O O B-Survey I-Survey I-Survey O O O O O O O B-Telescope I-Telescope O B-Instrument I-Instrument I-Instrument I-Instrument I-Instrument B-Instrument O O O O O B-Organization I-Organization I-Organization O O B-Observatory I-Observatory I-Observatory O B-Dataset I-Dataset I-Dataset O O O O O O O B-Survey O B-Survey I-Survey O O B-Survey I-Survey I-Survey O B-Survey I-Survey I-Survey O O O O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O O B-Grant O O O O O B-Survey I-Survey I-Survey O O B-URL O B-Survey O O O B-Location O O O O B-ObservationalTechniques O O O B-Telescope I-Telescope O B-Dataset I-Dataset I-Dataset O O O O O O O B-Survey O O B-Survey I-Survey I-Survey I-Survey I-Survey O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O B-Survey I-Survey B-Survey I-Survey B-Survey I-Survey B-Telescope B-Survey I-Survey B-Telescope B-Telescope O B-Wavelength O B-Wavelength O B-Survey O O O O B-Organization B-Location O B-Organization B-Location O B-Observatory O O O O O B-Survey O O B-URL O O O B-Person O O O B-Organization O B-Fellowship I-Fellowship I-Fellowship I-Fellowship O O O B-Organization I-Organization I-Organization I-Organization O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O B-Organization O O B-Grant B-Person O O O O O B-Organization I-Organization I-Organization B-Fellowship I-Fellowship B-Fellowship B-Person O O O O O O B-Organization I-Organization I-Organization O O B-Fellowship I-Fellowship B-Fellowship O O B-Person I-Person I-Person O O O O O O O O

ACKNOWLEDGEMENTS The SAMI Galaxy Survey is based on observations made at the Anglo-Australian Telescope. The Sydney-AAO Multi-object Integral field spectrograph (SAMI) was developed jointly by the University of Sydney and the Australian Astronomical Observatory. The SAMI input catalogue is based on data taken from the SDSS, the GAMA Survey and the VST ATLAS Survey. The SAMI Galaxy Survey is funded by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and other participating institutions. The SAMI Galaxy Survey website is http://sami-survey.org/ . GAMA is a joint European–Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the SDSS and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel -ATLAS, GMRT ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/ . Support for AMM is provided by NASA through Hubble Fellowship grant HST-HF2-51377 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. SMC acknowledges the support of an Australian Research Council Future Fellowship (FT100100457). MSO acknowledges the funding support from the Australian Research Council through a Future Fellowship (FT140100255). We thank Á. R. López-Sánchez for assisting in the proofreading of the paper.


O O B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O O O O O O O B-Organization B-Grant O B-Grant B-Grant B-Grant B-Grant B-Grant B-Grant B-Grant O B-Grant O O O O O O O B-Observatory O B-Identifier B-Observatory O O O O B-Observatory O O O O B-Organization B-Location O B-Organization B-Location O O B-Organization B-Location B-Organization O B-Organization B-Location O B-Organization B-Location I-Location I-Location O O O O B-Location I-Location I-Location O B-Observatory I-Observatory I-Observatory O O O B-Observatory B-Organization O B-Observatory O O O O O O O O O O O O O O O B-Organization I-Organization I-Organization B-Organization O O B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory O B-Observatory O B-Software B-Identifier B-Citation I-Citation I-Citation I-Citation O

We appreciate Takashi Miyata, Takafumi Kamizuka, and Ryou Ohsawa for fruitful discussions regaring our study. This work is supported by JSPS KAKENHI Numbers 18K13590, 19H05088, 19K03932, 18H05441, 17H01103, 18K03709, 18K13595, and 17K14244. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2017.1.00124.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. Data analysis was partly conducted with the common use data analysis computer system at the Astronomy Data Center, ADC, of the National Astronomical Observatory of Japan. Facility: ALMA. Software: CASA v5.1.1; (McMullin et al. 2007 ).


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

it is as follows: (a) the parameter h eff is sampled, marginalized over the velocity field; (b) the conditional probability of the parameter σ NL is drawn with the other parameters fixed; (c) a constrained realization of the density field is drawn assuming a ΛCDM power spectrum (Hoffman Ribak 1991 ); (d) a new set of distances is established from the sampled constrained realization with probabilities set by the current values of h eff , σ NL , and the velocity field, within priors on the distances. The process is carried through ∼10 3 MCMC steps until convergence. The procedure has been carried out on multiple constrained realizations and mock catalogs. With the current analysis, Graziani et al. ( 2019 ) find h eff = 1.02 ± 0.01 and σ NL = 280 ± 35 km s −1 . The resultant model makes predictions for the morphology and motions of structure locally within the ΛCDM framework and linear perturbations. Our present interest is in voids. It will be asked to what degree the overall model is in agreement with the excellent knowledge we have of the motions of very nearby galaxies. 2. Morphologies of Nearest Voids The Local Void does not have a simple shape. Moreover, as the void is followed to shallower levels it merges with adjacent voids, as part of a continuous network that extends beyond the volume that can currently be mapped. The three-dimensional interplay between complex high- and low-density structures is visually confusing. We should not have the ambition to get into great detail. As a prelude, previous efforts to identify nearby voids can be mentioned, derived from regions of emptiness in maps of the distribution of galaxies in redshift surveys. Among the earliest were the seminal studies of the Boötes Void by Kirshner et al. ( 1981 ) and the void in front of the “Great Wall” Coma and Abell 1367 clusters (Gregory Thompson 1978 ). On very large scales there is the pioneering work by Batuski Burns ( 1985 ) and Einasto et al. ( 1994 ) on the concentrations and absences of rich clusters. More nearby, and pertaining to the distribution of individual galaxies, of note is the work of Kauffmann Fairall ( 1991 ) and Fairall ( 1998 ). The latter gives a list of 33 void-like regions within 8500 km s −1 . Elyiv et al. ( 2013 ) have produced a more quantitatively rigorous catalog of 89 voids within 3000 km s −1 ; spherical regions with no known galaxies brighter than M K = −18.4. Typically these entities are modest in size, with radii ∼6 Mpc. It is well documented that voids network, and their dimensions as constrained by the exclusion of galaxies depend on the intrinsic properties of the galaxy samples. Sparse filaments of dwarf galaxies can snake through regions devoid of bright galaxies. This phenomenon has particularly been noted by Lindner et al. ( 1995 ) and Pustilnik et al. ( 2019 ) within the volume that attracts our attention. The linkage of the region under consideration including the Local Void to very extensive voids has been claimed by


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Proposal I-Proposal B-Proposal I-Proposal I-Proposal I-Proposal I-Proposal I-Proposal I-Proposal O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software I-Software B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O B-Model O O O O O O O O O B-Software B-Citation I-Citation I-Citation I-Citation O B-Citation O B-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O B-Software I-Software O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O B-Software O B-Software O O O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O

for the cold molecular phase. As this is the phase with the strongest magnetic fields as well as the phase in which star formation occurs, it is important to follow the evolution of the magnetized ISM together with its chemical evolution. In this study, which is part of the SILCC project (SImulating the Life-Cycle of molecular Clouds 1 ), we investigate the interplay between the chemical evolution of the SN-driven ISM and the magnetic field evolution. We use three-dimensional MHD simulations including a chemical network and shielding from an interstellar radiation field to explore how magnetic fields alter the formation of molecular gas in the ISM. We present the details of the simulations, the numerical methods and parameters, describing the chemistry network, the SN implementation, the list of simulations and the initial conditions in Section 2 . We discuss the morphological evolution of the simulations in Section 3 . The chemical evolution is described in Section 4 together with the magnetic field evolution and the influence of self-gravity. In this section we also evaluate the thermal, magnetic and kinetic energy density (Subsection 4.1 ). In Section 4.2 we show the magnetic field scaling and present our conclusions in Section 5 . 2 NUMERICAL METHOD, PARAMETERS AND SIMULATIONS 2.1 Numerical method We simulate the chemical, thermal and dynamical evolution of gas driven by SN feedback in a periodic volume using the Eulerian, adaptive mesh refinement (AMR), three-dimensional, magnetohydrodynamic code flash 4 (Fryxell etal. 2000 ; Dubey etal. 2008 ). To solve the ideal MHD equations, we use the directionally split three-wave MHD solver hll3r (Bouchut, Klingenberg Waagan 2007 , 2010 ; Waagan 2009 ; Waagan, Federrath Klingenberg 2011 ). Our simulations account for the effects of gas cooling and chemical evolution (Section 2.2 ) and SN feedback (Section 2.3 ) using an approach very similar to the one adopted by Gatto etal. ( 2015 ). Further details of our modifications to flash can also be found in Walch etal. ( 2015 ) and Girichidis etal. ( 2016 ). 2.2 Gas cooling and chemistry The simplified chemical network follows the evolution of seven chemical species (H , H, H 2 , C , O, CO and free electrons) and is based on the hydrogen chemistry of Glover Mac Low ( 2007a , b ) and the CO chemistry of Nelson Langer ( 1997 ). We model the effects of radiative cooling and diffuse heating by cosmic rays, X-rays and the interstellar radiation field (see Glover etal. 2010 ; Glover Clark 2012 ; Walch etal. 2015 ). Cooling by metals in hot ionized gas is modelled using rates from Gnat Ferland ( 2012 ), as outlined in Walch etal. ( 2015 ). Dust shielding and molecular self-shielding are included using the TreeCol algorithm developed by Clark, Glover Klessen ( 2012 ). Full details of the implementation of the chemical network and the coupling of TreeCol with flash are described by Walch etal. ( 2015 ) and Wnsch etal. (in preparation). In the simulations presented


O O O O O O O B-Organization I-Organization I-Organization B-Location O O B-Grant O O O O O O B-Grant I-Grant O O B-Organization I-Organization I-Organization I-Organization O O O O O B-Telescope I-Telescope I-Telescope I-Telescope O O B-Organization I-Organization I-Organization O B-Location O O O O O B-Organization I-Organization I-Organization B-Organization O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject

Acknowledgements The authors acknowledge support from the National Science Centre, Poland, through grant 2016/21/B/ST9/01455. This work was partially supported by Program 12 of the Russian Academy of Sciences. Partly based on observationswith the Effelsberg 100 m telescope of the Max-Planck-Institut für Radioastronomie in Effelsberg. We wish to thank the Maser Monitoring Organisation (M2O), a group of loosely organised radio telescopes that coordinates and cooperates for observations, for its prompt response for our request for confirmatory observations and increased monitoring of water masers associated with W49N.


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

distribution of central densities in SIDM haloes with cross-sections of this order of magnitude. This signature has not been studied in detail and in this section we present such an analysis based on our cluster-sized simulations. To get a simple measure of the mass assembly history of our halo sample, we trace back in time each halo from z = 0 through successive snapshots, picking the most likely progenitor based on proximity of mass and distance from the selected halo in consecutive snapshots. From this exercise, we define a measure of the ‘formation’ time of a halo as the redshift z f where it acquires a fraction f mass of the mass contained within the scale radius r −2 at z = 0, where r −2 is the radius at which the logarithmic slope of the density profile is −2: 5 This definition gives us a way to explore if the dispersion in the central densities at a fixed V max is related to the time when the mass in the inner regions of haloes was assembled. We note that using equation ( 10 ) for small values of f mass leads to a biased definition of formation time in the SIDM case since once within the region of the SIDM core, the enclosed mass is clearly smaller in the SIDM case than in the CDM case, leading to an artificially larger formation time in the former. In fact, SIDM and CDM haloes assemble at roughly the same time, since self-interactions start to be important later in the evolution of haloes. Equation ( 10 ) is nevertheless sufficient and simple enough for the purpose of this section. We have verified that using the formation time assigned to CDM haloes to the corresponding SIDM haloes leaves our results qualitatively unchanged. Fig. 9 shows the density at a fixed radius (4ε) as a function of V max (a proxy of halo mass) for our halo sample in the different cosmologies. We have chosen r = 4ε ≈ 22 kpc h −1 as the minimum radius we can safely trust in our density profiles. The size of each symbol is proportional to the formation redshift z f (defined with f mass = 1/2), with larger symbols corresponding to haloes formed earlier. It is well known that in CDM, at a fixed radius, more massive haloes (larger V max ) are denser than less massive ones, while at a fixed mass ( V max ), more concentrated haloes form earlier than less concentrated ones. Both of these features are present in Fig. 9 , although the trends are noisier due to the limited mass range we cover and the low number of haloes in our sample. Figure 9. Density at four times the softening (≈22 kpc h −1 ) versus the maximum velocity of the halo. The type of simulation is shown by the colour (blue for CDM, green for SIDM0.1 and red for SIDM1) and the symbol size indicates the square of the formation redshift [where the halo had obtained half its mass within M ( r −2 )], i.e. smaller (larger) symbols indicate haloes formed later (earlier). For CDM and SIDM0.1 we see a mild trend between the central density and the maximum velocity of a halo, which is absent negligible in the case of


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

to depend on any model assumptions, so we will use the calibration based on V − V HB for these 19 clusters. For the remaining clusters, the absolute magnitude calibration would be the best choice, but we only have F606W photometry available for two additional clusters. So instead of presenting results from three different calibrations, the metallicities for all the remaining six clusters are derived from the luminosity approach. When necessary, these six clusters are marked in plots and tables with an asterisk. Using the set of average reduced equivalent widths as given in Table 3 , we now calibrate them with mean cluster metallicities from the literature (from Dias et al. 2016 ). The upper plot in Fig. 14 shows the metallicities from the literature as a function of reduced equivalent width. The three lines show a linear, quadratic, and cubic fit to the data, taking into account the errors on both axes (which are small), together with their corresponding root mean squares (rms). The coefficients for all relations are given in Table 4 for the following equation: Fig. 14. Upper panel : the metallicity from the literature ( Dias et al. 2016 ) is plotted over the mean reduced equivalent width of each cluster, derived from the quadratic relation on all RGB stars based on V − V HB . Three polynomials of different degree are fitted to the data and the rms for each is given in the legend. Lower panel : quadratic fits to three more calibrations using the same metallicity scale (only brighter than HB, and based on M and L ) are provided for comparison, together with all four on a different metallicity scale (C09, from Carretta et al. 2009a ). Table 4. Coefficients for CaT-metallicity calibration as given by Eq. ( 7 ) for three different polynomial degrees. Since both the Bayesian (BIC) and the Akaike (AIC) information criteria give the best results for the quadratic relation, we chose this for further analyses. Therefore, the relation between reduced equivalent width and metallicity is given by: The metallicities derived from this relation show two systematic errors: the mean metallicity of a cluster will be the value of the relation given above at the mean reduced EW of the cluster and, therefore, will have a small offset to the literature value for most clusters. Furthermore, the slope of the relation at any given point defines the metallicity spread. Following previous studies (see, e.g., Vásquez et al. 2018 ), the uncertainties are calculated as the quadratic sum of the uncertainty σ W ′ and the rms for the used relation as given in Fig. 14 . For the quadratic relation this yields: where rms is the root mean square for the used relation and σ r is the propagated uncertainty for σ W ′ : The derived metallicities and their uncertainties for all previously discussed calibrations are listed in Table 8. We already demonstrated that all previously discussed approaches yield a similar W ′. The lower panel in Fig. 14 shows that the metallicity calibrations


O O O O O O O O O O O O O O O O O B-Wavelength O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O B-Wavelength O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O O O O B-Wavelength O B-Wavelength O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject I-CelestialObject I-CelestialObject B-CelestialObject O B-CelestialObject O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O B-Wavelength O O O O O O O O O O O B-Wavelength O B-Wavelength O O O O O B-Wavelength O O O O B-Wavelength O B-Wavelength O O O O O B-Wavelength O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

were measured with an accuracy of (Table 4 ). Moreover, we were unable to measure [O iii ]λ4959 and [O iii ]λ5007 lines for these objects. These two regions were also excluded from further analysis. 3.2 Oxygen and nitrogen abundances We are unable to use the direct T e -method to estimate the oxygen and nitrogen abundances, since auroral lines in spectra of H ii regions from our sample, such as [O iii ]λ4363 or [N ii ]λ5755, are too faint to be detected. Thus, we need to use empirical calibrations. Such methods are well developed (Kobulnicky Kewley 2004 ; Pettini Pagel 2004 ; Pilyugin Thuan 2005 ; Bresolin 2007 ; Pilyugin et al. 2010 ; Pilyugin Mattsson 2011 ; Marino et al. 2013 ; Pilyugin Grebel 2016 ). The most modern two-dimensional R and S-calibrations (Pilyugin Grebel 2016 ) are the simplest and the most popular now. The R-calibration, based on the [O ii ] and [N ii ] strong emission lines, is more resistant to the presence of diffuse ionized gas (see e.g. Sanders et al. 2017 ; Kumari et al. 2019 ; Poetrodjojo et al. 2019 ), but it is sensitive to extinction errors. The S-method, based on the [N ii ] and [S ii ] lines, does not depend strongly on extinction, but it cannot be used for the nitrogen abundance estimates (Pilyugin Grebel 2016 ). Among the other methods we distinguish the NS-calibration (Pilyugin Mattsson 2011 ), which is based on the [O iii ], [N ii ], and [S ii ] emission-line intensities (see discussion in Gusev et al. 2012 , 2014 ). All the empirical calibrations are constructed under some assumptions. One of them is that the H ii regions are in the low-density regime (Pilyugin Mattsson 2011 ; Pilyugin Grebel 2016 ), which is typical for the majority of extragalactic H ii regions (Zaritsky et al. 1994 ; Bresolin et al. 2005 ; Gutiérrez Beckman 2010 ). The dependence n e versus [S ii ]λ6717/[S ii ]λ6731 degenerates at low n e (Proxauf et al. 2014 ), thus we give upper limits of n e in Table 3 for most H ii regions. Large upper limits of n e for H ii region nos. 12, 15, and 16 in NGC 3963 are rather a result of large line flux errors. However, one H ii region of our sample, no. 6 in NGC 3963, is an apparently high-density object (see Table 3 ). The empirical calibrations are not applicable to such regions. As a result, this region was eliminated from further consideration. We calculated the oxygen and nitrogen abundances in H ii regions using three different calibrations discussed above. Data of emission lines fluxes from Table 4 were used. For several regions, where the [O iii ]λ4959 or [N ii ]λ6548 lines fluxes have not been measured, we adopt I ([O iii ]λ4959+[O iii ]λ5007) = 1.33 I ([O iii ]λ5007) and I ([N ii ]λ6548+[N ii ]λ6584) = 1.33 I ([N ii ]λ6584) according to results of Storey Zeippen ( 2000 ). The resultant oxygen abundances from the R, NS, and S-calibrations abundances, and nitrogen abundances from the R and NS-calibrations, and NS-calibration electron temperatures are given in Table 5 . Table 5. Oxygen and


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

The authors thank Javier Coronado-Blázquez, Jose Luis Contreras, and the anonymous referee for helpful comments. A.D. is thankful for the support of the Ramón y Cajal program from the Spanish MINECO. R.W. was supported by a grant from VILLUM FONDEN (Project No. 16599). J.F. is supported by NASA under contract S-15633Y. M.A. and A.D. acknowledge funding support from NSF through grant AST-1715256. K.H. acknowledges support from the Icelandic Research Fund, grant No. 173728-051. D.H. and L.M. acknowledge funding support from NASA through grant 80NSSC18K1720.


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

galaxies (Fig. 11 , upper right panel). Due to the relatively small range in redshift across the sample, this difference is not likely due to evolution. In the case of inner (r) and outer (R) rings, a small dependency is found (a p -value of 5.46 per cent). Both galaxies with outer and inner rings in the isolated sample are on average found at slightly larger distances than those without these features. We repeated the tests above to look for bias due to galaxy inclination (Fig. 12 ), but in all cases we found no apparent dependency, consistent with what we illustrated in Figs 6 – 8 . However, whether or not a spiral galaxy is identified as either grand design or flocculent is clearly dependent on both distance and inclination. This is illustrated in the top two panels of Fig. 16 . The sample grand design spirals seem to be more easily identified at lower inclinations and on average are more distant than the sample flocculent spirals. The dependence on inclination may not be unexpected because spiral arm contrast is lower the more inclined the disc galaxy. The dependence on distance could reflect the fact that grand design spirals tend to occur in more luminous systems than do flocculent spirals, and hence can be recognized to greater distances (Section 7). 8.2 Morphological features and isolation Using the same approach as above, we analysed whether the distributions of two environmental parameters: η k and Q Kar, p (Verley et al. 2007b ), were altered by restricting to subsamples of specific morphological features. η k is the logarithm of the local volume density of neighbouring galaxies, based on the projected separation to the k th neighbour and the distance estimate of the galaxy in question (Verley et al. 2007b ). Only neighbours that have diameters that are 0.25–4 times that of the target galaxy are considered, as larger (smaller) galaxies are assumed to be foreground (background) objects. However, this does leave open the possibility that some low-mass dwarf companions might be neglected. Typically k = 5, but in some cases the available optical field is too small to identify five neighbours and a lower value of k is used instead. Q Kar, p is a logarithmic and dimensionless measurement of the ratio of a galaxy’s internal gravitational binding force to external tidal forces, which assumes that galaxy diameter is a proxy for total mass (Verley et al. 2007b ). For CIG galaxies the mean values of η k and Q Kar, p are 1.4 and −2.7, and their standard deviations are 0.6 and 0.7, respectively. Figs 13 and 14 show the comparative histograms for stage, family, and inner and outer varieties versus η k and Q Kar, p ; the comparisons for AC are shown in the middle two panels of Fig. 16 . We find no dependency on the isolation parameters regarding the detection of inner or outer rings and bars, which could either be a sign of the minimal impact of the environment on the generation of these features, or could be indicating that the scatter in


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

2006 ). The observations were carried out over five nights in 2017 March, in clear weather or thin clouds, with seeing 06–10. We used a pixel scale of 025, the 210 grating in H -band and a slit width of 10, yielding a spectral resolution of R ∼ 3000. We adopted a two-point dithering pattern and an exposure time of 5 minutes per frame, for a total on-source time of 21 hr (summed from both spectrographs). To facilitate acquisition, we used a multi-object mask and chose PA = 20° to align the slit to the major axis of the galaxy (Section 2.1 ). The data were reduced using the flame pipeline (Belli et al. 2017 ), which outputs a rectified, sky-subtracted, wavelength-calibrated two-dimensional (2D) spectrum. The corresponding H α PV diagram is shown in Figure 1 (right, color scale). 2.4. One-dimensional Kinematic Profiles To create the one-dimensional (1D) velocity and dispersion profiles in CO, we proceed as described by Genzel et al. ( 2017 ): we first fit a Gaussian profile to the CO line emission in each spaxel of the data cube, smoothed over three spaxels to ensure sufficient signal-to-noise ratio (S/N) in the outer parts of the galaxy. Accounting for the galaxy’s systemic velocity, this results in the 2D velocity map. From this we determine PA kin = 23° as the axis with the steepest velocity gradient. It agrees with PA H , and with the PA of the H α slit observations (Sections 2.1 , 2.3 ). The CO 1D velocity and dispersion profiles are then constructed from 075 diameter apertures (as a compromise between the CO data resolution and the seeing-limited H α data) with the center spaced by 024 along PA kin . To create the 1D profiles in H α , we extract spectra in overlapping bins of two to four spatial pixels and fit a Gaussian profile to the H α line emission. The choice of the number of spatial pixels used for the extraction of individual data points does not substantially affect the extracted values, but allows for increased S/N in the outer disk regions. We trace H α out to 19 kpc (NE, ∼4.4 R e , H ) and 13 kpc (SW, ∼3.1 R e , H ), and CO out to 12 kpc (∼2.8 R e , H ). These physical radii at z ∼ 1.4 are equivalent to probing the rotation curve out to 23–35 kpc for a galaxy of this stellar mass at z ∼ 0 (van der Wel et al. 2014 ). Figure 2 shows the 1D velocity and dispersion profiles in CO and H α along PA kin in observed space. The uncertainties are derived from the Gaussian fits described above where noise has been taken into account. The two tracers agree, indicating that they trace the same mass distribution, most reliably in the outer disk where beam-smearing effects become less important. Figure 2. One-dimensional velocity (left) and velocity dispersion (right) profiles along the kinematic major axis in CO(3–2) (blue circles) and H α (red diamonds). Due to the spatial resolution of the observations, neighboring data points are not independent. The projected distance increases from NE to SW of the kinematic center of the galaxy. The


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

We thank the anonymous referee whose comments helped improve this manuscript. We thank Endrik Krügel for helpful discussions and for providing the original version of his MC code, and Frank Heymann for discussions on the implementation of perspective-projection ray tracing. We are grateful to Sebastian Wolf for discussions, comments and suggestions which improved the content of this manuscript. PS is supported under DFG programme No. WO 857/10-1


O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software I-Software B-Person O O O B-Organization I-Organization I-Organization B-Fellowship I-Fellowship I-Fellowship I-Fellowship I-Fellowship I-Fellowship O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization B-Person O O O O O O O O O O B-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization I-Organization O O O O B-Person I-Person O O O O O O O O O O O O O O O O O O O O O O B-Person I-Person O B-Person I-Person O O O B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person O O B-Person I-Person O O O O B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O B-Person I-Person O B-Person I-Person O O O O O B-Person I-Person O B-Person I-Person O B-Telescope O O O B-Person I-Person O O O O O O O O O O O B-Person I-Person O O O O O O O O O B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O B-Telescope I-Telescope O O O O O O O O O O B-Person I-Person O O O O B-Person I-Person O B-Person I-Person O O O O O O O O B-Person I-Person O O O O O O O O O O O O O B-CelestialObject I-CelestialObject B-Person O B-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O O O O O O O O O O O B-Person I-Person I-Person O O O O O B-Software O O O B-Person I-Person O O O O O B-Software O O O O O O O O O B-Telescope O O O O B-Telescope O O O O O B-Organization I-Organization I-Organization I-Organization O O O O B-Telescope O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset O O O O O O O O O O O O O B-Archive I-Archive I-Archive I-Archive I-Archive I-Archive I-Archive I-Archive B-Archive B-Organization O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O B-Organization O B-Grant O O B-Archive O B-Telescope O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Grant O O O O O O O O O O O O O B-Database I-Database I-Database O O B-Archive I-Archive I-Archive O O O O O B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Grant I-Grant I-Grant

We are thankful to the anonymous referee(s) for an insightful report that improved the paper, and in particular for advocating more conservative assumptions about the completeness of the Kepler pipeline. R.I.D. gratefully acknowledges the National Science Foundation Graduate Research Fellowship under grant DGE-1144152 and the Miller Institute for Basic Research in Science, University of California, Berkeley. J.A.J. is grateful for the generous grant support provided by the Alfred P. Sloan and David Lucile Packard foundations. We are grateful to Smadar Naoz for many enlightening discussions and comments, including opening our eyes to other possibilities in Section 4 , for which we also thank Simon Albrecht and Fred Rasio. Many thanks to Adrian Barker, Rick Greenberg, Renu Malhotra, Francesca Valsecchi, and especially Brad Hansen for tidal insights; to Katherine Deck, Will Farr, Vicky Kalogera, Yoram Lithwick, and Matthew Payne for helpful dynamical discussions; to Will Farr and Moritz Günther for helpful statistical discussions; to Courtney Dressing and Francois Fressin for Kepler assistance; and to Joel Hartmann for helpful discussions about the ground-based sample. We are grateful to Subo Dong for constructive comments on a manuscript draft. Thanks to Joshua Carter, Boas Katz, Doug Lin, Geoff Marcy, Darin Ragozzine, Kevin Schlaufman, and Aristotle Socrates for helpful comments, and to Thomas Barclay, Christophe Burke, Jon Jenkins, and Jason Rowe for helpful discussions of Kepler 's completeness for giant planets. We benefitted from helpful conversations with Thomas Barclay about candidate vetting and Howard Isaacson and Rea Kolb about false positives. We are very grateful to Chelsea Huang for helpful discussions and for providing us with a detrended light curve for KIC 6805414. R.I.D. thanks David Charbonneau, Sean Andrews, Debra Fischer, Matt Holman, and Abraham Loeb for helpful comments on the thesis chapter version of this manuscript. Special thanks to J. Zachary Gazak for helpful modifications to the TAP code. We thank Dáithí Stone for making his library of IDL routines available. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate. We are grateful to Kepler team for their work in making this revolutionizing mission possible and making the rich Kepler data set available. Some of the data presented in this paper were obtained from the Multimission Archive at the Space Telescope Science Institute (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. This research has made use of the Exoplanet Orbit Database, and the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.


O O O B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O B-Instrument O O O B-Person O B-Person I-Person O O O O O O O O O B-Person I-Person O B-Person I-Person O O O O O O O O O B-Person O O O O B-Organization I-Organization B-Fellowship I-Fellowship I-Fellowship B-Fellowship B-Fellowship O O B-Person O O O O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O O B-Grant B-Person O O O O O O O B-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization O O O O O O B-Person O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O O O O O O O O O B-Archive I-Archive I-Archive B-Archive O O O O O B-Observatory I-Observatory I-Observatory I-Observatory O O B-Archive I-Archive I-Archive I-Archive B-Archive O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O O O O O O O O B-Software B-Citation I-Citation O B-Software B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O B-Software B-Citation I-Citation O B-Software B-Citation I-Citation I-Citation O B-Software I-Software O B-Person I-Person O B-Software B-Citation I-Citation O O O O O O O B-Organization B-Archive I-Archive I-Archive B-Person O O O O B-Software O O B-Software I-Software I-Software

ACKNOWLEDGEMENTS We thank John O’Meara, Scott Burles, David Tytler, and Chris Gelino for their feedback about HIRES data used by BT ; Tristan Dwyer for running his code identifying absorption systems for us; Serj Balashev and Michael Wilczynska for useful discussions; an anonymous referee for valuable comments. EOZ was supported by an Australian Government Research Training Program (RTP) Scholarship. Work of SRS was funded by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. EOZ is grateful for the hospitality of the Institute of Theoretical Astrophysics at the University of Oslo during his visit in 2016 January–February. JKW thanks the Centre for Mathematical Sciences in Cambridge for an enjoyable and productive Sabbatical visit. This work is based on observations collected with the Keck Observatory Archive (KOA), which is operated by the W. M. Keck Observatory and the NASA Exoplanet Science Institute (NExScI), under contract with the National Aeronautics and Space Administration. A significant part of the analysis and figures were done using publicly available python packages: matplotlib (Hunter 2007 ), numpy (Van Der Walt, Colbert Varoquaux 2011 ), scipy (Oliphant 2007 ), astropy (Astropy Collaboration 2013 ), barak 11 by Neil Crighton, and qscan (Dumont 2017 ). This research has made use of NASA’s Astrophysics Data System. EOZ is also grateful to GitHub for the Student Developer Pack.


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

0.9 R ⊙ , see Section 2 ). The sound speed at the outer edge of the disc is usually in the regime dominated by the bound-free opacities and gas pressure. The mid-plane temperature could be estimated from the standard disc structure, as, e.g. in equation 5.49 of Frank et al. ( 2002 ), which yields T d ∼ 10 4 K. However, the disc in Cyg X-3 is embedded in the strong blackbody radiation of the donor, with T * ≈ 10 5 K. Thus, T d ∼ T * is likely. The sound speed is where Γ ≈ 5/3 is the adiabatic index, μ is the mean molecular weight, which is within μ ≈ 4/3 for fully ionized He and 4 for neutral He (which is most likely the main element of the WR star in Cyg X-3), and m p is the proton mass. At 10 5 K, He will be predominantly fully ionized, which yields H / R d ≈ 0.03. The field advection time-scale in the presence of magnetic outflows, equation ( 9 ), then becomes On the other hand, the standard viscous time-scale (i.e. in the absence of outflows), equation ( 10 ), is We see that the latter time-scale estimate is of the order of the average ∼50-d radio lag observed in Cyg X-3 . It may also be consistent with it for lower values of R d / R L and some plausible combinations of the parameters. Equation ( 6 ) estimates the value of the minimum strength of the magnetic field at R d required for the appearance of magnetic outflows. Using our estimates of the parameters of Cyg X-3 , it can be written as Current constraints give the average soft-state bolometric flux of erg cm −2 s −1 , as we discuss in Section 2 . Likely values of are then in the range of ≈100–200 G. Note that the dependence on the uncertain BH mass is weak. 3.3 The nature of the hard state In the low/hard state, a geometrically thick and hot inner accretion flow surrounded by an outer thin disc is present (e.g. see Yuan Narayan 2014 and the references therein). The hot accretion flow may be advection dominated (hereafter abbreviated as ADAF) though the degree of the flow advection at high Eddington ratios is uncertain. Also, the flow is likely to contain some condensed cold clumps. Still, for notational simplicity, we will use the term ADAF hereafter, but without implying a specific physics of the flow. Variations of the mass accretion rate trigger then state transitions. A hard-to-soft state transition occurs when the accretion rate increases above the critical value, . Then the ADAF cools efficiently and collapses to a thin accretion disc, which is accompanied by disappearance of jets. Steady jets are universally observed in the hard state of BH XRBs, which implies the presence of strong vertical/poloidal magnetic fields near the BH. The origin of such strong fields is unclear. If the ADAF extended all the way to the outer edge of the disc in this state, any outer field could be efficiently advected on the viscous time scale (Cao 2011 ). Equation ( 10 ) then yields ≈0.3 d for an ADAF extending to R d for typical values of H / R d = 0.5 and α = 0.1. In principle, this could also


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

is whether or not the GCS from NGC 1275 itself is contributing to the NGC 1272 totals particularly in ACS-3. From the NGC 1275 profile (see comments below and the lower panel of Figure 8 ), it appears that the contribution from NGC 1275 at the kpc projected distance of NGC 1272 will be at the level of only a few percent. If and when more complete imaging coverage of the core Perseus region is available, it will be possible to solve for the spatial distributions of both galaxies simultaneously and more accurately (see e.g., Wehner et al. 2008 , for an example in the Hydra cluster). For the central giant NGC 1275, the available material is much more limited and the conclusions about total GC population correspondingly rougher. We use field ACS-1 along with ACS-6 to set the local background, though here we are forced to assume that is roughly constant over the region concerned. The measurements are shown in the lower panel of Figure 8 , replotted from Figure 6 . The combination of this radial profile and the inner data from Penny et al. ( 2012 ) indicates that from outward, and is relatively flat inside 60′′. The main concerns in estimating its total population are the true local background level, and how far outward the NGC 1275 halo extends before fading into the ICM; the present data do not answer either of these satisfactorily. Here we simply use an outer radius (100 kpc) for integration of the profile and arcsec −2 as above. The result is then GCs for , not including any (unknown) systematic error in the adopted background. Extrapolating over all magnitudes as described above gives N GC = 13,500 ± 2000 and a specific frequency , including ±0.1 mag uncertainties in the GCLF turnover and dispersion as above. This total GC population and S N would make NGC 1275 quite similar to M87 in Virgo (e.g., Harris 2009b ). We emphasize once again that a better assessment will require a wider-field survey in which the GC populations in the member galaxies, and the IGCs, can both be determined more completely. Nevertheless, this very limited imaging material is enough to suggest that both of the dominant Perseus galaxies appear to have very populous GC systems. The closest analogy may be the more well known Coma cluster, where both NGC 4874 and 4889 have similarly large GC populations but where the central cD (NGC 4874) has the higher specific frequency. 5. Discussion: The IGC Population The data discussed in this paper consist of only 11 pencil-beam probes of the GC population, and thus fall far short of a genuine wide-field survey of the Perseus cluster. Only a more comprehensive survey to similar depth will be able to address the true spatial distribution of the IGCs and their total number. Nevertheless, we can make a very rough attempt to gauge the total GC population. We assume, following Figures 6 and 8 , that σ (IGC) is approximately constant at arcsec −2 in the inner part of the cluster and then falls off as further out. The inflection point


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

The author thanks Rubab Khan, Jamie Lomax, Philip Massey, Kathryn Neugent, Bertrand Plez, and Sarah Tuttle for using discussions regarding this work, as well as the anonymous referee for their helpful and constructive comments. This research was supported by a fellowship from the Alfred P. Sloan Foundation.


O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-CelestialObject I-CelestialObject O O O O B-CelestialObject I-CelestialObject B-CelestialObject O B-CelestialObject O O O O O O O O O B-CelestialObject I-CelestialObject O O B-Formula I-Formula I-Formula O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O B-CelestialObject B-CelestialObject I-CelestialObject O O O O B-CelestialObject I-CelestialObject O B-CelestialObject O B-CelestialObject I-CelestialObject O O O O O O O O O O O O B-CelestialObject O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O B-CelestialObject B-CelestialObject I-CelestialObject O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O

In this paper, we have used the E-MOSAICS simulations to quantify the stellar masses and accretion redshifts of five satellite galaxies that have been accreted by the Milky Way, as well as several other of their properties. These satellites are Kraken (previously referred to as the progenitor of the ‘low-energy GCs’ by Massari et al. 2019 ), Gaia -Enceladus, the progenitor of the Helmi streams, Sequoia, and Sagittarius. They likely represent the most massive objects that the Milky Way accreted since z = 4. We predict their properties by training an artificial neural network on the E-MOSAICS simulations, which follow the co-formation and co-evolution of galaxies and their GC populations. This network relates the ages, metallicities, and orbital properties of a group of GCs that formed in a common satellite progenitor to the properties of that progenitor. The conclusions of this work are as follows: The neural network is capable of predicting the stellar masses and accretion redshifts of the satellite progenitors to high precision, with a validation score of and a scatter of and around the true values, respectively. Omitting the GC age–metallicity information leads to considerably worse constraints on both quantities. Additionally, the accretion redshifts strongly rely on the GC orbital information (Fig. 1 ). Modulo a small number of changes, we adopt the proposed GC membership of the satellite progenitors from Massari et al. ( 2019 , see our Table 1 ). The five groups of GCs associated with the different satellite progenitors show clear differences in apocentre–eccentricity space and age–metallicity space. By comparing the orbital properties of GCs to those of fossil streams from Bonaca Kruijssen ( 2020 ), we find that the streams have similar orbital properties as the GCs associated with Kraken, Gaia -Enceladus, the progenitor of the Helmi streams, and Sagittarius. The Fimbulthul stream falls in between the orbital properties spanned by the GCs associated with Kraken and Gaia -Enceladus, suggesting that it may be the relic of a GC that formed in either of these two satellites. In age–metallicity space, the Kraken GCs have the highest metallicities at a given age, closely followed by the Gaia -Enceladus GCs, suggesting that these two galaxies were the most massive at any moment in time, with Kraken having a slightly higher mass (Figs 2 and 3 ). By applying the neural network to the Galactic GCs associated with the five satellite progenitors, we find that the satellites span a relatively narrow stellar mass range at the time of accretion, of M ⋆ = (0.6–4.6) × 10 8 M ⊙ . The top end of this range is occupied by Kraken, Gaia -Enceladus, and Sagittarius, with the latter two being the most massive. This differs from the stellar masses at any given moment in time, because the accretion redshifts of these satellites differ. By accreting later, Sagittarius was able to attain a higher mass, despite initially being considerably less massive than


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Archive I-Archive I-Archive I-Archive I-Archive I-Archive I-Archive B-Archive B-Person O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O O O O O B-Person O O O B-Organization O O O O O O O O O O O O O B-Fellowship I-Fellowship I-Fellowship

We are thankful to the anonymous referee whose detail in-depth comments helped us in improving the paper. This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Centre (HEASARC). ASM would like to thank Inter-University Centre for Astronomy and Astrophysics (IUCAA) for hosting him during subsequent visits. BR likes to thank IUCAA for hospitality and other facilities extended to him during his visit under their visiting Associateship programme.


O O O O B-Person I-Person O O O O O O O O B-Observatory O O O B-Observatory I-Observatory I-Observatory I-Observatory O O O B-Identifier O O O O O O O B-Database O O O B-Organization I-Organization O B-Organization I-Organization I-Organization I-Organization O B-Location O O B-Organization I-Organization I-Organization

We thank the referee, G. Wade, for useful comments. Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 191.D-0255. This work has made use of the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O B-Telescope O O O O O O O O O B-Citation I-Citation I-Citation O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O B-Telescope I-Telescope I-Telescope I-Telescope O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O B-CelestialObject B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-CelestialObject B-Citation I-Citation I-Citation O O B-CelestialObject B-Citation I-Citation I-Citation O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O

can be estimated combining the observed secular frequency shift (almost 0.8Hz over 17yr of observations) with that caused by the material torque (ranging between 0.35 and 0.56Hz, considering the NS radius and the corotation radius as possible truncation radii of the disc). The spin-down derivative required to compensate the estimated spin-up material torque and to produce the observed spin-down effect should range between 2.210 15 and 2.710 15 Hzs 1 , a factor of almost 1.5 and 1.8 higher, respectively, than the spin-down frequency derivative estimated ignoring the material torque. Using equation ( 5 ) and adopting the FPS equation of state previously described, we can estimate the NS dipole magnetic moment, hence the magnetic field at the magnetic caps in the range 2.210 8 B PC 310 8 G, very similar to the value suggested by Burderi etal. ( 2006 ). However, we note that other mechanisms such as magnetic propeller torque and gravitational radiation torque (see e.g. Hartman etal. 2008 , and references therein for more details) could contribute to the spin-down torque required to describe the observed secular spin evolution of SAXJ1808.43658. 4.2 Pulse energy dependence SAXJ1808.43658 shows an interesting behaviour of its pulse profile as a function of energy. As shown in Fig. 5 , the pulse fractional amplitude estimated from the fundamental component varies significantly between the XMMNewton and the NuSTAR observations of the source. We can exclude instrumental effects between the two satellites to explain such a discrepancy. Indeed, phase-coherence analyses on almost simultaneous XMMNewton and NuSTAR have been carried out on other sources (see e.g. Sanna etal. 2017 , for the AMXP IGRJ00291+5934) and they did not show any peculiar difference between the two detectors. Therefore, a possible explanation may be a significant source state variation occurred in the 3d gap between the two observations. A detailed study of this phenomenon is beyond the scope of this work, however, further investigations on the subject will be reported in a follow-up paper focusing on the spectral properties of SAXJ1808.43658 during its 2015 outburst (Di Salvo etal., in preparation). The peculiar trend showed by the fundamental component is consistent with that observed by the Rossi X-ray Timing Explorer during the 1998 outburst while it significantly differs from that of the 2002 (see e.g. Cui, Morgan Titarchuk 1998 ; Falanga Titarchuk 2007 ; Hartman, Watts Chakrabarty 2009a ). A similar behaviour has been observed for the other AMXPs such as IGRJ00291+5934 (Falanga etal. 2005 ; Sanna etal. 2017 ), IGRJ175113057 (Papitto etal. 2010 ) and XTEJ1751305 (Falanga Titarchuk 2007 ), although for the latter a good coverage of the trend below 4keV is lacking. On the other hand, the constant energy dependence of the fractional amplitude inferred from the second harmonic component seems to remain unvaried over the outbursts (see e.g. Cui etal. 1998 ; Hartman etal. 2009a ). The


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

We thank the staff of the Karoo Observatory for their invaluable assistance in the commissioning and operation of the KAT-7 telescope. The KAT-7 is supported by SKA South Africa and the National Science Foundation of South Africa. AMS gratefully acknowledges support from the European Research Council under grant ERC-2012-StG-307215 LODESTONE. We also thank the anonymous referee for their careful reading of this manuscript and useful comments.


O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O B-Wavelength O O O B-CelestialRegion O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model I-Model O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O

survey volume between the models, the luminosity functions agree well for magnitudes brighter than M UV = −16 mag. This is essentially due to the fact that for relatively small amplification factors, the variations of the amplification and the effective volume are anticorrelated and tend to maintain the same faint-end slope. Furthermore, relative errors on the effective volume are smaller than what is observed at M UV > −16 mag. Figure 10. Impact of cluster mass models on the UV luminosity function in MACS1149: Left: the effective survey volume (and associated 1σ uncertainties) as a function of absolute intrinsic magnitude for each lensing model. The effective volume depends mainly on the geometrical configuration, the magnification map, and the surface area (cf. Fig 9 ) of each model. Right: Derived UV luminosity function using each cluster model and associated uncertainties. All models support a turnover in the faint-end of the LF but at different magnitudes and with different slopes. Beyond an absolute magnitude of M UV = −15 mag, the differences are too large to robustly constrain the LF shape. In the next section, we use the same procedure in each cluster to assess the lensing uncertainties in the final LF that combines the six clusters. 7.3 Final constraints on the UV LF We now compute the final UV luminosity function at ∼6 resulting from the combination of all the individual clusters. In order to account for all the uncertainties described in the previous sections, we use MCMC simulations to explore the full error space of each galaxy and each effective volume in a given magnitude bin. At the level of an individual galaxy, the MCMC simulations take into account the magnification factor uncertainties by using multiple lensing models. Similarly to the previous section, we randomly sample the completeness functions and lensing models shown in the left-hand side of Fig. 10 for the corresponding cluster. For each galaxy magnitude, the MCMC procedure randomly samples the four effective volumes, which are based on four different lensing models, and their associated uncertainties. For each iteration, the total sample is distributed in magnitude bins M i , with a size of 0.5 mag, for which φ( M i ) is computed with the associated cosmic variance errors estimated from the = 7 lensing curve in fig. 2 of Robertson et al. ( 2013 ). These simulations have the advantage of accounting for uncertainties in M i when estimating uncertainties on φ( M i ) because individual galaxies can change bins at each iteration, and at the same time, rectifying the limitations of the binning procedure. Simultaneously, we also fit the resulting LF results at each iteration to determine the likelihood of the modified Schechter parameters. We here combine our results with the bright-end constraints derived by Bouwens et al. ( 2015 ) from wide-area blank field surveys. To account for the difference in the redshift selection function between their = 7 sample and our combined


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

medium these interactions can alter the structure of a galaxy, but the degree to which it is affected also depends on whether it was previously pre-processed. For example, dark matter haloes that were previously in a group environment tend to lose mass more slowly in the cluster environment as the more weakly bound dark matter in the outer halo was already removed in the group (Joshi, Wadsley Parker 2017 ; Han et al. 2018 ). Also, in some Virgo cluster dwarf ellipticals, counter-rotating cores have been observed (Toloba et al. 2014 ). Such features are very difficult to reproduce through the action of the cluster tides or harassment alone (González-García, Aguerri Balcells 2005 ). Thus, mergers in a previous group environment are likely required to explain the presence of such features in galaxies that are now cluster members. In addition to pre-processing, it is important to take into account that galaxies entering the cluster in a group face the combined environmental influence of both group and cluster, and it may be several gigayears until groups can be completely dissociated and virialized within the cluster (Cohn 2012 ). This process, arising in group-cluster mergers, is often referred to as post-processing . In Vijayaraghavan Ricker ( 2013 ), this process was studied using N -body cosmological simulations combined with idealized N -body/hydrodynamical simulations. They study the effects that the merger has on both group and cluster: there is an enhancement of the galaxy–galaxy merger rate during the pericentric passage of the group in the cluster; the merger shock leads to an increase in the ram pressure acting on both the group and the cluster galaxies; and the group galaxies may become tidally truncated. In this context it is clear that to fully understand the processes that are triggering changes in the cluster galaxy population it is crucial to understand their environmental history. While the quantity of pre-processed galaxies in clusters has been shown to be quite significant, as has the effect of the group environment upon them, our understanding of what happens to these galaxies once their hosts enter the cluster, the post-processing stage, has not been explored in so much detail. Thus, in the study presented here, we focus on the consequences of the group falling into the cluster environment. Using a set of high-resolution N -body simulations of galaxy clusters, we identify host and satellite haloes at different redshifts, and then track their evolution while they are under the influence of the cluster environment. We specifically include satellites that are stripped from their host halo by the cluster tides in order to better understand the role of the cluster in the dissociation of substructure. Understanding how groups breakup in clusters could potentially help us identify these pre-processed galaxies, once they have left their groups and mixed with the cluster population. However, as we will show later in this


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

for the reaction of C 2+ with normal H 2 . The circles indicate the binned values of the overall reaction rate coefficient k 3 ( T ) obtained in the present study from the measured decay of numbers of detected C 2+ due to the reaction with normal H 2 in the ion trap. The asterisks indicate the binned values of obtained from the measured time evolutions of the number of detected C + product ions. Note that are lower limits for k 3b ( T ). Vertical error bars indicate a statistical error with included uncertainty of the temperature ±5 K, which affects the reactant’s number density. The error band includes a 20% systematic uncertainty of the H 2 reactant pressure measurement as an independent influence. Horizontal error bars include in addition temperature uncertainty ±5 K and the binning of data. By measuring the time evolutions of the number of detected C + ions at different temperatures, we obtained the temperature dependence of the rate coefficient . We use an additional index C + to stress the fact that the value was obtained in the experiment by measuring the production of C + ions. We have in mind that we cannot prove that all the produced C + ions are confined in the ion trap, and a part of them can escape. That is why the measured value is a lower limit for k 3b ( T ). The temperature dependence of the measured reaction rate coefficient for production of C + ions in reaction of C 2+ dications with normal H 2 is also given in Figure 3 . Only a very small fraction of ions corresponding to the production of CH + (see the text above and example in Figure 2 ) was observed, that is why these data are not included in Figure 3 . In Figure 4 , the present k 3 ( T ) is compared with the results of previous experimental studies. Plotted are the present data (binned values as in Figure 3 ) and, for comparison, the values of k 3 ( T ) obtained at 300 K with the SIFT technique by Smith Adams ( 1981 ) and by Viggiano et al. ( 1979 ). We also include the value obtained by Gao Kwong ( 2003 ) at higher collisional temperatures ( T ≈ 2 630 K) using a cylindrical RF ion trap. The value of the Langevin collisional rate coefficient k L for collisions of C 2+ carbon dications with H 2 k L (C 2+ + H 2 ) = 3.2 × 10 −9 cm 3 s −1 is also plotted in Figure 4 . Note that the measured values of k 3 ( T ) are just small fractions of k L , i.e., 4%–5%. Figure 4. Temperature dependence of the reaction rate coefficient k 3 ( T ) of the reaction of C 2+ carbon dications with H 2 , comparison with previous results. The horizontal dashed line indicates the value of the Langevin collisional rate coefficient ( k L = 3.2 × 10 −9 cm 3 s −1 ). The circles indicate the values of k 3 ( T ) obtained in the present study for the reaction of C 2+ carbon dications with normal H 2 . The asterisks indicate values of the reaction rate coefficient , obtained from measured time evolutions of the number of detected C + ions. The values of are lower limits for the actual values of


B-Formula I-Formula I-Formula O O O O O B-Formula I-Formula I-Formula B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O B-CelestialObject O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O B-Software I-Software I-Software I-Software I-Software I-Software I-Software I-Software B-Software O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O B-Citation O O O O O O O B-CelestialObject O O O B-Software O O O B-Citation I-Citation I-Citation I-Citation O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject I-CelestialObject O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O B-CelestialObject O B-Citation O O O O O O B-CelestialObject O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O B-Citation O O O O O B-CelestialObject O O O B-CelestialObject O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O B-CelestialObject I-CelestialObject B-CelestialObject B-CelestialObject B-CelestialObject O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O B-CelestialObject O O O O O O B-Citation O O O O O O O O B-CelestialObject I-CelestialObject O O O B-CelestialObject I-CelestialObject I-CelestialObject O O O O O O B-CelestialObject O O B-CelestialObject I-CelestialObject O O O O O O O O O B-CelestialObject O O O O O O O O O O O O B-Citation O O B-Citation O O O B-CelestialObject O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O B-CelestialObject O O

[Fe/H] ∼ −1.6 and an outer halo of [Fe/H] ∼ −2.2 (Carollo et al. 2007 ). Although both components are expected to have contributions from accreted stars, it is the outer halo that is dominated by accreted stars (Tissera, White Scannapieco 2012 ). In the suite of eight MW-like model galaxies in the simulation by Tissera et al. ( 2012 ) the stars accreted from satellites have ages between 9.8 and 12.6 Gyr and metallicities of −1.45 [Fe/H] −0.86. Such ages and metallicities are consistent with the initial epoch of star formation and metallicity upper limits for the five disrupted satellites in our work (see Table 2 ). Using MW-like model galaxies based on the Evolution and Assembly of GaLaxies and their Environments (EAGLE) simulations by Mackereth et al. ( 2019 ) concluded that the MW’s assembly history was dominated by the accretion of a small number of satellites of stellar mass 10 8.5−9 M ⊙ , in reasonable agreement with our findings (see Table 2 ). 7.1 The Kraken/Koala satellite K19 describe their simulation of 25 galaxies with MW-like masses within the EAGLE galaxy formation model (Crain et al. 2015 ). Their suite of MW-like model galaxies undergoes an average of 4.4 ± 2.3 minor mergers. with mass ratios of 1:100 up to 1:4. This corresponds to satellite masses of 10 8−9 M ⊙ at the time of infall. They suggest that the vast bulk of the accreted GCs come from two nearly equal mass (∼10 9 M ⊙ ) satellites that merged in the last 10 Gyr. A third lower mass (∼10 8 M ⊙ ) satellite brings in a handful of GCs. Additional accreted satellites are of very low mass, containing few, if any, GCs. They associated the low-mass satellite with the Sgr dwarf galaxy and one of the massive satellites with the Canis Major dwarf galaxy (here called Gaia -Enceladus). The remaining, and perhaps most massive satellite, was not associated with any known feature of the MW by K19 but instead was given the name Kraken . They suggested the Kraken had a stellar mass of 2 × 10 9 M ⊙ and was accreted 6–9 Gyr ago. Although K19 do not associate any particular MW GC with the Kraken, they do list 18 GCs with the potential to be from the disrupted Kraken satellite. Here we have assigned those 18 GCs across the Gaia -Enceladus, Sequoia, Koala, Helmi satellites, and the high-energy group. So, while we do suggest that the MW accreted an additional satellite of mass M * ∼ 10 9 M ⊙ (i.e. Koala), we make different GC assignments to K19 . In their terminology, satellite 3 is the Sgr dwarf, satellite 2 is Gaia -Enceladus/Canis Major, and satellite 1 could be the Koala galaxy with ω Cen as its former nucleus. Our analysis indicates that the MW has accreted five satellites of mass 10 8−9 M ⊙ consistent with K19 . Overall, K19 concluded that the MW formed quite rapidly with no major mergers since z = 4 (i.e. the last 12.3 Gyr). This makes it somewhat atypical compared to their suite of 25 MW-like models. 8 CONCLUSIONS Using the IOM, the AMRs, and alpha-element ratios of MW GCs we


O B-Collaboration I-Collaboration O O O O B-Organization B-Organization O B-Organization B-Location B-Organization B-Organization O B-Organization B-Location B-Organization O B-Organization B-Location B-Organization O B-Organization B-Location B-Organization B-Organization B-Organization O B-Organization B-Location B-Organization B-Organization O B-Organization B-Location B-Organization O B-Organization B-Location B-Organization B-Location B-Organization I-Organization B-Location B-Organization B-Location B-Organization B-Location B-Organization B-Location B-Organization B-Location B-Organization O B-Organization B-Location O O O O B-Collaboration I-Collaboration O O O O O O O O O O O O O O O O O O O O O B-URL O O O O O O O B-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O B-Grant O O O O O O O O B-Organization B-ComputingFacility I-ComputingFacility I-ComputingFacility O O B-Organization I-Organization I-Organization O O B-Location B-Grant I-Grant I-Grant I-Grant I-Grant O O O O B-ComputingFacility I-ComputingFacility I-ComputingFacility O B-URL O O O O O O O O O O O O O O O O


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O O O O O O B-Software I-Software I-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O B-Instrument O O O O O O

models and derived systematics models and residuals for both transits in Fig. 2 . 3.2 Spectroscopic light-curve analysis To construct our spectroscopic light curves, we began by binning over the narrow wavelength regions shown in blue in Fig. 1 . For each transit, we extracted 53 individual light curves using a uniform bin width of 75 Å whilst avoiding the GMOS detector gaps. We show the resulting raw light curves in the left-hand panels of Figs 3 and 4 . The raw light curves for both transits show significant systematics which are largely independent of wavelength. This is a similar situation to that encountered for the FORS2 data set in Wilson et al. ( 2020 ) and we proceeded in much the same way by dividing each of the spectroscopic light curves by the common-mode corrections derived from the white light curves. Additionally, to remove any remaining high-frequency systematics, we also subtract the residuals from the white light curves fits. The net result of this procedure is to provide significant improvements to the precision of our transmission spectrum whilst preserving the relative values of the planet-to-star radius ratios. After applying the common-mode correction described above we find the best-fitting model for each spectroscopic light curve using the same process and the same systematics model as described in Section 3.1 . The main difference is that we fix the mid-transit time to the best-fitting value from the white light-curve analysis, whilst allowing the planet-to-star radius ratio, limb darkening parameters, linear baseline parameters and kernel hyperparameters to vary for each fit. As for the white light curves, we perform a joint analysis allowing for only a single planet-to-star radius ratio and limb darkening coefficient pair for each individual wavelength channel, but allow the hyperparameters to vary separately for each transit. To add another layer of flexibility to our model, we use wide Gaussian priors with a standard deviation of 0.1 for the limb darkening coefficients, with a mean given by the best-fitting values determined using pyldtk . We again used the same differential evolution algorithm to optimize the log-likelihood as for the white light curves, and fine-tuned using a Nelder–Mead simplex algorithm. Outliers deviating more than 4 σ from the predictive distribution were removed for each fit (this procedure typically clipped 1–2 points per light curve) and we used the same MCMC method described above to marginalize our posterior distributions. The best-fitting common-mode corrected GP models for the spectroscopic light curves are shown in the middle panels of Figs 3 and 4 and we list the measured planet-to-star radius ratios and uncertainties in Table 2 . Our GMOS transmission spectrum for WASP-121b is shown in Fig. 6 and the results are discussed further in Section 5 . Table 2. Transmission spectrum for WASP-121b recovered from the GMOS low-resolution spectroscopic light curves. 3.3 Modelling


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

This work is based on observations with the VLA, operated by the NRAO, and the 100-m telescope of the MPIfR (Max-Planck-Institut für Radioastronomie) at Effelsberg. This work is based in part on archival data obtained with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. This publication makes use of data products from the Wide-field Infrared Survey Explorer (WISE), 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. The author thanks the operators at the Effelsberg telescope for their support, the VLA team for performing the observations and help during data reduction, Bill Sherwood for processing the Effelsberg data at and Uli Klein for help with generating Fig. 27 . Aritra Basu, Michal Hanasz, Marita Krause, Woong-Tae Kim, and Fatemeh Tabatabaei are acknowledged for useful discussions and comments. Special thanks go to Marita Krause and Elly M. Berkhuijsen for careful reading of the manuscript, and to the anonymous referee for suggestions that helped to improve the paper. Financial support from DFG Research Unit FOR1254 is acknowledged.