ (Seigar 2011 ) but its stellar mass is some 50× larger. Thus, NGC 5846_UDG1 can be described as having a dwarf-like stellar mass with an overly massive dark matter halo. Assuming a total halo mass of 2 × 10 11 M ⊙ as described above, we can compare our half mass and TME mass with different halo mass profiles. We chose three profiles, namely the universal NFW profile (Navarro, Frenk White 1996 ), the mass profile resulting from the dwarf galaxy simulations of Di Cintio et al. ( 2014 ), which resembles a core-NFW profile, and the observationally motivated mass profile for dwarf galaxies from Burkert ( 1995 ). For details of the NFW and Di Cintio et al. profiles, see the appendix of Di Cintio et al. ( 2014 ). We show these three profiles, with the same total halo mass, compared to our half and TME masses measured at small radii in Fig. 5 . The plot shows that our mass measurements lie between the Di Cintio and Burkert mass profiles for a total halo mass as given by the total number of GCs. This suggests that NGC 5846_UDG1 may be better represented by a core mass profile as predicted in some simulations (e.g. Carleton et al. 2019 ) than a cuspy one. However, we cannot, at this stage, rule out other mass profiles and/or different total halo masses. Similar conclusions, favouring a cored mass profile, were reached by Gannon et al. ( 2020 ) for VCC 1287, and by Wasserman et al. ( 2019 ) for DF44. Figure 5. Halo mass profiles. Three halo mass profiles (Navarro et al. 1996 ,
Paper: 2019MNRAS.484.5377S (acknowledgments)
ACKNOWLEDGEMENTS We thank Martin Shepherd, David Hawkins, Russ Keeney, and Erik Leitch for advice and assistance with the design and construction of the backend. The work at the California Institute of Technology and Owens Valley Radio Observatory was supported by National Science Foundation (NSF) awards AST-0607857, AST-1010024, AST-1212217, and AST-1616227, and by NASA award NNX15AF06G. The work at Oxford was supported by funding from STFC, the Royal Society and the University of Oxford. CD acknowledges support from an ERC Grant no. 307209. OGK acknowledges the support of a Dorothy Hodgkin Award in funding his studies while a student at Oxford, and the support of a W.M. Keck Institute for Space Studies Postdoctoral Fellowship at Caltech. This work formed part of MAS’s PhD thesis (Stevenson 2013 ).
Paper: 2019A&A...627A..13B (acknowledgments)
The AGILE Mission is funded by the Italian Space Agency (ASI) with scientific and programmatic participation by the Italian Institute of Astrophysics (INAF) and the Italian Institute of Nuclear Physics (INFN). Investigation supported by the ASI grant I/089/06/2. We thank the ASI management for unfailing support during AGILE operations. We acknowledge the effort of ASI and industry personnel in operating the ASI ground station in Malindi (Kenya), and the data processing done at the ASI/SSDC in Rome: the success of AGILE scientific operations depend on the effectiveness of the data flow from Kenya to SSDC and on the data analysis and software management. We also acknowledge the use of innovative tools that are under development within the United Nations “Open Universe” initiative.
Paper: 2018ApJ...862...77C (acknowledgments)
The authors wish to thank the anonymous referee for a constructive report, which has improved the paper. We also wish to thank the Aspen Center for Physics for hosting two summer workshops, “The Obscured Universe: Dust and Gas in Distant Starburst Galaxies” in summer 2013 and “New Frontiers in Far-infrared and Sub-millimeter Astronomy” in summer 2016, whose stimulating conversations led to this work. The Aspen Center for Physics is supported by National Science Foundation grant PHY-1066293. C.M.C. thanks the National Science Foundation for support through grant AST-1714528. Additionally, C.M.C. and J.A.Z. thank the University of Texas at Austin College of Natural Sciences for support. J.S. thanks the McDonald Observatory at the University of Texas at Austin for support through a Smith Fellowship. E.dC. gratefully acknowledges the Australian Research Council for funding support as the recipient of a Future Fellowship (FT150100079). J.A.H. acknowledges support of the VIDI research programme with project number 639.042.611, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). S.L.F. acknowledges support from an NSF AAG award AST-1518183. This work relies in part on the COSMOS survey data set. COSMOS is based on observations from the NASA/ESA Hubble Space Telescope , obtained at the Space Telescope Science Institute, which is operated by AURA Inc., under NASA contract NAS 5-26555. It is also based on data collected at: the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan; the XMM-Newton and Herschel Space Observatory , ESA science missions with instruments and contributions directly funded by ESA Member States and NASA; the European Southern Observatory, Chile; Kitt Peak National Observatory, Cerro Tololo Inter-American Observatory, and the National Optical Astronomy Observatory, which are operated by AURA, Inc., under cooperative agreement with the NSF; the National Radio Astronomy Observatory, which is a facility of the NSF operated under cooperative agreement by Associated Universities, Inc.; the Canada–France–Hawaii Telescope operated by the National Research Council of Canada, the Centre National de la Recherche Scientifique de France and the University of Hawaii; and the James Clerk Maxwell Telescope, which is operated by the East Asian Observatory on behalf of The National Astronomical Observatory of Japan, Academia Sinica Institute of Astronomy and Astrophysics, the Korea Astronomy and Space Science Institute, the Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments, budgeted from the Ministry of Finance (MOF) of China and administrated by the Chinese Academy of Sciences (CAS). Many of the data sets this paper and analysis rely on were obtained on the summit of Maunakea on the island of Hawai’i. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.
Paper: 2018MNRAS.477.2599B (acknowledgments)
ACKNOWLEDGEMENTS MB and FH acknowledge partial financial support from the INFN TEONGRAV specific initiative. MB acknowledges the CINECA award under the ISCRA initiative, for the availability of high-performance computing resources and support. This work was supported by the H2020-MSCA-RISE-2015 Grant No. StronGrHEP-690904. AS is supported by a University Research Fellowship of the Royal Society. This work has made use of the Horizon Cluster, hosted by the Institut d’Astrophysique de Paris. We thank Stephane Rouberol for running smoothly this cluster for us.
Paper: 2019A&A...625A.141L (acknowledgments)
Acknowledgements We thank the anonymous referee for the insightful comments and suggestions that improved the paper in several ways. This research has been partly supported by the Lars Hierta Memorial Foundation, and the Royal Physiographic Society in Lund through Stiftelsen Walter Gyllenbergs fond and Märta och Erik Holmbergs donation. H.J. acknowledges support from the Crafoord Foundation, Stiftelsen Olle Engkvist Byggmästare, and Ruth och Nils-Erik Stenbäcks stiftelse. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This publication made use of the SIMBAD database, operated at CDS, Strasbourg, France; NASA’s Astrophysics Data System; and the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna.
Paper: 2020MNRAS.498.2663T (fulltext)
measure the target power spectrum , and ψ’s PDF, from the SB simulation. In using both from a single realization, the results may depend on peculiarities in that realization. The limitation is due to the lack of an available software to produce accurate fitting functions for the power spectrum and PDF of ψ. This introduced dependence is not a problem, as long as one has the aim of isolating the contribution from the PDF and the correlation of ψ to the specific realization examined, to assess how much information they convey with respect to the exact result. To emulate the final ψ field, we set the phases of the pre-translation Gaussian field to the phases of the initial density field of the realization. We do not smooth the measured ψ field, because both the power spectrum and the PDF are measured at the grid level, and one can check that the ψ field variance is already consistent with its power spectrum variance integrated up to the Nyquist frequency. In Fig. 12 , we can see that the PDF of the ψ realization from ITAM matches the one of the N -body simulation (much better than muscle , and particularly 2LPT), as it should by the virtue of the translation transform equation ( 2 ). Likewise, the Lagrangian-space ψ power spectrum of ITAM matches that of the N -body simulation better than the other prescriptions, as shown in Fig. 13 . The two standard simulation schemes have a mismatch, with 2LPT diverging the most at small scales and muscle departing the most on large scales. This confirms the detailed analysis of the power spectra of Lagrangian schemes conducted by Chan ( 2014 ), where it is also found that these techniques come short of matching the power spectrum measured in N -body simulations. Another benchmark of the goodness of a fast simulation scheme is the similarity between the resulting matter density and the N -body matter density. This is generally quantified by means of the cross-correlation spectrum, which for two fields A and B is Figure 12. PDFs of ψ for various schemes. The peak at ψ ∼ −3 is associated with collapsed halo particles. In muscle , halo particles give a Dirac-delta function at ψ = −3 (see equation 43 ). Figure 13. Transfer function of ψ, defined as the ratio of the power spectrum of ψ generated from the fast simulation scheme over the one measured from the N -body realization. The cross-correlations are plotted in the top panel of Fig. 14 . As we can see, the muscle implementation still performs better than ITAM, which in turn is better than 2LPT, at least on smaller scales. Examining this further, 2LPT is almost a local approach, in the sense that it considers the information of each voxel and its nearest neighbours (equation 37 ). ITAM by construction is non-local, as it involves the two-point correlation function. muscle is also non-local, but the non-locality is coming from a smoothing procedure on various scales centred around each voxel (equation 43 ). Considering that the one-point mapping of ITAM is very
Paper: 2019MNRAS.484.4401C (fulltext)
host halo mass, and the green valley transition time-scale, τ Green , for both disc and elliptical RS satellite galaxies. We select all = 0 RS satellite galaxies that, at the time the galaxy became green ( t Green ), were also satellites (corresponding to of the satellite sample), and calculate the ratio between the satellite galaxy’s stellar mass and the host halo mass, log 10 M * / M 200 , at t Green . Throughout this section host halo mass refers to the halo mass 1 of the central galaxy within which the satellite galaxies are embedded. Fig. 7 shows the green valley transition time-scale, τ Green , as a function of log 10 M * / M 200 at t Green for RS satellite galaxies that are discs (blue dots) and ellipticals (red dots) at = 0. It can be seen that for both disc and elliptical satellite galaxies, τ Green tends to increase with M * / M 200 . This is highlighted by the best-fitting linear regression of the τ Green −log 10 M * / M 200 relation for elliptical (red dashed line) and disc (blue solid line) satellite galaxies. We find that τ Green correlates more strongly with M 200 than with M * , so that satellite galaxies embedded in cluster-sized haloes transition the green valley faster than their same-stellar mass satellite counterparts residing in lower mass haloes. Figure 7. Green valley transition time-scale, τ Green , of satellite galaxies as a function of the ratio between stellar mass and host halo mass, log 10 M * / M 200 , at the time when the galaxy left the blue cloud, t Green . The data points with error bars show the median τ Green values in bins of log 10 M * / M 200 for galaxies that have been satellites since t Green and at = 0 have κ co ≥ 0.4 (shown as blue dots) or κ co 0.4 (shown as red dots). The error bars correspond to the 25th−75th percentiles of the τ Green distribution in each mass bin. The lines correspond to the best-fitting linear regressions. The red satellite galaxies tend to have quenched more rapidly if they are elliptical and if their stellar mass is smaller relative to the total mass of their host halo. This relation is mostly driven by a dependence on halo mass, rather than on stellar mass. Fig. 7 shows that satellite galaxies embedded in clusters (log 10 M * / M 200 − 4 with haloes more massive than ) tend to cross the green valley over shorter time-scales than satellite galaxies residing in lower mass haloes, regardless of the galaxy’s morphological type. However, the spread in the τ Green distribution is large. In the case of central galaxies, although most of them reside in cluster-sized haloes, we do not find any correlation between τ Green and halo mass or M * / M 200 . This is in agreement with previous studies that suggest that environmental quenching is mostly associated with satellite galaxies rather than centrals (e.g. Peng et al. 2010 ; Darvish et al. 2016 ). Central galaxies show properties that are more associated with the galaxies’ stellar mass (i.e. on average more massive galaxies are
Paper: 2020MNRAS.491.3217K (acknowledgments)
ACKNOWLEDGEMENTS The authors thank V. Bosch-Ramon and an anonymous referee for their useful comments. DK is supported by the Japan Society for the Promotion of Science KAKENHI Grant Numbers JP18H03722, JP24105007 and JP16H02170. MA is supported by the RIKEN Junior Research Associate Programme.
Paper: 2021A&A...650A.140M (fulltext)
methane possesses strong and distinctive absorption signatures. An annotated list of these attempts can be found in Zahnle et al. (2011) , Knutsen et al. (2021) , and in tabulated form in Pla-Garcia et al. (2019) . Hitchcock Lovelock (1967) pointed out that the composition of the Martian atmosphere could provide clues as to the possible presence of life on the planet. Therefore, methane has long been seen as the most accessible testimony of potential signs of life ( Atreya et al. 2003 , 2007 ) or of a remnant geophysical activity on Mars. Indeed, the presence of methane has such a strong connotation of Mars being potentially an active or even a life-hosting planet, contrary to all other chemical or geophysical evidence suggesting the opposite, that it has attracted the attention of a wide community extending far beyond those interested in Martian atmospheric composition. However, the succession of failed and successful attempts to detect methane has also created a blurred picture, and a comprehensive understanding of the existence and origin of methane on Mars has remained out of reach. Several attempts that were initially presented as positive detections were later either retracted or questioned. This is the case of the Mariner 7 detection (as explained above) and the Canadian-France-Hawaii Telescope (CFHT) observations of Krasnopolsky et al. (2004) – whose spectrum quality raised the suspicion of Zahnle et al. (2011) , who also questioned the seasonal variations of methane found by Geminale et al. (2008) based on a survey carried out with the Planetary Fourier Spectrometer (PFS). Finally, the methane mapping of Fonti Marzo (2010) using the Thermal Emission Spectrometer (TES) was later retracted by the same authors in Fonti et al. (2015) . Of all the detections made between 2003 and 2010, only a few survived the scrutiny of the community. The PFS 2003 detection reported in Formisano et al. (2004) , while bearing evident shortcomings regarding its resolving power and sensitivity, has resisted criticism and remains to this date the first non-rebutted detection of methane in the Martian atmosphere. In addition, Mumma et al. (2009) published the first ground-based evidence of methane collected the same year as the PFS, the scientific implications of which were bolstered by the revelation of puzzling spatial and seasonal variations attributed to a sudden release from the Nili Fossae region. Several studies have explored the chemical ramifications ( Lefèvre Forget 2009 ) as well as the spectroscopic consistency of these detections ( Zahnle et al. 2011 ). These works converged toward the idea that our current understanding of the physics and chemistry governing the Martian atmosphere is incompatible with the observed variable behaviour of methane, which should rely on an extraordinary, yet undefined mechanism. Together with the fact that detections had long lain on the fringe of the detection capability of all instruments used at that time, the