We thank Lindsey Bleem for providing Magellan Megacam LDSS3 images of SPT0254-58 and AS295 prior to RELICS to inform our HST observations of these clusters. We thank Florian Pacaud and Matthias Klein for discussions regarding our AS295 HST pointings. We thank Dale Kocevski for an image of RXC 0142+44 obtained with the University of Hawaii 2.2 m telescope. And we thank Stella Seitz et al. for sharing their HST observations of PLCK G287+32 obtained during the same cycle. We thank the STScI and SSC directors and time allocation committees for enabling these large observing programs. We are grateful to our HST program coordinator William Januszewski for implementing the RELICS HST observations. We thank Jennifer Mack for expert mentoring of our HST image reduction gurus RA and SO. And we thank Gabriel Brammer for providing an updated WFC3/IR hot pixel mask derived from science observations from GO 14114. We are grateful to the University of Arizona for hosting our team meeting. The RELICS Hubble Treasury Program (GO 14096) consists of observations obtained by the NASA/ESA Hubble Space Telescope (HST). HST is operated by the Association of Universities for Research in Astronomy, Inc. (AURA), under NASA contract NAS5-26555. Data from the NASA/ESA HST presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST), operated by the Space Telescope Science Institute (STScI). STScI is operated by the Association of Universities for Research in Astronomy, Inc. (AURA), under NASA contract NAS 5-26555. The HST Advanced Camera for Surveys (ACS) was developed under NASA contract NAS 5-32864. Spitzer Space Telescope data presented in this paper were obtained from the NASA/IPAC Infrared Science Archive (IRSA), operated by the Jet Propulsion Laboratory, California Institute of Technology. Spitzer and IRSA are operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. We gratefully acknowledge support from JPL for the Spitzer analysis. M.B. and V.S. also acknowledge support by NASA through ADAP grant 80NSSC18K0945, NASA/ HST through HST-GO-14096, HST-GO-13666, and two awards issued by Spitzer /JPL/Caltech associated with the SRELICS_DEEP and SRELICS programs. Part of this work by W.D. was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344. K.U. acknowledges support from the Ministry of Science and Technology of Taiwan (grant MOST 106-2628-M-001-003-MY3) and from Academia Sinica (grant AS-IA-107-M01). O.G. is supported by an NSF Astronomy and Astrophysics Fellowship under award AST-1602595. S.A.R. was supported by NASA grant HST-GO-14208 from STScI, which is operated by Associated Universities for Research in Astronomy, Inc. (AURA), under NASA contract NAS 5-26555. A.M. acknowledges the financial support of the Brazilian funding agency FAPESP (Post-doc fellowship—process No. 2014/11806-9). J.H. was supported by a VILLUM FONDEN Investigator grant (project No. 16599). Finally, we thank our referee for detailed comments that helped us improve the manuscript. Facilities: HST (WFC3 , ACS); Spitzer (IRAC); Planck ; MAST; IRSA.


adopt κ 140 μ m = 13.9 cm 2 g −1 ( Draine 2003a ). We also calculate the optical depth of the silicate feature τ sil , following the method defined by Imanishi et al. (2007) where an absorption-free continuum is assumed to be the power-law function determined from the flux densities at 7.1 and 14.2 μ m. As a result, τ sil is estimated to be 2.3 ± 0.2 at 9.8 μ m, the wavelength of the bottom of the absorption feature. We also performed the SED fitting of the heavily obscured AGN, using the Bayesian SED fitting code CIGALE ( Burgarella et al. 2005 ; Noll et al. 2009 ; Boquien et al. 2019 ), which includes energy balance between the dust absorption of the stellar/AGN emission and the dust re-emission in the mid- to far-IR, similarly to the study by Ciesla et al. (2015) . As a result, M star and L IR are estimated to be 8 × 10 9 M ⊙ and 6 × 10 10 L ⊙ , respectively. Comparing the result in Table 3 , we find that M star increases while L IR does not change. The increase in M star , however, does not change our conclusion below. 4. Discussion 4.1. Comparison with other AGNs We detect a deep ( τ sil ∼ 2.3) absorption feature due to silicate grains at around 10 μ m. We compared the optical depth of the silicate feature in LEDA 1712304 with those in other AGNs in a wide range of IR luminosities (10 10 L ⊙ L IR 10 13 L ⊙ ). The spectra of the AGNs to be compared are taken from those of the IR galaxies observed by S p i t z e r /IRS ( Stierwalt et al. 2013 ; Imanishi et al. 2007 ; Imanishi 2009 ; Roussel et al. 2006 ) with the threshold that the equivalent width of the PAH 6.2 μ m feature is smaller than 0.27 μ m ( Stierwalt et al. 2013 ). In addition, we also take AGNs with low IR luminosities ( L IR 10 11 L ⊙ ) from Wu et al. (2010) . Figure 3 shows the relation between the IR luminosity and the optical depth of the silicate feature for LEDA 1712304 and the AGNs. We estimated the optical depths of the AGNs of Stierwalt et al. (2013) , Wu et al. (2010) , and Roussel et al. (2006) by ourselves in the same manner as was performed for LEDA 1712304 with the method defined by Imanishi et al. (2007) , the spectra of which were obtained from the NASA/IPAC IR Science Archive (IRSA). On the other hand, we adopted the values given in each reference paper for the other AGNs. The blue dotted lines in Fig. 3 show the differences between our estimates and those by Stierwalt et al. (2013) , which confirms that the differences between the different methods are not large enough to change a global relation. Figure 3 shows that galaxies with low IR luminosities ( L IR 10 11 L ⊙ ) show significantly shallow silicate absorption features, as already pointed out by Stierwalt et al. (2013) . Imanishi (2009) suggested that the number of heavily obscured AGNs that have deep silicate absorption features ( τ sil > 2) increases with the IR luminosities of the host galaxies. Therefore, LEDA 1712304 may be a rare galaxy from the aspect of having both deep absorption feature τ sil ∼


This work was carried out as part of the ISIMA programme on Gravitational Dynamics held in 2014 July at CITA, Toronto. We acknowledge Pascale Garaud for the organization, the financial support and for providing a stimulating environment. MG thanks the Royal Society for financial support in the form of a University Research Fellowship (URF) and the European Research Council (ERC-StG-335936, CLUSTERS). RLS acknowledges the support of the Science and Technology Facilities Council (STFC) via the award of an STFC Studentship. RLS thanks Anna Lisa Varri for her support and guidance throughout this work. We thank Miklos Peuten, Antonio Sollima, Anna Lisa Varri and Alice Zocchi for discussions during the ‘Gaia Challenge’ meeting and Diederik Kruijssen,Vincent Hénault-Brunet, Jay Strader and Nate Bastian for comments on the manuscript. Finally, we thank the referee for constructive comments that helped to improve the paper.


catalog of the whole area covered by Herschel (HELP, Shirley et al. 2019 ) can be taken into consideration for the analysis. Moreover, new submillimeter surveys like TolTEC ( DeNigris Wilson 2019 ) or the future mid/near-infrared James Webb Space Telescope (JWST, Gardner et al. 2006 ) will certainly increase the area and/or the density of the background sources. Appendix A: The ingredients of the model The dark matter transfer function has been computed using Eisenstein and Hu’s fitting formula ( Eisenstein Hu 1998 ), which takes baryonic effects into account in a ΛCDM model. Having chosen an analytical computation of the power spectrum over the traditional numerical one using CAMB ( Lewis et al. 2000 ) is mainly due to computation time. The galaxy-dark matter cross-power spectrum has been computed through Eqs. ( 7 ) and ( 8 ), where the linear dark matter power spectrum is evolved to redshift z via the linear growth factor approximation of Carroll et al. (1992) . The HMF has of course been parametrized according to ( 1 ) for the two different fits we have described in Sect. 2.1 . The deterministic bias associated with each model has been derived using the peak background split as in Sheth Tormen (1999) . Furthermore, we have expressed the mean number of galaxies in a halo of mass M as where ⟨ N c g ⟩ M and ⟨ N s g ⟩ M are the mean number of central and satellite galaxies in a halo of mass M , respectively, expressed in terms of three HOD parameters ( α , log M 1 , log M min ) as and following Zehavi et al. (2005) and Zheng et al. (2005) . In essence, M min is the minimum mean halo mass required to host a (central) galaxy and M 1 > M min is the mean halo mass at which exactly one satellite galaxy is hosted. The mean number density of galaxies at redshift z is then given by The halo density profile has been assumed to match a Navarro-Frenk-White (NFW) profile ( Navarro et al. 1997 ). The normalized Fourier transform of the dark matter distribution within a halo of mass M is then given by ( Cooray Sheth 2002 ) where and ρ s are a scale radius and density that parametrize the profile, concentration parameter of a halo of mass M at redshift z , which satisfies for an NFW profile. The virial radius R vir has been computed through the virial overdensity at redshift z , using the fit by Weinberg Kamionkowski (2003) . It should be noted that we have not defined halos at a certain redshift as overdense regions of a constant factor (say 200) times the background or critical density, but using the virial overdensity instead, which depends on redshift. In practice, for a halo of mass M , we have adopted the concentration parameter by Bullock et al. (2001) , computed r s through ( A.1 ) and, subsequently, calculated ρ s using ( A.2 ). Appendix B: Additional tables and figures Fig. B.1. One- and two-dimensional (contour) posterior distributions from run 5 (in red) and run 1 (in blue), that is, a three-parameter and a two-parameter ST fit with 0 p 1 0.5,


Local Bubble. By analogy to the λ 5780.5 carrier, which appears to be a cation from the dependence of its EW on the ambient UV radiation field (Cami et al. 1997 ; Sonnentrucker et al. 1997 ; Vos et al. 2011 ), we suggest that the family of NIR DIBs also arises from cations. This idea is compatible with the DIB-PAH hypothesis, which proposes that the ionized PAHs are the carriers that have their transitions in NIR wavelength range (Mattioda et al. 2005 ; Ruiterkamp et al. 2005 ; Cox et al. 2014 ). Recently, in the first conclusive identification of DIB carriers, two DIBs at λ = 9577, 9632 Å were assigned to the absorption features of interstellar ionized buckminsterfullerene () (Foing Ehrenfreund 1994 ; Campbell et al. 2015 ). Ionized fullerenes and their derivatives are also expected to be the carrier sources of NIR DIBs. In Section 5.2 , we suggested that the λ 10504 carrier is destroyed by the ionizing photons in Cyg OB2, whereas those of the family DIBs can survive the Cyg OB2 environment. We also suggested that the λ 12623 carrier may be produced in Cyg OB2 (Section 5.2 ). The destruction of the λ 10504 carrier and possible production of the λ 12623 carrier are probably caused by the strong UV flux in the Cyg OB2 environment. The different environmental dependence of these two DIBs and the family DIBs would be due to the difference of their carrier properties, such as the ionization state and the stability against the UV photon flux. The FWHM of λ 10504 is ∼1.2 cm −1 , which is one of the smallest widths among 17 DIBs investigated in this paper, whereas the FWHM of λ 12623 is ∼2.4 cm −1 , which is one of the largest. If the profiles of the DIBs are unresolved rotational contours, the widths are mainly determined by the rotational constants of the carriers (Cami et al. 2004 ; Oka et al. 2013 ). Assuming that the geometries of the molecules are similar, the band widths of the smaller molecules should be broader due to the larger rotational constants. Therefore, the λ 10504 carrier would be larger than the λ 12623 carrier. Then, we speculate that the photodissociation of the λ 10504 carrier results in the production of λ 12623 carrier. 6. SUMMARY We investigated the environmental dependence of DIBs toward Cyg OB2 using WINERED spectrograph data. We observed the seven brightest OB stars in the J band in Cyg OB2, and detected all known NIR DIBs with large EWs toward all observed stars in the wavelength coverage of WINERED. Our findings are summarized below: 1. Five of the DIBs ( λλ 10780, 10792, 11797, 12623, and 13175) are well correlated, suggesting similar physical and/or chemical properties of their carriers. 2. The EWs of DIB λ 10504, which were suggested to be well correlated with the family of NIR DIBs in Paper I , are almost constant toward Cyg OB2. In contrast, the EWs of the family of NIR DIBs vary by a factor of two. We attribute the different behaviors of the DIB λ 10504 and the family to the environmental sensitivity of the DIB λ


DIJ acknowledges support from STFC via grant number ST/H002359/1, travel support from NewCompStar (a COST-funded Research Networking Programme), and useful feedback from members of the Continuous Wave group of the LIGO Scientific Collaboration and the Virgo Scientific Collaboration, including many discussions with Matt Pitkin and Graham Woan.


We thank Ivan Ramírez for providing his abundance differences and interpolation routines, which were used in Table 6 and Fig. 10 , and Karin Lind for helping with uploading the results on to the online non-LTE data base INSPECT. AMA and MA are supported by the Australian Research Council (ARC) grant FL110100012. RC acknowledges support from the ARC through DECRA grant DE120102940. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government.


ACKNOWLEDGEMENTS The authors would like to thank Sean Fillingham, Michael Cooper, Alex Drlica-Wagner, Denis Erkal, and Josh Simon for useful discussions. TK and JSB were supported by NSF AST-1518291, HST-AR-14282, and HST-AR-13888. MBK acknowledges support from NSF grant AST-1517226 and CAREER grant AST-1752913 and from NASA grants NNX17AG29G and HST-AR-13888, HST-AR-13896, HST-AR-14282, HST-AR-14554, HST-AR-15006, HST-GO-12914, and HST-GO-14191 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. Support for SGK was provided by NASA through the Einstein Postdoctoral Fellowship grant number PF5-160136 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060. MSP acknowledges that support for this work was provided by NASA through Hubble Fellowship grant #HST-HF2-51379.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. AGS was supported by an AGEP-GRS supplement to NSF grant AST-1009973. Numerical business was taken care of in a flash using computational resources of the Texas Advanced Computing Center (TACC; http://www.tacc.utexas.edu ), the NASA Advanced Supercomputing (NAS) Division and the NASA Center for Climate Simulation (NCCS), and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575. This work also made use of Astropy, 6 a community-developed core python package for Astronomy (Astropy Collaboration 2013 , 2018 ), matplotlib (Hunter 2007 ), numpy (van der Walt, Colbert Varoquaux 2011 ), scipy (Jones et al. 2001 ), ipython (Perez Granger 2007 ), pandas (McKinney 2010 ), mayavi (Ramachandran Varoquaux 2011 ), and the NASA Astrophysics Data System.


We thank Rick Bogart for useful discussions. The German Data Center for SDO (GDC-SDO), funded by the German Aerospace Center (DLR), provided the IT infrastructure to process the data. The HMI data used are courtesy of NASA/SDO and the HMI science teams. BWH acknowledges NASA support through grants 80NSSC17K0008, 80NSSC18K1125, and 80NSSC19K0267. We acknowledge partial support from ERC Synergy grant WHOLE SUN 810218.


Acknowledgments The authors acknowledge the comments and suggestions made by Sam Lindsay, assistant editor of MNRAS, and the comments of an anonymous referee, which helped us in improving the quality of our analysis and presentation. This study was possible thanks to DAIP-Ugto (1006/2016). RAO-M acknowledges support from CONACYT (Mexico) through PhD fellowship 322095, and CAPES (Brazil) through a PDJ fellowship from project 88881.030413/2013-01, programme CSF-PVE. The funding for SDSS 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 full acknowledgement can be found here: www.sdss3.org . This research has made use of the r language and environment for statistical computing, available at www.r-project.org . This paper also made use of the VizieR catalogue access tool, CDS, Strasbourg (Ochsenbein etal. 2002 ) and of the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration.


Acknowledgments We thank Dmitri Uzdensky and Mitch Begelman for fruitful discussions and for comments on the manuscript. VZ acknowledges support from NSF grant AST-1411879. JW is supported by the Wisconsin Alumni Research Foundation at the University of Wisconsin-Madison. SB is partly supported by NSF grant AGS-1261659, and by the Vilas Associates Award from the University of Wisconsin-Madison. Simulations were performed at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin under the NSF-Teragrid Project TG-PHY110016.


emission is integrated over a velocity range of [−12, 6] km s −1 . The axes are offsets with respect to a central position (i.e., R.A. (2000) = 5 h 52 m 129; decl. (2000) = 26°59′33′′). The positions of IRAS 05490+2658, IRAS 05488+2657, and IRAS 05483+2728 are denoted by stars (in red). We have also presented integrated intensity (moment-0) maps of 13 CO(1–0), C 18 O(1–0), and CS(2–1) emission in Figures 3 (a)–(c), respectively. The 13 CO moment-0 map is shown here only for comparison (see also Figure 2 ). The C 18 O line is a tracer of total molecular gas column density, while the CS line is known to depict denser molecular gas compared to the 13 CO data. Hence, the denser parts in the filament can be spatially examined in Figures 3 (b) and (c). The dense gas traced in the C 18 O(1–0) and CS(2–1) maps is found toward both the filament ends (i.e., the S242 H ii region and all the PGCCs). Figures 3 (d)–(f) show moment-1 (velocity field) maps of the 13 CO(1–0), C 18 O(1–0), and CS(2–1), respectively. With the help of moment-1 map, some signatures of velocity gradients are seen at both the ends of the filament (see Figure 3 (d)). Figure 3. (a) 13 CO(1–0) integrated intensity map in the direction of the site S242 (i.e., Moment-0 map). (b) Moment-0 map of C 18 O(1–0). (c) Moment-0 map of CS(2–1). (d) Moment-1 map of 13 CO. e) Moment-1 map of C 18 O. (d) Moment-1 map of CS(2–1). In all the moment-0 maps, the vertical bar shows the color-coded intensity in K km s −1 . In each moment-1 map, the vertical bar shows the color-coded velocity in km s −1 . Figures 4 and 5 display the 13 CO and CS(2–1) velocity channel maps from −1 to 5 km s −1 , respectively. Each channel map is obtained by integrating the emission over 1 km s −1 velocity intervals. These channel maps help us to examine the gas distribution in the filament for a given velocity interval. In this paper, the molecular gas in the filament is mainly studied in the velocity interval of [−1, 5] km s −1 . However, we find an additional velocity component at [−12, −6] km s −1 toward the northern part of the filament using the 13 CO line data (not shown in this paper). In this velocity range, there is no C 18 O(1–0) and CS(2–1) emission detected toward the filament. It implies that the gas at [−12, −6] km s −1 has low density. In the position–velocity (pv) plot, we find that the velocity component at [−12, −6] km s −1 is kinematically unrelated to the one at the [−1, 5] km s −1 (not shown in this paper). Furthermore, the emission at [−12, −6] km s −1 appears to be more extended to the north of the filament, which is not covered in this observational work. In order to make any further conclusion on the kinematical structure at [−12, −6] km s −1 , one needs to obtain further observations for a wide-scale area containing the north part of the filament. Figure 4. 13 CO(1–0) integrated velocity channel maps (starting from −1 km s −1 at intervals of 1 km s −1 ) in the direction of the site S242. In


We wish to thank Dr. Andrea Giuliani for useful discussions. The research that led to these results has received funding from the European Union Seventh Framework Programme (FP7-SPACE-2013-1), under grant agreement n. 607452, “Exploring the X-ray Transient and variable Sky – EXTraS”.


to examine several SNR candidates proposed in the MAGPIS survey. Fig. 21 Spectral index of the SNR G018.8+00.3 (THOR source G18.761+0.287). The black contours represent the continuum emission in steps of 10, 20, 30, and 40 mJy beam. MAGPIS ( Helfand et al. 2006 ) provides 49 new SNR candidates, which are only partly included in the SNR catalog by Green (2014) . Their criteria for a SNR candidate is that they have: 1) a high ratio between 20 cm continuum and 21 m flux density; 2) a counterpart at 90 cm continuum emission; and 3) a distinctive SNR morphology, e.g., shell-type or filled-center ( Dubner Giacani 2015 ). Followup observations for these candidates to determine the distance using H i absorption are presented in Johanson Kerton (2009) . Since MAGPIS has higher spatial resolution, it is more sensitive at detecting the mentioned SNR morphology. However, the THOR survey can help to characterize the radiation and distinguish between thermal and non-thermal radiation. Within the THOR region, we find 33 MAGPIS SNR candidates, which are listed in Table 4 . Only five of them are included in the SNR catalog presented by Green (2014) . In contrast to this, 24 of these MAGPIS SNR candidates have at least one counterpart in the WISE H ii region catalog presented by Anderson et al. (2014) . However, thanks to the high density of H ii regions within the Galactic plane, these can be chance alignments along the line of sight. Table 4 MAGPIS SNR candidates in comparison with THOR sources, Green SNR and WISE H ii regions. Notes. The SNR candidates are given in Helfand et al. (2006) . Since the SNR candidates can be clumpy, several THOR sources can be found within one MAGPIS SNR candidate so we list all corresponding THOR sources. The associated SNR names taken from Green (2014) are given, as well as associated WISE H ii regions given in Anderson et al. (2014) . The given H ii regions can be associated with the SNR, but they can also be foreground or background objects. Figure 22 shows the size for each H ii region. The diameter of the MAGPIS SNR candidates is taken from Helfand et al. (2006) , and the distance is determined via H i absorption and taken from Johanson Kerton (2009) . The spectral index is measured for the peak position of the THOR source (see Sect. 3.7 ) and can vary significantly within the source (see Fig. 22 ). (a) Spectral index map is shown in Fig. 22 . (b) Multiple H ii regions can be found within the region. (c) Determination of the spectral index is uncertain, since the source is not detected in all spectral windows. Fig. 22 Spectral index maps of extended MAGPIS SNR candidates. The color scale represents the spectral index from 1.5 to 0.5, the black contours show the continuum emission in steps of 5, 10, 15, 20, 40, and 100 mJy beam. The red and white circles indicate the sizes of the SNR candidates given in Helfand et al. (2006) and the sizes of the H ii regions given in Anderson et al. (2014) , respectively. In each panel the


It is a pleasure to thank the anonymous referee, Brett Andrews, Bob Benjamin, and Chris McKee for helpful comments and discussions. Some of the results in this paper have been derived using the HEALPix (Górski et al. 2005 ) and healpy packages. J.B. received support from a John N. Bahcall Fellowship and the W.M. Keck Foundation. H.W.R. received funding for this research from the European Research Council under the European Union’s Seventh Framework Programme (FP 7) ERC grant Agreement No. [321035]. T.C.B. acknowledges partial support for this work from grants PHY 08-22648; Physics Frontier Center/Joint Institute or Nuclear Astrophysics (JINA), and PHY 14-30152; Physics Frontier Center/JINA Center for the Evolution of the Elements (JINA-CEE), awarded by the US National Science Foundation. J.B. and H.W.R. acknowledge the generous support and hospitality of the Kavli Institute for Theoretical Physics in Santa Barbara during the “Galactic Archaeology and Precision Stellar Astrophysics” program, where some of this research was performed. 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 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.


of the model with the expected scaling relations described in Section 3.1 using km s −1 from Bernardi et al. ( 2003 ). The expected value is , which is in agreement with the model values in Table B4 , mostly the one of the most complete cluster model. The two other results are closer to the upper limit value because those models do not take in account the impact of the cluster convergence boosting the lensing power of the galaxy, therefore leading to an overestimation of the velocity dispersion of the lens. However the value of of the complete cluster model is ∼40 per cent higher than the one optimized in the cluster model. This could indicate that the standard galaxy is not constrained well enough, as the cluster model not including the GGL constraints provides different values. We find a similar problem in the cluster model of RX J2129 where . If the GGL lens follows the scaling relation as we assumed it, the velocity dispersion of the lens would be , which is far too low according to Fig. 12 to produce multiple images as we observe them even for a source with s = 10. Even with the boost of the cluster, a complete model constraining the GGL parameters with a source with s = 10 leads to a σ 0 = 166.8 . There is a ∼30 per cent variation with this value and the one derived from the scaled standard galaxies in the models presented in Table B2 . Either the standard galaxy parameters are not well constrained or our assumption about the GGL lens is wrong. Having a spectroscopic redshift for the GGL source would provide a way to constrain σ 0 independently of the scaling relation and would allow us to test the consistency of the results. Then, assuming that the galaxy follows this relation, we could constrain better the standard galaxy parameters directly using the locations of multiple images in the GGLs. For this reason, a spectroscopic follow-up of the 24 GGLs presented in Fig. A1 and Table A1 would improve greatly the model constraints for all those clusters. 5 SUMMARY AND CONCLUSIONS We visually inspect the full Hubble field of view of the 25 observed clusters from the CLASH survey in order to locate GGL events in the outskirts of those clusters. We find a selection of 24 candidate GGLs (some already known), and study in detail three of them presenting the following characteristics: a single lens, at least four distinct multiple images, and a separation from the BCG larger than 80 arcsec. For each of those GGLs and their associated cluster, we produce four parametric models of the DM distribution to study the influence of the cluster on the GGL modelling and the influence on the GGL on the cluster models. Through those models, we show that the modelling of the GGLs cannot be done properly without taking into account its environment. This can be achieved through a complete model of the neighbour structures or even with a simple parametrization of their effects like an external shear. A photometric redshift is accurate enough to properly


We thank the anonymous referee for very useful comments and suggestions that substantially improved the manuscript. We are also grateful to M. Kromer for useful discussions about theoretic progenitor scenarios and to A. De Cia, O. Yaron, D. Tal, D. Perley, K. Tinyanont, A. Waszczak, I. Arcavi, and S. Tang for performing observation and data reduction. This research is partly supported by the Swift Guest Investigator program and by the National Science Foundation. Y.C. and M.M.K. acknowledge support from the National Science Foundation PIRE program grant 1545949. A.G. and R.A. acknowledge support from the Swedish Research Council and the Swedish Space Board. E.Y.H. acknowledges the support provided by the Danish Agency for Science and Technology and Innovation through a Sapere Aude Level 2 grant. Some observations were obtained with the SuperNova Integral Field Spectrograph on the University of Hawaii 2.2 m telescope as part of the Nearby Supernova Factory II project, a scientific collaboration between the Centre de Recherche Astronomique de Lyon, Institut de Physique Nucl’eaire de Lyon, Laboratoire de Physique Nucl’eaire et des Hautes Energies, Lawrence Berkeley National Laboratory, Yale University, University of Bonn, Max Planck Institute for Astrophysics, Tsinghua Center for Astrophysics, and Centre de Physique des Particules de Marseille. Some data in this paper were obtained with ALFOSC, which is provided by the Instituto de Astrofisica de Andalucia (IAA) under a joint agreement with the University of Copenhagen and NOTSA. We also thank the RATIR project team and the staff of the Observatorio Astronómico Nacional on Sierra San Pedro Mártir. RATIR is a collaboration between the University of California, the Universidad Nacional Autónoma de México, NASA Goddard Space Flight Center, and Arizona State University, benefiting from the loan of an H2RG detector and hardware and software support from Teledyne Scientific and Imaging. RATIR, the automation of the Harold L. Johnson Telescope of the Observatorio Astronómico Nacional on Sierra San Pedro Mártir and the operation of both is funded through NASA grants NNX09AH71G, NNX09AT02G, NNX10AI27G, and NNX12AE66G, CONACyT grants LN 260369, and UNAM PAPIIT grant IG100414. A portion of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Copyright 2016 California Institute of Technology. All Rights Reserved. US Government Support Acknowledged.


brackets refer to the inverted mass hierarchy. Different columns give the numbers for different detection channels: the superscripts CC and el refer, respectively, to the dominant charged current process (inverse beta decay or absorption on the Ar nucleus) and to neutrino-electron scattering. The subscript β indicates the contribution of the β processes to those two channels. The total number of events is given in the last column. The results for Betelgeuse ( D = 0.2 kpc) can be obtained by rescaling by a factor of 25. Table 4 Same as Table 3 , but for the Progenitor The tables confirm that a large LS like JUNO has the best potential, due to its sensitivity at low energy, with events (depending on the type of progenitor) recorded from a star at D = 1 kpc. For these events, the contribution of the β p is at the level of 10%–30%, and is larger for the inverted mass hierarchy, for which the flux is larger; see Section 4.1 . For Betelgeuse, a spectacular signal of more than 200 events in two hours could be seen. One can define (optimistically) the horizon of the detector, D h , as the distance for which one signal event is expected. We find that JUNO has a horizon . Although it is disadvantaged by the higher energy threshold, SuperKamiokande and DUNE can observe presupernova neutrinos for the closest stars. For the most massive progenitor, SuperKamiokande could reach a horizon and record events for D = 0.2 pc. Of these, ∼10%–20% would be from β p. Looking further into the future, the larger water Cherenkov detector HyperKamiokande (Abe et al. 2011a )—with a mass 20 times the mass of SuperKamiokande—might become a reality. Assuming an identical performance as SuperKamiokande, HyperKamiokande will have a statistics of up to thousands of events, and a horizon of . 10 10 Due to its mass, HyperKamiokande will have a level of background that is ∼20 times higher than that of SuperKamiokande, and probably a higher energy threshold as well. Therefore, its performance will be worse, and the figures given here have to be taken as best case scenarios. At DUNE, a detection is possible only for the closest stars; the number of events varies between and , depending on the parameters, for . For the most optimistic scenario (the more massive progenitor and the inverted mass hierarchy), the horizon can reach . DUNE will observe a strong component due to β p, at the level of of the total signal. Therefore, in principle LAr has the best capability to probe the isotopic evolution of supernova progenitors. 5. Discussion We have presented a new calculation of the total neutrino flux from beta processes in a presupernova star, inclusive of time-dependent emissivities and neutrino energy spectra. This is part of a complete and detailed calculation of presupernova neutrino fluxes from most relevant processes—beta and thermal—done using the state-of-the-art stellar evolution code MESA. The beta neutrino flux is strongest in the channel, where it is comparable to the flux


Acknowledgements M. Maercker acknowledges support from the Swedish 83 Research Council under grant number 2016-03402. M.B. acknowledges funding through the uni:docs fellowship of the University of Vienna, and funding by the Austrian Science Fund FWF under project number P23586. E.D.B. acknowledges funding by the Swedish National Spaceboard. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research.


We acknowledge useful discussion with S. Ettori, G. Brunetti, G. Ghisellini, R. Decarli, E. Torresi, E. Lusso, S. Gallerani. We thank the anonymous referee for his/her useful comments and suggestions that improved the quality of this work. We acknowledge financial contribution from the agreement ASI-INAF n.2017-14-H.O and ASI-INAF I/037/12/0. The scientific results reported in this article are based on observations made by the Chandra X-ray Observatory. This work made use of data taken under the ESO program ID 095.A-0714(A). FC acknowledges funding from the INAF PRIN-SKA 2017 program 1.05.01.88.04.


is hotter. Consequently these two effects increase the significance of the CS. The vertical red dashdotted line indicates the f sky of the Planck Common Field mask. 3.4 The ISW for the coldest spots The ISW contribution to the coldest spots in simulations was measured and is shown in Fig. 5 . Here, we display the mean and 1 contours for all the full-sky minima and the most extreme 3percent (which approximately corresponds to the CSs p-value). The profiles are poorly constrained and very similar, with the more extreme case tending to be slightly more negative. The result illustrates that it is very likely that the ISW plays a minor role in the CS profile: 10percent of the full profile. The reconstructed ISW profiles (Nadathur etal. 2014 ; Rassat etal. 2014 ; Finelli etal. 2016 ; Planck Collaboration XXI 2016c ) appear to be consistent with the predicted ISW shown in Fig. 5 . The presence of prominant voids in the LOS (see Szapudi etal. 2015 ; Kovcs Garca-Bellido 2016 ) are therefore precisely what we would expect from CDM. Figure 5. The 1 contours of the ISW of all the full-sky minima are shown in blue. The most extreme 3percent are indicated by the grey hatched area. The mean for all the coldest spots and the most extreme 3percent are indicated by the blue and grey dashed lines, respectively. The ISW in either case is not very well constrained and consistent with zero but, on average, appears to contribute 10percent to the minimas profiles. 3.5 Dependence on angular scale Up to this point, we have used a preselected angular scale, R 5, where the CS was measured to be most significant by Planck Collaboration XVI ( 2016b ). However, our conclusions for the CS significance may not necessarily hold true for other angular scales. To test this, R is varied between 4 and 7, roughly equaling the range of R over which Zhang Huterer ( 2010 ) found the CS to be significant. The same procedure is carried out as before except with a smaller number of realizations (10000). In Table 1 , we summarize these results. The probability, , is roughly equal to the fraction of unmasked pixels of the effective mask. However, is found to be 0.85 for the angular scales considered. When the cut-sky significance 2 the probability is even higher (0.93). This makes it appropriate to compare the CS to full-sky minima in simulations where it is 2 for 4 R 7. Combined with previous studies (e.g. Vielva etal. 2004 ) means the CS is 2 for all angular scales. Table 1. The probability that the full-sky and cut-sky minima (Cold Spot) are equivalent for any and for the CSs is indicated by and , respectively, for each angular scale R . The significance of the CS is shown in comparison to the cut-sky and full-sky minima in CMB realizations. For each value of R (except R 5, where 100000 realization were previously made) 10000 CMB realizations were simulated. 4 CONCLUSIONS We measure the cut-sky and full-sky minima (Cold Spot) in 100000 simulations using the Planck Common Field mask,


atmospheric dispersion corrector in the fall of 2009, reducing the crosstalk below 1 per cent. Periodic monitoring of the crosstalk confirms that it has remained stable since 2009. However, higher crosstalk levels were likely present (with levels as high as 5 per cent) during the first three semesters of MiMeS LP observations. The absence of any significant impact of crosstalk on most MiMeS observations is confirmed through long-term monitoring of TC targets as standards, and is addressed in Section 4.2 . 5 The crosstalk evolution and mitigation is described in more detail by Barrick, Benedict Sabin ( 2010 ) and Silvester et al. ( 2012 ). Change of the ESPaDOnS CCD: until semester 2011A, ESPaDOnS employed a grade 1 EEV CCD42-90-1-941 detector with 2k × 4.5k 0.0135 mm square pixels (known as EEV1 at CFHT). This was replaced in 2011A with a new deep-depletion E2V CCD42-90-1-B32 detector (named Olapa). Olapa has exquisite cosmetics and much less red fringing than EEV1. Another major difference is that Olapa's quantum efficiency in the red is about twice as high as with EEV1. Commissioning experiments by CFHT staff, as well as within the MiMeS project, were used to confirm that observations acquired before and after the CCD replacement are in excellent agreement. This is discussed further in Section 4.2 . 2.4 Narval 2.4.1 Instrument Narval 6 is a near-twin of ESPaDOnS installed at the 2 m TBL in the French Pyrénées. It is composed of a Cassegrain polarimeter unit similar to that of ESPaDOnS, and a similar spectrograph located in the TBL coudé room. Compared to ESPaDOnS, the instrument was only adapted to the smaller telescope size. Small differences include the diameter of the entrance pinhole of the Cassegrain unit (2.8 arcsec for Narval, versus 1.6 arcsec for ESPaDOnS) and the lack of a fibre agitator. However, the sampling of the (sliced) pinhole image is identical to that of ESPaDOnS. The CCD used at TBL (a back illuminated e2v CCD42-90 with 13.5 μm pixels) differs from that used at CFHT. However, each Narval spectrum also captures 40 spectral orders covering a similar spectral range (370–1050 nm) with the same resolving power of about 65 000. All other technical characteristics of Narval are effectively identical to those of ESPaDOnS described in Section 2.3.1 . 2.4.2 Observations Observations of MiMeS targets with Narval were initiated in 2009 March, and continued until 2013 January. A total of 1213 h were allocated to the MiMeS programme, first in the framework of three single-semester programmes and then as an LP for five additional semesters (all of these observations are hereinafter considered to be ‘LP’ observations). TBL programme identifications associated with MiMeS were prefixed by the letter ‘L’, followed by the year (e.g. ‘12’ for 2012) and the semester (1 or 2 for semesters A and B, respectively), then ‘N’ for Narval, and the ID of the programme itself (e.g. ‘02’ in the case of the LP). The MiMeS Narval runs are thus L091N02,


R.S. acknowledges financial support under the research grant SR/FTP/PS-058/2012 by SERB, DST, and from IISER Bhopal. P.K.A. is supported by the Claude Leon Post-doctoral Fellowship program of the Claude Leon Foundation, South Africa, at UCT. We use the publicly available HEALPix (Górski et al. 2005 ) package available from http://healpix.sourceforge.net to perform forward and backward spherical harmonic transformations and for visualization purposes. We acknowledge the use of the Legacy Archive for Microwave Background Data Analysis (LAMBDA). LAMBDA is a part of the High Energy Astrophysics Science Archive Center (HEASARC). HEASARC/LAMBDA is supported by the Astrophysics Science Division at the NASA Goddard Space Flight Center.


Acknowledgments We thank Stevie Bergman, Jeffrey Filippini, William Jones, Lyman Page and George Efstathiou for useful discussions. SMF was partially supported by Science and Technology Facilities Council (STFC) in the United Kingdom. The Center for Computational Astrophysics is supported by the Simons Foundation. HVP was partially supported by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013) ERC GA no. 306478-CosmicDawn. LV acknowledges support by Spanish Ministerio de Economía, Industria y Competitividad (MINECO) under projects AYA2014-58747-P and MDM-2014-0369 of Institut de Ciències del Cosmos, Universitat de Barcelona (ICCUB) (Unidad de Excelencia Maria de Maeztu). JEG acknowledges support by Katherine Freese through a grant from the Swedish Research Council (Contract No. 638-2013-8993). The work of JE was performed within the LabEx Institut Lagrange de Paris (ILP) (reference ANR-10-LABX-63) part of the Idex Sorbonne Universités à Paris pour l'Enseignement et la Recherche (SUPER) and was supported by the Agence Nationale de la Recherche (ANR), as part of the programme Investissements d'avenir under the reference ANR-11-IDEX-0004-02.


ACKNOWLEDGEMENTS We thank Henrik Latter, Eugene Vasiliev, and Antranik Sefilian for useful conversations. The orbit integration in Section 7 was done using galpy ( http://github.com/jobovy/galpy ; Bovy 2015 ). The N -body calculations were done with mercury (Chambers 1999 ). CH is funded by a Science and Technology Facilities Council (STFC) studentship.


We thank K. Sumiyoshi and T. Yoshida for productive suggestions. This work was supported in part by Grants-in-Aid for Scientific Research of JSPS (26105517, 24340060) of the Ministry of Education, Culture, Sports, Science and Technology of Japan. Work at the University of Notre Dame (GJM) supported by the U.S. Department of Energy under Nuclear Theory Grant DE-FG02-95-ER40934.


have been considered (e.g. Lovell et al. 2014 ; Vogelsberger et al. 2016 ; Iršič et al. 2017b ; Robles et al. 2017 ), among which are the so-called warm dark matter models, where the corresponding particles have non-negligible thermal velocities. In particular, sterile neutrinos with masses of a few keV have been shown to be an interesting candidate for warm dark matter particles (e.g. Boyarsky, Iakubovskyi Ruchayskiy 2012 , and references therein). Moreover, the three sterile neutrino model, also known as the neutrino minimal standard model (νMSM), can also explain baryogenesis processes and neutrino oscillations (Boyarsky et al. 2012 , and references therein). Recently, sterile neutrinos have gained renewed attention following the apparent detections of a 3.5 keV X-ray line in several galaxy clusters as well as M31, and at the centre of the Milky Way (Boyarsky et al. 2014 ; Bulbul et al. 2014 ; Boyarsky et al. 2015 ); for a review see, e.g. Iakubovskyi ( 2014 ). However, sterile neutrino decay as a source of the line remains contentious (e.g. Anderson, Churazov Bregman 2015 ; Gu et al. 2015 ; Jeltema Profumo 2016 ). Recent results from the Hitomi collaboration (Aharonian et al. 2017 ) have ruled out an atomic transition as the origin of the 3.5 keV line, but a dark matter interpretation is still possible due to the different sensitivity of the Hitomi instrument to narrow (atomic) and broad lines. The 11σ detection of the 3.5 keV signal by NuStar (Neronov, Malyshev Eckert 2016 ) from a quiescent region of the sky and subsequent confirmation of this result with Chandra (Cappelluti et al. 2017 ) suggest that the line is not a statistical fluctuation nor an artefact of the XMM–Newton ’s instrumentation. It is therefore important to find complimentary probes of these models as well as supporting future X-ray observatory missions such as XARM and ATHENA that will provide still stronger X-ray constraints. Due to their appreciable thermal velocities at early times, warm dark matter particles free-stream out of density perturbations and are responsible therefore for a cut-off in the linear power spectrum and by extension in the formation of structures at small scales. The exact scale at which the suppression happens is strongly related to the dark matter particle production mechanism. For example, for 7 keV sterile neutrinos with a lepton asymmetry (defined in Section 3 ) much smaller or larger than L 6 = 8 this happens at the scale of progressively more massive dwarf galaxies (Lovell et al. 2016 ), while in a CDM model the suppression happens at much smaller scales. Quantifying the number of small mass structures is therefore an essential approach to distinguish among different dark matter models. For example, Kennedy et al. ( 2014 ) and Lovell et al. ( 2016 ) have used semi-analytic models to compare the abundance of luminous Milky Way satellites predicted by sterile neutrino dark matter models with the observed number of dwarf spheroidals, and


On the other hand, when V 0 = −2 V A (Figure 1 (c)), the forward LH-polarized Alfvén/proton-cyclotron wave at V 0 = 0 becomes backward RH-polarized mode, and the forward RH-polarized fast-magnetosonic/whistler wave with λ p k 1 at V 0 = 0 turns to the backward LH-polarized mode. For the forward fast-magnetosonic/whistler wave with λ p k > 1, its polarization remains the same as that at V 0 = 0. Comparing the ratio of to in the plasma frame to that in the spacecraft frame, we find that this ratio is independent of V 0 . Note that this conclusion can be confirmed by the expressions ( 6 )–( 8 ). Therefore, the correlation coefficient between B ⊥ and V ⊥ () is a useful parameter to identify the direction of the observed wave in the plasma frame, e.g., corresponding to the backward wave and for the forward wave. Then, by combining the information of the polarization and the direction of S ∥ , we can clearly identify the nature of the electromagnetic wave in the spacecraft frame. As shown in Table 1 , Alfvén/proton-cyclotron and fast-magnetosonic/whistler waves have different behaviors in the parameter space of , S ∥ , and polarization. Table 1 Linear Responses of Low-frequency Electromagnetic Waves in the Spacecraft Frame where V 0 > V A ( V 0 − V A ) 3. Application: Two Observation Events Figure 2 shows two events containing both LH- and RH-polarized low-frequency electromagnetic waves in the Earth’s magnetosheath by using Magnetospheric Multiscale ( MMS ) spacecraft observations (Burch et al. 2016 ). The particle moments are measured from the Fast Plasma Investigation (Pollock et al. 2016 ). The electric and magnetic fields are from the Electric Double Probe (Ergun et al. 2016 ; Lindqvist et al. 2016 ) and the FluxGate Magnetometer (Russell et al. 2016 ). These two events have the same distribution, that is, the waves with the ellipticity ∼−1 have frequencies larger than that with the ellipticity ∼1. The former and latter correspond to the LH-polarized and RH-polarized waves, respectively. Note that the ellipticity is defined as the ratio of the intermediate variance value to the maximum variance value in performing minimum variance analysis to the magnetic field. For nearly parallel and antiparallel waves, the definition of the ellipticity is consistent with the polarization defined in the field-aligned coordinates. The parallel Poynting flux is different for these two events, that is, in Event 1 and in Event 2. In these two events, ions have the perpendicular temperature anisotropy , and electrons exhibit the parallel temperature anisotropy . Furthermore, using the bulk ion flow velocity and the magnetic field data in Figure 2 , we find in Event 1 and in Event 2. Figure 2. Two examples of the wave event containing both LH- and RH-polarized low-frequency electromagnetic waves observed by MMS : (I) Event 1 occurred in the time interval of 12:27:00 UT−12:37:00 UT on 2018 October 7, and (II) Event 2 during 08:30:00 UT−09:00:00 UT on 2018 November


keV counts cm −2 s −1 , where the effective area of the XRT and NICER were taken as 110 and 1900 cm 2 , respectively. The optical and UV magnitudes have been dereddened. The first clear detection of SAX J1808.4−3658 with NICER during the rise was made on August 6, 22:00 utc , when the net (background-subtracted) count rate was 9.16 ± 0.16 counts s −1 . We fit the spectrum of that GTI with an absorbed power law ( tbnew × pegpwrlw in xspec ), with the photoionization cross-section set to vern and the abundances set to wilm . Our best fit (χ 2 = 88 for 108 degrees of freedom) yields an N H of 0.33 ± 0.03 atoms cm −2 , a power-law photon index of 2.43 ± 0.07, and an unabsorbed 0.5–10 keV flux of (2.91 ± 0.11) × 10 −11 erg cm −2 s −1 . A potential detection was made one GTI earlier, on August 5 22:46 utc , when the net count rate (1.44 ± 0.06 counts s −1 ) was higher than during all observations preceding it (maximum of 0.85 counts s −1 ). However, comparison to contemporaneous background measurements indicated that the flux was still background dominated, and there was no statistically significant detection. Between July 30 and August 5, the net count rates varied between −0.22 and 1.44 counts s −1 , with an average of 0.402 ± 0.003 counts s −1 . This rate is significantly above zero, possibly due to contaminating sources in NICER’s ∼30 arcmin 2 field of view. The variations in the pre-outburst net count rate are likely due to limitations of the background model (which is still being refined). The 1σ standard deviation in the net count rate before the first detection is 0.32 counts s −1 . We consider the average pre-outburst net count rate plus three times the 1σ standard deviation, 1.37 counts s −1 , to be a good general estimate of the NICER detection limit of SAX J1808.4−3658. Using the best-fitting model to the spectrum of the first detection, this count rate corresponds to an unabsorbed 0.5–10 keV flux of ∼4 × 10 −12 erg cm −2 s −1 . 3 RESULTS The multiwavelength light curve of the 2019 rise to outburst of SAX J1808.4−3658 is shown in Fig. 1 . Activity of the source was first noticed in the optical i ′ -band observations, beginning July 25 (MJD 58689), as the i ′ -band magnitude fluctuated between 0.7 and 1.0 mag brighter than the quiescent level of approximately 19.8 mag. Then, between August 3.85 and 5.98 (MJD 58698.85 and 58700.98), the i ′ -band intensity increased to approximately 2 mag brighter than the quiescent level, and continued to increase over the coming days as the outburst progressed. The peak of the 2019 outburst was reached at around August 10 (MJD 58705) in optical, and August 14 (MJD 58709) in X-ray (Fig. 1 ). The first Swift /UVOT detection occurred on August 4 (MJD 58699.71), 10 d after the first sign of increased optical activity in the source, and 2 d before the first X-ray detection. The UV magnitude in all bands rose sharply by approximately 3 mag over the next 4 d until the outburst peak was reached. We note that


ACKNOWLEDGEMENTS AKB, TD, and ADM were supported by the National Science Foundation through grant number 1616168. TDM acknowledges funding from NSF ACI-1614853, NSF AST-1517593, NASA ATP NNX17AK56G, and NASA ATP 17-0123. The BLUETIDES simulation was run on the BlueWaters facility at the National Center for Supercomputing Applications. ADM also acknowledges support through the U.S. Department of Energy, Office of Science, High Energy Physics, under Award Number DE-SC0019022.


the NSs and BHs form according to the ‘rapid’ and ‘delayed’ SN models of Fryer et al. ( 2012 ) and pulsation pair-instability SN (PPSN) and pair-instability SN (PSN) models of Belczynski et al. ( 2016b ). The NSs and BHs receive natal kicks based on SN fallback on to them, as in Belczynski et al. ( 2008 ). Such fallback slows down the remnants, causing BHs of ≳10 M ⊙ ( Paper I ) to retain in the clusters right after their birth. Furthermore, NSs formed via electron-capture SN (Podsiadlowski et al. 2004 ) also receive small natal kicks and retain in the clusters. In , the PN treatment is handled by the algorithm (Mikkola Tanikawa 1999 ; Mikkola Merritt 2008 ). Such a PN treatment allows for GR evolution of the innermost NS- and/or BH-containing binary of an in-cluster (i.e. gravitationally bound to the cluster) triple or higher order compact subsystem, in tandem with the Newtonian-dynamical evolution of the subsystem (Kozai–Lidov oscillation or chaotic three-body interaction), potentially leading to the binary’s (in-cluster) GR in-spiral and merger. The treatment also undertakes the GR evolution of in-cluster NS/BH-containing binaries that are not a part of a higher order subsystem. As discussed in Paper II (see also the references therein), the moderate density and velocity dispersion in the model clusters make them efficient factories of dynamically assembling PN subsystems, particularly, those comprising BHs. As also discussed in Paper II (see also Anagnostou, Trenti Melatos 2020 ), the vast majority of the GR mergers from these computed model clusters are in-cluster BBH mergers. As also demonstrated therein (see also Banerjee 2020 ), the final in-spiralling phases of such merging BBHs sweep through the LISA and deci-Hertz GW frequency bands before merging in the LVK band. 2.2 Calculation of intrinsic merger rate density from computed model clusters To obtain the intrinsic merger rate density at the present cosmic epoch, ‘mock detection experiments’ are performed in a ‘Model Universe’ that is constructed out of the computed model clusters of Paper II . An ideal GW detector (a detector with zero noise floor) is considered that can detect GW arriving from all GR mergers within a comoving volume whose boundary is at a redshift z max . z max represents an (artificial) horizon, for average source inclination, for a realistic GW detector like LVK. For LVC O1/O2 observing runs, z max ≈ 1 (Abbott et al. 2019b ; Chen et al. 2021 ). However, since an ideal GW detector is considered here, z max will be varied to also address future, ground-based GW detectors of > Hz frequency band (e.g. LIGO A+ upgrade, Einstein Telescope, Cosmic Explorer). If the GR compact-binary merger rate density (per unit comoving volume), , in the Universe has an inherent dependence on the merger event redshift, z event , then the present-day, intrinsic merger rate density, , would depend on the detector horizon, z max (Abadie et al. 2010 ; Abbott et al. 2019b ). Conversely,


estimates of the relative abundances of the COMs detected in L1157-B1. With this goal in mind, we obtained their excitation conditions and column density carrying out a simple rotational diagram analysis. We computed the molecular abundances with respect to H 2 by using the whole CO column density in the B1 direction, as estimated by Lefloch etal. ( 2012 ), N (CO) = and assuming a standard relative CO abundance X (CO) = N (CO)/ N (H 2 ) = 10 4 . The obtained temperatures, column densities and abundances are summarized in Table 1 . The rotational diagrams of the newly detected COMs are shown in Fig. 2 . For all the detected species, the line emission can be reasonably well fitted by one single gas component whose rotational temperature is around 2030K (Table 1 ). Glycolaldehyde has a slightly larger rotational temperature (50K) but also a larger error bar (10K). Considering only the lines observed with E up , a lower rotational temperature () would provide a very good fitting. Figure 2. Rotational diagrams of COMs detected towards L1157-B1. From the top to bottom: C 2 H 5 OH, H 2 CCO, HCOCH 2 OH and CH 3 OCH 3 . The best fit is drawn by the red line. For HCOCH 2 OH, the fit to the transitions with is drawn by the blue dashed line. Column densities vary from 210 12 cm 2 (formamide) to 110 15 cm 2 (methanol). Incidentally, the column density of the other COMs shows a relatively small range of variation, from 2 to 510 13 cm 2 , namely less than a factor of 3. In other words, with the exception of formamide, which is significantly less abundant, the other detected COMs have abundances a factor of 2050 lower than methanol. We searched for acetic acid (CH 3 COOH), the other isomer of glycolaldehyde and methyl formate, using the frequencies given in Ilyushin, Kleiner Lovas ( 2008 ). We failed to detect it. We could place an upper limit on its abundance, adopting T rot similar to that of methyl formate () and glycolaldehyde (), and the partition function given by Calcutt etal. (in preparation). We obtain a 3 upper limit of 510 9 at (10 8 at ): Acetic acid is less abundant by a factor of 35 with respect to methyl formate. 3.3 Comparison with hot corinos When compared to other solar-like star-forming regions where these COMs were previously detected (IRAS 162932422: Jaber etal. 2014 ; Coutens etal. 2015 ; Jrgensen etal. 2016 ; IRAS 2A and IRAS 4A: Taquet etal. 2015 ), L1157-B1 appears to be a site were COMs are very efficiently produced. The molecular abundances are higher in the shock than in the hot corinos by a factor of 210, depending on the species. The dimethyl ether abundance measured in IRAS 162932422 is the only exception. We note that only relatively small COMs are detected in the shock region, whereas larger COMs, for example, propanone (CH 3 COCH 3 ) and ethylene glycol [(CH 2 OH) 2 ], are detected in the inner protostellar environments (e.g. Maury etal. 2014 ; Jrgensen etal. 2016 ) but not yet in the shock. More observational data from


of the angular velocity of the black hole. This could lead to quasi-periodic signatures in the observed radiation, especially if the radiation mechanism at the current sheet is to some extent beamed. However, in an actual accretion disc the flux tubes may be much more irregular and variable, leading to more stochasticity in the observed signal. The main dissipation mechanism is likely to be reconnection at these current sheets. Since the environment around the black hole is filled by soft optical/UV photons from the accretion disc, particles accelerated in the reconnection process will be quickly cooled through inverse Compton scatterings and may also initiate pair cascades on the soft photons. The reconnection outflow may be strongly Compton dragged, and the reconnection proceeds in a regime as described by Beloborodov ( 2017 ) where the spectrum is dominated by bulk Comptonization from the reconnection outflow and has a high-energy cut-off near the pair production threshold. To get an estimation of the dissipation rate in realistic systems, let us consider a typical Seyfert galaxy, MCG-6-30-15, which has a supermassive black hole of mass M = 2 × 10 6 M ⊙ , bolometric luminosity , and X-ray luminosity (Reynolds et al. 1997 ). Measuring B in terms of the characteristic field G (Phinney 1983 ), we get If the magnetic field near the black hole reaches a value B ∼ B Edd , then the amount of power dissipated due to the kink instability will be enough to account for all the X-ray emission. The kind of relatively strong magnetic field with small coherence length-scales may be a generic outcome of MRI in the accretion disc, or associated with hotspots on the accretion disc as the flux tubes emerge buoyantly similar to solar magnetosphere. As we have seen from GRMHD simulations, when the accretion disc is threaded by a strong net magnetic flux, it tends to become magnetically arrested and form a powerful jet (McKinney Blandford 2009 ; Avara, McKinney Reynolds 2016 ); however, in situations where the net magnetic flux is small, the disc magnetic field may be dominated by the locally amplified components. MRI and the subsequent buoyancy or gravitational instabilities can produce fields with alternating polarities (e.g. Davis, Stone Pessah 2010 ; Zhu Stone 2018 ); they may also be quite asymmetric. As these structures are fed to the black hole, we may see continuous flux tube inflation and kink instability happening on a much more stochastic basis. We have seen from the simulations that the kink instability tends to happen when the confinement from the surrounding field is strong; this confinement can be produced by magnetic field from currents in the outer disc, or a disc wind that exerts pressure towards the central axis region. We have so far approximated the black hole as a resistive membrane in a flat space–time model. Whether the other GR effects are important will be tested using a time-dependent, GR force-free code in the future. Meanwhile,


by allowing toroidal fields of opposite sign to exist on the two sides of the equator more easily. One way of capturing the physics of this in a 3D non-axisymmetric model may be to include a quenching of turbulent diffusivity in the regions of strong magnetic field. We plan to explore the effect of this in future. With these two opposite results at hand, one crucial question is: which of the two results is closer to reality? Although we cannot assert this with confidence at this stage, we believe that the 2D kinematic dynamo result that the dipolar parity is preferred on increasing diffusivity is the more appropriate result. Although in this paper we are taking account of the 3D nature of magnetic buoyancy and, in that sense, treating magnetic buoyancy more realistically, we still have not taken account of flux depletion from the convection zone and its bottom in an appropriate way. This is probably one important reason why our results are not matching with the results of previous 2D models (Chatterjee et al. 2004 ; Hotta Yokoyama 2010 ). We are right now exploring possible schemes to take account of the flux depletion due to magnetic buoyancy in a realistic way. We believe that this flux depletion is quite important in the solar dynamo. Choudhuri Hazra ( 2016 ) found that the Waldmeier effect cannot be reproduced from a theoretical dynamo model unless the flux depletion is taken into account. We have a future plan of incorporating flux loss due to magnetic buoyancy in a realistic way in our 3D kinematic dynamo model and then studying the parity issue more carefully. Since we are interested in a dynamo solution which has dipolar parity, we have converged on the reference solution presented here. If we decrease diffusivity, then we are led to the case where the meridional circulation provides the main transport mechanism for the poloidal field. On the other hand, if we increase diffusivity, then we obtain the quadrupolar mode. This is what has led us to choose the value 5 × 10 11 cm 2 s −1 for diffusivity inside the convection zone. 4. THE BUILD-UP OF THE POLAR FIELD After constructing the self-excited dynamo model, we now study how individual sunspot pairs contribute to the building up of the polar field and address the question of whether our understanding gained from this study necessitates the revision of some insights we have from SFT models. For this study, we shall put individual sunspot pairs on the solar surface by hand and look at the evolution of the magnetic field. In other words, we shall now not try to construct self-excited periodic solutions, although we shall keep using the same values of different parameters that we had used for constructing the self-excited periodic solution. We start our simulation by putting a single pair of bipolar sunspots in the northern hemisphere at different emergence angles λ emg and let it evolve under the axisymmetric mean flows and diffusion to see the development of the polar field. We have


We thank Bill Harris and Eric Pelligrini for interesting discussions. C.S.H. and R.E.P. thank the the Zentrum für Astronomie der Universität Heidelberg (ZAH), Institut für Theoretische Astrophysik (ITA), and the Max-Planck-Institut für Astronomie (MPIA), for their generous support during R.E.P.’s sabbatical leave (2015/16) and C.S.H.'s extended visit (2015 October–November). R.E.P. is supported by Discovery Grants from the Natural Sciences and Engineering Research Council (NSERC) of Canada. C.S.H. acknowledges financial support provided by the Natural Sciences and Engineering Research Council (NSERC) through a postgraduate scholarship. The FLASH code was in part developed by the DOE-supported Alliances Center for Astrophysical Thermonuclear Flashes (ASCI) at the University of Chicago. This work was made possible by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca ) and Compute/Calcul Canada. R.S.K. acknowledges support from the European Research Council under the European Communitys Seventh Framework Programme (FP7/2007-2013) via the ERC Advanced Grant “STARLIGHT: Formation of the First Stars” (project no. 339177). R.S.K. further acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG) in the Collaborative Research Center SFB 881 The Milky Way System (subprojects B1, B2, and B8) and in the Priority Program SPP 1573 Physics of the Interstellar Medium (grant nos. KL 1358/18.1 and KL 1358/19.2).


The research leading to these results has received funding from the European Community’s Seventh Framework Program (FP7/2007-2013) under grant agreement No. 269194 and also under the auspices of the US DoE by Los Alamos National Laboratory under Contract No. DE-AC52-06NA25396


field is obtained using the cloud-in-cells particle assignment scheme (CIC; e.g. Hockney Eastwood 1981 ). We introduce some scatter before the mapping procedure in a way that is different from the original EZmock paper. The new scattering formula is where and are the ZA density field after and before the scattering, respectively, and G (λ) is a random number drawn from the Gaussian distribution with width λ. The exponential function, exp[ G (λ)], is used to avoid negative densities. The term including ρ a introduces a non-linear mapping, which converges to 1 when is large. It plays a similar role as the density saturation parameter in the original EZmock paper. Furthermore, we apply a density cut ρ c (called the density threshold in Chuang et al. 2015a ) on the ZA density field before applying the scatter formula to make sure that there is no object produced in a low-density region. The mock catalogue is then populated by randomly picking a fraction of the original ZA particles in each cell, which is equal to the number of haloes attributed to it by the PDF mapping procedure described above. Finally, we assign the velocity by multiplying the displacement field by a factor f , and combining it with a three-dimensional Gaussian random motion with width σ . The factor f is computed by where f () and H () are the growth rate and Hubble parameter, respectively, at the redshift of the simulation box. Thus, we have five effective bias parameters ( n d , A , λ, ρ c and σ ) to adjust EZmocks. In contrast to Chuang et al. ( 2015a ), we do not modify the input power spectrum for calibration because we want to observe the impact of different cosmological models, as explained in the next section. 2.2 Simulation set-up We use the same input power spectrum as the one used by Chuang et al. ( 2015a ), that is ΛCDM Planck cosmology with {Ω M = 0.307115, Ω b = 0.048206, σ 8 = 0.8288, n s = 0.96}, and a Hubble constant () given by h = 0.6777. The output galaxy catalogues are at redshift = 0.5618. Again, following Chuang et al. ( 2015a ), we calibrate the number density to 3.5 × 10 −4 , which is similar to that of the SDSS-III/Baryon Oscillation Spectroscopic Survey (BOSS) galaxy sample at ∼ 0.5 (Reid et al. 2016 ). The simulation boxes are 2.5 h −1 Gpc each side. The particle mesh used is 960 3 . We construct three sets of 3000 EZmock simulation boxes with three sets of parameters. These three sets of boxes share the same 3000 Gaussian random fields. Table 1 lists the values of these parameters. The first set is the reference, whose clustering statistics are similar to those in Chuang et al. ( 2015a ) but not the same, because the EZmock methodology we are using here is slightly different, as mentioned above. Table 1. The three sets of EZmock parameters used in this study. We construct 3000 EZmock simulation boxes for each set of parameters (i.e. 9000 boxes in total). The second set of 3000 EZmocks has a different amplitude in the input power spectrum (i.e.


Thanks are given to the anonymous referee for the critical comments that motivated new calculations that reinforced the robustness of the analysis to be carried out. This work has been supported by the grant AYA2015-66506-P from the Spanish Ministry of Economy and Competitiveness (MINECO).


sources HD 100546 and TW Hya. 2. For both disks, we fitted fully parametric physical models to the spectral energy distribution, spatially resolved CO emission, and a range of far-infrared emission lines. The most important difference from past physical models is that spatially resolved CO line emission is also reproduced. 3. We confirm earlier findings that the HD 100546 disk atmosphere has either an interstellar gas-phase carbon abundance ([C]/[H] , if = 10) or is moderately depleted ([C]/[H], if = 100); and that the TW Hya disk atmosphere is two orders of magnitude deficient in gas-phase volatiles ([C]/[H], [O]/[H], and ). 4. Upper limits on HD emission for HD 100546 put an upper limit of on the gas to dust ratio in the disk. 5. The star HD 100546 has solar-like photospheric abundances of volatile elements (C, N, O) but a strong depletion of rock-forming elements (e.g. Fe, Mg, Si). 6. Most carbon atoms in the HD 100546 disk gas have not passed through a hydrocarbon more complex than CH during the lifetime of the system. 7. An underabundance of volatile elements in the disk atmosphere can develop when gas is mixed through the midplane and the temperature there allows freeze-out of major carrier species onto settled grains. An analytical formulation of this process is presented in Sect. 6.2 . 8. The volatiles return to the gas in the inner disk, as evidenced by their enhanced abundance relative to refractory elements in the gas accreting onto both stars. Appendix A: HD 100546 photospheric abundances Table A.1 Photospheric abundances derived for HD 100546. Notes. Number of lines used in the abundance fitting is shown in Col. 4. Asterisks denote very uncertain results. Solar abundances are from Asplund et al. (2009) . The photospheric properties of HD 100546 were derived through directly fitting synthetic spectra to the optical spectrum of the star. This process gives a measurements of , , , and chemical abundances. The synthetic spectra were calculated using the Z eeman spectrum synthesis program ( Landstreet 1988 ; Wade et al. 2001 ), using model stellar atmospheres from ATLAS9 ( Kurucz 1993 ), and atomic data from the Vienna Atomic Line Data Base (VALD; Kupka et al. 1999 ). The best-fit stellar parameters were derived using and automatic minimization routine, in which all parameters were fitted simultaneously ( Folsom et al. 2012 ). This fitting process was performed on none independent spectral windows (416427, 440460, 460480, 490510, 510520, 520540, 540560, 620650, and 710750 nm). The final best fit parameters are the averages of the individual windows, and the uncertainties are the standard deviations. In cases where a chemical abundance for an element was derived in only three or fewer windows, the uncertainties were increased to account for the full window-to-window variation, as well as any potential uncertainties due to line blending and continuum normalization. Great care was taken to avoid any lines contaminated by emission. Weak


We thank the Ulysses project at ESA and NASA/JPL for effective and successful mission operations and an anonymous referee for improving the presentation of our results. H.K. is also grateful to Klaus Hornung for valuable discussions during the preparation of this manuscript. This research was supported by the German Bundesministerium für Bildung und Forschung through Deutsches Zentrum für Luft-und Raumfahrt e.V. (DLR, grant 50 QN 9107). H.K. and P.S. gratefully acknowledge support by MPI für Sonnensystemforschung. P.S. acknowledges support by Deutsche Forschungsgemeinschaft (DFG) grant KR 3621/1-1. Facility: Ulysses .


the 2MASS All Sky Data Release. 4 The zero-points with their standard errors are taken from Maíz Apellániz Pantaleoni González ( 2018 ). For the WISE photometry, we calculate synthetic magnitudes on the AB magnitude scale and then apply the corrections to Vega magnitudes from Jarrett et al. ( 2011 ) for which the estimated error is 1.45 per cent (0.016 mag). For the IUE NEWSIPS spectra, we adopted a zero-point error of 4 per cent from Nichols Linsky ( 1996 ). 3.3 Interstellar reddening Fig. 3 shows a selection of HARPS spectra of AI Phe obtained from the ESO Science Archive Facility in the region of the Na i doublet. There are no detectable interstellar absorption lines in this region and so the reddening towards AI Phe must be very close E ( B − V ) = 0 (Karataş Schuster 2010 ). Accordingly, we set a Gaussian prior on the reddening E ( B − V ) = 0 ± 0.005 and set a lower limit E ( B − V ) ≥ 0. Figure 3. HARPS spectra of AI Phe in the region of the Na i doublet. The spectra have been normalized and vertically offset for clarity. The rest wavelengths of the Na i lines are indicated by vertical dashed lines. 3.4 Priors on infrared flux ratios The near-infrared (NIR) flux from solar-type stars compared to the total flux or the flux at optical wavelengths shows a well-defined relationship with T eff that is almost linear and that has little dependence on metallicity or surface gravity. This is the basis of the infrared flux method (Blackwell, Shallis Selby 1979 ; Casagrande et al. 2010 ) or the use of colours such as () to estimate effective temperatures for FGK-type stars (Boyajian et al. 2013 ). There is only one direct measurements of the flux ratio for AI Phe at wavelengths longer than 1 μm. We were concerned that not imposing any additional constraints on the flux ratio at these wavelengths could mean that the resulting models become unrealistic, e.g. the () colours computed using samples from P ( M | D ) might show a much large scatter than is observed in real stars. This could happen if the use of distortion coefficients allows for models where the flux from one star is unrealistically high at NIR wavelengths while the other is too low, unless some constraint is placed on the flux ratio at these wavelengths. We address this concern by making the assumption that the stars in AI Phe behave similarly to other dwarf and sub-giant FGK-type stars in the solar neighbourhood in order to put some constraint on the flux ratio in the 2MASS J, H , and K s bands, and the WISE W 1, W 2, W 3, and W 4 bands. We use stars from the Geneva–Copenhagen survey (Holmberg et al. 2009 ) to define relationships between T eff and ( V − J ), ( V − H ), etc. The values of T eff , E ( B − V ), log g , and [Fe/H] for each star are taken from Casagrande et al. ( 2011 ). We define separate relations for the F7 V star and the K0 IV star based on a different subsample of stars with similar properties to each. For the F7 V star, the subsample is defined by the following


It is a pleasure to thank G. Bodo, M. Chandra, S. Davis, J. Dolence, J. Goodman, G. Lesur, V. Paschalidis, J. Simon, and J. Stone for useful discussions. We also thank the anonymous referee for a very useful report. The numerical calculations presented here were performed on the Blue Waters supercomputer at NCSA. B.R.R. was supported by an Illinois Distinguished Fellowship and by NSF grant AST-1333612. Work at Los Alamos National Laboratory was done under the auspices of the National Nuclear Security Administration of the US Department of Energy. C.F.G.’s work was also supported in part by a Romano Professorial Scholar appointment, a Simons Fellowship in Theoretical Physics, and a Visiting Fellowship at All Souls College, Oxford.


We thank the referee for their insightful suggestions, which led to improvements in the presentation of the paper. IR also thanks F. Cattaneo for his important and informative discussions about magnetic field generation during WD smoldering. This work was supported by NSF Grant AST-0709181 and by NSF Graduate Research Fellowship Grant DGE-1144082.


from the Kerr solution is found, this approach is not suitable to measure the deviation from the Kerr geometry and it is necessary to use a different method. Among the many proposed parametric black-hole spacetimes in literature, here we use the Johannsen metric (Johannsen 2013 ). In its simplest form, which is the version employed in our work, the Johannsen metric has only one deformation parameter, named α 13 (see Johannsen 2013 for the origin of this parameter). In Boyer–Lindquist coordinates, the line element reads (we use units in which G N = c = 1)where , , , andFor α 13 = 0, we recover the Kerr metric. In order to have a regular exterior region, we have to impose (as in the Kerr metric, for there is no event horizon and the central singularity is naked) and the following restriction on α 13 (Tripathi et al. 2018 ) Note that α 13 enters the metric coefficients g tt , g tφ , and g φφ . It thus affects the structure of the accretion disk, modifying the Keplerian gas motion and moving the ISCO radius. Qualitatively speaking, α 13 > 0 ( 0) increases (decreases) the strength of the gravitational force, so it moves the ISCO radius to higher (lower) values and this explains the strong correlation with the spin parameter in the plots that will be presented in the next sections. 3. Data Reduction and Analysis LMC X-1 was discovered in 1969 as the first extragalactic X-ray binary (Mark et al. 1969 ; Price et al. 1971 ). The system consists of a stellar-mass black hole and an O-giant companion star. The distance of the source, the black-hole mass, and the inclination angle of the orbit, which are three key-quantities in the continuum-fitting method, have been estimated to be D = 48.10 ± 2.22 kpc, M = 10.91 ± 1.54 M ⊙ , and i = 3638 ± 202, respectively (Gou et al. 2009 ; Orosz et al. 2009 ). LMC X-1 is characterized by a quite stable bolometric luminosity, which is about 16% of its Eddington luminosity L Edd (Gou et al. 2009 ) and thus nicely meets the standard criterion required to use the continuum-fitting method: sources with an accretion luminosity in the range 5%–30% L Edd (McClintock et al. 2014 ). The measurement of the spin parameter of the black hole in LMC X-1 using the continuum-fitting method was presented in Gou et al. ( 2009 ), and here we follow that study. There are 55 pointed observations of LMC X-1 with the Proportional Counter Array (PCA) on board RXTE (Swank 1999 ). To use the continuum-fitting method, it is desirable to choose thermal dominant spectral data, which are defined by three conditions (Remillard McClintock 2006 ): (i) the flux of the thermal component accounts for more than 75% of the total 2–20 keV unabsorbed flux, (ii) the root mean square (rms) variability in the power density spectrum in the 0.1–10 Hz range is lower than 0.075, and (iii) quasi-periodic oscillations are absent or very weak. Imposing these three conditions, we only have three observations, which are named “gold spectra” in Gou et al. ( 2009 ) as


The authors are grateful for useful suggestions from the CBC group of the LIGO Scientific–Virgo Collaboration. In particular, we are grateful to Walter Del Pozzo for useful advice, Vivien Raymond for helpful discussions, Simon Stevenson for careful reading, and Neil Cornish for beneficial comments. B.F. was supported by the Enrico Fermi Institute at the University of Chicago as a McCormick Fellow. This work was supported in part by the Science and Technology Facilities Council. P.B.G. acknowledges NASA grant NNX12AN10G. S.V. acknowledges the support of the National Science Foundation and the LIGO Laboratory. J.V. was supported by STFC grant ST/K005014/1. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative agreement PHY-0757058. This work used computing resources at CIERA funded by NSF PHY-1126812, as well as the computing facilities of the LIGO Data Grid including: the Nemo computing cluster at the Center for Gravitation and Cosmology at the University of Wisconsin–Milwaukee under NSF Grants PHY-0923409 and PHY-0600953; the Atlas computing cluster at the Albert Einstein Institute, Hannover; the LIGO computing clusters at Caltech, and the facilities of the Advanced Research Computing @ Cardiff (ARCCA) Cluster at Cardiff University. Some results were produced using the post-processing tools of the plotutils library at http://github.com/farr/plotutils and skyarea library at https://github.com/farr/skyarea . This paper has been assigned LIGO document reference LIGO-P1500117.


energy. Figure 7. Drift along time of the CoM of the bound core with respect to the CoM of the whole system, for one of the simulations of set 1A. The Lagrangian radii are evaluated in percentage of the total mass of the cluster and so all the bodies, including possibly escaping IMBHs and growing (in mass) objects, are taken into account. Of course, the escaping IMBHs lead to a natural increase of the high-percentage Lagrangian radii so that in Fig. 5 we display only up to the 50 percent Lagrangian radius ( R 1/2 ). Note that growing massive objects, although they remain inside the bound core of the system and close to its CoM, do not a priori coincide with this CoM (see Fig. 8 that shows, also, how the growing BH movement is since the beginning well within the half-mass radius) and could, consequently, induce sharp variations in the latter evolution of the low-percentage Lagrangian radii. Figure 8. Black line gives the distance ( r smo ) of the growing supermassive object to the CoM of the bound ‘core’ of the system, for one arbitrarily chosen simulation of set 1A, taken as example. For comparison, we also give in purple the half-mass radius of the system. The curves are plotted starting from the time when the SMBH ‘seed’ is already grown by 42 IMBH mergers. The late time evolution of the average half-mass radius R 1/2 (50 percent Lagrangian radius), which is a good definition of the system radial scale, is well fitted by a linear relation: For set 1 and t > 15 yr, the values of the parameters are leading to a root mean square error of the fit equal to 0.8 mpc for R 1/2 . For set 2 and t > 500 yr, the values are giving a root mean square error equal to 5 mpc. The average half-mass radius at the final simulation time for set 1 is equal to 24 mpc (37 mpc for set 2), that is 50 times (8 times for set 2) the initial half-mass radius, R 1/2 (0) = 2 −1/3 R 0 . Even with our original safety margin of one order of magnitude for the initial cluster radial size, at the end of the simulation the core of the cluster extends much farther out than the area allowed for our purpose of mimicking the presence of an SMBH by a dense cluster of IMBHs. This is true for every simulation of set 1 and not only on average. This is even more the case for set 2, where no safety margin was taken. This result is not surprising: We actually expected that the extreme conditions required (a stable system of IMBHs of total mass and maximal size ≤5 mpc) were very unlikely to be reached. As we see in the next section, in both set 1 and set 2 the IMBH cluster undergoes various merger episodes. This has relevant consequences, whose main result is the formation of a very massive BH as coming out from the dominant object growing up after successive merger events. Hence, we conclude that, as expected on basic theoretical understanding, the answer to the first of the issues we raised in introduction is negative: A cluster of IMBHs dense enough to mimic the dynamical role of an


Acknowledgements The authors thank the referee for their comments that allowed us to improve the article. D. Hypolite and S. Mathis acknowledge funding by the European Research Council through ERC SPIRE grant 647383 and the CNES PLATO grant at CEA Saclay. The authors also wish to thank the MESA website. M. Rieutord acknowledges funding by the French Agence Nationale de la Recherche (ANR), under grant ESRR (ANR-16-CE31-0007-01). This work was also supported by the Centre National de la Recherche Scientifique (CNRS, UMR 5277), through the Programme National de Physique Stellaire (PNPS), and by the CNES PLATO grant at IRAP.


The authors wish to thank the anonymous referee, whose helpful comments greatly improved the quality of this manuscript. This work was supported by Kepler Cycle 2 GO grant NNX11AB71G and Cycle 3 GO grant NNX12AC79G and NSF grant AST13-11678. We are pleased to thank John Gizis for advance use of his GO program’s long-cadence data on GJ 1243. We thank John P. Wisniewski and Adam F. Kowalski for continuing discussions on stellar activity, and Rakesh Yadav for very useful comments on an early draft of this manuscript. J.R.A. Davenport thanks Morgan Fouesneau for help with Python code. Kepler was competitively selected as the tenth Discovery mission. Funding for this mission is provided by NASA’s Science Mission Directorate.


We thank the anonymous referee for their thoughtful comments and useful suggestions. This material is based upon work supported by the National Science Foundation under grant No. 1716260. Additional support for this work was provided by a NASA Keck PI Data Award, administered by the NASA Exoplanet Science Institutem and by the Department of Astronomy and the Institute for Astrophysical Research at Boston University. A.W.M. is supported through Hubble Fellowship grant 51364 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. F.A. received funding from the French “Programme National de Physique Stellaire” (PNPS) and the “Programme National de Planetologie” of CNRS (INSU). D.H. is supported by the Collaborative Research Centre SFB 881 “The Milky Way System” (subproject A4) of the German Research Foundation (DFG). This research made use of the Massachusetts Green High Performance Computing Center in Holyoke, MA. The data presented herein were obtained at the W.M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W.M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.


We would like to thank Carlos de Breuck and Theresa Nilsson for their support for the observations. This publication is based on data acquired with the Atacama Pathfinder EXperiment (APEX) which is a collaboration between the Max-Planck-Institut für Radioastronomie, ESO, and the Onsala Space Observatory. We acknowledge the use of the GILDAS software for the data reduction. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Advanced Grant RADIOLIFE-320745.


We acknowledge the comments received from an anonymous referee, which helped to improve the quality of this paper. GALC acknowledges the support of an STFC PhD studentship. This research utilised Queen Mary's MidPlus computational facilities, supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1.


The authors acknowledge the continuous and fruitful interactions with Jorma Harju, and an anonymous referee for helping improve the manuscript. S.K. acknowledges support from Xueying Tang and an NRAO Student Observing Support grant. J.C.T. acknowledges support from the University of Florida Research Opportunity Seed Fund and the Florida Space Inst. V.W. acknowldeges funding by the French INSU/CNRS program PCMI, the Observatoire Aquitain des Sciences de l’Univers and the European Research Council (ERC Grant 336474: 3DICE).


ACKNOWLEDGEMENTS We thank Robert Izzard and Conor Byrne for helpful discussions. The Armagh Observatory and Planetarium is funded by direct grant from the Northern Ireland Dept for Communities. The authors acknowledge support from the UK Science and Technology Facilities Council (STFC) Grant No. ST/M000834/1.


hot Jupiters from Kepler , coupled with the complex targeted nature of the survey (cf. the untargeted surveys used in our sample), means that these results need to be approached with some caution. Our result () confirms the giant planetmetallicity correlation seen in previous studies, but suggests that it is marginally weaker for hot Jupiters than it is for the longer period giant planets such as in the survey outlined above. We summarize our result and the previous results in Table 1 . Table 1. values. Our value is lower than all previously published results in Table 1 . We are within 1 of the result of Johnson etal. ( 2010 ); however, if we accounted for the mass dependency in our calculation as they have, we would expect our value for to decrease further (though not significantly so, as the stellar masses in our sample have a range of only 1M ). We have 2.32 and 2.31 from Valenti Fischer ( 2005 ) and Schlaufman ( 2014 ), the two other results from RV surveys, respectively. While these hint at a difference in the strength of the correlation between cool and hot Jupiters, we have 1.55 from Guo etal. ( 2017 ) and 2.75 from Petigura etal. ( 2018 ), the two hot Jupiter specific studies. Though again it should be noted that both hot Jupiter studies have a very small sample size. 5.2 Potential biases The metallicity offset ( c ) is needed to calibrate between the metallicities in Besanon and SWEET-Cat, but adds an extra degree of uncertainty compared with a survey that has a uniformly determined set of metallicities for both hot Jupiter hosts and field stars. However, the metallicity offset appears fairly well constrained from the sample distribution in Fig. 4 , and is relatively small in comparison to the overall spread of metallicities (cf. Fig. 3 ). The metallicity offset does correlate with (see Fig. 4 ), which results in a relatively large and slightly asymmetric uncertainty on . There is also a correlation between the radius of a star and its metallicity: the increase in opacity with the presence of metals results in the star having a larger radius. As transit depth decreases with the square of a stars radius, planets would be more difficult to detect around higher metallicity stars via the transit method. This would act to decrease our value of , but it has been found by Petigura etal. ( 2018 ) that planet detectability does not significantly depend on stellar metallicity. RV surveys will preferentially find planets around metal-rich stars as it is easier to perform the method when there are stronger and/or more metal lines present in the host star spectra. We do not expect the detection of a planet via the transit method to depend significantly on metallicity of the host star and this is one of the advantages of using a sample of hot Jupiter planets from transit survey discoveries. However, it should be noted that confirmation of planets from transit surveys is based on RV follow-up, which will still be subject to the bias described above.


J. A. López-Vázquez and S. Lizano acknowledge support from PAPIIT-UNAM IN101418 and CONACyT 23863. J. Cantó acknowledges support from PAPIIT-UNAM-IG 100218. We thank an anonymous referee for useful suggestions that improved the presentation of this paper.


The authors were supported by a grant from the NASA Living with a Star Program to Ames Research Center. The HMI data used are courtesy of the NASA/ SDO and the HMI science teams. We gratefully acknowledge many useful discussions with Nagi Mansour, Alan Wray, Alexander Kosovichev, and Philip Scherrer.


We thank the entire Herschel Gould Belt Survey team for their efforts in obtaining and preparing the data used for the dense core catalogs we employed in this paper. We particularly appreciate the efforts of Bilal Ladjelate, Vera Könyves, and Alexander Men’shchikov in their contributions to the getsources extractions used for our analysis. J.K., J.D.F., E.R., M.C.C., and C.D.M. acknowledge the financial support of a Discovery Grant from NSERC of Canada. R.K.F. is a Dunlap Fellow at the Dunlap Institute for Astronomy Astrophysics. The Dunlap Institute is funded through an endowment established by the David Dunlap family and the University of Toronto. P.C., J.P., A.P., and A.C.T. acknowledge the financial support of the European Research Council (ERC; project PALs 320620). The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This research made use of astrodendro, a Python package to compute dendrograms of Astronomical data ( http://www.dendrograms.org/ ). This research also made use of Astropy ( http://www.astropy.org ), a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ). Facilities: GBT , Herschel .


Acknowledgements AM acknowledges funding support from Spanish public funds for research under project ESP2015-65712-C5-5-R from the Ministerio de Industria, Economía y Competitivida (MINECO) and the Fondo Europeo de Desarrollo Regional (FEDER), and from project RYC-2012-09913 under the Ramón y Cajal programme of the Spanish MINECO. The authors want to acknowledge the efforts and very constructive comments and suggestions of the anonymous referee. We also acknowledge Brian Boland and Jose Delgado for the English edition of this manuscript.


case, even if the Coulomb collision scenario outlined above was not valid, the waves generated during the magnetic field reconfiguration could discriminate between fully stripped and normal ions for a short time before being dissipated, resulting in a charge state composition similar to the Outlier one. If such a reconnection event occurred high in the corona where the plasma density is low enough, ionization would not be able to replenish the ions left behind, leaving the decrease in their abundance intact. However, if the wave–plasma interaction was depositing energy to all ions except the fully stripped ones, the Outlier wind should exhibit larger ion temperatures in normal ions than in the fully stripped ones. Also, the presence of an additional energy source to this wind, which is unavailable to the normal slow wind and not directly associated with the reconnection region, would cause the ion temperature in the Outlier wind to be larger than in the normal wind. Figure 10 shows, however, quite the opposite: (1) all heavy ions have the same temperatures in the Outlier wind, and (2) these ion temperature values are lower than in the normal slow wind. However, the signatures of wave–plasma interaction at the reconnection point could easily be lost during the Outlier wind propagation in the heliosphere after the initial release, so that the comparison shown by Figure 10 does not necessarily rule out this scenario. The acceleration of ions during magnetic reconnection in solar flares has also been investigated with simulations and analytic studies by Drake et al. ( 2009 ). They found that the ions with a high mass-to-charge ratio can behave like pickup particles and be strongly heated more easily than protons as they cross the reconnection exhaust boundary; the threshold for ions to be heated also depends on the plasma β . Whether the depletion of the fully stripped ions in the Outlier slow wind is attributed to the lack of this type of acceleration needs further exploration. 5. CONCLUSIONS In this work, we have studied the Outlier slow solar wind, a subset of slow solar winds that shows a peculiar charge state distribution, and compared its characteristics to those of the normal slow solar wind. The Outlier wind, initially identified through a C 6+ /C 5+ ratio that is much lower than expected, was found to be a subset of slow solar winds with the same properties and source regions as the latter, with the exception of a very large decrease in the abundance of fully stripped ions, which causes a decrease of C and He abundance, and much lower ion temperatures. We argue that no matter what the Outlier wind origination mechanism is, the source region must be above 1.6 solar radii and the generating process must be episodic. We discuss three possible scenarios that could explain the peculiar properties of the Outlier wind: wave–plasma interaction, depletion already contained in coronal loops, and Coulomb collisions. In the first scenario, waves