O O O O O O O O O O O O O O O O O O O O O B-Model B-Model O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O B-Model B-Model O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model B-Model O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model B-Model O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O B-Model O B-Model O

/yr) L and D n 4000. 8 The goal is to check how these properties vary according to the employed classification: GMM, BPT and WHAN. To that end, we portray their statistical properties as boxplots in Figs 9 11 , as well as their summary statistics in Table 3 , which shows the values for the median, first (Q1) and third (Q3) quartiles, and the interquartile range (IQR Q3Q1). Figure 9. Metallicity distributions portrayed as boxplots for GMM, BPT and WHAN diagram classes, from top to bottom. For the GMM we can see the GCs displayed in the following order: GC4, GC1, GC3, GC2 following increasing log[N ii ]/H, i.e. the x -axis of the BPT diagram. The order of the remaining groups is given by the KL distance to the GMM components. The width of boxes is proportional to the square root of the number of galaxies within each bin and the whiskers extend to the most extreme data point, which is within the 50per cent interquartile range (IQR). To better illustrate the overall distribution of the sample, for the boxplots of the BPT diagram we have omitted part of the outlier zone, zooming into the interquartile distance. Figure 10. Average stellar age distributions portrayed as boxplots for GMM, BPT and WHAN diagram classes, from top to bottom. The GCs and remaining groups are ordered as in Fig. 9 . Figure 11. D n 4000 distributions portrayed as boxplots for GMM, BPT and WHAN diagram classes, from top to bottom. The GCs and remaining groups are ordered as in Fig. 9 . Table 3. Summary statistics for the Z /Z L , log( t /yr) L and D n 4000 galaxy properties. Shown are the median, IQR and the first and third quartile for the properties of the GMM, BPT and WHAN groups. On the boxplots, the groups are vertically aligned based on their proximity in terms of the KL distance, and the fiducial order roughly follows increasing values of log[N ii ]/Hin the BPT diagram (i.e. from left to right: SF, composite, AGN). As we can see from the boxplots, by aligning these groups in this way, a positive monotonic relationship between the classes and the median values of Z /Z L , log( t /yr) L and D n 4000 distributions is revealed. The trend is a consequence of the AGN host galaxies having different characteristics from their inactive counterparts, preferentially populating the so-called green valley and red sequence of the colourmass diagram (e.g. Schawinski etal. 2010 ). Thus, as we move towards the right-hand side of the BPT diagram, one is mostly looking at early-type galaxies, that are characterized by older and more metallic stellar populations, and higher values of D n 4000 (e.g. Poggianti Barbaro 1997 ; Schawinski etal. 2010 ; De Souza etal. 2016 ). Notably, the GMM solutions automatically find groups that share meaningful physical properties, beyond the features used in the clustering algorithm. An inspection of Table 3 confirms that the statistical properties are quite similar between the GMM groups and their respective counterparts on the BPT and WHAN diagrams.


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We wish to thank the anonymous referee for carefully reading our manuscript and making a number of very useful comments. We wish to thank Eric Gérard, and Laurence Alsac and other Nançay Radio Telescope staff members for their technical advice and assistance. TJ and RM are grateful for the bursary provided by the South African SKA Project Office; S.B., T.J., R.C.K.K., M.R. and PS were supported by the South African National Research Foundation. The Nançay Radio Astronomy Station is operated as part of the Paris Observatory, in association with the Centre National de la Recherche Scientifique (CNRS) and partially supported by the Région Centre in France. This research has made use of the HyperLeda database ( http://leda.univ-lyon1.fr ), 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 and the Sloan Digital Sky Survey which is managed by the Astrophysical Research Consortium for the Participating Institutions.


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We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research. The authors would like to thank Alycia Weinberger for providing the published IRS spectrum of BD+20 307, and Rui Wang for the help in illustration preparation. This work is based on observations made with the Spitzer Space Telescope , which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. This work was partially supported by contracts 1255094 and 1256424 from Caltech/JPL to the University of Arizona, and by NASA Grant NNX10AD38G. Facilities: AAVSO , Spitzer (IRAC)


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This research was funded by the European Union’s Seventh Frame-work program under grant agreement 337595 (ERC Starting Grant, “CoSMass”). EV acknowledges funding from the DFG grant BE1837/13-1. EV also acknowledges support of the Collaborative Research Center 956, subproject A1 and C4, funded by the Deutsche Forschungsgemeinschaft (DFG). JD acknowledges financial assistance from the South African SKA Project (SKA SA; www.ska.ac.za ).


O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O

In agreement with this trend, shows a slow first core outflow; Fig. 9 shows the radial velocity in a slice through the first core at ρ max ≈ 10 −7 g cm −3 . Although a similar ‘X’-shaped pattern is visible for all the non-ideal MHD models, the pattern in has only a narrow band of gas that is slowly outflowing ( r ≲ 0.3 km s −1 ), while the surrounding material is falling at a slightly faster rate; the gas is accreting faster along both the equatorial and polar directions than along the diagonals. Given the high angular momentum in the disc of , outflows are not required to carry angular momentum away. Figure 9. Radial velocity in a slice through the first core for each of the calculations. The white contour is r = 0. There are no first core outflows in the purely hydrodynamics model, and there are narrow, slow outflows embedded in the centre of the diagonal infall of . By contrast, as the ionization rate is decreased for the models with , the speed of the first core outflows increases and broadens. In all cases, the lower ionization rate reduces the magnetic field strength that accounts for the broadening of the outflows. Thus, at any given ζ cr , the difference in outflow speed is necessarily a result of the Hall effect. 1 In the models with , the Hall effect spins down the gas above and below the first core, which reduces the toroidal component of the magnetic field. As previously shown (e.g. Bate et al. 2014 , WBP2018 ), lower ratios of toroidal-to-poloidal magnetic field strengths result in faster outflows. Shortly after the formation of the first core, the trend of decreasing ratios of toroidal-to-poloidal magnetic field strengths corresponds to increasing outflow speeds. Vaytet et al. ( 2018 ) modelled the collapse through the first and stellar core phases using ζ cr = 10 −17 s −1 , however, their model included only ambipolar diffusion and Ohmic resistivity. They found no first core outflows, akin to our but contrary to . Their model had an initial m = 2 perturbation and an initial faster rotation. Thus, their initial conditions promoted disc formation and hence they found results similar to our , in which the Hall effect is responsible for promoting disc formation. 4.5 Counter-rotating envelopes During the first core phase, a counter-rotating envelope forms in , and at its most massive contains ∼10 −3 M ⊙ and extends to r ∼ 30 au. The counter-rotating envelope dissipates with time and disappears completely by ρ max ≈ 2 × 10 −9 g cm −3 , just before the disc forms. This envelope is smaller and less vertically extended than those found by Tsukamoto et al. ( 2015b , 2017 ). Their initially stronger magnetic field strength and faster rotation likely required the larger envelope at larger radii to conserve angular momentum. Thus, counter-rotating envelopes are likely a transient feature, with their properties dependent on the Hall effect, ionization rates (e.g. Wurster, Bate Price 2018b ), and initial conditions. 5 SUMMARY AND CONCLUSION


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product. We recommend not using the unofficial LAB HEALPix map any longer. 6.2. Spectral data The full spectral data will be provided for download in FITS binary table format on a HEALPix grid (nside-1024, Galactic coordinates are used for indexing). Because the EBHIS Milky Way spectral data set has a size of about 50 GByte, we split it into smaller chunks, namely into the 192 HEALPix pixels of the nside-4 tessellation. Because EBHIS is only available for declinations above , not all of the nside-4 pixels contain data, and these are therefore omitted. Additionally, smaller spectral data cubes of size are available for download in FITS image format. 7. Summary We presented the first data release of the Effelsberg-Bonn H i Survey (EBHIS), elaborated on the recent changes to the data processing software and techniques compared to Winkel et al. (2010) , and studied the quality of the resulting data products in great detail. EBHIS is a 21-cm H i line survey of the sky north of , comprising full spatial sampling and high sensitivity. At full spectral resolution of , EBHIS has an average noise level of . Its angular resolution is . About 1000 h of observing time with the Effelsberg 100-m telescope were spent until the completion of the first coverage that forms the basis for the first data release. Owing to the large bandwidth of 100 MHz, not only is the Milky Way emission covered in EBHIS, but also H i in the Local Volume out to a redshift of . The presented data products comprise the Milky Way emission. The analysis of the extragalactic part of the survey will be published elsewhere. EBHIS was designed to replace the northern-hemisphere portion of the important Leiden/Argentine/Bonn Survey (LAB; Kalberla et al. 2005 ), which has about a factor of 4 lower angular resolution and severely suffers from the insufficient spatial sampling (beam-by-beam only; compare Kerp et al. 2011 , e.g., their Fig. 7). Great effort went into developing new data reduction techniques, including but not limited to software radio-frequency-interference mitigation, a frequency-dependent flux-density calibration scheme, and robust baseline estimation in the 2D time-frequency domain. Furthermore, the very successful stray-radiation prediction algorithms, which were also employed for the LAB and Galactic All-Sky Survey (GASS; McClure-Griffiths et al. 2009 ; Kalberla et al. 2010 ; Kalberla Haud 2015 ), have been adapted to the Effelsberg telescope. We carefully assessed the data quality of the EBHIS spectra and inferred column densities. Our investigation revealed a mismatch in intensity calibration of GASS with respect to EBHIS and LAB, which led to a revised calibration scale in the third data release of GASS ( Kalberla Haud 2015 ). EBHIS and LAB show a near-perfect one-to-one relation in column densities and brightness temperatures. From a comparison of EBHIS and LAB, and from the overlaps of the EBHIS fields themselves, we estimated the uncertainties of the EBHIS column


O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

could introduce spurious velocities along the line of sight. In order to test this methodology, we use the GALAXY mock catalogue, in two different scenarios. The first scenario is an ideal one, with no mask or selection function: we apply the three approaches on a box populated with our mock catalogue, and compare the recovered radial velocities with the real ones provided by the catalogue. When performing this comparison, we evaluate a 3D grid for the original galaxy radial velocity by assigning to each cell the radial velocity of the galaxy falling nearest to the cell centre, that is, we adopt a nearest particle (NP) method. For different radial bins of the wavevector, we evaluate the correlation coefficient () computed from the cross-power spectrum between the recovered and the original radial velocity fields and their respective auto-spectra: In this expression, stands for , that is, the radially averaged power spectrum of the line of sight velocity modes. The superscripts stand for “original” and “recovered” components, respectively, and the asterisk denotes the complex conjugate operation. Fig. 3 Correlation coefficient of the recovered line-of-sight velocities with the actual ones in our GALAXY mock catalogue. Solid, dotted, and dashed lines refer to the LINEAR, LOG-LINEAR, and LOG-2LPT approaches, respectively. The red lines consider the ideal scenario without any sky mask or selection function, while the black ones are for the same sky mask and selection function present in the real CGC. In Fig. 3 the red lines display the correlation coefficients for the ideal scenario. Solid, dotted, and dashed lines refer to the LINEAR, LOG-LINEAR, and LOG-2LPT approaches, respectively. On large scales (low modes), the three approaches provide correlation coefficients that are very close to unity, while they seem to lose information on small scales in the same way. We note that a direct comparison to the results of K12 is not possible, since, in our case, we do not use the full dark matter particle catalogue, but only a central halo one. After the real mask and selection function obtained from the CGC is applied to the GALAXY mock catalogue, then the recovery of the peculiar velocities from the three adopted approaches worsens considerably. The black lines in Fig. 3 display, in general, much lower correlation levels than the red ones. The LINEAR approach seems to be the one that retains most information on the largest scales, and it out-performs the LOG-LINEAR and the LOG-2LPT methods on all scales. We hence expect the LINEAR approach to be more sensitive to the kSZ effect than the other two methods, particularly on the largest scales. Once the velocity inversion from the CGC density contrast has been performed, we compute the spatial correlation function between the recovered velocities and the kSZ temperature anisotropies, where is estimated as in Eq. ( 4 ), and the ensemble average is obtained after running through all galaxy pairs lying a


O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O B-Instrument O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O B-Instrument I-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model O O B-Model I-Model O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O B-Instrument I-Instrument O O O B-Instrument O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

modes. Simulations suggest that the algorithm should be able to reach one-shot correction resulting in a λ /1000 nm rms WFE with approximately 10 7 photons, and that with one iteration it should be able to correct up to ∼ λ /8 of rms WFE. In Sect. 5 , we demonstrated the principle with the vAPP in SCExAO, both with the internal source and on-sky, by measuring and controlling the 30 lowest Zernike modes and improving the raw contrast between 2.5 and 4 λ / D by a factor ∼2. Furthermore, we showed that the algorithm can correct WFEs of 300 nm and 150 nm rms with the internal source and on-sky, respectively. Although the contrast gains, both with the internal source and on-sky, are moderate, it does demonstrate that the coronagraphic PSFs of the vAPP can be used for wavefront sensing. The FPWFS performance of the SCExAO vAPP can be improved in various ways: (1) better model calibration to increase the contrast gain, (2) measuring more and higher spatial frequency modes to increase the area of correction, and (3) improving the convergence speed of the algorithm. For this demonstration the model was calibrated ad hoc by tuning the pupil undersizing, rotation, and the detector pixel scale by hand. These parameters should be more accurately measured or be part of the fitted parameters. To measure more and higher spatial frequency modes we want to replace the current Zernike mode basis with a pixel-mode basis ( Paul et al. 2013a ). This should also reduce the effect of unsensed modes on the estimation, when estimating a truncated mode basis ( Paul et al. 2013b ). Improving the convergence speed of the algorithm would result in a higher correction cadence or the correction of a larger mode basis in a similar time. A relatively simple improvement would be to use the multi-processing capabilities in the latest python libraries not yet available on SCExAO, this would enable multi-core processing and give a maximum speed improvement of a factor of 40. Theoretically, the best possible performance we can then expect is a wavefront estimation with sub 1 nm WFE every six seconds for targets down to a m H = 8 (five seconds of integration and one second for estimation). Other coronagraph model improvements include the integral field spectrograph CHARIS, an accurate model of the focal-plane filter and the incoherent background due to residual turbulence effects. This is because science observations with the SCExAO vAPP are done with CHARIS. Preventing detector saturation due to bright fields of the vAPP requires a focal-plane neutral density filter that attenuates the bright fields. Therefore, exposures with the focal-plane filter in place will have a larger dynamic range. This increase in dynamic range will make the incoherent background, which is due to uncorrected atmospheric turbulence, a more prominent feature in the image compared to the current on-sky results. Thus, the incoherent background needs to be included within the coronagraph model as well (


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We thank Hector Gil-Marín for useful discussion. DJ thanks Max-Planck Institute for Astrophysics for hospitality. NM acknowledges support from the Science and Technology Facilities Council (ST/L00075X/1).


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O B-Wavelength I-Wavelength O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O B-Wavelength O O O O O O O O O O B-Wavelength B-Wavelength I-Wavelength O O B-Wavelength O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Observatory I-Observatory O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O B-Wavelength O O O O O O O B-CelestialObject I-CelestialObject O B-Wavelength O O O O O O B-Dataset I-Dataset B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O

in SNRs. We also investigated the opposite problem assuming that this canonical paradigm is valid and put constraints on the parameter space spanned by the ambient gas density around the shock and electron-to-proton energy fraction. Overall,the derived flux upper limits are not in contradiction with the canonical CR paradigm. Assuming this paradigm holds true, we can constrain typical ambient density values around shell-type SNRs to n ≤ 7 cm −3 and electron-to-proton energy fractions above 10 TeV to ε ep ≤ 5 × 10 −3 . Finally, we compared the presented flux upper limits to the flux measurements of the seven non-interacting shell-type SNRs detected both in the radio and VHE range. We found evidence of correlation between the ratio of VHE γ -ray luminosity to radio luminosity, ( L VHE ∕ L radio ), and source age. This correlation can be explained by invoking the theory of magnetic field amplification at SNR shocks, which accounts for the rapid decrease in radio luminosity by predicting a declining magnetic field strength as the shock slows down with increasing source age. Further development in the SNR population from the observational point of view should be achieved with the next generation instrument: the CTA observatory. In this work we have also estimated the performance of this future observatory to probe the ambient gas density and the SN blast energy parameter space. The results suggest that this instrument will be an important leap forward in the investigation of the Galactic SNRs and will likely be able to confirm or invalidate the CR paradigm. Appendix A Additional material Table A.1 Results overview. Notes. The SNR morphology type is indicated by S for shell-type and C for mixed morphology SNRs. The type of the latter is indicated as thermal (t), plerionic (p), or both (tp). If molecular cloud interaction is probable or certain (information taken from SNRcat), this is indicated by an asterisk. For sources with an observational live time ≲1 h, instrumental effects can impact the result and should be used with care. R ON are the radii of the circular analysis regions. Distance assumptions correspond to the arithmetic mean of the density range limits reported in SNRcat. Table A.2. Literature estimates on source ages, distances, and ambient densities. Notes. Some of the ambient density values are not direct measurements but are derived from fits to the X-ray spectra. In some cases correlations with high density gas clumps are observed or inferred. The corresponding densities inside the clumps are denoted by an asterisk. Table A.3. Spectral parameters of the shell-type SNRs detected in both the radio and VHE bands. Notes. Except for the value for RX J1713-3946, all radio flux density values are taken from Green’s catalogue Green (2014) . Also given are the references to the models applied in Sect. 5 . ( a ) Assuming a systematic flux error of 20%. ( b ) Sum of north-east and south-west regions. ( c ) Value from ( Acero et al. 2009 )


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O

± 71, 604 ± 21, and 569 ± 22, respectively, consistent with the adopted value given the size of the 95% CIs. Plots of this experiment’s results are not shown owing to their similarity to Figure 6 , with the exception that the θ p plots for the and data show more scatter and larger 95% CIs than the results shown in Figure 6 for the data. The conclusion of the first experiment is that different data are an unlikely explanation for the differences between the adopted value of θ o and the previous estimates. The second experiment fits ellipses to isophotes in sky and galaxy coordinates using the method of J87 to determine whether the results for this method depend on the coordinate system used. Both and for the NIR data are used for determining whether the results for this experiment depend on whether or not the data are filtered for 180° symmetry. Different initial values for the position angles in sky coordinates, φ oi , and phase angles in galaxy coordinates, θ oi , are used for determining whether the results for this experiment depend on these initial values. Initial values of i = 0.23 are used for sky coordinates, and i = 0.10 for galaxy coordinates. These i are adopted from the mean values found by M95 . The ellipse centers are assumed to be the kinematic centers adopted in this paper. The results are shown in Figure 26 . Figure 26. Results for ellipses fit to isophotes. Panel (a) shows the results for sky coordinates. Panel (b) shows the results for galaxy coordinates. The data type and initial values for the different markers are indicated in each panel. The red filled circles and green filled triangles are slightly offset from the center of each ring of data for clarity. The cyan shaded region in panel (b) shows the 95% CI for the adopted value of θ o . The results for sky coordinates shown in panel (a) of Figure 26 are in excellent agreement with the results shown in Figure 2 of M95 . The mean of the φ o results for the data is 927 ± 19 when φ oi = 931 and 931 ± 22 when φ oi = 568. The mean of the φ o results for the data is 925 ± 35 when φ oi = 931. These are all consistent with the mean of the previous estimates given the size of the 95% CIs. The results for galaxy coordinates are shown in panel (b) of Figure 26 . The mean of the θ o results for the data is 534 ± 54 when θ oi = 206 and 547 ± 62 when θ oi = 598. The mean of the θ o results for the data is θ o = 551 ± 42 when θ oi = 206. These are all consistent with the adopted value of θ o given the size of the 95% CIs. The results for the second experiment are inconsequential to the goal of the three experiments, so plots of them are not shown. The results for in sky coordinates consistently differ from the initial values by only a few percent and converge to a mean of ≈0.23. The results for in galaxy coordinates converge to a mean of 0.16 for the data when θ oi = 206, 0.17 for the data when θ oi = 598, and 0.14 for the data when θ oi = 206. There are two conclusions from the


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Identifier O O O O O O O O O O O O O O B-Dataset O B-Wavelength I-Wavelength O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O B-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula B-Dataset O O O O O O O O O O O O O O O O B-Identifier O O B-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O B-Citation O B-Citation I-Citation I-Citation O B-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O

the horizontal and vertical components to contribute equally to the line-of-sight field when ∼ 1/6 radian, which is about 9° and almost one day from central meridian. This may explain why it is difficult to extract the canopy fluctuations from the background at locations more than 2–3 days west of the central meridian. 3.3. Inward Motions To study the much slower motions with speeds km s −1 , we again use running differences, but with much longer lags ∼20–30 min. The differences remove the saturated fields near the inner edge of the penumbra. The longer time lags allow the slowly moving features to change their positions by greater amounts and therefore provide stronger signals for their detection in the difference images. Figure 8 compares a jmap of the line-of-sight field for AR12218 (top) with a running difference jmap of this field (middle) and a running difference jmap of 6173 Å continuum intensity (bottom), all with slit orientations of φ = 358°. The difference jmaps were obtained using 22.6 min lags. Short strips of unsubtracted continuum jmaps are shown at the right to indicate the location of the sunspot umbra and penumbra. Also, yellow arrows are oriented at slopes corresponding to 0.5 km s −1 and placed in rows to provide a speed reference. Figure 8. φ = 358° jmaps of the line-of-sight field (top), its running difference (middle), and the continuum running difference (bottom) for AR12218. The difference jmaps show inward tracks (short arrows) along the edge of the masked umbra. Yellow lines (middle panel) separate inflows from outflows whose curved tracks resemble the legs of the quasi-parabolic track of a nearby supergranule (arrow in top panel). The middle panel shows numerous tracks extending inward and outward on both sides of the sunspot. Some of the inward tracks are marked by short arrows. These tracks have speeds comparable to the 0.5 km s −1 reference arrows, and often decelerate at rates ∼0.1 m s −2 as they move toward the gray band that masks the sunspot umbra. In the bottom panel, ingoing tracks of continuum intensity (occasionally indicated by short arrows) are visible along the edge of the masked umbra. We suppose that these continuum tracks are produced by penumbral grain, which also move inward with speeds ∼0.5 km s −1 (Muller 1973 , 1976 ; Zirin Wang 1989 , 1991 ; Lites et al. 1998 ; Sobotka et al. 1999 ; Sobotka Sütterlin 2001 ; Georgakilas et al. 2002 ). The inward tracks of the magnetic field are usually longer than the continuum tracks and show more curvature. This may be due to the greater height of formation of the Fe I 6173 Å line than of the 6173 Å continuum. In the middle panel many tracks curve outward from the mid-penumbra, decelerating as they approach the network region of positive-polarity (white) flux near R / R SG = −2. These outward tracks occasionally join inward tracks to form in/out pairs similar to the parabolic profile of diverging motion in the neighboring supergranule (indicated by the


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ACKNOWLEDGEMENTS TM acknowledges the support of the Australian Research Council through grant FT150100099. DLK acknowledges support by NSF grant AST-1412421. This scientific work makes use of the Murchison Radio-astronomy Observatory, operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. Support for the operation of the MWA is provided by the Australian Government (NCRIS), under a contract to Curtin University administered by Astronomy Australia Limited. We acknowledge the Pawsey Supercomputing Centre that is supported by the Western Australian and Australian Governments. The NASA/IPAC Extragalactic Database (NED) 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 material from the Bordeaux VLBI Image Database (BVID). This work was supported by the Centre for All-sky Astrophysics (CAASTRO), an Australian Research Council Centre of Excellence (grant CE110001020).


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spectra, the instrumental differences in spectral observations render the SEDs globally more homogeneous. To compare these to models, we made use of the VO SED Analyzer (VOSA; Bayo et al. 2008 ) which provides χ 2 and Bayesian fitting to an array of models and templates. For this data, we adopted the χ 2 fitting to BT-Settl models (Allard, Homeier Freytag 2012 ) with the following limits: 700 T eff 4000 K, and . We also limited the absorption in the V band to 0.001 and turned off the excess fitting option as the non-black body distribution of the spectra of these objects was causing the excess fitting procedures to ignore the mid-IR magnitudes. As an example in Fig. 5 , we plot the VOSA fit of 0227-1624 to the BT-Settl model spectra. 0227-1624 is a 16th -band magnitude L1/L0.5 object at 20 pc with a 8 per cent error on the distance for which we have magnitudes in 11 bands. The model slightly underestimates the near- and mid-infrared bands but follows well for the optical bands. Reasons for this over luminosity are beyond the scope of this paper as it will require a more in-depth study of the models and the fitting of VOSA. Figure 5. An example VOSA χ 2 fitting of the 0227-1624 spectral energy distribution to BT-Settl models. The optical to J bands appear to fit well but the model underestimates the flux in the other near and mid-infrared bands reasons for which are discussed in Section 4.4 . The Gaia G observation is plotted in orange because we manually removed it from the fit. We find the nominal G passband tends to over estimate the flux of the L and T dwarfs which can be seen in Fig. 5 . This systematic excess is also reflected in the model (the blue points) as this uses the G passband for the transformation, however, we felt this known systematic error made using the Gaia magnitude for the 38 objects in the Gaia DR1 to constrain the fits premature. The problem of the G passband is noted in the Gaia documentation, 2 and there is an empirical correction proposed(Maíz Apellániz 2017 ). In Fig. 6 , we plot the RAD1 radius from the VOSA fits to BT-Settl models against the near-infrared spectral types. RAD1 is the ‘scaling radius’, i.e. the radius required to fit the observations to the model based on the parallactic distance. From the PARSEC sample, we removed all objects which are known or suspect unresolved binary systems and known or candidate moving group objects (see Section 5.3 ) leaving 60 objects. We removed the moving group objects as these are in general young and we wanted to be sure that our sample was dominated by objects older than 1 Gyr. In Fig. 6 , the targets plotted as diamonds are literature objects and the PARSEC sample are plotted as asterisks. The radius of objects with ages greater than 1 Gyr in this spectral range is predicted to be a minimum at the spectral type that corresponds to the hydrogen burning limit (e.g. Chabrier et al. 2009 ; Burrows, Heng Nampaisarn 2011 ). Objects with earlier spectral types than this


O O B-Person I-Person O O O B-Telescope B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O B-Person I-Person B-Person I-Person O B-Person I-Person O O O O B-Person I-Person O O O O O O O O O O O O O B-Person I-Person O O O O O O O O O O O B-Person O O O O B-Grant I-Grant O B-Organization O B-Location O O O O O O O B-Database O O O B-Organization B-Location I-Location O O O O O B-Database O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-Organization I-Organization B-Survey I-Survey I-Survey B-Survey O B-Survey O O O O O B-Organization I-Organization O B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory O O O B-Organization I-Organization I-Organization O O B-Grant B-Grant B-Grant B-Grant B-Grant O O O O O O O B-Organization B-Organization B-Archive I-Archive I-Archive O O O O O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization I-Organization I-Organization I-Organization

We thank Xiaohui Sun for providing the Urumqi 6 cm Stokes I image prior to its availability on the survey website. The paper benefitted from discussions with Jonathan Tan, Neal Evans, and Diederik Kruijssen. We thank our referee Jeff Mangum for a helpful and rapid referee report. We are grateful to the editor, Malcolm Walmsley, for additional comments and for catching many small but significant errors. E.R. is supported by a Discovery Grant from NSERC of Canada. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France. The original description of the VizieR service was published in ( Ochsenbein et al. 2000 ). This publication makes use of molecular line data from the Boston University-FCRAO Galactic Ring Survey (GRS). The GRS is a joint project of Boston University and Five College Radio Astronomy Observatory, funded by the National Science Foundation under grants AST-9800334, AST-0098562, AST-0100793, AST-0228993, AST-0507657. This research has made use of the NASA/ IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.


O O O O O O O O B-Observatory I-Observatory I-Observatory I-Observatory B-Observatory O O O O O O O O O O O O O O O O O O O B-Organization B-Organization I-Organization I-Organization O O B-Grant O O O O O O O B-Database O O O B-Organization B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Location I-Location O O O O O B-Database O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O B-Database O O O B-Organization B-Location I-Location B-Citation I-Citation I-Citation I-Citation O O O O O O O B-Software I-Software I-Software O O B-Organization B-Observatory I-Observatory B-Location B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O O O O B-Database O O O O O O O B-Database I-Database I-Database O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O O O O B-Survey I-Survey I-Survey I-Survey I-Survey I-Survey I-Survey I-Survey I-Survey B-Survey B-URL O O O O O B-Telescope I-Telescope I-Telescope B-Telescope O B-Telescope O O O O B-Location I-Location I-Location I-Location O O B-Organization I-Organization I-Organization O O B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory O O B-Organization I-Organization I-Organization I-Organization I-Organization O B-Survey O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O B-Dataset O O O O O B-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O O B-Organization B-Organization I-Organization I-Organization I-Organization I-Organization O B-Grant O O O O O O O O O O O O O B-Survey I-Survey I-Survey I-Survey I-Survey I-Survey I-Survey I-Survey I-Survey O O O O O O O O O B-Telescope I-Telescope I-Telescope I-Telescope O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O O B-Survey I-Survey I-Survey I-Survey I-Survey O O O O O O O B-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization O O O O O O O O O B-Telescope I-Telescope I-Telescope I-Telescope O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O B-Software B-Software I-Software I-Software I-Software I-Software B-Software O O O O B-Observatory I-Observatory I-Observatory I-Observatory O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O O O B-Organization I-Organization I-Organization O O O O O O O O O B-Organization I-Organization I-Organization B-Organization O B-Mission O B-URL O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization B-URL O O O O B-Organization O O O O O O O O O O O O O B-Collaboration I-Collaboration I-Collaboration O O O O B-Person I-Person I-Person O B-Person I-Person O O O O O O O O O O B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O O O O O O O O O O O O


O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O B-Location I-Location I-Location O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant

ACKNOWLEDGEMENTS I would like to thank the anonymous referee and editors for their useful suggestions, which helped to improve this article. This work has been supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia under Project 176011: ‘Dynamics and kinematics of celestial bodies and systems’.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

with the lowest velocities ( V 290 km s −1 ) are clearly divided into two groups. One group is an undisturbed low-velocity SW, the second group includes points near the heliospheric plasma sheet and inside weak and very slow ICMEs. Using the regression model with different slopes in regions of low and high velocity (Table 2 ), we calculated hourly values of the expected temperature T exp and the temperature index K T = T / T exp for all hourly data from 1965 July–2019 November. The distribution of the temperature index is presented in Fig. 2 . The distribution is unimodal, peaked, right-skewed, and has a long tail in the region of large values. In Fig. 2 the tail is cut off, because the maximum value of the temperature index is too large ( K T max = 39.5) and the overwhelming majority of the observations have temperature index K T 5. A large number of high K T values is understandable: as a rule, the observed temperature is higher than expected where different-speed SW streams interact (e.g. Elliot et al. 2005 ). There are many such areas: they are in front of almost all HSS from CHs and in front of all ICMEs with sufficiently high speed. In other words, the events in a distribution tail in a region of large values of temperature index may be associated with CIRs or sheaths before high-speed ICME. Figure 2. Distribution of the temperature index K T (from 1965 July–2019 November). Analysis of the minimal (for each FD) values of the temperature index K T min for a large number of events (766 FDs) allowed us to establish a correspondence between K T min and the type of solar source modulating cosmic rays. Fig. 3 presents the distribution of K T min for groups of FDs associated with different types of source (1, ejection from an active region accompanied by a solar flare; 2, CME associated with a solar filament eruption from a region beyond a sunspots group; 3, mixed events with contributions of HSS from CHs and CMEs; 4, clear influence of HSS from CH without other influences), and for the full group of events (group 5). It is clearly visible that the distribution of K T min values for group 4 (CH-associated events) differs significantly from the distributions for groups 1 and 2 (CME-associated events). The K T min means (0.36 ± 0.02 in group 1 and 0.38 ± 0.02 in group 2) and medians (0.28 and 0.31 respectively) are approximately equal for the two groups associated with CMEs. At the same time, these statistics differ significantly from the mean (0.59 ± 0.01) and median (0.58) in the CH-associated group. The distribution of the mixed events group (mean 0.50 ± 0.02, median 0.47) is between those of the CME and CH groups and is close to the distribution of the full group of events (group 5: mean 0.48 ± 0.01, median 0.49). The results obtained therefore reveal that we can use K T min as a tool to help in the identification of FDs with solar sources. Figure 3. Box plots of the temperature index minimal values K T min (from 2000 January–2016 November) in


O O O O O O O O O B-Person I-Person I-Person O B-Person I-Person O O O O O O O O O O B-Person O O O O O B-Organization I-Organization I-Organization O O O B-Grant O O O O O B-Organization I-Organization I-Organization I-Organization O O O O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant

We acknowledge the anonymous referee for useful comments, and Tim De Zeeuw and Wyn Evans for additional comments. This material is based upon work of LC supported in part by the National Science Foundation under grant no. 1066293 and the hospitality of the Aspen Center for Physics. We acknowledge financial support from PRIN MIUR 2010-2011, project ‘The Chemical and Dynamical Evolution of the Milky Way and Local Group Galaxies’, prot. 2010LY5N2T.


O O O O O O O O O O O O O O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O B-Grant B-Grant O B-Grant O O B-Grant I-Grant I-Grant I-Grant O B-Organization O O B-Grant B-Person O O O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant

We thank the anonymous referee for valuable comments that helped to improve our manuscript. This work is supported by the National Natural Science Foundation of China under grants 11133001, 11333004, and 11303015, and the Strategic Priority Research Program of CAS under grant XDB09000000. Z.L. acknowledges support from the Recruitment Program of Global Youth Experts.


O O O O O O O O O O O B-Mission O O O O O O B-Organization I-Organization I-Organization I-Organization O O O O O O O O B-Location B-Organization I-Organization I-Organization O O O O O O O O B-Mission O O O O O O B-Organization I-Organization I-Organization B-Person O B-Person O O O O O B-Organization O B-Grant B-Person O B-Person O O O O O B-Organization O B-Grant B-Grant O B-Grant O B-Organization O B-Grant O O O B-Organization I-Organization B-Person B-Person O B-Person O O O B-Location B-Organization O O B-Grant

We thank all the outstanding men and women who have made IBEX a successful mission. Work at the Los Alamos National Laboratory was carried out under the auspices of the U.S. Department of Energy. This research was carried out partly under the IBEX mission, which is part of the NASA Explorer Program. M.I.D. and E.J.Z. were supported in part by NASA grant NNX16AG83G. J.H. and N.P. were supported in part by NASA grants NNX15AN72G, NNX16AG83G, and 80NSSC18K1649NS, and NSF award OAC-1811176. The work at CBK PAN (M.B., J.M.S, and M.A.K.) was supported by Polish NCN grant No. 2015/19/B/ST9/01328.


O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O B-CelestialRegion I-CelestialRegion I-CelestialRegion O O O O B-Telescope B-Wavelength O O O O O O O O B-Wavelength O O O O O O O O O O O O O B-Wavelength O O O O O O O O B-Telescope O O O O O O O O O O O O O O O B-Wavelength O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

with increasing radius). However, since we have high-resolution HST imaging for all our galaxies, we prefer to relax this assumption and provide a 2D SFR map. Unlike the emission-line ratios , the modelled emission-line luminosities do have an azimuthal dependence. We assume that the distribution of stellar light in the HST images provides a rough approximation for the relative SFR distribution. In the MUSE-Deep UDF fields we use the deep HST F775W imaging. In all other fields we use the F814W band. Although ideally we would like to use photometry that covers the rest-frame ultraviolet light in each galaxy, the necessary deep multiwavelength HST imaging does not exist for all fields. To strike a good balance, we use the F775W and F814W filters, since they are providing the S/N required. This has the additional advantage that, because these filters have similar pivot wavelengths, we also ensure a degree of consistency between the fields. We construct SFR maps of our galaxies by cropping the HST images to only include flux within an ellipse of radius 4 × r d along the major axis. This ellipse has the same morphology (RA, Dec, inc., PA) as above. We inspect each image and alter the mask if necessary to ensure that we include all flux from the object and to remove other objects or defects. When necessary, we interpolate over these rather than mask them. Negative flux values are set to zero. The final result is an SFR map that represents the relative spatial distribution of the SFR. The absolute SFR values are determined by normalizing the map to the total SFR, SFR tot , which is a free parameter in the model. From experience with our C17 work, we found that it was quite important to include the complex spatial variations of the SFR in our model. Using an SFR map (as opposed to simply assuming an exponential disc) appeared to improve the accuracy of our model. However, including an SFR map does not correct for all effects of clumpy star formation. Indeed, a limitation of our model is that, by construction, we do not accommodate for galaxies with strong azimuthal metallicity variations 12 ; we should anticipate that we may observe galaxies where the bright star-forming clumps have uncharacteristically low/high metallicities. However, it should be noted that, for the low-redshift galaxies where azimuthal metallicity variations have been quantified, the variations are typically found to be quite small (≲0.05 dex; e.g. Sánchez et al. 2015 ; Zinchenko et al. 2016 ; Sánchez-Menguiano et al. 2017 ), and there is only the odd example of a galaxy with variations as large as ≈0.2 dex (e.g. Vogt et al. 2017 ). That said, even if there were large azimuthal variations in our galaxies, it does not mean that we cannot still make inferences in these cases because, in effect, we likely still measure the radial metallicity profile of brightest star-forming regions (i.e. a quasi flux-weighted metallicity profile). Nevertheless, this would be in contrast with


O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization I-Organization O O B-Location B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O B-Survey O O O B-URL O B-Person O O O O O O O O O B-Organization I-Organization I-Organization B-Organization O B-Location O O O O B-Organization I-Organization I-Organization O B-Organization B-Grant O B-Grant B-Person O O O O O O O O B-Grant I-Grant B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O O B-Location B-Organization I-Organization I-Organization

We thank an anonymous referee for a careful review of this paper, which has improved the clarity of its presentation. Funding for SDSS-III was 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 Web site is http://www.sdss3.org/ . Y.S.L., acting as the corresponding author, acknowledges support from the National Research Foundation (NRF) of Korea grant funded by the Ministry of Science and ICT (No.2017R1A5A1070354 and NRF-2018R1A2B6003961). T.C.B. acknowledges partial support for this work from grant PHY 14-30152; Physics Frontier Center/JINA Center for the Evolution of the Elements (JINA-CEE), awarded by the U.S. National Science Foundation.


O O B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O O B-Person O O O O O B-Location I-Location B-Grant I-Grant I-Grant B-Grant O O O O B-Grant O O O B-Organization O O O B-Grant I-Grant I-Grant O O O O O B-Location B-Organization I-Organization O O B-Grant O O O B-Organization O B-Grant

We thank I. Reiss, I. Gurwich, B. Katz, M. Pohl, M. Ostrowski, F. Zandanel, R. Mukherjee, and L. Rudnick for useful discussions. The research of U.K. has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no PCIG09-GA-2011-293975 and from the IAEC-UPBC joint research foundation (grant No. 257), and was supported by the Israel Science Foundation (grant No. 1769/15) and by the GIF (grant I-1362-303.7/2016).


O O O O O O O O O O O O O B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O O O O O O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Grant O O O B-Grant I-Grant I-Grant I-Grant O O B-Organization I-Organization I-Organization I-Organization B-Grant I-Grant I-Grant O O O O B-ComputingFacility I-ComputingFacility I-ComputingFacility O O B-Organization I-Organization O O O O O O O O B-Organization I-Organization I-Organization I-Organization O O O O B-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility O B-URL O O O O O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant B-Organization O O B-Grant O B-ComputingFacility I-ComputingFacility O O O O O B-ComputingFacility I-ComputingFacility B-Person O O O O B-Fellowship I-Fellowship I-Fellowship

ACKNOWLEDGEMENTS We thank an anonymous referee for thoughtful comments. We thank our colleagues (J. Schaye, M. Schaller, R. Crain, and R. Bower) for sharing with us the data from the eagle simulation. This study was funded by the Science and Technology Facilities Council (grant number ST/F001166/1) and by the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office (AP P7/08 CHARM). For this study, the DiRAC Data Centric system at Durham University was used. This system is run by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ); the equipment was funded by BIS National E-Infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC; as a part of the National E-Infrastructure. M.S. is supported by an STFC post-doctoral fellowship.


O O O B-Person I-Person I-Person I-Person O O O O O O O O O O B-Person O O O O O B-Fellowship I-Fellowship I-Fellowship O B-Fellowship O O O O O O O O O O O O O O O O O

ACKNOWLEDGEMENTS We thank Dr. A. Y. Potekhin for useful discussions during the early stages of this work. DGC acknowledges the financial support by CONICYT Becas-Chile fellowship (No. 72150555). We are grateful to an anonymous referee for a careful reading and constructive comments on the paper.


O O O B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Organization O O O B-Grant I-Grant O O B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization O B-Organization O B-Grant I-Grant

The authors thank Vivien Bonvin, Patrick Kelly, and Tommaso Treu for very helpful discussions of this paper. We would also like to thank the referee for their insightful comments, which certainly strengthened this work. Support for this work was provided by NASA through grant No. HST -AR-15050 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS 5-26555.


ACKNOWLEDGEMENT The authors thank the anonymous referee for his/her valuable comments, which helped them to improve the manuscript substantially.


O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model I-Model O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O O

( Stimpfl et al. 2006 ). Model B (green curve) is constructed by matching the surface potential energy distribution shown in Stimpfl et al. (2006) . Here sites with binding energies smaller than 8420 K (70 kJ mol −1 ) are included, contrary to model A. Finally, model C (blue curve) includes 8% of surface sites with adsorption energies higher at 25 260 K (210 kJ mol −1 ), which were not taken into account in model A although claimed by the atomistic model of Stimpfl et al. (2006) . The surface energy potential distribution of the adsorption sites of the crystal surface determines the coverage of the adsorbed water molecules. As shown in the right plot of Fig. B.1 , in model C the surface coverage is 5% higher than in model A at 700 K and n H 2 O = 6 × 10 10 cm −3 . The surface potential energy distribution of model B allows only half the coverage than seen in model A and C, due to the higher hydrophobicity of the surface. We can conclude that the choice of the adsorption energy of the sites does not explain the factor of three difference in surface coverage between our work and the Muralidharan et al. (2008) simulations. Fig. B.1. Surface potential energy distribution ( left plot ) and surface site density ( middle plot ) of three models: A (red curve), B (green curve), and C (blue curve). The surface coverage for the three models is calculated at 700 K and n H 2 O = 6 × 10 10 cm −3 ( right plot ). Fig. C.1. Effect of water cluster on surface coverage data. Left panel : surface coverage (H 2 O nm −2 ) as a function of time (s) at n H 2 O = 6 × 10 10 cm −3 and T = 700 K, with cluster effect (pink curve) and without (light blue curve). Right panel : snapshots of the grid resembling the [1 0 0] forsterite surface showing the adsorption energy of the occupied sites without (middle plot) and with (right plot) cluster effect. The color scale indicates the adsorption energy in the range from 13 000 K (blue) to 25 000 K (yellow). Appendix C: Water cluster simulation In our MC models the water molecules randomly “walk” on the surface until they adsorb at a favorable site. As two water molecules sit on neighboring sites, a hydrogen bond links the two species, and a dimer can be created with the total binding energy higher than the ones of single molecules. This generates the formation of water clusters for which the binding energy increases with increasing size ( González et al. 2007 ). In two dimensions, the binding energies increase with the number of water molecules present in the cluster, while for a 3D cluster Lin et al. (2005) show that the binding energy mainly depends on the number of water molecules close to the surface. In this work we used a simple way to calculate the binding energies, which increase linearly with the number of neighboring water molecules ( Dartois et al. 2013 ), but we limited the number of water molecules to four. Water clusters have been implemented in previous MC simulations to study the formation of water on carbon grains


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-CelestialObject O O O O O O O O O O B-CelestialObject O B-CelestialObject O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O

thermal stress during the perihelion passage could trigger the observed repetition of dust jet activities. On the other hand, the penetration of the heat wave to the bottoms of some of the dormant pits might reactivate the sublimation process. Figure 9 shows a projection of the jet source number density distribution in Imhotep and Bes onto the shape model of the comet, oriented to match the viewing geometry of an NAC color-composite image. This illustrates the possible correlation between the two main clusters of activity in Imhotep and the prominent water-ice-enriched areas in the same region, as described in Oklay et al. (2017) . These flat areas contain exposed surface material with high volatile material content, which can sublimate away when illuminated. The cliffs surrounding the source terrain focus the gas flow that is released from the ice patches and lead to the jet formation, a process described in Vincent et al. (2016a ) for other regions of 67P. Fig. 9 Comparison of the two higher concentration regions in Imhotep and Bes with an NAC color-composite image from Oklay et al. (2017) . It is interesting to note that almost every cliff on 67P is likely to produce a jet, as long as a gas flow exists near by. The source of the gas flow is typically the boulder talus at the foot of a collapsed cliff. Although the process is similar here, we note that the two cliffs in Imhotep do not show much rubble in their taluses and are considerably brighter than other areas. In addition, the terrain at the foot of the cliffs appears smooth and subdued, and the low spectral slope (typical of water-rich content) has persisted for extended periods (>1 yr, see Oklay et al. 2017 ). This means that the source of volatiles in these two areas is not simply fresh debris from the collapsing cliffs, but probably local water reservoirs buried just below these areas that constantly feed the jets. This would explain why these areas remain spectrally distinct for a long time, and why more jets can be generated here than in other regions with similar morphologies. In a recent paper, Kramer et al. (2017) have also identified localized regions of enhanced activity using gas-monitoring data from the ROSINA instrument. Although the uncertainty of their source locations is large because the collimation factor of gas flows is lower than that of dust jets, they also found clustering of activity in zones that correspond to our inversion. 6 Summary and discussion We have examined the time evolution of the spatial distribution of the dust jet sources on comet 67P from November 2014 to March 2016 based on an analysis of the OSIRIS imaging data. The main results are listed below. 1. Using a ray-tracing method ( Spitale Porco 2007 ; Farnham et al. 2013 ; Lin et al. 2015 ; Vincent et al. 2016a ), we identified 3530 collimated jets by visual inspection. We find that the location of the jet source regions is correlated with theseasonal variation of the sunlit latitude of 67P. 2.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O B-Model B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model I-Model O O B-URL O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O B-Telescope O O O B-Telescope I-Telescope I-Telescope I-Telescope O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O B-Telescope O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O B-Survey I-Survey O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O B-Telescope O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O B-Organization I-Organization O O O B-Dataset O O O O O O O O O B-Dataset O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O B-Wavelength B-Wavelength O B-Wavelength I-Wavelength O B-Wavelength O O O O B-Wavelength O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O B-Telescope O O O O O O O O O O O O O O B-Wavelength B-Wavelength O B-Wavelength I-Wavelength O B-Wavelength O O O O B-Wavelength O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation O O B-Survey B-Dataset O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O B-Wavelength I-Wavelength O O O O O B-Wavelength I-Wavelength O B-Wavelength I-Wavelength O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

different sub-types of carbon stars in line indices and multi-band photometric space. Finally, a short conclusion is drawn in Section 5 . 2. DATA 2.1. The LAMOST Data The LAMOST, also called the Guo Shou Jing telescope, is a 4 m reflective Schmidt telescope with 4000 fibers on a 20 square degree focal plane (Cui et al. 2012 ; Zhao et al. 2012 ). The unique design of LAMOST enables it to take 4000 spectra in a single exposure to a limiting magnitude as faint as r = 18 at the resolution R = 1800. The LAMOST survey will finally obtain more than 5 million low-resolution stellar spectra after its five year survey (Deng et al. 2012 ). Previous works have shown that LAMOST observations are biased more to giant than dwarf stars, since the limiting magnitude is relatively brighter (Liu et al. 2014a ; Wan et al. 2015 ). By the end of 2014, the LAMOST team had released the DR2 catalog, which contains 4,136,482 targets including 3,784,461 stars. In DR2, there are about 0.5 million late type giant stars (Liu et al. 2015a ), implying that there should be large numbers of carbon stars. 2.2. Line Indices The typical spectra of carbon stars are characterized by strong C 2 , CN, and CH absorption bands (Secchi 1869 ). Therefore, we measure the line indices of carbon related molecular lines to identify carbon stars. We show several sample LAMOST spectra for typical carbon stars in Figure 1 . The features of the C 2 swan bands at 4737, 5165, and 5635 Å, and red CN molecule bands at 7065 and 7820 Å are prominent in these spectra, while the detailed features are different in different sub-types. Compared to the traditional approach to classifying spectral types, using line indices allows a semi-automatic classification. With multiple spectral line indices, various spectral types may transitionally change from one to another, which naturally reflects the variation of the astrophysical parameters, e.g., effective temperature, surface gravity, and metallicity, from type to type (Liu et al. 2015b ). Figure 1. LAMOST spectra for typical carbon stars. The features of the C 2 swan bands at 4737, 5165, and 5635 Å and red CN molecule bands at 7065 and 7820 Å are indicated. In order to identify carbon stars, we adapt the Lick line indices defined by Worthey ( 1994 ) and Worthey Ottaviani ( 1997 ). For the LAMOST DR2 spectra, we use the measured results from Liu et al. ( 2015b ). The Ba ii (4554 Å), molecular bands C 2 (around 5635 Å), CN (7065 Å), and CN (7820 Å) are also measured based on the wavelength definition listed in Table 1 . Table 1 Line Indices Definition Used in This Work The line index in terms of equivalent width (EW) is defined by the following equation (Worthey 1994 ; Liu et al. 2015a ):where F λ and F C are the fluxes of the spectral line and continuum, respectively, which are functions of wavelength λ . The pseudo-continuum F C is estimated via linear interpolation of the fluxes located in the “shoulder” region on either side of each index bandpass. The line


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h is the Planck constant, k is the Boltzmann constant, T d is effective dust temperature, and β is the emissivity index, which determines the steepness of the Rayleigh–Jeans slope of the spectrum. The python implementation emcee (Foreman-Mackey et al. 2013 ) was used to build a Markov Chain Monte Carlo (MCMC) analysis of the fitted SED for each data set, allowing the dust temperature and normalization as free parameters to sample the posterior probability distribution of the model. Where possible, we also leave β as a free parameter in the model, allowing for a test of the range of dust emissivities that are consistent with the data. However, fitting for β requires at least four data points to constrain the peak and Rayleigh–Jeans slope of the modified blackbody function. For many sources, our Herschel /SPIRE data are the only measurement in the FIR–submillimetre regime. Thus, we assume a value of β = 1.5 for these sources, as is frequently applied in the literature (e.g. Magnelli et al. 2012 ). Various combinations of T d and β have been found for samples of high-redshift quasars. For example, Priddey McMahon ( 2001 ) find an average of T d = 41 ± 5 K and β = 1.95 ± 0.3, whereas Beelen et al. ( 2006 ) find an average of T d = 47 ± 3 K and β = 1.6 ± 0.1 for their sample. For the sources that were not detected, or had only one detection to constrain the fit, we assume the median fitted dust temperature of the sample (38 K, see Fig. 6 ) and β = 1.5, and fit only for the normalization. For these sources with only one detection, we fit the 16th and 84th percentile values of the median fitted temperatures to estimate our errors on the FIR luminosity. As β is highly correlated with T d , errors on the derived properties of sources without β-fitting may be underestimated. However, the FIR luminosity is not strongly affected by our assumptions due to the joint dependency of T d , β and normalization, so this will not have a significant effect on the inferred values of L FIR or SFR. This is unsurprising, as the luminosity is derived by the integral of the fit defined by the data points. Figure 6. Histogram of effective dust temperatures for 53 quasars in the sample with temperature fitting, excluding those without a known redshift and synchrotron-dominated sources. The median dust temperature is K. Where two dust temperatures are fit, only the colder component is included here. For the purpose of spectral fitting, the ten sources without a known redshift are assumed to have z = 1.8, equivalent to the median redshift of the sample. The choice of redshift significantly affects the luminosity distance; thus, these objects are not included in the overall statistics. For three sources (APM 08279+5255, H1413+117, and iras F10214+4724), there are sufficient data in the mid-IR (MIR) to motivate fitting a two-temperature dust model. Table 3 shows the number of sources fitted with each combination of spectral parameters. Table 3. Number of sources fitted with


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We thank an anonymous referee for useful comments that helped to improve the paper. We are grateful to Ted Bergin for the use of his non-equilibrium code. We benefited from many discussions with him and his (former) graduate student Ilse Cleeves on astrochemical codes and analysis. Additionally, we thank Til Birnstiel, Roy van Boekel, Cornelis Dullemond, Thomas Henning, Hubert Klahr, Oliver Gressel and Dmitry Semenov for interesting discussions and feedback on this work. The work made use of the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca ) and Compute/Calcul Canada. AJC acknowledges funding from the National Sciences and engineering Research Council (NSERC) through the Alexander Graham Bell CGS/PGS Doctoral Scholarship. REP is supported by an NSERC Discovery Grant. MA acknowledges funding by an Ontario Graduate Scholarship (OGS) and NSERC CGS-M scholarship. REP also thanks the MPIA and the Institut fur Theoretische Astrophysik (ITA) in the Zentrum fur Astronomie Heidelberg for support during his sabbatical leave (2015/16) during the final stages of this project. AJC also thanks MPIA and the Institut fur Theoretische Astrophysik (ITA) in the Zentrum fur Astronomie Heidelberg for their hospitality during his 1 month stay in 2016.


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(1) perturb the previous step to propose a new set of parameters θ * drawn from the prior; (2) use fleck to compute the smoothed-amplitude distribution for a large sample of stars, (3) for use as a summary statistic, we compute the two-sample Anderson–Darling statistic A 2 for comparing how close the trial smoothed-amplitude distribution is to the observed one (Anderson Darling 1952 ; Scholz Stephens 1987 , see discussion in Appendix 6); (4) if , we accept the proposed step and add it to our chain; (5) go back to step (1), and repeat. We select , not far from the minimum value of the Anderson–Darling statistic . The approximate posterior distributions produced by ABC should approach the true posterior distributions in the limit that , provided that the Anderson–Darling statistic is a sufficient statistic. In practice it is difficult to prove that a statistic is sufficient, so we note that the posterior distributions shown here are valid given the hypothesis that the Anderson–Darling statistic is a sufficient one. The posterior distributions from the ABC analysis illustrate the three-way degeneracy between starspot latitudes, radii, and contrasts for stars with unknown inclinations. For a fixed number of spots, small spots spread randomly over all latitudes generate rotational modulations similar to larger spots concentrated near the poles. Similarly, small spots with extreme intensity contrasts () reproduce similar rotational modulations to larger spots with less extreme spot contrasts (). This exercise is a demonstration of why it is so difficult to invert light curves of rotational modulation and recover unique starspot properties—a wide range of spot parameters can produce similar light curves. Figure 6 shows the simulated smoothed-amplitude distributions (gray histograms) with spot parameters drawn randomly from the posterior distributions, compared with the observed smoothed-amplitude distribution for stars in each association (colored histograms). This figure illustrates how the ABC algorithm minimized the Anderson–Darling statistic between the simulated and observed smoothed-amplitude distributions. Most simulated and observed distributions are statistically indistinguishable according to the Anderson–Darling statistic. Figure 6. Observed smoothed light-curve amplitude distributions (colored histograms) compared with simulated smoothed-amplitude distributions (gray) drawn from the posterior distributions for the spot parameters from the ABC technique with fleck. The goal of the ABC technique is to search for starspot parameters that produce simulated smoothed-amplitude distributions that are statistically indistinguishable from the observations. The poorest “fit” is M67, the oldest cluster, for which the simulations produce a more strongly peaked smoothed-amplitude distribution near 0.5%. The lack of a peak in the observed smoothed-amplitude distribution for M67 is likely real; observational bias in favor of detecting large