The authors cordially thank the referee for helpful comments on the manuscript. They furthermore thank Eiichiro Komatsu for helpful discussions about the definition of f NL with respect to the commonly used WMAP parametrization. JC is supported by the Royal Society as a Royal Society URF at the University of Manchester, UK. ED acknowledges support from the DOE under grant No. de-sc0008016. MK was supported by NSF Grant No. 0244990, NASA NNX15AB18G and the Simons Foundation.


it is challenging to reproduce this effect in our simulations without additional feedback. Stars from cold accretion mostly formed at high redshifts in small haloes that later assembled into the massive galaxies. The most efficient way to remove them from our simulations is to have stronger winds, i.e. stronger mass-loading factors in those haloes. However, having too much winds early will unavoidably fail to match observations at higher redshifts. The right-hand panels of Figs 10 and 11 show that cold accretion has nearly stopped after = 2 for these galaxies but hot accretion and wind recycling continue growing rapidly at low redshifts, and is responsible for most of the excess stars formed. After = 2, the stars in massive galaxies that must be prevented from forming come from almost equal parts: hot accretion, hot wind re-accretion, and cold wind re-accretion. Hence, preventing hot gas from cooling and forming stars at these times will eliminate both the hot mode accretion and hot wind re-accretion and will lessen the tension in massive haloes. There are several potential mechanisms, such as AGN and cosmic ray heating, that could reduce the amount of this cooling gas but they are not yet included in our simulations. The hot wind recycling would be harder to affect by extra heating, because the higher metallicity of this gas makes it cool faster. In hydrodynamic simulations, mixing between ejected wind elements and the surrounding gas may have a large impact on the amounts of hot and hot wind accretion. It is, however, unclear whether this type of feedback could prevent the ∼1/3 of stars formed through cold wind re-accretion after = 2 from forming, which is also necessary to match the observations. It is possible that a more accurate treatment of the cloud-CGM interaction would allow a larger fraction of these winds to become hot, alleviating this problem. 6 THE HIGH-RESOLUTION SIMULATION OF THE REFERENCE MODEL In this section, we present key results from the high-resolution RefHres simulation. It adopts the new wind launch algorithm as described in the previous sections using our fiducial set of wind parameters listed in Table 1 . It is also implemented with the numerical improvements to the SPH hydrodynamics introduced by Huang et al. ( 2019 ). We will focus on those predictions that have changed significantly from Huang et al. ( 2019 ) and from our previously published work with our new wind algorithm. 6.1 The stellar content Fig. 14 shows that the GSMFs from our fiducial simulation, shown as black lines in each panel, are mostly consistent with observations at all redshifts from = 0 to 4. The agreement is particularly good at the faint end except for = 0, where our simulation slightly underproduces the number of these low-mass galaxies. At the massive end, our GSMFs agree with observations at = 1 and 4. However, our fiducial simulation produces too many massive galaxies at = 0 and too few massive galaxies at = 2, even after taking account


We are grateful to Jacek Niemiec, Martin Pohl, Oleh Kobzar, Arianna Ligorini, and Artem Bohdan for fruitful discussions. Numerical computations and analyses were carried out in part on Cray XC30 and computers at Center for Computational Astrophysics, National Astronomical Observatory of Japan. This work used the computational resources of the K computer provided by the RIKEN Advanced Institute for Computational Science through the HPCI System Research Project (Project ID: hp150263), and was supported in part by JSPS KAKENHI grant No. 17H02877. This work also used the computational resources of the HPCI system provided by Information Technology Center, Nagoya University through the HPCI System Research Project (Project ID: hp170158).


ACKNOWLEDGEMENTS A.C., M.S. and E.Z. acknowledge support from CONICET, and E.Z. acknowledges support from SeCyT-UNC.


for VLM stars and BDs have high uncertainties at ages younger than 1 Myr ( Baraffe et al. 2002 ), we assumed an age of 1 Myr for [GKH94] 41. The temperature of 2795 K then corresponds to a mass of 41 . If the uncertainty of 1.5 subclasses in the spectral type is taken into account, a possible mass range for [GKH94] 41 is from 14 to 97 . We also calculated the luminosity of the source itself (without any contribution from its surrounding envelope) to constrain the mass range. We first dereddened the 2MASS -band magnitude () using the extinction law with ( Mathis 1990 ) and the visual extinction ( Bulger et al. 2014 ). The value was obtained from the best-fit atmospheric model (see Bulger et al. 2014 and references therein) over the 2MASS bands. Using the bolometric correction in -band versus spectral type relation as given in Filippazzo et al. (2015) , we derived a luminosity of 0.025 . Figure 3 shows the luminosity versus temperature diagram for [GKH94] 41 with the theoretical evolutionary models from Chabrier et al. (2000) and Baraffe et al. (1998) . The location of [GKH94] 41 in the diagram indicates that the upper limit (including the uncertainties) to its current mass is just above the substellar limit and below 0.09 , which is consistent with the upper limit value of 97 estimated above. We therefore conclude that the upper limit to the mass of [GKH94] 41 is in the range of 1497 . Second, we estimated the envelope mass of [GKH94] 41 based on the modeling of a modified blackbody with fluxes from 70 m to mm wavelengths (Table 2 ) as used to estimate the envelope mass of IC348-SMM2E (see Palau et al. 2014 ). We obtain the dust temperature , dust emissivity and envelope mass by searching these three parameters minimizing the in the following ranges: from 10 K to 40 K, from 0.1 to 1.8 and from 1 to 60 . We assumed a dust opacity coefficient dependence on wavelength with , cm g (see Palau et al. 2014 and references therein) and a gas-to-dust ratio of 100. The best fit (Fig. 4 ) then gives = 34 K, = 2 , and = 0.4. The obtained dust emissivity index value is significantly lower than the typical value of about 1.4 for class I objects in Taurus ( Chandler et al. 1998 ). This is probably because the best fit is obtained from only three available data points. If we take = 1.4 and = 34 K, we obtain an envelope mass of 8 , a factor of 4 higher than the value estimated from the fitting. We therefore take the final mass of [GKH94] 41 to be 49 , which the sum of the current mass (41 ) and the envelope mass (8 ). If the uncertainty in the spectral type is taken into account, this upper limit will be 49. As the outflow process also ejects accreting material and dissipates material in the envelope, the final mass of [GKH94] 41 thus will very likely be below the substellar limit. Fig. 3 H-R diagram for [GKH94] 41 and IRAS 04191+1523B. Isochrones and mass tracks from the theoretical evolutionary models of Chabrier et al. (2000) for and Baraffe et al.


Acknowledgements ME and HH acknowledge support from the European Research Council under FP7 grant number 279396. The authors thank Jerome Amiaux, Mark Cropper, Jean-Charles Cuillandre, Thomas Kitching, Yannick Mellier, Jason Rhodes, and Gijs Verdoes Kleijn for discussions and comments. In this project we used the scikit-learn (Pedregosa et al. 2011 ), pandas (McKinney 2010 ), scipy (Jones et al. 2001 ), matplotlib (Hunter 2007 ), and ipython (Perez Granger 2007 ) software packages. This research has made use of NASA's Astrophysics Data System.


I appreciate support of this publication by the Solar Physics Laboratory of Goddard Space Flight Center. I thank the referee for suggesting a better ordering of the manuscript. I am grateful to PMOD/WRC, Davos, Switzerland, for supplying their irradiance record (Frohlich 2006 ), composite_42_65_1709.dat, with new data from Virgo on the ESA/NASA Satellite Mission, SOHO .


The authors would like to thank J. D. P. Kenney, E. J. Murphy, and Olaf Wucknitz for useful comments on the article.


fluencies (chemical equilibrium). The chemical equilibrium during ice processing by photons, electrons or ions occurs at large fluences when parent and daughter species reach abundances that keep each other's abundance constant during reciprocally production-destruction reactions in the presence of incoming energy. This steady state scenario explains the horizontal plateau in chemical abundances observed at fluences larger than ∼1.4 × 10 13 ion cm −2 (see for example Fig. 4 ). Additionally, in the case of ion bombardment, the sputtering may affect drastically the molecular abundances as function of time, mainly in the beginning when parent species are more affected by sputtering. At high fluences, the molecular abundance reaches the chemical equilibrium, the sputtering affects equally all the species and their column densities decrease (in a constant rate) as function of fluence. The chemical equilibrium is also affected by the sample temperature since volatile products tend to desorb from the ice. More details on chemical equilibrium induced by radiation in ices are given elsewhere (Vasconcelos et al. 2017 ). Studying the radiolysis of N 2 : CO (1:1) ice at 10 K by electron beam irradiation with final energy dose similar to the exposure of astrophysical ice to 5 × 10 6 year in the ISM, Jamieson et al. ( 2005 ) have found the result N 2 O/N 2 > 6.0 × 10 −4 . Bombarding N-rich ices by protons and considering the band strengths in the infrared for N 2 O and N 2 derived by Jamieson et al. ( 2005 ), Loeffler et al. ( 2010 ) have found N 2 O/N 2 ∼ 2.0 × 10 −3 for the equilibrium chemistry attainted at longer fluencies. This value is very similar to the one observed in the gas phase at Sgr B2, as shown above (Womack, Ziurys Wyckoff 1992 ). The current manuscript investigates the survival of solid N 2 O against ionizing field (1.5 MeV N + cosmic ray analogues). From the measured destruction cross-section (σ d ) and the ion flux (Φ) in the laboratory, the photodissociation rate at laboratory (k d = σ d × Φ), turns out to be about 7.0 × 10 −4 ions s −1 . Same equation can be employed to derive the photodissociation rate in space. Considering first order kinetics for the destruction of N 2 O by swift ions, the evolution of N 2 O abundance as a function of time is described by equation ( 25 ) where N(t) and N(t 0 ) represents the molecular abundance at a given time (t) and at the beginning of experiment. κ d is the dissociation rate in units of ions s −1 , σ d is the dissociation cross-section in units of cm 2 and Φ is the ion flux in units of ions cm −2 s −1 . To extrapolate the current laboratory results to astrophysical environments, the flux of medium-mass ions (3 Z 11) having energies between 0.1–10.0 MeV/u estimated by Pilling et al. ( 2013 ) is used. The values considered are 3.0 × 10 −1 and 1.0 × 10 −1 ion cm −2 s −1 , for the ISM and inside the Solar system at Pluto orbit, respectively. Fig. 5 presents the evolution of N 2 O abundance, for ices


This work is dedicated to the memory of our friend and colleague, Mike Bradford. SDO data are provided courtesy of NASA/ SDO and AIA and HMI science teams. EUVI data are provided courtesy of the STEREO Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) team. FIRS has been developed by the Institute for Astronomy at the University of Hawai‘i, jointly with the National Solar Observatory (NSO). The FIRS project was funded by the National Science Foundation Major Research Instrument program, grant number ATM-0421582. T.A.S. gratefully acknowledges colleagues at the High Altitude Obseratory for interesting discussions regarding this work during a scientific visit. This work also benefited from discussions at the International Space Science Institute (ISSI) meetings on “Sub-arcsec Observations and Interpretation of the Chromosphere” and “Implications for Coronal Heating and Magnetic Fields from Coronal Rain Observations and Modelling.” This research has made use of NASA’s Astrophysics Data System. The authors thank the referee for a careful review and fruitful comments. Facilities: Dunn(FIRS) , SDO (AIA) , SDO (HMI) , STEREO (EUVI-A). Software: SSWIDL, SolarSoft, Crispex (Vissers Rouppe van der Voort 2012 ), HeLIx + (Lagg et al. 2004 ; Lagg 2007 ), PIKAIA (Charbonneau 1995 ).


between and M * in the top panel of Figure 1 for isolated galaxies with ALFALFA data and the residuals () between the observed and the predicted in the bottom panel. Equation ( 2 ) is plotted as the solid curve. The dashed lines in the bottom panel are ±1 dex from the relation, which roughly corresponds to the 3 σ scatter. We use the −1 dex residual as a reference point throughout the paper as an arbitrary line of demarcation. Figure 1. Top panel: the isolated to M * relation of gas-rich galaxies. Only isolated galaxies with detections from the 70% ALFALFA survey are plotted here as black contours and dots. The fit from Equation ( 2 ) is plotted as the solid curve and noted in the legend. Bottom panel: H i mass residual ( −1 dex) as a function of stellar mass with the same symbols at the top panel. The ±1 dex (3 σ ) residuals are shown as dashed lines. 2.4. Emission Line Measurements Optical emission line flux ratio diagnostics are often used to differentiate between various physical phenomena that ionize the ISM of galaxies Baldwin et al. ( 1981 ), Veilleux Osterbrock ( 1987 ). Differentiating between excitation sources (actively accreting black holes, star formation, shocks from AGB stars, etc.) depends on the ionization mechanism strength. Reines Volonteri ( 2015 ) measure emission line strengths of ∼67000 galaxies selected from the NSA catalog. The authors cull the spectral catalog with an S/N cutoff of three and an equivalent width cutoff of one for the H α , [N ii ], [O iii ] emission lines and an S/N cutoff of two for the H β line. They iteratively subtract continuum and absorption lines using the models of Tremonti et al. ( 2004 ) from the observed spectrum, carefully masking out the emission lines. Next, a one- or two-component Gaussian is fit to the [S ii ] doublet and used as a template for the remaining emission lines of interest, excluding [O iii ]. We use the data set from this study without any modifications. 2.5. BPT Emission Line Diagnostics We examine the emission line diagnostics of all galaxies from Reines Volonteri ( 2015 ), regardless of environment or H i data using the [O iii ]/H β and [N ii ]/H α narrow-line BPT diagram (Baldwin et al. 1981 ; Veilleux Osterbrock 1987 ; Kewley et al. 2006 ). We adopt the division into the star-forming, composite, and AGN categorization that has been performed by Reines Volonteri ( 2015 ) using the Kewley et al. ( 2001 ) and Kauffmann et al. ( 2003 ) categorization. The Kewley et al. ( 2001 ) demarcation line represents the division between a theoretical maximal starburst model and emission that can only be explained by AGN activity. The Kauffmann et al. ( 2003 ) demarcation line is an empirical separation between the star-forming sequence and the AGN sequence. Galaxies below the Kauffmann et al. ( 2003 ) line are considered to be purely star-forming and galaxies above the Kewley et al. ( 2001 ) line are considered Seyferts and LINERs. The region between these two demarcation lines


Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS website is http://www.sdss.org/. This publication makes use of data products from the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, and NEOWISE , which is a project of the Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the National Aeronautics and Space Administration.


This work is supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under the grant 113F354. MAA is a member of the Science Academy (Bilim Akademisi), Turkey. We thank Patrick Weltevrede for kindly providing us with unpublished post-glitch timing data, and Danai Antonopoulou, Anna Watts, Cristobal Espinoza and Patrick Weltevrede for useful discussions and for sharing with us their preprint with an alternative approach to the post-glitch behaviour of PSR J1119−6127. We thank the referee for useful comments.


This work was supported by a grant from the US Air Force Office of Scientific Research in the Basic Research Initiative “Understanding the Interaction of CMEs with the Solar–Terrestrial Environment,” and by an STFC consortium grant to the universities of Dundee and Durham. The authors thank C. Prior, M. Weinzierl, P. Wyper, and T. Whitbread for useful discussions concerning the development of this work, which made use of the Hamilton HPC Service of Durham University. SOLIS data used here are produced cooperatively by NSF/NOAO and NASA/LWS. Facility: SOLIS. Software: Python, SunPy (SunPy Community et al. 2015 ), SciPy (Jones et al. 2001 ), NumPy (van der Walt et al. 2011 ), Mayavi (Varoquaux Ramachandran 2011 ), Matplotlib (Hunter 2007 ), FRoDO (Lowder 2017 , https://github.com/lowderchris/FRoDO ).


This work uses data obtained through the Telescope Access Program (TAP), which is funded by the National Astronomical Observatories of China, the Chinese Academy of Sciences (the Strategic Priority Research Program, “The Emergence of Cosmological Structures” Grant No. XDB09000000), and the Special Fund for Astronomy from the Ministry of Finance. The observations reported in this study were obtained at the MMT Observatory, a joint facility of the Smithsonian Institution and the University of Arizona. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This work is supported by the National Basic Research Program of China (973 Program)(2015CB857004), and the National Natural Science Foundation of China (NSFC, Nos. 1320101002, 11433005, 11421303, and 11673004), and the Yunnan Applied Basic Research Projects (2014FB155). E.W. was supported by the Youth Innovation Fund by the University of Science and Technology of China (No. WK2030220019). Facility: MMT. Software: SExtractor (Bertin Arnouts 1996 ), xfitfibs, HSRED, STARLIGHT (Cid Fernandes et al. 2005 ), MPFIT (Markwardt 2009 ), PyNeb (Luridiana et al. 2015 ).


We thank Lydia Heck and James Willis for their assistance with computational challenges. We thank the anonymous referee for their helpful comments. This work was supported by the Science and Technology Facilities Council (STFC) grants ST/P000541/1 and ST/L00075X/1, and used the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grants ST/H008519/1 and ST/K00087X/1, STFC DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure. J.A.K. is funded by STFC grant ST/N50404X/1. V.R.E. acknowledges support from STFC grant ST/P000541/1. R.J.M. acknowledges the support of a Royal Society University Research Fellowship. L.F.A.T. and D.G.K. acknowledge support from NASA Outer Planets Research program award NNX13AK99G. L.F.A.T., D.C.C., D.G.K., and K.J.Z. acknowledge support from NASA Planetary Atmospheres grant NNX14AJ45G. C.L.F. was funded in part under the auspices of the U.S. Dept. of Energy, and supported by its contract W-7405-ENG-36 to Los Alamos National Laboratory.


This work is partly supported by the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013 Grant Agreement No. 247060-PEPS and grant No. 320478-TOFU). B.D. thanks the University of Exeter for support through a Ph.D. studentship. D.S.A. acknowledges support from the NASA Astrobiology Program through the Nexus for Exoplanet System Science. N.J.M. and J.G.’s contributions were in part funded by a Leverhulme Trust Research Project Grant, and in part by a University of Exeter College of Engineering, Mathematics and Physical Sciences studentship. This work used the DiRAC Complexity system, operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility. This equipment is funded by BIS National E-Infrastructure capital grant ST/K000373/1 and STFC DiRAC Operations grant ST/K0003259/1. DiRAC is part of the National E-Infrastructure. This work also used the University of Exeter Supercomputer, a DiRAC Facility jointly funded by STFC, the Large Facilities Capital Fund of BIS and the University of Exeter.


An anonymous referee’s comments and suggestions are gratefully acknowledged.


frequency of the observed maser transition. We use A = 5.570 × 10 −8 s −1 , the highest Einstein coefficient in Lankhaar et al. ( 2018 ) for the ν = 24959.079 × 10 6 Hz CH 3 OH maser transition, and T b = 10 6 –10 7 K, ΔΩ ≃ 0.03–0.003 for the 25 GHz maser line (Leurini et al. 2016 ). Using these values in Equation ( 2 ), we obtain R ≤ 3 ×10 −3 s −1 . Meanwhile, g Ω ≈ 170 s −1 in our observations of OMC-1. This means that R ≪ g Ω, and thus it is unlikely that a rotation of the axis of symmetry for the molecular quantum states is responsible for the shape of the Stokes V profile. Finally, Houde ( 2014 ) found that maser radiation scattering off foreground molecules can increase the antisymmetry in the Stokes V spectral profile of SiO masers. Consequently, if the Stokes V profile were ascribed to the Zeeman effect, one would obtain a much larger value for the magnetic field traced by these SiO masers. While we cannot rule out this effect in our 25 GHz CH 3 OH maser observations, our values are not orders of magnitude higher than the fields expected in such regions, unlike in SiO masers. We will discuss this in more detail in the next subsection. 4.3. Magnetic Fields and Densities If the observed Stokes V profile is caused by a magnetic field in the source, and is not due to any instrumental effects or other non-Zeeman causes discussed in Section 4.2 , then we have a magnetic field from our 25 GHz Class I CH 3 OH maser observation of OMC-1 that is three to four times larger (depending on which hyperfine is responsible for the maser) than the highest magnetic field we have detected to date in Class I CH 3 OH masers at 44 GHz (Momjian Sarma 2017 ). Such a value is not implausible, however; if we assume that the fields are amplified in proportion to the density in the shocked regions where these masers occur, thenwhere B pre and B post are the magnetic fields in the pre- and post-shock regions respectively, and n pre and n post are the densities in these regions. We can use Equation ( 3 ) to calculate B pre and compare it to magnetic fields calculated from other observations. Since our measured fields are in the range 171–214 mG depending on which hyperfine is responsible for the maser transition (Table 3 ), we will use = 171 mG in our calculations; this is convenient because values corresponding to = 214 mG can then be found by multiplying our results by a factor of 1.25. Leurini et al. ( 2016 ) have found that bright Class I methanol masers likely occur in regions with densities in the range 10 7–8 cm −3 , so we will calculate corresponding to both n post = 10 7 and 10 8 cm −3 . For densities in the pre-shocked gas we draw upon the work of Kwan et al. ( 1977 ), who found that a minimum pre-shock density of 10 5 cm −3 is required to produce the observed intensities in the H 2 ν = 1−0 emission lines in OMC-1. Thus, densities in the pre-shocked material of interest are likely of the order of 10 5 cm −3 or higher, but unlikely to be as high as, or


We thank E. Zirnstein and P. Mostafavi for helpful discussions about PUI pressures and densities, shocks, and wave speeds in the heliosheath. We are grateful to both the Voyager and IBEX teams. This work was supported by NASA Grant 80NSSC18K0237 and by the IBEX mission, which is part of NASA’s Explorer Program. J.D.R. was supported under NASA contract 959203 from the Jet Propulsion Laboratory to the Massachusetts Institute of Technology.


g ′) and Δμ( r ′ − i ′), with the maximum at only ρ = 0.30 and p = 0.08. The only exception is for the PAS profiles from the r ′ − i ′ breaks sub-sample, where there is a correlation of ρ = 0.49 and p = 0.02. This correlation is due to the same points as the correlation of h 0 with Δ h 0 ( r ′ − i ′0) in the PAS. 4.4.4 Correlations with absolute magnitude, maximum rotation and specific angular momentum Martín-Navarro et al. ( 2012 ) found that the truncation radius of a galaxy is strongly correlated with the maximum rotational velocity v rot of a galaxy, having a correlation of ρ truncation = 0.81. The breaks in their sample were correlated at only ρ break = 0.50. In contrast, Pohlen Trujillo ( 2006 ) report no correlation with rotation. They do report a weak correlation of the brightness at the feature with the absolute magnitude M B,abs . We explore possible correlations of the feature radii R f , surface brightness at the feature μ and differences in brightness between bands at the feature μΔ with absolute magnitude M abs and maximum rotation v rot in Table 7 . We find only one weak correlation in the difference between the g ′ and r ′ surface brightness at the feature radius, as measured with the EP method, with the rotation velocity v rot . The correlation strength is ρ = 0.36 and p = 0.03. Martín-Navarro et al. ( 2012 ) also perform a correlation test of the break and truncation radii with the specific angular momentum j , calculated using the empirical expression by Navarro Steinmetz ( 2000 ), 4 As this is a rescaling of v rot , the correlation remains the same. They find that the feature radius only correlates well beyond r b = 8 kpc. For a smaller disc with v rot 100 kms −1 , r break and v rot are essentially unlinked. When examining the full radii range, we also do not find a statistically significant correlation. The same holds when limiting ourselves to all features beyond r > 8. 4.4.5 Effect of inclination In Section 4.4.2 , we found that the PAS profiles typically tend to have sharper breaks than the ellipse-fit profiles and EP. As the PAS projects data as if it was edge-on, it is also interesting to compare the effect of inclinations in this. To this end, we have tested the correlation of a / b (major axis over minor axis) with scalelengths h 0 and h f , h 0 / h f and R f / h f , in Table 7 . The results are also visualized in Fig. 12 . Figure 12. Correlation of various parameters with inclination (expressed as a / b ). Top row shows with scalelength h 0 (left) and h f (right). Bottom row has the h 0 / h f (right) and R f / h f (left) correlations. The statistical correlation tests are plotted in each panel. Boxes represent the PAS points, while circles represent the EP points. Open markers represent truncations, while filled markers represent boxes. We find no significant correlation between a / b and inner scalelength h 0 . There may be a negative trend visible in the truncation-only sample, as both the EP and PAS profiles


Acknowledgements A. P. acknowledges support from CONACyT 238631. G. B. acknowledges financial support under the INTEGRAL ASI-INAF agreement 2013-025.R01. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research has made use of data from the MOJAVE database that is maintained by the MOJAVE team ( Lister et al. 2009 ). This work was supported by UNAM-PAPIIT IA101214 and IA102816. We thank Prof. Robert Antonucci for his careful revision of the paper and useful comments.


ACKNOWLEDGEMENTS This work was supported by the ERC Consolidator Grant CMBSPEC (No. 725456) as part of the European Union’s Horizon 2020 research and innovation programme. JC was supported by the Royal Society as a Royal Society URF at the University of Manchester, UK.


ACKNOWLEDGEMENTS We thank Nir Shaviv and Reetanjali Moharana for helpful discussions. The research was supported by an ERC advanced grant TReX and by the CHE-ISF I-CORE center for excellence.


transform Fig. A.4 Illustration of the backward interpolation. Interpolation is performed for inertial knots (bottom line) on intervals between comoving knots (top line). Interpolation weights are indicated on sloped lines connecting corresponding knots between the frames. Linear interpolation is used for internal inertial knots that lie on the internal comoving intervals with l.h.s. (solid) and r.h.s. (dotted) weights. Weights are named with respect to the comoving frame so that the line slopes are opposite to those in the forward interpolation. Constant extrapolation with unity weights is used for inertial knots exactly matching comoving knots , or for inertial knots that lie on the or intervals. Both end cases are given in gray color. Once has been fully accumulated, we compute the comoving profile ratio and keep it in memory for all frequency knots simultaneously. This makes the backward transform much easier than the forward one, because we can employ a standard interpolation. The backward transform can be directly evaluated because it is just an interpolation from the comoving frame to the inertial frame (see green notations in Fig. A.2 ): As follows from Fig. A.4 , each depends on either one or two comoving knots . It depends on two knots if the inertial knot lies on the comoving interval , where both and are internal knots (), that is, . In this case we do linear interpolation: For the backward transform, knots and weights are always denoted by and with no superscript to tell them from knots and weights for the forward transform. It depends on one knot if the inertial knot matches the comoving knot , that is, . It also depends on one knot if the inertial knot lies on intervals before or after , which means or , because we use constant extrapolation. Therefore, in those three cases: Appendix A.2: General interpolation One can perform the searches and interpolations described in Sects. A.1.4 – A.1.5 on the fly. This does not require any additional storage, and can be used for a frequency grid of arbitrary spacing, but the algorithm is slow. We call this algorithm general interpolation. Appendix A.2.1: General forward transform We search for all comoving knots that lie on the intervals and , that is, whose comoving frequencies are either between and , or between and . We take the current inertial knot and its left neighbor and right neighbor . We use and to keep the neighbors within the frequency grid range . For these knots we compute their frequencies in the comoving frame, which we denote by Therefore, the comoving knots related to the inertial knot are those whose frequencies belong to the or intervals. The knots lie either on the left or on the right side of , and they might fall outside the frequency coverage of the line. We thus have four different cases to test for and apply Eqs. ( A.15 )–(A.16). Algorithm 1 shows the forward transform in detail. Appendix A.2.2: General backward transform Algorithm 2 gives the backward transform,


to be robust as they are obtained without any assumptions. Table 3. Ro-vibrational partition function values computed at different temperatures. Notes. (a) Includes all states up to 400 cm −1 : the ground, v 11 = 1, v 18 = 1, and v 17 = 1 states. (b) Includes all states up to 1600 cm −1 . To compute the ro-vibrational partition function of the manifold containing all states up to 1600 cm −1 (referred to as “Manifold 2” in Table 3 ), we have applied a vibrational correction to our new ground state partition function values. The correction factor was calculated at each temperature summing up the contribution of all the vibrational states below 1600 cm −1 . Since we have no direct information about the vibrational energies of the states between 400 and 1600 cm −1 , they were either estimated under the harmonic approximation or taken from low-resolution measurements ( Bak et al. 1975 ). Higher energy levels were not considered in the calculation because they do not contribute significantly to Q even at 300 K. The vibration-rotation partition functions of the three manifolds of aminoacetonitrile, computed at temperatures between 2.725 and 300 K, are listed in Table 3 . Based on our results, we prepared new catalog entries for aminoacetonitrile in the ground and excited states. The line intensity of each transition was calculated using the partition function values from Col. 4 of Table 3 (“Manifold 2”), while the permanent dipole moment values μ a = 2.577(7) D and μ b = 0.575(1) D were taken from Pickett (1973) and assumed not to change upon the vibrational states. 4. Astronomical observations 4.1. Observations We used the ReMoCA imaging spectral line survey carried out toward the protostellar cluster Sgr B2(N) with ALMA. Details about the observational setup and data reduction of this survey were reported in Belloche et al. (2019) . In short, the observations covered the frequency range from 84.1 GHz to 114.4 GHz with five tunings, with a spectral resolution of 488 kHz (1.7–1.3 km s −1 ). The angular resolution (HPBW) varied between ∼0.3′′ and ∼0.8′′, with a median value of 0.6′′, which corresponds to ∼4900 au at the distance of Sgr B2. The rms sensitivity ranged from 0.35 mJy beam −1 to 1.1 mJy beam −1 , with a median value of 0.8 mJy beam −1 . The field was centered at ( α , δ ) J2000 = (), a position that is halfway between the two main hot molecular cores, Sgr B2(N1) and Sgr B2(N2), which are separated by 4.9′′ or ∼0.2 pc. Here we analyze the spectrum at the offset position Sgr B2(N1S) defined by Belloche et al. (2019) to reduce the optical depth of the continuum emission, which is partially optically thick toward the peak position of the main hot core Sgr B2(N1). Sgr B2(N1S) is located at ( α , δ ) J2000 = (, ), about 1′′ to the south of Sgr B2(N1). In Belloche et al. (2019) , the continuum and line emission of the ReMoCA survey was separated in the image plane because the hot cores detected in the field of view have different systemic


ACKNOWLEDGEMENTS We would like to thank Ming Sun for providing us with their data. We also thank Helen Russell, Stephen Walker and Elena Rasia for helpful discussions about X-ray data analysis. We are grateful to Volker Springel for making the arepo moving-mesh code available to us and to the Illustris collaboration for their development of the Illustris galaxy formation model on which this work is based. NAH is supported by the Science and Technology Facilities Council (STFC). EP acknowledges support by the Kavli Foundation. DS and SS acknowledge support by the STFC and the European Research Council Starting Grant 638707 ‘Black holes and their host galaxies: co-evolution across cosmic time’. This work made use of the following DiRAC facilities ( www.dirac.ac.uk ): the Data Analytic system at the University of Cambridge [ funded by a BIS National E-infrastructure capital grant (ST/K001590/1), STFC capital grants ST/H008861/1 and ST/H00887X/1, and STFC DiRAC Operations grant ST/K00333X/1] and the COSMA Data Centric system at Durham University (funded by a BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/K00087X/1, DiRAC Operations grant ST/K003267/1 and Durham University). DiRAC is part of the National E-Infrastructure.


gas is released in the inner region of the disk, a global transonic structure of such a slim disk surrounding a BH is crucial in modeling the observations. In this work, we derive a global solution to a slim accretion disk with outflows accelerated by radiation force of the disk. The calculations can be applied to model the accretion disks surrounding either massive BHs or stellar mass BHs. We describe our model calculations and results in Sections 2 and 3 . The last section contains the discussion of the results. 2. Model A set of equations describing a slim disk with outflows in cylindrical coordinates are summarized in the previous works (see Gu Lu 2007 ; Kato et al. 2008 ). We consider a steady axisymmetric accretion disk in this work, which is described by the momentum equations, energy equation, continuity equation, and the equation of state. The r -component of the momentum equation isAs was done in many previous works, we adopt the pseudo–Newtonian potential,to simulate the gravity of the BH, where M is the BH mass, and is the Schwarzschild radius (Paczyńsky Wiita 1980 ). The continuity equation is We integrate Equation ( 1 ) over z and obtainwhere Equation ( 3 ) is used, Σ is the surface density, Π is the vertically integrated pressure, Ω is the angular velocity of the gas in the disk, and Ω K is the Keplerian angular velocity. The φ -component of the momentum equation iswhere the α -viscosity of t rφ = − αp is adopted. Integrating Equation ( 5 ) over z -direction, we havewhere the velocity of outflow at the disk surface is v H = v z ( H ), as the outflows driven by the radiation force are considered in our model. By integrating Equation ( 3 ) vertically, it becomesThe term 2 ρv H in this equation represents the mass loss rate in outflows from the unit area of the disk surface. If we further integrate Equation ( 7 ) over r -direction, it becomeswhere the integral constant C can be determined with suitable boundary conditions. As the mass loss rate in the outflows from the unit area of the disk surface are , we haveso the mass accretion rate in the disk at radius r isand the total mass loss rate in the outflows driven from the disk region between r and r out is Substituting Equation ( 7 ) into Equation ( 6 ), we haveWe integrate the angular Equation ( 12 ) over r , which leads torespectively, where Equation ( 9 ) is used, is the angular momentum removed by the outflows from the corresponding disk region, is the mass accretion rate swallowed by the BH, and is rate of angular momentum of the accretion gas flowing into the BH ( j in is the specific angular momentum of the accreting gas swallowed by the BH). The vertical integrated equation of state iswhere μ is the mean molecular weight ( μ = 0.62 is used in this work), and T 0 is the temperature at the mid-plane of the disk (see Equations (7.43)–(7.45) in Kato et al. 2008 ). The isothermal sound speed is . The energy equation of the disk iswhere is the gravitational power released


obs , and I obs were then, for the i th value, and“Observed” values of p ′ and were calculated using Equations ( 2 ) and ( 8 ), respectively, for a given value of . We repeated this process 10 4 times. Figure 2 shows a single realization of our Monte Carlo simulations for three cases: α = 1, 0.5, and 0 (constant polarization fraction). In all cases, p 100 mJy beam –1 = 0.025. The results are shown without any debiasing of the polarization fraction and colored according to their S/N in I and p ′. Figure 2. Monte Carlo simulations of the observed polarization fraction p ′ as a function of S/N I / σ QU for three cases: α = 0 (top), 0.5 (middle), and 1 (bottom). In all cases, p 100 mJy beam –1 = 0.025. Results are shown without debiasing. The polarization fraction p ′ is given as an absolute value (not as a percentage), so any value p ′ > 1 (marked in bold on plot axes) is unphysical. Points are colored according to their S/N in I and p ′; gray points meet none of the criteria shown in the legend. The solid black line shows p ′ = μ p , the mean of the Rice distribution, for the given values of α and . The dashed black line shows the true polarization fraction p = ( I / σ QU ) − α . The dotted line shows the null-hypothesis/low-S/N relation . In the cases where α 1, the dotted–dashed line marks the critical value . Figure 2 shows that where I / σ QU ( I / σ QU ) crit , the distribution shows an identical p ′ ∝ I −1 behavior in all cases, and that the underlying power-law behavior does not dominate over the I −1 dependence until I / σ QU ≫ ( I / σ QU ) crit . Thus, unless a reasonable number of data points have S/Ns significantly greater than the critical value, the true power-law behavior is not recoverable. The overall behavior of the recovered polarization fraction is well described by the mean of the Rice distribution. 4.1. Uncertainty on Polarization Fraction In keeping with standard practice in observational polarimetry and the default behavior of the POL-2 pipeline (e.g., Kwon et al. 2018 ), we estimated the uncertainty on the polarization fraction using the relation(e.g., Wardle Kronberg 1974 ). We note that this result is derived using classical error propagation and so assumes that δQ , δU , and δI are small and uncorrelated. We here take and . As discussed above, measurement errors δp calculated using Equation ( 19 ) can only be treated as representative of a Gaussian distribution around p ′ when p ′/ δp is large. 4.2. Fitting Methods We investigated the results of fitting two models to our Monte Carlo simulations. Fitting was performed using the scipy routine curve _ fit . In all cases, we attempted to recover the input values of and α , assuming that σ QU is a fixed and directly measurable property of the data set. We supplied our calculated δp values to curve _ fit as 1 σ uncertainties on p ′. Although this is technically valid only at high S/N, the default behavior of this fitting routine is to consider only the relative magnitudes of


the contribution of a magnetic background turbulence, like that provided by an SSD, to α . Here, α M can be obtained already by SOCA (Rädler Rheinhardt 2007 ). Second, it can be interpreted as reflecting the quenching of α by such that with its magnitude growing, α M also grows, but opposite in sign to α K , resulting in a reduced total α . This can be obtained via closure approaches like the τ or eddy-damped quasi-normal Markovian approximations (Pouquet et al. 1976 ). Turbulent diffusivity in incompressible flows is estimated asIf the Strouhal number u rms τ / l , l a characteristic scale of the flow, is assumed to be unity, we can estimate τ = 1/ k f u rms . To obtain the fluctuating fields b ′, j ′, we have removed the large-scale field via filtering out the contributions of the k = 1 mode from the magnetic field snapshots. We estimated the growth rates by first calculating α and η t for individual snapshots using data from runs with high snapshot frequency during exponential growth. Then, we used Equation ( 10 ) to get λ for an individual snapshot and subsequently time-averaged over the growth phase. We find results that are at least approximately aligned with the directly measured values with better agreement at low ; see Figure 18 . Toward high , the results begin to diverge, most strongly at the highest . This appears to indicate that the closure estimates can work surprisingly well but do not catch all of the details. Figure 18. Growth rates ( 10 ) based on closure estimates of α and η t at the exponential growth stage, compared to directly estimated ones. But how does α behave at the saturation stage? Brandenburg et al. ( 2008 ) measured α and η t quenching, with both decreasing as functions of . For comparison, we calculated α K,M from the saturation stages of our runs and discovered similar results; see Figure 19 . The normalized α K approaches a constant, whereas α M keeps getting stronger with . Our results are close to Brandenburg et al. ( 2008 , their Figure 3) within our range of , including substantially more points though. The total α decreases with growing , reaching eventually a tentative plateau. Figure 19. α K and α M at the saturated stage as functions of , normalized by α 0 = u rms /3. Bars represent the fluctuation level of α derived from their standard deviations. The jump at is due to an unclear disagreement between resolutions 128 3 , 64 3 , and 256 3 , while that at is due to different saturation stage lengths in the runs with resolutions 256 3 and 512 3 . 5.5. Magnitude Distribution of the Magnetic Field Next, we investigate how the probability density functions (pdf’s) of the magnetic field differ in the regimes where SSD or LSD alone and SSD and LSD together are acting. While spectra gave us information about the distribution of the magnetic field across different scales, pdf’s can further reveal details of the structure of the field in different spatial directions. Additionally, we calculated kurtoses of these


We would like to thank the SPIRITS team (PI: Mansi Kasliwal) for assistance with the inclusion of the SPIRITS data in four of our galaxies and Chris Clark for his help with the P – L distance uncertainty calculation. This work is supported by Spitzer via grant GO11041 and the NASA Astrophysics Data Analysis Program grant No. NNX16AT56G. R.D.G. was supported by NASA and the United States Air Force. O.C.J. has received funding from the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement No. 665593 awarded to the Science and Technology Facilities Council.


did for the Schwarzschild case (Chattopadhyay Kumar 2016 ), but now for Kerr metric. And then using these values, we look for transonic jet solution. We iterate c φ and n c for the same values of Z φ and r b and by estimating the value of from the disc. This goes on until the entropy of the transonic jet is in between the PSD and pre-shock disc or . Once the transonic solution is found, then from Section 2.4 , we compute the , which is fed to equation ( 35 ) to recalculate the r sh . Once the new r sh is obtained, steps (iii)–(v) are repeated but with the new value of , till r sh converges to a value. The resulting jet is the self-consistent jet solution driven by disc properties. While the jet solution is being obtained, shock transition in jets is also studied. 4 RESULTS We are connecting two flows, namely accretion and jet, and since both the flows are quite complicated, we would first present all possible advective accretion solutions, then all possible jet solutions and then self-consistent accretion-jet solutions. 4.1 Accretion In this section, we present only accretion solution, i.e. we follow steps (i)–(iii) of Section 3 . BH accretion disc is necessarily transonic, i.e. the solutions falling on to a BH pass through at least one sonic point. Therefore, a lot of insight can be gained by studying the critical point conditions. For a given value of and λ, the critical points can be uniquely determined. Let us denote and . Therefore, using the critical point conditions (equations 19 and 20 ) in equation ( 16 ), we obtain energy as a function of r c for given values of λ, a s and ξ (equation 21 ). We plot with r c for a s = −0.99 (Fig. 1 a), a s = −0.5 (Fig. 1 b), a s = 0.0 (Fig. 1 c), a s = 0.5 (Fig. 1 d), a s = 0.8 (Fig. 1 e) and a s = 0.99 (Fig. 1 f). Each of the curves plotted from top to bottom is for λ = 1.5–4.0 with interval dλ = 0.5 and with same composition parameter, ξ = 1.0. Critical points are formed because gravity increases the infall velocity, but because accretion is an example of convergent flow, the temperature of the flow also increases during accretion (compressional heating). As temperature increases, sound speed also increases, but in accretion the rise of sound speed is less than the bulk velocity. Therefore, the flow that is subsonic at large distances becomes supersonic within a certain point (critical point). However, if the infalling matter is rotating, then the centrifugal term competes with the gravitation term in a way that MCP may form. As a s increases, frame dragging enhances the centrifugal term significantly, therefore, even matter with low angular momentum exhibits MCP, for example the flow with λ = 2 [dotted in Figs 1 (a)–(f)] exhibits MCP for a s = 0.99 but not for a s ≤ 0.8. On the other hand, if the flow is retrograde, then MCP occur only for higher angular momentum flow. Fig. 1 (c) is similar to fig. 3a of Chattopadhyay Chakrabarti ( 2011 ). This phenomenon is better exhibited with ‘kite-tail’ plot.


area enclosed by the dark bubble. Finally, Figure 10 gives an estimate of the order of magnitude of the upflows and also of the delays between them. The upper panel shows Si iv velocities as a function of time for eight pixels within the dark bubble (purple lines). The spatial average of those pixels has been smoothed in time using a five-step running average. This smoothed mean is represented by the black line. For comparison, an average of pixels outside of the flux emergence region is plotted as a cyan line. Note the bump produced in the velocities at around 09:03:00 UT, which is only detected by the pixels belonging to the bubble but not by the pixels outside the magnetic bubble. The bump corresponds to upflows of about 8 km s −1 in the transition region. Note also how some individual pixels within the bubble present transient upflows at other times (e.g., at 08:55:00 UT) that are not captured by the smoothed mean. It is important to realize that the 8 km s −1 upflows found represent the result of a single Gaussian fit to the Si iv spectra. Double Gaussian fits show that the Si iv line is split into two components during the event, as is also evident from Figures 7 and 9 . The more blueshifted component shows velocities of 40–50 km s −1 in bursts, presumably due to reconnection between the emerging bubble and the ambient field (indicating that the bubble has reached the dark appearing canopy of H α fibrils), while the other component is blueshifted by at most a few km s −1 . The Doppler shift derived from the single Gaussian fit of Figure 10 is therefore showing the combined effect of reconnection bursts and the rising bubble, and cannot be considered a real velocity but rather a time marker. Figure 10. Upper panel: Si iv velocities as a function of time for several pixels within the dark bubble (purple lines) and their smoothed mean (black line). Note the bump produced in the velocities at around 09:03:00 UT, marking the existence of an upflow of more than 8 km s −1 in the transition region. Compare to the averaged velocities of pixels outside of the flux emergence region (cyan line). Lower panel: velocities measured in the photosphere (green line and dashed black line), low chromosphere (red line), and transition region (solid black line) as a function of time. Note the successive bumps occuring with a certain delay in the different lines, proving how the upflowing plasma traverses different layers in the solar atmosphere. The Ti ii velocity has been multiplied by 10 for display purposes. The lower panel plots velocities and velocity proxies measured in the photosphere (green and dashed black line), low chromosphere (red line), and transition region (black line) as a function of time. In principle, we would expect that the Fe i intensity darkening and the Ti ii velocity upflow, seen at about 08:55 UT, would occur simultaneously, since both sample the photosphere at similar heights. However, there is a slight delay between the green and


( 15 ). For θ > 0, all modes with m ≥ 1 are populated, whereas the approximation () contains only m = 2. For θ = π /2, h × vanishes identically and the relative amplitude of the modes m ≠ 2 with respect to the mode m = 2 is the largest one, reaching ∼75% for m = 3 and ∼50% for m = 4 when a = 0.98 M . Some tests of our computations, in particular comparisons with previous results by Poisson (1993a ; a = 0) and Detweiler (1978 ; a = 0.5 M and a = 0.9 M ) are presented in Appendix A . 3. Signal-to-noise ratio in the LISA detector The results in Sect. 2 are valid for any BH. We now specialize them to Sgr A* and evaluate the S/N in the LISA detector, as a function of the mass μ of the orbiting object, the orbital radius r 0 and the spin parameter a of Sgr A*. 3.1. Computation Assuming that its noise is stationary and Gaussian, a given detector is characterized by its one-sided noise power spectral density (PSD) S n ( f ). For a gravitational wave search based on the matched filtering technique, the S/N ρ is given by the following formula (see e.g. Jaranowski Królak 2012 ; Moore et al. 2015 ): where is the Fourier transform of the imprint h ( t ) of the gravitational wave on the detector, h ( t ) being a linear combination of the two polarization modes h + and h × at the detector location: In the above expression, ( t , r , θ , φ ) are the Boyer–Lindquist coordinates of the detector (“Sgr A* frame”), while F + and F × are the detector beam-pattern coefficients (or response functions), which depend on the direction (Θ, Φ) of the source with respect to the detector’s frame and on the polarization angle Ψ, the latter being the angle between the direction of constant azimuth Φ and the principal direction “+” in the wavefront plane (i.e. the axis of the h + mode or equivalently the direction of the semi-major axis of the orbit viewed as an ellipse in the detector’s sky) ( Apostolatos et al. 1994 ). For a detector like LISA, where, for high enough frequencies, the gravitational wavelength can be comparable or smaller than the arm length (2.5 Gm), the response functions F + and F × depend a priori on the gravitational wave frequency f , in addition to (Θ, Φ, Ψ) ( Robson et al. 2019 ). However for the gravitational waves considered here, a reasonable upper bound of the frequency is that of the harmonic m = 4 (say) of waves from the prograde ISCO of an extreme Kerr BH (see Fig. 4 ). From the value given by Eq. ( 5 ), this is f max = 2 × 7.9 ≃ 15.8 mHz, the multiplication by 2 taking into account the transition from m = 2 to m = 4. This value being lower than LISA’s transfer frequency f * = 19.1 mHz ( Robson et al. 2019 ), we may consider that F + and F × do not depend on f (see Fig. 2 in Robson et al. 2019 ). They are given in terms of (Θ, Φ, Ψ) by Eq. (3.12) of Cutler (1998) (with the prefactor appearing in Eq. (3.11) included in them). Generally, the function S n ( f ) considered in the LISA literature, and in particular to present the LISA sensitivity


such as the malfunction of satellites, radiation hazards for astronauts, and the interruption of radio communication and electric power (e.g., Schrijver et al. 2014 ; Ponomarchuk et al. 2015 ). Hence, it is very important to establish a reliable system for flare prediction to effectively prevent various types of damage caused by powerful flare events. The triggering mechanism of solar flares is not well understood, thus many flare prediction models have been developed with different methods in recent years. Some of them are based on statistical methods (Song et al. 2009 ; Mason Hoeksema 2010 ; Bloomfield et al. 2012 ; Barnes et al. 2016 ), while most of them are based on machine-learning methods. Traditional machine-learning methods commonly used for flare prediction cover artificial neural network (Qahwaji Colak 2007 ; Ahmed et al. 2013 ; Li Zhu 2013 ; Nishizuka et al. 2018 ), k-nearest neighbors (Li et al. 2008 ; Huang et al. 2013 ), support vector machine (Yuan et al. 2010 ; Bobra Couvidat 2015 ; Nishizuka et al. 2017 ; Sadykov Kosovichev 2017 ), random forests (Liu et al. 2017 ; Florios et al. 2018 ), and ensemble learning (Colak Qahwaji 2009 ; Huang et al. 2010 ; Guerra et al. 2015 ). Because flares originate from the magnetic field around sunspots in an active region (AR), many of the above studies utilize photospheric magnetic observational data of AR for binary class prediction. Both statistical methods and traditional machine-learning algorithms learn from features that are manually selected and extracted from the observational data. The predictive performance of these methods mainly relies on the quality of the features used. Deep learning neural networks (DNNs), which are as a new branch of machine learning, have become a highly reliable technique for solving large-scale learning problems in astronomy and other branches of science (Abraham et al. 2018 ). A famous method of DNNs, convolutional neural networks (CNNs; LeCun et al. 2015 ), have become very popular in the area of image processing and computer vision. It could learn directly from the raw data instead of features. CNNs are usually composed of several convolutional layers, and each convolutional layer is trained to automatically select and extract increasingly complex features from input data. There are some literatures reporting that CNNs have been successfully applied to solar flare prediction. Park et al. ( 2018 ) applied a CNN model to predict a binary class flare within 24 hr, which is based on a combination of GoogLeNet (Szegedy et al. 2014 ) and DenseNet (Huang et al. 2016 ). After trained by full-disk solar line of sight (LOS) magnetograms, their model could only make binary class prediction for a ≥C-class flare, which lacked research on the prediction for a ≥M-class major flare. Huang et al. ( 2018 ) presented a CNN model to predict a binary class flare, which is based on the classic CNN model architecture including two convolutional layers with 64 11


The authors would like to thank Bence Bécsy, Gergely Dálya, and Ákos Szölgyén for useful discussions and for valuable comments on the manuscript. Péter Raffai is grateful for the support of the Hungarian Academy of Sciences through the ‘Bolyai János’ Research Scholarship programme. We also acknowledge financial support from the NASA Astrophysics Theory Program under grant no. NNX11AE05G (to ZH) and from OTKA under grant no. 101666 (to ZF).


all remaining isomers are located among the second peak nuclei, the most notable of which include 125,127 Te. Finally, we identify the particularly interesting isomer in 129 Te. It remains important across all epochs of our simulation, from 0.01 days to 1 year, reflecting a complex interplay between the β -decay feeding parent, thermal excitation, and de-excitation. We identify 195 Pt as the dominant astromer located in the third r- process peak (green). In contrast with the conventional assumption that this isotope is stable upon population via the ground state β decay of 195 Ir, our calculations show that the isomer of 195 Pt may be populated directly from the β decay of the isomer of 195 Ir. The 195 Pt isomer de-excites on a timescale of ∼4 days, which is relevant to the study of electromagnetic signals associated with the r process. In the interest of readability, Figure 2 shows only those astromers that rank in the top five by AIR. When we expand our enumeration of r -process astromers to include the top 10, we identify only 11 additional astromers. This highlights the effectiveness of AIR as a filter for distinguishing astromers from the broader population of isomers: the list of isomers identified as influential astromers is relatively insensitive to the particular choice of AIR top- N . We summarize our expanded (top 10) list of astromers in Table 1 . Table 1 Astromer Importance Rating (AIR) Top 10 Selected at Each Time Point t ≳ 15 Minutes Notes. We use g and m to indicate the ground state and isomer, respectively. The isomer energy is E m , and the J π are the spin and parity of the respective levels (parentheses denote uncertain J π ). The half-lives ( T 1/2 ) and β -decay branching for the isomer ( B m β ) are as measured in the laboratory; B m β is the percent of isomer decays, which are β decays rather than internal transitions to another nuclear state. T pop is the approximate timescale on which the isotope is populated, and the Notes column gives some brief comments on the nucleus. a EM signals (detectable x -rays and γ -rays) are possible, but we do not study them carefully here. b Nearly all parent β decay feeds the isomer, effectively bypassing the longer-lived ground state. c To explore the 195 Pt isomer population, we assume all 195 Os β decay feeds the 195 Ir isomer (actual feeding unknown). Notably, there are three species in Table 1 that do not appear in Figure 1 : 93,95 Nb and 131 Xe. This is because while the isomers might not be heavily populated relative to the ground state (low R ), they have comparatively high activities a and large thermal population imbalances I . The astromers identified in Table 1 that change β -decay rates will affect r -process heating. Some (e.g., 115 Cd, 127,129,131 Te) will defer heating by delaying decay, while others (e.g., 85 Kr, 128,130 Sb) partially counteract this trend by accelerating decay. Furthermore, astromers may generate identifiable x -ray or γ -ray signals either directly


ranges as Lohfink et al. ( 2012b ), we obtained a reduced χ 2 value of 1.099 (null hypothesis probability of 0.065). Considering the fact that our data selection criteria and extraction regions for XIS source and background data are not completely identical, we conclude that our blurred reflection fit results are consistent with those of Lohfink et al. ( 2012b ). The blurred reflection model fitted by Lohfink et al. ( 2012b ) and the MYTORUS model fit (see Table 2 ) give reduced χ 2 values that are comparable, which indicates that the two model fits are statistically similar to each other. In our blurred reflection model fit described above, the value of C PIN: XIS was fixed at 1.16, as was in Lohfink et al. ( 2012b ). When we allowed C PIN: XIS to be a free parameter, the value of χ 2 dropped by only 1.6, and we obtained , which encompasses the value of 1.16. Figure 5. A comparison, in the 4–8 keV band, of the best-fitting baseline face-on MYTORUS fit with a ‘blurred reflection’ model, similar to that fitted by Lohfink et al. ( 2012b ), showing how the MYTORUS model can eliminate the need for a relativistically broadened Fe Kα line. Both models include narrow Fe xxv and Fe xxvi Lyα emission lines from distant ionized material (but the fitted Fe xxv flux for the blurred reflection model is negligible). (a) Unfolded photon spectrum (black) overlaid with the total MYTORUS model (red; see Table 2 , column 2). The Compton-scattered continuum from the MYTORUS model shown in blue is able to account for excess emission in the Fe K band instead of a relativistically broadened Fe Kα line. (b) Unfolded photon spectrum (black) overlaid with the total multi-component Lohfink et al. ( 2012b ) disc-reflection and Comptonization model in red (see the text for details). The blurred reflection component is shown in blue. Above the blue wing of the relativistic Fe Kα line, the reflection continuum drops by more than an order of magnitude but rises again at high energies. Note that in this model fit, the narrow Fe Kα is unresolved because Lohfink et al. ( 2012b ) did not include any broadening for this component. Panels (c) and (d) show the data-to-model ratios corresponding to (a) and (b), respectively, showing that both models give good fits to the data. Note that, although the MYTORUS component and blurred reflection component (shown in blue in panels (a) and (b), respectively) appear to be markedly different from each other, smearing due to the limited CCD detector energy resolution has a considerable impact on the ability of the data to distinguish between the two models. This is illustrated in Fig. 6 . The model components in blue in Figs 5(a) and (b) show how the Compton-scattered continuum from the MYTORUS model and the relativistically broadened Fe Kα line with blurred disc continuum respectively provide very different solutions to the same data. At a disc inclination angle of 48°, the relativistic line has significant emission in the blue wing but it


We thank Sebastian Lopez for initial discussions about using Zn and Cr ii to measure Δα/α, and Julija Bagdonaite and Jonathan Whitmore for assistance with Fig. 15 . MTM thanks the Australian Research Council for Discovery Projects grant DP110100866 which supported this work. Some of the data presented herein were obtained at the W.M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W.M. Keck Foundation. We 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. Other data presented herein were based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme IDs 65.O-0158, 67.A-0022, 072.A-0346, 074.A-0201, 079.A-0600, 082.A-0682, 082.A-0569, 083.A-0874 and 084.A-0136.


ACKNOWLEDGEMENTS PQM gratefully acknowledge the financial support of CONICYT/Becas Chile. This work was also supported by the Portuguese Science foundation (FCT) through national funds (Investigador contract of reference IF/00894/2012 and UID/FIS/04434/2013) and POPH/FSE (EC) by FEDER through COMPETE2020 (POCI-01-0145-FEDER-030389 and POCI-01-0145- FEDER-007672). Funds were also provided by the project FCT/CNRS: PICS 2014. OK acknowledges financial support from the Knut and Alice Wallenberg Foundation, the Swedish Research Council, and the Swedish National Space Board.


in two distinct emission regions. We used compTT plus compPS , or two compTB . The dominant emission results, by the Comptonization, in a disk geometry of seed photons at 0.1 keV by a plasma with keV and optical depth 3. This emission can be interpreted as arising in a region above the accretion disk and contributes to the 70 of the total flux. We call this component hard Comptonization. In addition, a secondary emission component, described by a spherical geometry, is due to seed photons at 0.8 keV Comptonized by a plasma with keV and optical depth 8 using a compTT model or with keV using a compTB . This emission can be interpreted as originating from a region closer to the neutron star around the boundary layer and contributes about 30 of the total flux. We call this component soft Comptonization. The persistent emission spectrum of the LMXB GS 1826-23, observed jointly by Chandra and RXTE, has been described by Thompson et al. ( 2005 ) with a similar model. This model uses dual Comptonization of 0.3 keV soft photons by a plasma with keV and , which is interpreted as emission from accretion disk corona, plus Comptonization of hotter 0.8 keV photons by a 6.8 keV plasma, which is interpreted as emission from the boundary layer. The parameters are therefore very similar to those reported here for 4U 1705-44. Moreover, a dual Comptonization model was successfully applied to the broadband spectra of the low/hard state of the black hole candidate Cyg X-1 by Makishima et al. ( 2008 ) with Suzaku data, by Di Salvo et al. ( 2001 ) and Frontera et al. ( 2001 ) with Beppo SAX data, and by Gierliski et al. (2007) and Ibragimov et al. ( 2005 ), who used simultaneous Ginga, RXTE and GRO/OSSE data. Takahashi et al. ( 2008 ) modeled the spectra of GRO J1655–40 taken with Suzaku in a similar way. Recently, the two compTB model was applied to six neutron star LMXBs (see Seifina et al. 2015 ). This model describes a scenario in which the Keplerian disk is connected to the neutron star by the transition layer (see Titarchuk et al. 1998 ), where the hot electrons scatter off the soft photons from the neutron star photosphere of temperature and the soft disk photons of temperature giving rise to two Comptonized components. We stress that no reflection component is needed in the two scenarios described above. 5.2. Banana states and high energy tail The absorbed Comptonized component with reflection does not describe the soft/high state spectrum. In addition to a dominating Comptonized emission, the continuum broadband spectrum of the source during the soft state requires a blackbody component at lower energies and a hard component to model the hard tail or excess. The dual Comptonization scenario successfully describes the broadband spectra of the banana state in that it models the soft dominating emission and the hard tail well. Contrary to the island state, the dominating Comptonization emission is soft and is expected to originate in the hot plasma


This work is based on observations made with the JAST/T80 telescope at the Observatorio Astrofísico de Javalambre (OAJ), in Teruel, owned, managed and operated by the Centro de Estudios de Física del Cosmos de Aragón. We acknowledge the OAJ Data Processing and Archiving Unit (UPAD) for reducing and calibrating the OAJ data used in this work. Funding for the J-PLUS Project has been provided by the Governments of Spain and Aragón through the Fondo de Inversiones de Teruel, the Aragón Government through the Reseach Groups E96 and E103, the Spanish Ministry of Economy and Competitiveness (MINECO; under grants AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789 and ICTS-2009-14), and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). R.L.G acknowledges support from “Obra social de la fundación bancaria Ibercaja”. K.V. acknowledges the Juan de la Cierva Incorporación fellowship, IJCI-2014-21960, of the Spanish government. R.A.D acknowledges support from CNPq through BP grant 312307/2015-2, CSIC through grant COOPB20263, FINEP grants REF. 1217/13-01.13.0279.00 and REF 0859/10-01.10.0663.00 for partial hardware support for the J-PLUS project through the National Observatory of Brazil. L.G. was supported in part by the US National Science Foundation under Grant AST-1311862. R.M.G.D was supported by AYA2016-77846-P, AYA2014-57490-P, and Junta de Andalucía FQ1580. J.A.H.J. and S.A. thank the Brazilian institution CNPq for financial support through postdoctoral fellowship (project 150237/2017-0 and 300336/2016-0, respectively). R.L.O. was partially supported by the Brazilian agency CNPq (Universal Grants 459553/2014-3, PQ 302037/2015-2, and PDE 200289/2017-9). R.L.G wants to thank his thesis directors and CEFCA staff for all the teaching, support and feedback received during the development of this work; to the J-PLUS Collaboration for the feedback received; to the SELGIFS Collaboration ( http://astro.ft.uam.es/selgifs/ ) for the training in IFS data management, and to R.M. González Delgado for the data provided for the comparison in Fig. 8 . The authors thank the referee for the thorough review. This research made use of AstroPy ( Astropy Collaboration 2013 ), Matplotlib ( Hunter 2007 ) and NumPy , part of the Python Software Foundation. (Python Language Reference, version 2.7. Available at http://www.python.org ). This study uses data provided by the Calar Alto Legacy Integral Field Area (CALIFA) survey ( http://califa.caha.es/ ). Based on observations collected at the Centro Astronómico Hispano Alemán (CAHA) at Calar Alto, operated jointly by the Max-Planck-Institut für Astronomie and the Instituto de Astrofísica de Andalucía (CSIC). This study uses data provided by SDSS. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the US Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/ . SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France ( Wenger et al. 2000 ).


a low-order tunable filter will be described. We present Fabry–Perot data cubes for the large-diameter expanding structures around a giant H ii region, the centre of the 30 Doradus starburst (or 30 Doradus giant star-forming region), also called the Tarantula Nebula, in the Large Magellanic Cloud (LMC). The study of shells and bubbles, due to stellar winds and supernova explosions, within giant H ii regions is crucial for understanding the processes of mass and energy transfer from the stellar component to the interstellar medium and the processes of triggering of star formation. Although kinematic studies of supergiant shells and superbubbles in giant H ii regions are common in the literature, carried out both with long-slit and, more recently, with 3D spectroscopy, they have not had the necessary coverage and spatial resolution to answer a few key questions. Even the origin of the supersonic motions present in these regions is still a matter of debate (Melnick et al. 1999 ). Moreover, it is still not understood if the kinematics of giant H ii regions are due to an underlying diffuse component or to superposed multiple kinematic components. Having high spatial and spectral resolution velocity maps, as those delivered by SAM-FP, is crucial to tackle these problems. A few authors have studied the kinematics of the ionized gas in the 30 Doradus nebula, e.g. Chu Kennicutt ( 1994 ), Melnick et al. ( 1999 ), Redman et al. ( 2003 ), Torres-Flores et al. ( 2013 ), and references therein. All of these studies reported broad and possibly multiple components widespread in the nebula, which indicate a complex structure. These studies were either performed (i) with long-slit echelle spectroscopy (e.g. Chu Kennicutt 1994 ; Melnick et al. 1999 ), probing motions and physical conditions in small (parsec) and larger (tens of parsecs) scales but covering only a limited number of lines of sight; or (ii) with fibres, as the work of Torres-Flores et al. ( 2013 ), with a somewhat larger field of view, but probing the nebula in a sparse manner (every 5 × 5 pc 2 , with a filling factor of 0.0028). The advantage of this study is that it combines 3D spectroscopy information with continuous coverage of the field over a fairly large field of view, with a pixel size (binned 4 × 4) of 0.18 × 0.18 arcsec 2 (or 0.045 × 0.045 pc 2 ). These data show that the kinematic profiles change on scales of a tenth of a parsec over the whole field. The wealth of details seen in Hubble Space Telescope ( HST ) images of nearby giant H ii regions can now be investigated using similar-quality kinematic data taken with SAM-FP with a laser guide star. The main purpose of this paper is to highlight the potential of Fabry–Perot maps obtained with adaptive optics in the study of 30 Doradus and similar giant H ii regions. A high-order Fabry–Perot was used to observe the ionized gas within 30 Doradus, in its strongest optical line: Hα. In Section 2 , we first describe the new mode of SAM with a


The authors thank Cecilia Ceccarelli, Daniele Galli, and Vincent Tatischeff for valuable discussions. We acknowledge the financial support of the Agence National pour la Recherche (ANR) through the COSMIS project. This work has been carried out thanks to the support of the OCEVU Labex (ANR-11-LABX-0060) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the “Investissements d’Avenir” French government programme managed by the ANR. M.P. and A.M. also acknowledge the support of the CNRS-INAF PICS project “Pulsar wind nebulae, supernova remnants and the origin of cosmic rays”.


The authors gratefully acknowledge the anonymous referee for a careful reading of the manuscript and very helpful comments. We are deeply thankful to Dr. Mark G Rawlings for supporting our observations with SCUBA-2. We also appreciate Dr. Manolis Rovilos, Dr. Elisabete da Cunha, and Dr. Stéphanie Juneau for their helpful comments on SEABASs and MAGPHYS . The Submillimeter Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. The James Clerk Maxwell Telescope has historically been operated by the Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the United Kingdom, the National Research Council of Canada and the Netherlands Organisation for Scientific Research. Additional funds for the construction of SCUBA-2 were provided by the Canada Foundation for Innovation. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III website is http://www.sdss3.org/ . SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. This publication makes use of data products from the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. This research is based on observations with AKARI, a JAXA project with the participation of ESA. Y.T., W.H.W., C.F.L., and Y.Y.C. acknowledge the support from the Ministry of Science and Technology of Taiwan ( MOST 105-2112-M-001-029-MY3). T.N. is financially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (16H01101 and 16H03958).


of equation 33 ). Finally, in Fig. 4 we show the temperature moment itself as given in equation ( 33 ). Figure 2. Kappa function f κ ( v , η(Θ, κ)) for isentropic electron flows for different values of kappa as a function of the particle velocity v in units of U 0 = 100 km s −1 . One can see that different transitions from a Maxwellian core to a power-law distribution are occurring along the heliosheath streamlines at an isentropic electron behaviour. Figure 3. The differential temperature moment of the electrons defined by the integrand of equation ( 33 ) is plotted as a function of the velocity v in units of U 0 = 100 km s −1 for different values of the parameter κ. This shows that for different permitted combinations of the parameters κ and Θ the contributions to the effective temperature are due to the higher electron velocities, the lower are the associated kappa values. Figure 4. The electron temperature, as a velocity-moment of the distribution function given by equation ( 33 ) as a function of the upper velocity border v ∞ in units of U 0 = 100 km s −1 , is shown as a function of the actual kappa value for isentropic electron flows. 4 NON-ISENTROPIC ELECTRON FLOWS AND THE ELECTRON ENTROPY In case that the electron flow along the streamlines, contrary to the assumption in the section before, develops non-isentropically due to the presence of energy sources or sinks, i.e. if the entropy of the electron fluid changes with the flow line element s , then one has to consider the following more complicate relation along streamlines: where d Q according to thermodynamical convention describes energy gains and losses per unit of the streamline element s in the co-moving volume Δ V , and T = T κ ( s ) is the local kappa temperature. Looking at equation ( 34 ), one can already qualitatively say that practically no change of the entropy has to be faced, if the temporal energy change compared to the given energy content Q is negligible, i.e. the latter condition for would lead to a quasi-isentropic behaviour. In order to check on this condition, we may now look into the pressure transport equation (equation 14 ) where one can identify two processes contributing to the change of the energy density Q ( s ) = ε( s ), namely: connected with the process of magnetic cooling (see Section 2 , equation 8 ) which is given by and the process of velocity-space diffusion (see Section 2 , equation 11 ) leading to the following term: Hence one can express the energy change per time by . Since we have already derived the solution for the local pressure P κ ( s ), one thus, based on that, can also obtain the entropy evolution with the following equation (see Section 2 , equation 14 ): where the actual kappa temperature is given by (see Heerikhuisen et al. 2008 ; Scherer et al. 2018 ) The kappa-entropy S κ ( s ) can then be calculated with the above differential equation, using the equation for P κ ( s ) (equation 14 ) derived further above in this paper in


Thanks to an anonymous referee for useful comments on the paper. This work has been funded with support from the Spanish Ministerio de Economía y Competitividad through the grant AYA2012-32295. JS acknowledges financial support from the Alexander von Humboldt foundation. CRA is supported by a Marie Curie Intra European Fellowship within the 7th European Community Framework Programme (PIEF-GA-2012-327934). RGD acknowledges support through the grant AYA2010-15081. This work is partially based on data obtained as part of the WHT service programme. The WHT and its service programme are operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. We thank the WHT staff for performing the observations. The work is also based on data from Sloan Digital Sky Survey. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the US Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society and the Higher Education Funding Council for England. The SDSS website is http://www.sdss.org/ . The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory and the University of Washington.


ACKNOWLEDGEMENTS The authors are grateful to the anonymous referee for the useful comments on the manuscript. The Hydrangea simulations were in part performed on the German federal maximum performance computer ‘Hazel Hen’ at the maximum performance computing centre Stuttgart (HLRS), under project GCS-HYDA/ID44067 financed through the large-scale project ‘Hydrangea’ of the Gauss Centre for Supercomputing. Further simulations were performed at the Max Planck Computing and Data Facility in Garching, Germany. This project received funding from the EU Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement 747645 (ClusterGal). This work used the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility. DiRAC is part of the National E-Infrastructure. This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grants ST/H008519/1 and ST/K00087X/1, STFC DiRAC Operations grant ST/K003267/1, and Durham University. This work has furthermore benefitted from the use of Py-SPHViewer (Benitez-Llambay 2015). STK and DJB acknowledge support from STFC through grant ST/L000768/1. RAC is a Royal Society University Research Fellow. CDV acknowledges financial support from the Spanish Ministry of Economy and Competitiveness (MINECO) through grants AYA2014-58308 and RYC-2015-1807.


The Fermi LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l’Energie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d’Études Spatiales in France. This work performed in part under DOE Contract DE-AC02-76SF00515.


We acknowledge the anonymous referee for fruitful comments. We warmly thank Franck Le Petit for his assistance in the use of the PDR code. C.F. acknowledges the financial support for this work provided by the French space agency CNES along with the support from the Italian Ministry of Education, Universities and Research, project SIR (RBSI14ZRHR). We acknowledge the funding from the European Research Council (ERC), projects PALs (contract 320620) and DOC (contract 741002). This work was supported by the French program Physique et Chimie du Milieu Interstellaire (PCMI) funded by the Conseil National de la Recherche Scientifique (CNRS) and Centre National d’Etudes Spatiales (CNES) and by the Italian Ministero dell’Istruzione, Universitá e Ricerca through the grant Progetti Premiali 2012—iALMA (CUP C52I13000140001).


Acknowledgments C.C. gratefully acknowledges support from the National Natural Science Foundation of China (NSFC), grant No. 11750110410.


The authors are grateful to the staff of IRAM for their help during the observations of the IRAM 30 m data, and in particular to the Director for allowing us to use the telescope during the DDT proposal D04–15. This work was partly supported by the Italian Ministero dell’Istruzione, Università e Ricerca through the grant Progetti Premiali 2012–iALMA. P.C. and A.V. acknowledge the financial support of the European Research Council (ERC; project PALs 320620). J.M.-P. acknowleges partial support from the MINECO under grants AYA2010-2169-C04-01, FIS2012-39162-C06-01, ESP2013-47809-C03-01, and ESP2015-65597-C4-1.


detected in the spectra, as illustrated for two objects in Figure 9 . In such instances, we simply excluded this narrow wavelength region from the fits. We are confident that ignoring the carbon opacity in our models only marginally impacts the derived values of and log g . Indeed, the carbon abundances measured in DOZ white dwarfs typically fall in the range −3.0 log C/He −2.0 (Dreizler Werner 1996 ; Reindl et al. 2014a ; Werner et al. 2014 ), and the corresponding opacity barely influences the He i and He ii line profiles at high temperatures according to the model-atmosphere calculations of Reindl et al. ( 2018 ). Figure 8. Best model-atmosphere fits to the optical spectra of DA white dwarfs with high signal-to-noise observations (S/N > 30). The observed and synthetic spectra, normalized to a continuum set to unity, are displayed as black and red lines, respectively. The fits are offset vertically from each other by 0.6 for clarity. The SDSS name, the spectral type, and the best-fitting atmospheric parameters are given for each object. The positions of the H i lines are marked by blue ticks. Figure 9. Same as Figure 8 , but for DO/DB white dwarfs. The positions of the He i and He ii lines are marked by green and magenta ticks, respectively. 4.2. Hybrid White Dwarfs Special care was needed to properly analyze the 127 stars in our sample exhibiting both hydrogen and helium spectral features. As indicated by Figure 5 , the great sensitivity of the spectrum to the chemical structure of the atmosphere can be exploited to discriminate between homogeneous and stratified distributions of hydrogen and helium (Bergeron et al. 1994 ; Manseau et al. 2016 ). To this end, we fitted the optical spectra of our hybrid white dwarfs with both our homogeneous and stratified model-atmosphere grids. The physical quantity used to measure the composition (the helium-to-hydrogen number ratio He/H in the homogeneous case, the fractional mass of the hydrogen layer q H in the stratified case) was treated as an additional free parameter in the optimization process. We then compared the two solutions and adopted the best-fitting one. Figure 10 contrasts the homogeneous and stratified solutions for four hybrid white dwarfs in our sample. Once again, while only the blue optical spectra are displayed here, redder wavelengths, up to λ = 7000 Å, were included in the fits as well. For each star, the parameters corresponding to the adopted chemical configuration are highlighted in red. The top two white dwarfs are relatively hot, in which situation the comparison rests largely on the He ii λ 4686 line and to a lesser extent on the He i λ 4471 line. We can safely assert that the first object has a homogeneous atmosphere, because our stratified models produce a He ii λ 4686 feature that is too shallow. Conversely, the second object bears the signature of a stratified atmosphere, the He ii λ 4686 profile predicted by our homogeneous models being too deep. The bottom two


The authors would like to thank Diego Muñoz and Dong Lai for providing the results of their simulations and for many useful discussions. We thank Suzanne Hawley and the Astrophysical Research Consortium (ARC) for their generous allocation of ARCSAT commissioning time as well as Flynn Hasse and the WIYN 0.9 m synoptic observers, Stephen Gilliam, François Dufour, and William Romanishin. This work makes use of observations from the LCOGT network and observations obtained with Apache Point Observatory’s 0.5 m Astrophysical Research Consortium Small Aperture Telescope. B.M.T. acknowledges support from a Sigma Xi Grant-in-Aid of Research and the University of Wisconsin–Madison Graduate School.


al. ( 2018 ) carried out polarimetric observations of the NEO (3200) Phaethon at very large phase angles. The analysis of their data led Devogèle et al. ( 2018 ) to conclude that Phaethon is most likely a collisional fragment of the B-class asteroid (2) Pallas, in particular a fugitive from Pallas’ family, confirming previous results based exclusively on spectral reflectance data by De León et al. ( 2010 ). The observations by Devogèle et al. ( 2018 ) and Ito et al. ( 2018 ) are also shown in Fig. 1 . We note that at phase angles ∼100° (not shown in Fig. 1 ), the two polarimetric datasets are not consistent: around phase angle 105°, polarization values from Ito et al. ( 2018 ) reach about 50 per cent while at phase angle 101°, Devogèle et al. ( 2018 ) measure only 38 per cent. An explanation for this discrepancy, possibly due to surface heterogeneity of Phaethon, is beyond the scope of this paper. However, at phase angles , it clearly appears that Bennu exhibits values of linear polarization much higher than those of Phaethon. Our measurements, as well as the very recent observations of (152679) 1998 KU2 (Kuroda et al. 2018 ) are the highest ever found for a NEO observed at large phase angles. Another Solar system object that exhibits such high-polarization values is comet C/1995 O1 (Hale–Bopp), which was observed by Manset Bastien ( 2000 ) in a 9 nm wide gas-free filter centred around λ = 694 nm. Hale–Bopp measurements are also shown in Fig. 1 . The resemblance between the polarimetric data of Bennu and those of Hale–Bopp is striking, and although reinforces the conclusion about similarity between F-type objects and cometary dust proposed by Kolokolova Jockers ( 1997 ), it should be treated with caution, due to the big differences between the two physical environments: in one case we have an obviously active and very large comet, in the other one a much smaller asteroid with no detection of a gas or dust coma. Our data show that the phase-polarization curve of Bennu is steeper than the extrapolation of phase-polarization curves of main-belt B-class asteroids observed at phase angles , and resembles that of F-class asteroids. At phase angles , the polarimetric behaviour of Bennu is probably in between that typical of the F and B classes. Dynamical modelling has suggested that Bennu has most likely have evolved from the Inner part of the Main Belt (IMB; a 2.5 au; Walsh et al. 2013 , and references therein) possibly within either the ‘New’ Polana or Eulalia families (Bottke et al. 2015 ). If this is true, taking into account the finding by Cellino et al. ( 2001 ) that most of Polana family’s members belong to the F-class, our polarimetry might support the hypothesis that Bennu originated in that family, since Eulalia is a C-class asteroid. Based on what is currently known about the similarity in polarimetric behaviour of the asteroid F-class and of some cometary bodies, (101955) Bennu could be compatible with cometary characteristics and a


fit all transtions with K ′′ a up to 20 (excluding lines of K ′′ a = 18, which showed a clear trend in residuals between 197.48 and 221.45 GHz) for frequencies below 251 GHz. But in the range 310–506 GHz, the assignments appeared difficult in spite of larger intensities. As for other vibrational states of g - n -PrCN, the oblate pairing consisting of a -type and b -type transitions facilitated the assignments. However, the increasing separation of paired transitions with increase in quantum numbers made assignments more difficult combined with the blending and broadening in the high frequency range and the possible splits caused by internal rotations. Therefore very secure assignments with K ′′ a up to 4 or 5 (for each sub-branch) were first made. Even after the improvement of the parameters several lines at each K ′′ a could not be correctly fitted showing systematic residuals. All these lines are in the ranges of J ′′ from 68 to 73 and from 89 to 93. We attributed this as due coupling with other vibrational states. Finally 935 R -branch a -type transitions were identified in all four frequency ranges. The assignment of b -type transitions was not easy due to low intensities at low frequencies and blending and broadening at high frequencies. The confidently assigned b -type transitions at high frequency were all oblate paired with intense a -type transitions with K ′′ a less than 6. Totally, 487 b -type transitions were fitted. Q -branch b -type transitions were useful to correctly determine the parameter D K . We identified internal rotation causing splits (with the components separated by around 0.6 MHz) at frequencies between 89.25–126.75 GHz, in transitions with large J ′′. In this range, Q -branch b -type transitions have K ′′ a less than 11 and those that could be securely assigned have J ′′ less than 45. For Q -branch b -type transitions with K ′′ a between 13 and 18 in the range 171–251 GHz, only prolate pairs were assigned. These transitions have low J ′′ and hence are not affected by internal rotation. Finally 466 Q -branch b -type transitions were correctly fitted. Moreover, 110 hyperfine split transitions were fitted with fixed hyperfine parameters from v 30 = 1. These are R -branch a -type transitions with J ′′ approaching K ′′ a (less than 11), b -type transitions (both R -branch and Q -branch) with K ′′ a less than 4 at frequencies below 70 GHz. The derived parameters are shown in Table 6 . The fitted transitions are in Table 18 (available at the CDS), and those confidently assigned but not correctly fitted with systematic residuals are separated in Table 19 (available at the CDS). Using the newly derived molecular parameters, confident predictions of a -type transitions can be made up to J ′′ = 68 for K ′′ a ≤ 5 below 380 GHz, and J ′′ = 41 for K ′′ a ≤ 41 below 251 GHz (excluding lines of K ′′ a = 18 that show residuals that cannot be fitted). For b -type transitions, all R -branch predictions with K ′′ a ≤ 11 below 251 GHz


This work would have been impossible without the vigorous work of Fernando Selman and George Hau, MUSE Paranal instrument scientists. We are grateful to Francesca Iannuzzi and Francesca Fragkoudi for very useful input and discussions on bars in simulated galaxies, particularly on box/peanuts. It is also a pleasure to thank Eric Emsellem for useful discussions on the inner components of barred galaxies and Michael Vlasov for his permission to use a chart from his atlas. We are very grateful to the anonymous referee for a thoughtful and constructive report. M.K.S. and J.F.-B. acknowledge support from grant AYA2013-48226-C3-1-P from the Spanish Ministry of Economy and Competitiveness (MINECO). J.F.-B. also acknowledges support from the DAGAL network from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007–2013/ under REA grant agreement number PITN-GA-2011-289313. P.S.-B. acknowledges support from the Ramón y Cajal programme, grant ATA2010-21322-C03-02 from the Spanish Ministry of Economy and Competitiveness (MINECO). Based on observations made with ESO telescopes at the La Silla Paranal Observatory under programme ID 060.A-9313.