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

driven by neutrino heating, where the MRI significantly enhances the convection and angular momentum transport (Sawai Yamada 2014 ). Although neutrino heating plays an important role, neutron-rich matter is ejected especially in polar directions with a smaller influence of -capture on neutrons due to shorter expansion/explosion timescales. We compute additional models to investigate the effect of magnetic fields in explosion dynamics. However, simulating MRI-driven explosion models with different initial magnetic fields requires a huge amount of computer resources because the saturation of the MRI is strongly depending on the magnetic fields, i.e., all explosion models have different criteria for numerical convergence (see Figure 6 of Sawai Yamada 2016 ). In the current study, therefore, we adopt a more simplified parametric method based on the above MRI-driven explosion model (shown in Figure 1 ), whose numerical convergence has been confirmed. 8 8 We confirmed that a higher-resolution model, of which the finest grid is 30 m, shows the same result of r-process nucleosynthesis (plotted in Figure 4 ) as well as explosion dynamics. Instead of changing magnetic fields, we vary the time evolution of neutrino luminosity by multiplying a scale factor as , 0.20, 0.30, 0.40, 0.50, 0.60, 0.75, and 1.25. We suppose that explosion models with a lower are dominated by magnetic fields rather than neutrino-heating. To evaluate the strength of magnetic fields in jets, we consider the minimum value of the ratio of gas to magnetic pressure (), i.e., Since the has a large fluctuation, we calculate the mean value denoted by during jet propagation (from jet launching to the time the shock front reaches 2000 km). A lower is obtained in models with lower , where , 0.038, 0.047, 0.060, 0.071, 0.083, 0.15, 0.20, and 0.30 in ascending order of . We expect neutron-rich material to be mostly ejected in magnetically driven jets rather than heating-driven ejecta. In the jet direction, neutrino emission can be weakened due to lower mass accretion onto the proto-NS. Adopting a lower , therefore, is consistent with decreasing in the polar direction. We label the standard MRI-driven explosion model with as the h -model, indicating neutrino heating is dominant, while the case of the magnetically driven jet with is named as the m -model. We choose the case of as a typical intermediate explosion model as the i -model. As the behavior of the explosion varies gradually depending on , the distinction of models has uncertainties, e.g., the m -model can be categorized in the i -model as an extreme case. We adopt and 0.75 models as a variation of the i -model, denoted by i -model(−) and i -model(+), respectively. The h -model(+) and m -model(+), based on and 0.40, respectively, are also referred to in order to discuss the impact of neutrino absorption. The , entropy ( S ), and for the selected models are shown in Figure 2 , when the shock front reaches ∼1000 km. For the m -model, we


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This work was granted access to the HPC resources of CINES under the allocations x2016046955 and A0020406955 made by GENCI. The work of S.C. has been supported by the ERC Project No. 267117 (DARK) hosted by Université Pierre et Marie Curie (UPMC) – Paris 6 and the ERC Project No. 614199 (BLACK) at Centre National De La Recherche Scientifique (CNRS). AB acknowledges support from NSERC (Canada) through the Discovery Grant program, from Institut Lagrange de Paris (ILP), and the Pauli Center for Theoretical Studies ETH UZH. S.C. is also grateful to the University of Zurich’s Institute for Computational Sciences’s Center for Theoretical Astrophysics and Cosmology and the Institut d’Astrophysique de Paris for hospitality during his recent sabbatical visit. S.C. thanks Ewan O’Sullivan for the precious advise on the production of realistic X-ray images.


B-Citation I-Citation O B-Mission O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O B-Telescope B-Telescope O O B-Wavelength I-Wavelength O O O O O B-Telescope B-Telescope 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 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-Dataset I-Dataset I-Dataset O B-Telescope O O O O O O O B-ObservationalTechniques I-ObservationalTechniques O O O O O O O O O O B-Telescope O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O B-Mission O O B-CelestialObject I-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 B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-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 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 B-Dataset I-Dataset I-Dataset O B-Citation I-Citation I-Citation I-Citation O O O B-Archive I-Archive I-Archive I-Archive I-Archive O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B-CelestialObject I-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 B-Instrument O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O B-Instrument

al. 2014 ). BRITE-Constellation obtained observations of the Cygnus-II field from June 1 to November 25, 2015. The star 43 Cygni was observed for only 13 days with BRITE-Lem (BLb) in the blue filter and for 156 days with BRITE-Toronto (BTr) in the red filter, both in chopping mode, where the position of the target star within the CCD plane is constantly alternated between two positions about 20 pixels apart on the CCD. This procedure was adopted to mitigate the impact of high dark current in CCDs. A detailed description of this technique is given in Popowicz et al. (2017) . As the blue data set from BLb did not have sufficient quality for an asteroseismic analysis, we omitted it from our further investigation. Fig. 1 Complete BRITE-Toronto light curve of 43 Cygni ( top ) and zoom into a 20-day subset ( bottom ). The BRITE-Constellation data of 43 Cyg were corrected for instrumental effects according to the iterative procedure described by Pigulski et al. (2016) . This procedure was, however, modified to include two-dimensional decorrelations. Since 43 Cyg shows variability with large amplitude, decorrelations were made using residuals from a fit consisting of a sum of sinusoidal terms. For 43 Cyg, the model consisted of five terms: four modes with frequencies 1.250 d, 1.288 d, 0.963 d, and 1.516 d, plus a combination frequency (1.250 d0.963 d = 0.287 d). This model was used for all BTr setups. The fit was recalculated at each step of the decorrelation. The whole procedure included outlier rejection, 1D and 2D decorrelations, and removal of corrupted orbits (e.g. affected by poor stability of the satellite). Decorrelations, both 1D and 2D, were made using parameters provided in Data Release 3 (see Popowicz et al. 2017 , or the BRITE Public Data Archive 2 for the explanation of parameters) and a given satellite orbital phase as an additional parameter. The decorrelation was made sequentially, allowing for multiple decorrelations with the same parameter (or a pair of parameters). At each step, the parameter showing the strongest correlation was chosen for correction. The strength of a correlation was defined as the degree of reduction in variance due to decorrelation with a given parameter (for 1D) or a pair of parameters (for 2D). The iterations were stopped when all correlations (both 1D and 2D) resulted in a variance reduction smaller than 0.05 per cent. The whole procedure was run independently for each setup. Outliers and the worst orbits were rejected at least twice during the whole procedure. After decorrelating the data, the blue and red data were separately combined, taking the mean magnitude offsets between the setups into account. The complete BTr light curve of 43 Cygni consists of 98 481 data points and is shown in the top panel of Fig. 1 , while the bottom panel shows a zoom into a 20-day subset. 2.2. HERMES spectra We obtained ten high-resolution spectra with high signal-to-noise ratio (S/N) for 43 Cygni using the HERMES


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

defined by the effective beam at the given frequency. The algorithm starts optimization at the MHW2 source location, assuming initially a circular Gaussian source with the FHWM of the PSF. Optimization of all six parameters converges in a reasonable number of iterations, usually less than 1000. Appendix B.2: Error estimation The downhill simplex method does not provide any information on the uncertainties in the optimized parameters. We estimate errors using a Markov Chain Monte Carlo (MCMC) method to sample the posterior distribution. In our case, the burn-in time can be very small since to start the chain the simplex-optimized value of the parameter from the previous step can be used, and it is usually very close to the peak of the distribution. The length of the MCMC chain is set to 1000 samples by default. To obtain the proper sampling in the MCMC chain it is necessary to define the optimal step size in the parameter space. This determination is complex since there are more than 100 000 sources in the catalogue to process; it would be impossible to adjust every MCMC chain, since it would take an excessive amount of time to try several values of the sampling step using an iterative approach. Hence we decided to use an analytical approximation for the errors of most of the Gaussian profile parameters, following Hagen Dereniak (2008) . We used an analytical approximation for the separable 2D Gaussian profile model; the errors are derived for five parameters; namely position coordinates, flux density, and two non-equal width values. The calculated errors were used to find the value of the step size in the parameter space to start the MCMC sampler, which delivered the error on the tilt angle. This combined technique allowed us to avoid most of the problems related to undersampled or oversampled MCMC chains. Appendix B.3: Validation of the method using model data The results of the Gaussian fit were compared with the MHW2 positions and flux densities for a test set of sources with known parameters. The PR2 100 GHz map with injected sources and cold cores was used to check the 2D Gaussian fitting algorithm. The cold cores were modelled as 2D Gaussian profiles with uniformly distributed random orientation angles. The major FWHM was in the range from 4.́5 to 19, and the ellipticity parameter, , varied from 1 (circular source) to 7 (highly elliptical source). All sources were uniformly distributed across the sky. Plots comparing the flux density values for MHW2 and Gaussian fitting for this test set are shown in Figs. B.1 B.4 . Both methods give almost the same point source flux-density values (Figs. B.1 and B.2 ), but MHW2 gives less scatter for the fainter point sources. For the cold cores, Gaussian fitting works better and gives much less bias, especially for the bright sources (Figs. B.3 and B.4 ). The visible digitization of the flux densities reflects the fact that only integer values were used for the flux densities of injected sources. The


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5 and 6 arcmin from each cluster centroid generated a set of background values distributed symmetrically about the global background computed in Section 4.2 with no evidence of clusters with high MIR aperture flux measurements displaying enhanced local background values. Identification of X-ray faint, MIR bright clusters in Fig. 2 is therefore not influenced by the application of a global background correction to MIR cluster aperture fluxes. It is furthermore unlikely that the non-cluster masking procedure applied to the computation of X-ray aperture fluxes results in the exclusion of bona-fide cluster flux from the aperture measurement. As mentioned previously, XMM–LSS pipeline detections corresponding to non-C1/C2 sources were masked from the aperture measurement. An alternative masking procedure was tested, this time excluding source areas corresponding only to point sources classified as P1 (high significance, low extension), with little or no qualitative difference in the results presented above. We also compare the X-ray and MIR brightness measures of each cluster sample in terms of their luminosity using the measured redshift for each cluster and the assumed cosmological model to convert the measured aperture flux in either the X-ray or IRAC1 band, respectively, to a rest-frame [0.5–2] keV X-ray and K -band stellar luminosities. In addition to the luminosity distance to each source, the correction to luminosity requires the application of a k -correction. For the X-ray brightness, this correction is computed using the same T = 2 keV plasma model used to convert count rates to flux. In the MIR, we employ a k -correction derived from a 1 Gyr, solar metallicity burst of star formation which evolves passively from a formation redshift z f = 10.This model is taken from Willis et al. ( 2013 ) and matches the red-sequence evolution of the XMM–LSS distant cluster sample. Furthermore, as demonstrated by van der Burg et al. ( 2014 ), the stellar mass in SpARCS distant clusters is dominated by M * galaxies located on the red sequence. The evolving spectrum was realized using the galaxev 2003 stellar population synthesis code (Bruzual Charlot 2003 ). Computing theMIRluminosityof a galaxy cluster within a spatial aperture effectively involves integrating the cluster galaxy luminosity function (LF) down to a sensitivity limit which itself may be expected to be a function of redshift. In order to determine whether this is likely to be an important bias in the aperture luminosity values computed for this , we performed the following analysis: taking the 3.6 m flux limit of the optical–MIR catalogue as 3.7 μJy, we computed the corresponding K -band luminosity of a galaxy with this flux over the redshift interval 0.8 z 1.6 (the extent of the SpARCS sample). We next considered this luminosity as the limiting value as a function of redshift to which we integrated the K -band cluster galaxy LF presented by De Propris et al. ( 2007 ). The relative change in


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We thank Drs. Yu Bai, Yi Hu Song Wang, and Hang Gong for very helpful discussions. J.F.L. acknowledges support from the National Science Foundation of China through NSFC grants 11233004 and 11333004. W.W. is partially supported by NSFC grants 11203035 and 11233004 for this work.


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Acknowledgements We thank the engineers and staff at the Mullard Radio Astronomical Observatory for maintenance and operation of AMI, which is supported by Cambridge and Oxford Universities. We are grateful for IT knowledge exchange with the SKA project. The AMI telescope, AMS and TC gratefully acknowledge support from the European Research Council under grant ERC-2012-StG-307215 LODESTONE. We thank our anonymous referee for constructive comments that have improved our paper. CJR acknowledges an OCE Fellowship from CSIRO. IH and MLB are supported by a European Research Council Starting Grant (grant number 280127). MLB is an STFC Advanced/Halliday fellow (grant number ST/I005129/1). YCP acknowledges support from a Trinity College Junior Research Fellowship. This research has made use of data from the OVRO 40-m monitoring program (Richards et al. 2011 ) that is supported in part by NASA grants NNX08AW31G, NNX11A043G, and NNX14AQ89G and NSF grants AST-0808050 and AST-1109911. This work has also made use of the NASA/IPAC Extragalactic Database (NED), operated by JPL under contract with NASA, and NASA's Astrophysics Data System (ADS).


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This work was supported in part by NASA-ADAP grant NNX12AI49G and NSF grant 1411773. This research made use of Astropy 16 16 astropy.org (Astropy Collaboration et al. 2013 ), TOPCAT, 17 17 www.star.bris.ac.uk/∼mbt/topcat (Taylor 2005 ), and STILTS 18 18 www.star.bris.ac.uk/∼mbt/stilts (Taylor 2006 ). All figures made use of matplotlib 19 19 matplotlib.org (Hunter et al. 2007 ). Figures 1 , 3 , 8 , 11 , and 20 were made using densityplot 20 20 github.com/CKrawczyk/densityplot (Krawczyk Peters 2014 ). Special thanks go to Alex Gray for developing the code for the NBC KDE algorithm and to Erica Smith for carefully reviewing the manuscript. 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/ . 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/ .


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T.E., T.K.G., M.J.R. and C.D.W. were visiting astronomers at the InfraRed Telescope Facility, which is operated by the University of Hawaii under Cooperative Agreement no. NNX-08AE38A with the NationalAeronautics and Space Administration, Science Mission Doctorate, Planetary Astronomy Program. We thank the IRTF staff for the support of TEXES observations. Observations with TEXES were supported by NSF Grants NNX-14AG-34G for T.K.G. and AST-0708074 for M.J.R. T.K.G. acknowledges support by NASA Grant NNX14AG34G. T.E. and B.B. acknowledge support from CNRS and Programme National de Planétologie (INSU), and T.F. aknowledges support from UPMC. T.W. acknowledges support from the University of Versailles-St-Quentin and the European Commission Framework Program FP7 under Grant Agreement 606798 (Project EuroVenus). T.E. acknowledges support from the Jet Propulsion Laboratory as a Distinguished Visiting Scientist. We are grateful to Emmanuel Marcq for helpful comments regarding the discussion.


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This work is partly supported by China Ministry of Science and Technology under State Key Development Program for Basic Research (2012CB821800, 2015CB857100) and the National Natural Science Foundation of China No. 11373038, No. 11373045, Strategic Priority Research Program of the Chinese Academy of Sciences, No. XDB09010302. SSRO acknowledges support from NASA through Hubble Fellowship grant #51311.01 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, INC., for NASA, under contract NAS 5-26555. The orion calculations were performed on the Trestles XSEDE cluster and on the Yale University Omega cluster. We are grateful to the referee for their detailed comments and suggestions.


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with any field characteristics obtained from circular polarization modeling. Considering our ZDI results, the mean vertical field corresponds to the surface-averaged unsigned radial field reported in Table 3 . Our = 98–168 G is a factor of 2.9–5.6 lower than the vertical surface fields suggested by MacDonald Mullan ( 2017b ). If, instead, we choose to compare their predictions with our measurements, the theoretically estimated field should be multiplied by assuming isotropic field vector orientation. This yields a total surface field strength of 850–950 G, which is 3.3–4.0 times lower than determined in our paper. To summarize, theoretical models by Feiden Chaboyer ( 2013 ) appear to predict a more realistic surface field strength for the components of YY Gem than the one found in the approach by MacDonald Mullan ( 2014 , 2017b ). However, this conclusion should be taken with caution given the ambiguity of relating the vertical surface field parameter of the latter theoretical calculations to observations. 7.3. Estimating Magnetic Flux from X-Ray Luminosity In the absence of direct constraints on the surface magnetic properties, many studies resort to an approximate estimate of the mean magnetic field strength. The X-ray emission is often considered to be a particularly useful magnetic proxy. Pevtsov et al. ( 2003 ) demonstrated that the total unsigned magnetic flux measured for solar magnetic regions at different spatial scales shows a power-law dependence on the X-ray spectral radiance. The few measurements reported at that time for active late-type stars fell on the extrapolation of this relation to higher magnetic flux values. Feiden Chaboyer ( 2013 ) recalibrated this relation using Stokes I field strength measurements of low-mass dwarfs. Both Feiden Chaboyer ( 2013 ) and MacDonald Mullan ( 2014 ) attempted to verify their theoretically predicted surface field strengths with indirect field strength estimates using the X-ray emission of YY Gem. This exercise was, however, hampered by the contamination of the ROSAT X-ray measurements of YY Gem (Voges et al. 1999 ) by the nearby early-type companions Castor A and B. More recent Chandra observations of YY Gem (Hussain et al. 2012 ) provided images with a high angular resolution, allowing one to separate the low-mass binary from the A-type stars. The combined quiescent X-ray luminosity of YY Gem is reported to be (3–3.5) × 10 29 erg s −1 . Assuming equal luminosities of the components and using their fundamental radii yields = 770–820 G with the calibration by Feiden Chaboyer ( 2013 ). This is significantly smaller than the observed obtained in our study, indicating that the magnetic flux versus X-ray luminosity relation may be appropriate only for large stellar samples but is unlikely to be precise enough to provide a meaningful field strength estimate for individual stars. Another problematic issue with applying this relation to active stars, apparently entirely overlooked in the


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f dif – decorrelation bandwidth, t dif – scintillation time, τ sc – scattering time, θ H – angular diameter of the scattering disc, d s – distance to the scattering screen, ρ dif – scale of diffraction pattern, n – spectral index of ISM inhomogeneities. Dynamic spectra of observations conducted in 2014 and 2015 show clearly distinguishable inclined structures that indicate the presence of angular refraction in a given direction. Time and frequency structure functions obtained from analysis of the dynamic spectrum in 2015 showed their power-law character with an equal index of α = 1.13 ± 0.01, which corresponds to n = 3.13 ± 0.01. The similarity of their inclinations indicates strong refraction in the direction to the pulsar. In the 2014 observation, we had a strong modulation in frequency and time, so a quality structure function could not be obtained. Such a modulation is well known as time and frequency ripples caused by the interference of diffracted and direct rays in dual-reflector radio telescopes. It was found that the diffracted rays from the sub-reflector edge can interfere with the main beam to produce a gain ripple of sinusoidal form for the dual reflector system. Analysis of the dynamic spectrum for 2012 gave the same slope of the structure functions, α = 0.83 ± 0.04. For 2014 and 2015 observations, the drift of diffraction spots in the dynamic spectrum is clearly visible. In Bhat et al. ( 1999 ), it was noted that such drift behaviour is typical for this pulsar. Such a drift can last for quite a long time. This effect was observed during the entire series of observations conducted in 1993–1994 (110 days). This suggests that the structure leading to the refraction of radiation passes the line of sight for a time longer than 110 days. Analysis of the complex covariation function module for the space–ground interferometer yielded the scale of the diffraction pattern. In Fig. 6 , this function is shown for the 2015 observation for the RadioAstron–Arecibo (RA–AR) baseline. Applying equation ( 8 ), we found a value of the spatial coherence function B u = 0.25 ± 0.04. Using the values α = 1.1 and , we obtained ρ dif = (6.0 ± 0.9) × 10 9 cm. The distance from the observer to the screen d s was estimated using ( 10 ). Since the tangential velocity of the pulsar is , we neglected the velocity of the Earth and the screen. Accordingly, d s / D = 0.64 ± 0.06, . Using ( 11 ), we calculated , . We estimated the scattering time to be 0.76 μs. Figure 6. PSR B0834+06. Average covariation function from complex cross-spectra (observation 08.04.2015, baseline RadioAstron–Arecibo). The dashed line corresponds to the OFF-pulse window. For 2014, we did not obtain a qualitative covariation function, because of the influence from frequency ripples. In the observation of 2012, the baseline projection of the space–ground interferometer was 2.05 × 10 10 cm. For this day, we found the value B u = 0.38 ± 0.09 and, using α = 0.83, we determined the diffraction


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to interpret the OPR values of H ( Crabtree et al. 2011c , b ) and subsequently studied in more detail for the specific system ( Gomez-Carrasco et al. 2012 ). The mechanistic distinctions were also recently considered for observations of the OPRs of HO ( Herbst 2015 ) and HCl ( Neufeld et al. 2015 ), providing new insights to explore. A rigorous determination of the mechanisms in a particular system requires a full quantum scattering treatment, but as pointed out by Gomez-Carrasco et al. (2012) , full quantal computations are rarely achievable because of their complexity and the computational cost. The next best approach is the semi-classical trajectory method, in which atoms and molecules undergo classical trajectories on a quantum mechanical PES. It is this approach that is used in this paper. Of course, both destruction and conversion processes must also be considered in determining OPRs, and such processes are also contained in our chemical simulations. In the remainder of the paper, we first consider in Sect. 3 the astronomical observations leading to new OPR measurements for HCl in an extragalactic absorber. Then we discuss in Sect. 4 our modeling approach and present its outcomes for the scrambling versus hydrogen abstraction/proton hop mechanisms. Section 5 describes our semi-classical treatment of reaction ( 4 ), which models show to be the dominant formation process for HCl. Finally, Sect. 6 summarizes our results and draws the main conclusions of the present study. 3. Observations 3.1. Description of the observations The ortho and para forms of HCl were observed with the Atacama Large Millimeter/submillimeter Array (ALMA) during Early Science cycle 2, in summer 2014. Two different tunings were observed to cover the – transition of ortho-HCl (rest frequencies 189 GHz, redshifted to 100 GHz, within ALMA Band 3) and the – transitions of para-HCl (rest frequencies 485 GHz, redshifted to 257 GHz, within ALMA Band 6), respectively, as listed Table 1 . The array was maintained in a configuration providing a synthesized beam resolution of better than 0.5, sufficient to resolve the two point-like lensed images of PKS 1830211, separated by 1. The bandpass calibration was done on the bright quasar 1924292, and the gain calibration was solved by regular visits on 18322039 during the observations. The correlator was set up with spectral windows of 1.875 GHz, providing a velocity resolution of 1.3 km s (Band 6) and 2.9 km s (Band 3), after Hanning smoothing. The data calibration was performed within CASA 1 following a standard procedure. A further run of self-calibration using the bright continuum of PKS 1830211 helped us to significantly improve the data quality. The final spectra were extracted toward both lensed images of PKS 1830211, using the CASA-python task UVMULTIFIT ( Martí-Vidal et al. 2014 ), by fitting a model of two point sources to the interferometric visibilities. The ALMA spectra of HCl toward PKS 1830211 are shown in Fig. 1 . The


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each particle by decomposing the gravitational forces into a long-range term, computed from Particle-Mesh methods, and short scale interactions from nearest neighbours using Tree methods. The code is MPI parallel and can be used with periodic boundary conditions in the comoving frame. The scaling of the code with the number of particles is , so it can handle a large number of particles with reasonable computational resources. The simulations were run in the MareNostrum supercomputer at BSC-CNS 4 using a modified version of GADGET-2 in which the free parameters of the code were chosen such that the force computation was accurate enough for PBH binaries to form and survive without being disrupted by pure numerical errors. We took the gravitational softening length ε = 0.001 pc and a time-step of Δ t = 10 −6 in log ( a ). These parameters have a significant impact in the orbit stability, so we ensured that our choices are optimal and that a further reduction of both ε and Δ t does not yield any significant improvement in orbit stability. Previous analytic estimates suggest that the first PBH pairs begin to appear approximately at the radiation-matter energy equipartition epoch ( z ∼ 10 3 ; Nakamura et al. 1997 ; Sasaki et al. 2016 ) or, depending on the PBH fraction, even earlier – during the radiation-dominated epoch. Therefore, we had to modify the cosmological equations in the GADGET-2 code to be valid during the radiation dominated epoch of the Universe since we start our simulations at z init = 10 5 . As was discussed earlier, in this work we focus on the formation of PBH pairs for different fractions of PBH to the total DM component. So, when f PBH 1.0, we have to split the DM into two types: ‘PBH DM’ and ‘normal DM’ (see Section 1 ). As it is shown in Section 3 , in our simulations virialized structures start to form as early as z ≈ 10 3 , which could be caused by the initial Poisson distribution of PBHs. This differs significantly from the case of standard ΛCDM model, where the first virialized structures start to form at z ≃ 40. Therefore, we assume that the ‘normal DM’ behaves as an uniform medium in our simulations. Nevertheless, we will discuss later why the formation of virialized structures is important and how it affects the behaviour of PBH pairs (see Section 3.3 ). In order to quantify the range of redshifts in which this approximation is applicable, we calculate the fraction of virialized normal DM using the analytical Press–Schechter formalism for haloes more massive than 30 M ⊙ (see Press Schechter 1974 ). We find that the fraction of virialized structures made of normal DM is less than 50 per cent at z > 10. (see Fig. 1 ). Figure 1. The fraction of virialized structures is made of normal DM for haloes more massive than 30 M ⊙ , as a function of redshift z . Thus, the presence of normal DM influences the evolution of PBH DM only through its contribution to the computation of the Hubble function in the GADGET-2 code: For our


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We are grateful to the referee for his/her constructive comments. This paper is based on data obtained at the European Southern Observatory, Chile, with proposals ESO 68.C-0149(A), ESO 70.C-0008(A) and ESO 382.C-0195(A). This work has been funded by the Spanish Ministerio de Economía y Competitividad (MINECO) under project AYA2011-22614. JGR acknowledges support from Severo Ochoa excellence program (SEV-2011-0187) postdoctoral fellowship.


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ACKNOWLEDGEMENTS This paper is based on observations collected with the MPG 2.2 m telescope, with the Danish 1.54 m telescope and with the Zeiss 1.23 m telescope. The first two telescopes are both located at the ESO Observatory in La Silla, Chile, while the latter is at the Centro Astronómico Hispano Alemán (CAHA) in Calar Alto, Spain. Operation of the MPG 2.2 m telescope is jointly performed by the Max Planck Gesellschaft and the European Southern Observatory. Operation of the Danish 1.54 m telescope in 2015 was financed by a grant to UGJ from the Danish Natural Science Research Council (FNU). Operations at the Calar Alto telescopes are jointly performed by the Max Planck Institute for Astronomy (MPIA) and the Instituto de Astrofísica de Andalucía (CSIC). GROND was built by the high-energy group of MPE in collaboration with the LSW Tautenburg and ESO, and is operated as a PI-instrument at the MPG 2.2 m telescope. The reduced light curves presented in this work will be made available at the CDS ( http://cdsweb.u-strasbg.fr/ ). LM acknowledges support from the Italian Minister of Instruction, University and Research (MIUR) through FFABR 2017 fund. LM acknowledges support from the University of Rome Tor Vergata through ‘Mission: Sustainability 2016’ fund. GC acknowledges the support by the National Natural Science Foundation of China (Grant No. 11503088, 11573073, 11573075) and the project ‘Technology of Space Telescope Detecting Exoplanet and Life’ from National Defense Science and Engineering Bureau civil spaceflight advanced research project (D030201). IJ acknowledges the Austrian Forschungsförderungsgesellschaft FFG project ‘RASEN’ P847963. TCH acknowledges financial support from KASI. DMB acknowledges the support of the NYU Abu Dhabi Research Enhancement Fund under grant RE124. MK acknowledges the support of the International Postdoctoral Fellow of Independent Research Fund Denmark. CITEUC is funded by National Funds through FCT – Foundation for Science and Technology (project: UID/ Multi/00611/2013) and FEDER – European Regional Development Fund through COMPETE 2020 – Operational Programme Competitiveness and Internationalisation (project: POCI-01-0145-FEDER-006922). We thank Fei Yan for useful discussion about the WASP-74 b transmission spectrum. The following internet-based resources were used in research for this paper: the ESO Digitized Sky Survey; the NASA Astrophysics Data System; the SIMBAD data base operated at CDS, Strasbourg, France; and the arXiv scientific paper preprint service operated by Cornell University. Based on data collected by MiNDSTEp with the Danish 1.54 m telescope at the ESO La Silla Observatory.


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

In Fig. 11 we have plotted both IGAPS r and GaiaG magnitudes on the horizontal axis: despite their being very similar for modest r − i , the relation between them has a growing colour dependence for large r − i , as can be seen in Fig. 12 . A minor factor affecting Fig. 11 is that some IGAPS sources might not have a measured r magnitude (either r I or r U ), and so could not be included. Fig. 12. Differences between IGAPS r I and GaiaG magnitudes as a function of IGAPS r I − i colour. The colour scales according to the density of sources in each bin, with square root intervals. Yellow represents the lowest density of at least four sources per 0.02 × 0.02 mag 2 bin. In the same two areas of 1 sq. deg, we have compared the IGAPS catalogue with Pan-STARRS ( Chambers et al. 2016 ). In this case there are more sources in the Pan-STARRS catalogue. In Fig. 13 we can see that Pan-STARRS is only slightly deeper in the r band. In this figure we are directly comparing Pan-STARRS and IGAPS AB magnitudes that are the same by construction. Crowding accounts for less of the difference in this comparison since both catalogues come from ground-based photometric surveys with a similar pixel scale (0.333 vs. 0.258′′ pixel −1 ) and typical seeing. Fig. 13. Results of the cross-match between IGAPS and Pan-STARRS at l = 60°. In blue, rAB magnitude distribution for the IGAPS sources. In red, r magnitude distribution from Pan-STARRS. In green, sources with both IGAPS and Pan-STARRS values. In cyan, sources in IGAPS not crossmatched with Pan-STARRS. In orange, sources in Pan-STARRS but not in IGAPS. When two r magnitudes are available for an IGAPS source ( rAB I and rAB U ) the mean value is plotted. If only one of the two magnitudes has a value, that value is used and the object is included. 8.5. The fully calibrated colour–colour diagrams The creation of the IGAPS catalogue adds to the available colour–colour diagrams. The first of these to mention is the g − r U , r I − i diagram, which uses the three fully calibrated broad bands. An example, constructed as a density plot from the Galactic longitude range 60° l 65°, is shown in Fig. 14 . The tracks overplotted in red have been computed via synthetic photometry using library spectra (see Appendix D ). As the main sequence (MS) and giant tracks sit very nearly on top of each other, we show only the MS track as a red solid line. A reddening line for an A2V star is also included as a dashed line. The comparison of the catalogue data with these reference tracks points out that all stars to K-type fall within a neat linear strip that follows the reddening vector. Only the M stars break away from this trend, creating the roughly horizontal thinly populated spur at g − r U ∼ 1.5 where nearly unreddened M dwarfs are located. This can be echoed at greater g − r U and r I − i by an even sparser distribution of stars to the right of the main stellar locus. Indeed, in the example shown in Fig. 14 , the density of stars


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magnitudes using the parsec isochrones. We then pair the single stars to make binaries assuming a non-random pairing of the binary components. According to the hydrodynamical simulations of star cluster formation by Bate ( 2009 ), the multiplicity fraction is approximately proportional to the logarithm of the primary mass. As a result, we assume that the probability that a single star with mass m prim is the primary component of a binary, scales linearly with the logarithm of its mass, i.e. Prob ∝ log 10 ( m prim ). We also assume a flat distribution function for the mass ratio 0 ≤ q ≤ 1.0. We then calculate the total J and K s magnitudes of each binary from the sum of the luminosities of its components. We also add photometric errors to the magnitudes of single and binary stars. The photometric errors resemble the photometric errors of PPMXL at each magnitude. Using this procedure, we make a synthetic CMD of a cluster with single stars and binary systems. We then apply our photometric membership criterion (Section 4.2 ) on this synthetic CMD through a blind test and count the number of single stars and binaries. Photometrically, a single star is any point that lies within 2.5σ of the isochrone and a binary is any non-single star whose vertical deviation from the isochrone is less than 0.75 mag. Finally, we change the low-mass and the high-mass slopes of the MF (α low , α high ) in ranges (0.70, 1.25) and (2.00, 2.60), respectively, until we find the best match with the observed MF (using K-S test) and the observed binary fraction. For each set of the simulation parameters, we have simulated 40 clusters with different random seed numbers, to reduce the errors caused by statistical fluctuations. Fig. 8 , shows the synthetic CMD of one of our simulated clusters (left-hand panel) and the detected single stars and binaries after applying the blind photometric test. In this figure, the true binary fraction is 23 per cent in the mass range 0.10 ≤ M / M ⊙ ≤ 4.68, but the observed binary fraction is only ∼6 per cent. Figure 8. (Left) Synthetic CMDs of simulated binaries (red filled circles) and single stars (black open squares) for a true binary fraction of 23 per cent over the whole mass range (0.1 m / M ⊙ 4.68). The scatter of single stars is due to the added photometric errors which resemble actual photometric errors in PPMXL. (Right) The detected binaries (red filled circles) as well as single stars (blue filled circles) after applying the blind photometric test on the simulated data. As it can be seen, many of the binaries have been identified as single stars. The observed binary fraction is ∼6 per cent. The parsec isochrone which is used in the blind photometric test, is shown by the black solid line. Table 4 compares the binary fractions of Alpha Persei from different studies. According to the outcome of our Monte Carlo simulations, the binary fraction of the cluster over the mass range 0.70 m / M ⊙ 4.68 is 34 ± 12 per cent, in agreement with


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

of a magnetohydrodynamic (MHD) fluid is that weak collisionality permits approximate conservation of particle adiabatic invariants and so when frozen-in magnetic field is locally increased or decreased by the plasma motions, perpendicular ( p ⊥ ) and parallel ( p ∥ ) pressures evolve differently (Chew, Goldberger Low 1956 ; Kulsrud 1983 ), giving rise to local pressure anisotropies. Pressure-anisotropic plasma is unstable when the relative pressure anisotropy is large enough compared to 1/β (otherwise Lorentz forces stabilize the plasma), namely, (the mirror threshold given here is approximate; see Hellinger 2007 ). These instability conditions are easily achievable in the ICM (see Schekochihin et al. 2005 and references therein). The instabilities are very fast in time and microscopic in space compared to any conceivable macroscopic dynamics because they happen essentially on ion Larmor scales. It is reasonable to expect that their effect will be to keep the plasma locally (at most) marginal with respect to the conditions ( 3 ) and ( 4 ); solar-wind measurements lend strong support to this expectation (Hellinger et al. 2006 ; Bale et al. 2009 ). In order to make that happen, the firehose and mirror fluctuations must impose an upper bound on the effective rate of change of the magnetic field (which gives rise to the pressure anisotropies) and/or increase the effective collisionality of the plasma (which relaxes the anisotropies) – either scenario can lead to macroscopic dynamics dramatically different from the conventional MHD evolution. This topic was discussed at length (if speculatively) by Mogavero Schekochihin ( 2014 ), with particular focus on the question of how, under pressure-anisotropic conditions, the magnetic field might be able to grow from a weak primordial seed (believed to be anywhere between 10 −21 and 10 −9 G, corresponding to β ∼ 10 32 − 10 8 ; see review by Durrer Neronov 2013 ) to its observed value ( B ∼ 10 −6 G, β ∼ 10 2 ), even as the rate at which it is allowed to change locally is constrained by the condition where ν eff is either the Coulomb collision frequency or some effective collisionality determined by particle scattering (if any) off microscopic firehose or mirror fluctuations (see further discussion in Section 4.2 ). This is the question of plasma dynamo – magnetogenesis in a weakly collisional turbulent plasma. Besides being of astrophysical (origin of observed fields) and fundamental (what happens?) physical interest, it is likely soon to be attacked not just theoretically, but also experimentally, with the advent of laboratory experiments aiming to reproduce the plasma dynamo process (Spence, Reuter Forest 2009 ; Meinecke et al. 2014 , 2015 ; Forest et al. 2015 ; Plihon et al. 2015 ). The first demonstration of this process in a 3D kinetic numerical simulation by Rincon et al. ( 2016 ) has indeed confirmed the ubiquitous appearance of mirror and firehose fluctuations, although a detailed investigation of


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I gratefully acknowledge the anonymous referee for useful comments that helped to clarify several aspects of the present work and I thank the Departamento de Ciencias Básicas, Facultad de Ingeniería Universidad Autonoma del Caribe. This work has been supported by the Vice-Rectoría de Investigación y Transferencia, Universidad Autonóma del Caribe through project PRYINT-049 2015.


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has not taken place yet). Future observations of the disc may provide additional constraints, considering there is indirect evidence for a much larger binary population within it. For example, binaries could account for the observed radial trend in disc membership among young stars in the Galactic Centre (Naoz et al. 2018 ). The remainder of this paper is organized as follows. In Section 2 , we review the effects of mass ratio on binary disruptions. In Section 3 , we describe the approximate method we use to model disruptions. In Section 4 , we describe how the probability of not producing massive S-stars varies with binary parameters. In Section 5 , we discuss the effects of stellar disruptions. Finally, we summarize our conclusions in Section 6 . 2 MASS RATIO AND THE HILLS MECHANISM When an unequal mass binary is disrupted, the primary star’s final orbit will be closer in energy to that of the original binary than the secondary star’s. This is because the primary star is closer to the binary’s centre of mass, and experiences a smaller tidal force. Analogously, when a single star is disrupted the most tightly bound debris comes from its surface (Rees 1988 ). Stars can either gain or lose energy in the disruption, depending on the binary’s phase. The final (specific) energy of star i will be (see e.g. Kobayashi et al. 2012 ) where ε o is the initial energy of the centre-of-mass orbit, M is the mass of the SMBH, r t is the tidal radius (approximately ( M / m bin ) 1/3 a bin , where m bin and a bin are the binary mass and semimajor axis, respectively). The δ i factor is the characteristic distance between the star and the centre of mass. This is approximately qa bin /(1 + q ) for the primary and a bin /(1 + q ) for the secondary, where q is the ratio of the secondary mass to the primary mass. Thus, the post-disruption (specific) energies of the primary (star 1) and the secondary (star 2), are where k is a factor of order unity that depends on the binary’s eccentricity, phase, and orientation (see GM20). In the parabolic limit, the corresponding semimajor axes are The absolute value of the primary’s semimajor axis is larger by 1/ q . 3 MONTE CARLO EXPERIMENTS We explore how the mass and semimajor axis distribution of the bound stars depend on the properties of the progenitor binary population. In particular, we construct mock binary samples from the distributions in Table 1 . For each binary, we calculate post-disruption energies of its member stars according to equation ( 2 ), assuming the initial semimajor axis of the binary’s centre-of-mass orbit is 0.05 pc. The scatter from the binary’s phase and orientation is encapsulated by k . We use the suite of ∼500 000 archain binary disruption simulations from GM20 to construct an empirical distribution for this parameter. Although the final energies of the stars depend on the binary mass, semimajor axis, and its centre-of-mass orbit, the k -distribution will not depend on these properties. Thus,


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The authors of this paper want to thank Dr J. Koppenhoefer for his support in the provision of the Pan-STARRS1 catalogue data and Dr K. Beisser for making available the Monte-Carlo simulation software for the dust coma and tail of comets. We also wish to acknowledge the important contributions for this work resulting from the Pan-STARRS1 Survey catalogue data. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation under Grant No. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE). This research was supported by the cluster of excellence ‘Origin and structure of the universe’ of the Deutsche Forschungsgemeinschaft DFG.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B-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-Model I-Model I-Model 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 O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O B-Software 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 B-Collaboration O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

galaxy. Some arcs are longer than others, some may wind in the opposite direction to the majority and some ‘arcs’ are actually just noise mistaken for an arc. We found that the most reliable measure of the winding direction of the galaxy was a length-weighted vote of the winding direction of all the detected spiral arcs. Figure 3. Steps sparcfire (Davis Hayes 2014 ) takes in describing a spiral galaxy image. (a) The centred and de-projected image. (b) Contrast-enhanced image. (c) Orientation field (at reduced resolution for display purposes). (d) Initial arm segments found via hierarchical agglomerative clustering (HAC) of nearby pixels with similar orientations and consistent logarithmic spiral shape, overlaid with the associated logarithmic spiral arcs fitted to these clusters. (e) Final pixel clusters (and associated arcs) found by merging compatible arcs. (f) Final arcs superimposed on image (a). Red arcs wind S-wise, cyan arcs wind Z-wise. To demonstrate that this measure is unbiased with regard to spin direction, we refer to Fig. 4 , which shows a scatter plot of the left-to-right mirrored versus unmirrored value of the galaxy's pitch angle as measured by sparcfire ; in a perfect reversal, the two should be negatives of each other. We find that in 29 094 out of 29 250 cases (99.47% of cases), the two are negatives of each other to within 10 −4 deg. Even more relevant to this paper, we find that in all but five cases (99.983% of cases), the chirality of the mirrored image is correctly flipped compared to the unmirrored case. Thus, the chirality determination of sparcfire is unbiased, with respect to flipped images, to better than two parts in 10 4 . (It is instructive to discuss why a deterministic algorithm like sparcfire is not 100% unbiased to image flips; we discuss some of the issues in the appendix.) Figure 4. Galaxy-level pitch angles reported by sparcfire using unmirrored and left-to-right mirrored input images across 29 250 ‘clear’ spiral galaxies (see text for definition). These galaxy-level pitch angles are calculated as the arc-length-weighted average of all arcs agreeing with the dominant winding direction (as determined by an arc-length-weighted vote). We see that for almost all galaxies the measured pitch angle almost exactly negative, as it should be. The diagonal line gives y = − x ; cases on this line are visually underrepresented due to overlap. More importantly, only five cases out of 29 250 disagree on chirality, showing that sparcfire is chirality unbiased to a level of almost one part in 10 000. We then ran sparcfire on the entire Galaxy Zoo sample of galaxies, in order to determine the chirality of galaxies in an unbiased manner. However, we still used the human GZ1 determination of P S + P Z to select which galaxies actually display spiral structure. The second quarter of Table 2 details the statistical significance of the winding direction bias, as a function of the human confidence in observed spiral structure,


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2013 ), due to a likely very late thermal pulse (born-again scenario). Moreover, while most HE-PNe show N/O ratio larger than 1 (N rich), SuWt 2 has an N/O = 0.8 which makes it somewhat different. Most of the HE-PNe in our group (Tables 3 and 4 ) lie in the low-left corner of the PNe regime on the Hα/[N ii ] versus Hα/[S ii ] diagnostic diagram (Sabbadin, Minello Bianchini 1977 ; Gonçalves et al. 2003 ) of Galactic PNe (Fig. 9 ). Akras Gonçalves ( 2016 ) have recently demonstrated that in this regime also lie shock-excited regions, e.g. low-ionization structures such as knots and jets. Since these PNe do not exhibit high velocities, the scenario of shocks in such large and HE-PNe seems unlikely. Interestingly, some HE-PNe exhibit uncommonly bright [N i ] λ5200 and [O i ] λ6300 emission lines, which are strong indicators of shock activity. Recently, Ali et al. ( 2015 ) showed that a reverse shock of only 25 km s −1 in the PN G342.0–01.7, due to its motion through the ISM, can successfully explain the strong neutral emission lines. The low photoionization rate of their central stars, (very low L ) and large nebular size, would make the contribution of shocks easily perceptible even at small velocities (Akras Gonçalves 2016 ), or produce emission line spectra that resemble those of shock-excited regions (Raga et al. 2008 ). Figure 9. A diagnostic diagram of Galactic PNe by Sabbadin et al. ( 1977 ) with the density ellipses introduced by Riesgo López ( 2006 ) and for the HE-PNe in Tables 3 and 4 . Table 3. Chemical abundances † and dereddened emission lines of HE-PNe. † Chemical abundances are given in the notation of 12+log[ n (X)/ n (H)], †† These values have been derived from photoionization models. a Relative to Hβ= 100, b Average value from seven slit positions, c This value is a lower limit and corresponds only to N + . d This ratio was calculated based on the assumption of N/O = N + /O + , e The night sky emission line at 6300 Å may have not been properly subtracted. f They have been dereddened using R = 3.1 Table 4. Stellar and physical parameters of HE-PNe. † The stellar parameters have been derived from photoionization models. a These kinematical ages have been calculated by us, assuming a nominal expansion velocity of 40 km s −1 . b A possible born-again PN. 3 out of 11 HE-PNe are seen to exist outside the normal region in Fig. 9 , namely SBW 1, ESO 166–PN 21, and WeBo 1. SBW 1 and WeBo 1 are the youngest members in this group, and their central stars are such luminous that their high photoionization rate overcomes the shock excitation. Unfortunately, the stellar parameters of these HE-PNe are still unknown. Regarding ESO 166–PN 21, the [N ii ]/Hα and [S ii ]/Hα line ratios correspond to an average value from seven slit positions (for more details see in Peña et al. 1997 ). Studying the spectrum of each slit position separately, we deduce that the most distant regions (A1 and A5 in Peña et al. 1997 ) exhibit stronger [N ii ] and [S ii ]


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Acknowledgments We thank the Referee for providing a constructivereport that has improved the presentation of the results. We thank P. Nulsen for providing the software required to produce the SB fit reported in Fig. 4 and M. Markevitch for useful discussion. AI thanks A. Botteon and G. Lanzuisi for useful discussions. MG, GB acknowledge partial support from PRIN-INAF 2014. EOS was supported in part by NASA grants GO5-16123X and GO6-17121X. CLS was supported in part by NASA Chandra grants GO4-15123X, GO5-16131X, and GO5-16146X. KWW was partially supported by Chandra grants GO5-16125X and GO6-17108X, and NASA ADAP grant NNH16CP10C.


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may have been found in the form of braided velocity-coherent substructure in the Taurus B211–3 filament ( Hacar et al. 2013 ). Another major result from Herschel in nearby clouds is that most (75%) low-mass prestellar cores and protostars are found in dense, supercritical filaments for which the mass per unit length exceeds the critical line mass of nearly isothermal long cylinders (e.g. Inutsuka Miyama 1997 ), /pc, where km s is the isothermal sound speed for molecular gas at K (e.g. Könyves et al. 2015 ). These Herschel findings support a scenario for low-mass star formation in two main steps (cf. André et al. 2014 ): First, large-scale compression of interstellar material in supersonic MHD flows generates a cobweb of filaments 0.1 pc in width in the ISM; second, the densest filaments fragment into prestellar cores (and subsequently protostars) by gravitational instability above , while simultaneously growing in mass through accretion of background cloud material. In addition to the relatively modest filaments found in non-star-forming and low-mass star-forming clouds, where rarely exceeds ten times the thermal value of , significantly denser and more massive filamentary structures have also been observed in the most active giant molecular clouds (GMCs) of the Galaxy and may be the progenitors of young massive star clusters. The DR21 main filament or ridge is probably the most emblematic case of such a massive elongated structure with about inside a 4.5 pc long structure (i.e., /pc; Motte et al. 2007 ; Schneider et al. 2010 ; Hennemann et al. 2012 ). Other well-known ridges include Orion A ( Hartmann Burkert 2007 ), Vela-C ( Hill et al. 2011 , 2012 ), IRDC G035.39–00.33 ( Nguyen Luong et al. 2011 ), and W43-MM1 ( Nguyen-Luong et al. 2013 ; Louvet et al. 2014 ). These ridges, which exceed the critical line mass of an isothermal filament by up to two orders of magnitude, are believed to be in a state of global collapse, to be fed by very high accretion rates on large scales ( Schneider et al. 2010 ; Peretto et al. 2007 , 2013 ), and to continuously form stars and clusters. The formation of these ridges is not yet well understood but may result from the large-scale collapse of a significant portion of a GMC ( Hartmann Burkert 2007 ; Schneider et al. 2010 ). Whether the low-mass star formation scenario summarized above – or an extension of it – also applies to regions dominated by hyper-massive clumps and ridge-like structures ( Motte et al. 2016 ) is not yet known. In particular, further work is needed to confirm that the inner width of interstellar filaments remains close to 0.1 pc in regions of massive star formation beyond the Gould Belt, where the moderate angular resolution of Herschel (–36 at –500 m) is insufficient to resolve this characteristic scale. At a distance of 1.7 kpc, NGC 6334 is a very active complex of massive star formation ( Persi Tapia 2008 ; Russeil et al. 2013 ) with about 150 associated luminous stars of O- to


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Acknowledgments The authors are very grateful to the reviewer of this article for the helpful comments and suggestions that led to the improve of the quality of this work. The authors wish to thank the São Paulo State Science Foundation (FAPESP, grants 13/15357-1 and 14/06762-2) and CNPq (grants 150360/2015-0 and 312313/2014-4) for their generous support of this work.


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We thank Simone Aiole, Seshadri Nadathur, Mark Neyrinck, and István Szapudi for discussing this work with us. BRG and AJH acknowledge support of the European Research Council through the Darklight ERC Advanced Research Grant (# 291521). AK was supported by the Spanish Ministerio de Economía y Competitividad (MINECO) under projects FPA2012-39684, and Centro de Excelencia Severo Ochoa SEV-2012-0234. The results were derived with cosmopy ( www.ifa.hawaii.edu/cosmopy ) and healpix with healpy (healpix.jpl.nasa.gov, code.google.com/p/healpy). 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.


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Acknowledgments This research is supported by the Canada Research Chair programme and Natural Science and Engineering Research Concil of Canada. DW is supported by European Research Commission grant ERC-StG-6771177 DUST-IN-THE-WIND.