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

Rayleigh tail of the dust blackbody spectrum, we do not expect a significant shift in the sources probed or in the polarization of each source between 95 and 150 GHz. Supporting this position, Bonavera et al. ( 2017b ) found the DSFG 〈 p 〉 to not vary with frequency from 143 to 353 GHz. For a reasonable mask flux threshold of 2 mJy, which is achievable by existing experiments like SPT-3G, the total polarized foreground power is less than the EE power spectrum out to l ≲ 5700 (l ≲ 4700) and the BB lensing power spectrum out to l ≲ 5300 (l ≲ 3600) at 95 (150) GHz. Figure 5. CMB polarization surveys will be minimally impacted by polarized source power after masking radio and dusty galaxies above reasonable flux thresholds. The predicted AGN power at 95 GHz (left-hand panel) and 150 GHz (right-hand panel) for the 〈 p 2 〉 measured in this work is shown by the solid red (yellow) line for a 50 (2) mJy masking flux threshold in temperature. The filled area represents the 1σ uncertainties on 〈 p 2 〉 (no uncertainty in the source distribution has been included). The dashed blue line and the filled blue area show the mean and the 1σ upper limit on the predicted DSFG polarized power, respectively. We have assumed that the fractional polarization of the DSFGs remains constant from the value measured at 150 GHz down to 95 GHz. The total polarized foreground power will be less than the CMB EE power spectrum out to l ≲ 5700 (l ≲ 4700) and CMB lensing BB power spectrum out to l ≲ 5300 (l ≲ 3600) at 95 (150) GHz. The green arrow in the right-hand panel shows the 95 per cent CL upper limit from the recent SPTpol EE power spectrum measurement (Henning et al. 2018 ). The polarization fraction measurements in this work support the expectation that extragalactic foregrounds will be fractionally smaller for CMB polarization than temperature measurements, thus allowing more modes to be used in polarization analyses. The equivalent angular multipole for temperature is l ∼ 4000 and l ∼ 3100 at 95 and 150 GHz, respectively – more modes will be available to cosmological studies in polarization than temperature. Note that the results are not particularly sensitive to the flux cut since the polarized foreground power envelope is being driven by DSFGs. The DSFG intensity power varies slowly with mask threshold since most DSFGs are fainter than 2 mJy. It is also worth noting that better measurements of the DSFG 〈 p 2 〉 are likely to make the AGN power, and thus masking threshold, somewhat more important. We have reasons to believe that the polarization of DSFGs is lower than AGN, but 1σ limits in this work are higher (at 150 GHz) for DSFGs due to the limited number of DSFGs in the sample. The inferred power from the measured 〈 p 2 〉 values in this work are compatible with current observational constraints. Using the measured EE bandpowers from the SPTpol survey at 150 GHz, Henning et al. ( 2018 ) places a 95 per cent confidence level upper limit of on polarized point


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The authors would like to thank Antonio Bibiano and the anonymous referee for their insightful comments. GMS is supported by a Swinburne University SUPRA postgraduate scholarship. DC acknowledges the receipt of a QEII Fellowship from the Australian Research Council. The Millennium and milli-Millennium Simulations used in this paper were carried out by the Virgo Supercomputing Consortium at the Computing Centre of the Max Planck Society. The data bases used in this paper and the web application providing online access to them were constructed as part of the activities of the German Astrophysical Virtual Observatory (GAVO).


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We thank B. Lembège for valuable discussions. The numerical simulations were performed on the Fujitsu PRIMERGY CX400 at Kyushu University. M.N. is supported by “Boursier du Gouvernement Français.” S.M. is supported by a Grant-in-Aid for Scientific Research (C) 25400479 from JSPS. We are also grateful to ISSI (Bern) for their support of the team “Physics of the injection of particle acceleration at astrophysical, heliospheric, and laboratory collisionless shocks.”


O O O O O O O O O O B-Instrument O O O O O B-Telescope O O O O O O O O O O B-Wavelength B-Model 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 B-Instrument O O O O B-Telescope O O O O O O O B-CelestialObject O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O B-Instrument O O O O O O B-Telescope B-Instrument O O O O O O O O O O O O O O O O O B-Instrument O O B-CelestialObject O B-Instrument 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 B-Telescope O O O O O O O O O O O B-Telescope B-Instrument O O O O O O O O O B-Telescope O B-Telescope 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-Instrument 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 B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O O B-Software 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 B-CelestialObject O O O O O O O O B-CelestialObject O O O O O O O O B-CelestialObject O O O O O O O O B-CelestialObject 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 B-Software I-Software I-Software 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 B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-Software O O O O O O O B-Model I-Model I-Model I-Model O O O O O O O B-Software O O O O O O O O O

of all neighboring sources in 120 radius fitting to the IRAC frame a higher-resolution image (the HST detection mosaic) convolved with a kernel reconstructed from the empirical IRAC PSFs and the PSF of the prior image. Aperture photometry (with a diameter of 18) is then performed after removing the models of the neighboring objects. A correction based on the PSF and on the light profile of the source under analysis is finally applied to obtain total fluxes. Table 4 gives the observed IRAC fluxes. As with the HST data, the photometry has been corrected for Galactic extinction (Schlafly Finkbeiner 2011 ), although this extinction does not affect the measurements, given the lack of precision in the IRAC data. Table 4 Candidate Super Eight Spitzer IRAC Magnitudes Notes. The 1 σ uncertainties are quoted for the non-detection upper limits. a There are no IRAC observations for Super8-6. The IRAC uncertainties were measured in the same manner as described in Stefanon et al. ( 2017 ). Briefly, the rms was calculated for the pixels in the apertures using the residual frames (science frame with all objects subtracted). Systematic errors from the kernel reconstruction are also taken into account and the final result was scaled using the aperture correction. We present the cutouts from the available HST bands in Figure 1 – 8 , as well as the Spitzer IRAC cutouts for the objects with available data. Figure 1. HST and Spitzer cutouts for Super8-1. The cutouts are 5′′ × 5′′ and centered on the candidate galaxy. The central circles have a radius of 09, matching the IRAC aperture, and are included to guide the eye. For the IRAC cutouts, both the original images (top) and images after neighbor subtraction (bottom) are shown. The bottom right figure gives the photometry and fit SED for Super8-1. The observed photometry is shown with black open squares, while the fit is shown with dark pink open circles. The fit spectrum is shown with a solid pink line. The upper limits indicate the 1 σ uncertainties. The probability distributions from the redshift fitting performed using EAZY (pink) and BPZ (green) are given in the lower rightmost panel. Figure 2. Same as Figure 1 but for Super8-2. Figure 3. Same as Figure 1 but for Super8-3. Figure 4. Same as Figure 1 but for Super8-4. Figure 5. Same as Figure 1 but for Super8-5. Figure 6. Same as Figure 1 but for Super8-6. Figure 7. Same as Figure 1 but for Super8-7. Figure 8. Same as Figure 1 but for Super8-8. 4. Photometric Redshift Fitting We performed photometric redshift fitting using the EAZY fitting code (Brammer et al. 2008 ). This algorithm can quickly fit linear combinations of spectral templates that are based on semianalytic models (SAMs), rather than spectroscopic samples. Brinchmann et al. ( 2017 ) determined that the least-biased EAZY template for high-redshift galaxy fitting is the eazy _ v1.3 template. This template is based on the original EAZY template that was developed from synthetic galaxy photometry using


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This work was supported under NASA Contract No. NNG08FD60C, and made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA).


O O O B-Person I-Person I-Person B-Person I-Person B-Person I-Person B-Person I-Person O B-Person I-Person O O O O B-Person I-Person O O O O B-Person I-Person O O O B-Person I-Person O O O O O O O B-CelestialObject I-CelestialObject O O B-Person I-Person O O O O O O O B-Observatory O O O O O O O O O B-Observatory O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O B-Survey O O O O B-Organization O B-Grant O B-Organization O O B-Organization I-Organization I-Organization B-Person O B-Person O O O B-Organization O B-Grant O B-Grant B-Survey O O O O O O B-Organization I-Organization I-Organization I-Organization O O O B-Telescope I-Telescope I-Telescope O O O O O B-Grant B-Organization I-Organization I-Organization I-Organization B-Organization O O B-Grant I-Grant I-Grant B-Location B-Organization I-Organization I-Organization 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 I-Grant B-Person O O O O B-Organization B-Fellowship I-Fellowship I-Fellowship I-Fellowship O O B-Fellowship O O B-Person O O O O O B-Organization O O B-Grant O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant O O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Person O O O B-Organization O B-Grant B-Person O O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant O O B-Organization I-Organization I-Organization I-Organization O O B-Grant B-Person O B-Person O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O B-Grant I-Grant I-Grant I-Grant O B-Person O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Person O O O O B-Organization O O B-Grant B-Grant O B-Grant O O O O O O O O B-Organization I-Organization I-Organization I-Organization O O O B-Observatory I-Observatory I-Observatory O B-Telescope I-Telescope O O O O B-Location I-Location I-Location I-Location O B-Organization I-Organization I-Organization I-Organization O O B-Location B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O B-Location B-Organization I-Organization I-Organization I-Organization I-Organization O B-Telescope I-Telescope I-Telescope O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory B-Location I-Location I-Location O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O O O O B-Organization O B-Grant O O O O O O O B-Instrument O O O O O B-Organization O B-Grant O O B-Organization B-Grant I-Grant I-Grant I-Grant B-Grant O O O O O B-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O B-Telescope O O O O O O O O B-Location I-Location B-Location O B-Location O O O O O O O B-Telescope I-Telescope I-Telescope I-Telescope O O B-Observatory I-Observatory I-Observatory B-Location B-Software O O O O B-Observatory I-Observatory I-Observatory I-Observatory O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O O O O O B-Organization I-Organization I-Organization O O O B-Survey O B-Survey O O O O O B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization I-Organization O B-Location B-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization I-Organization I-Organization O B-Location B-Organization O B-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization O B-Location O B-Survey O O O B-URL O O O O O O O O O O B-Survey I-Survey I-Survey I-Survey I-Survey O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O B-Database I-Database I-Database B-Database O O O O O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O B-Software I-Software I-Software I-Software B-Software O O O O O O B-Organization I-Organization I-Organization B-Organization B-Location

The authors thank Jennifer van Saders, Curtis McCully, Peter Brown, Saurabh Jha, and Louisa Diodato for discussions and encouragement, Toru Yusa for aid in translation, Joe Antognini for reading, and Ryan Foley for providing us with the spectra of SN 2009ig. We thank Neil Gehrels for approving our ToO requests and the Swift science operation team for performing the observations. We thank LCOGT and its staff for their continued support of ASAS-SN. We also thank the anonymous referee for his/her helpful comments and suggestions improving this manuscript. Development of ASAS-SN has been supported by NSF grant AST-0908816 and CCAPP at the Ohio State University. C.S.K. and K.Z.S. are supported by NSF grants AST-1515876 and AST-1515927. ASAS-SN is supported in part by the Mt. Cuba Astronomical Foundation. Operations of the Cassius ASAS-SN station are partially funded from project IC120009 Millennium Institute of Astrophysics (MAS) of the Millennium Science Initiative, Chilean Ministry of Economy. B.S. is supported by NASA through Hubble Fellowship grant HF-51348.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS 5-26555. T.W.-S.H. is supported by the DOE Computational Science Graduate Fellowship, grant number DE-FG02-97ER25308. Support for J.L.P. is in part provided by FONDECYT through grant 1151445 and by the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to the Millennium Institute of Astrophysics, MAS. J.F.B. is supported by NSF grant PHY-1404311. S.D. is supported by “the Strategic Priority Research Program—The Emergence of Cosmological Structures” of the Chinese Academy of Sciences (grant No. XDB09000000). E.H. and M.S. are supported by the Danish Agency for Science and Technology and Innovation realized through a Sapere Aude Level 2 grant. P.R.W. is supported by the Laboratory Directed Research and Development program at LANL. CSPII is supported by the NSF under grants AST-0306969, AST-0607438, and AST-1008343. This paper uses data products produced by the OIR Telescope Data Center, supported by the Smithsonian Astrophysical Observatory. The Liverpool Telescope is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias with financial support from the UK Science and Technology Facilities Council. The Nordic Optical Telescope is operated by the Nordic Optical Telescope Scientific Association at the Observatorio del Roque de los Muchachos, La Palma, Spain, of the Instituto de Astrofisica de Canaries. This paper is based in part on observations obtained through the CNTAC proposal CN2015A-119. This paper used data obtained with the MODS spectrographs built with funding from NSF grant AST-9987045 and the NSF Telescope System Instrumentation Program (TSIP), with additional funds from the Ohio Board of Regents and the Ohio State University Office of Research. The LBT is an international collaboration among institutions in the United States, Italy, and Germany. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile. IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web site is http://www.sdss.org/ . This research has made use of data from the AAVSO Photometric All Sky Survey, whose funding has been provided by the Robert Martin Ayers Sciences Fund. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research has also made use of the XRT Data Analysis Software (XRTDAS) developed under the responsibility of the ASI Science center (ASDC), Italy.


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Acknowledgements We thank Julien Devriendt, Bhuvnesh Jain, Vinu Vikram and Risa Wechsler for helpful discussions during the course of this work, and Tessa Baker, Gary Mamon, Stacy McGaugh and an anonymous referee for comments on the manuscript. HD received support from the Balzan foundation via New College, Oxford, the U.S. Department of Energy under contract number DE-AC02-76SF00515, and St John's College, Oxford. PGF is supported by ERC H2020 693024 GravityLS project, the Beecroft Trust and STFC. GL acknowledges financial support from ‘Programme National de Cosmologie and Galaxies’ (PNCG) of CNRS/INSU, France. GL is supported by ANR grant ANR-16-CE23-0002. This work made use of the Dark Sky simulations, which were produced using an INCITE 2014 allocation on the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory. We thank the Dark Sky collaboration for creating and providing access to this simulation, and Sam Skillman and Yao-Yuan Mao for running rockstar and consistent trees on it, respectively. Additional computation was performed at SLAC National Accelerator Laboratory. This research was supported by the DFG cluster of excellence ‘Origin and Structure of the Universe’ ( www.universe-cluster.de ). This work was partly made in the ILP LABEX (under reference ANR-10-LABX-63) supported by French state funds managed by the ANR within the Investissements d'Avenir programme under reference ANR-11-IDEX-0004-02.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey I-Survey I-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

the CO abundance model and the spatial resolution of the synthetic observations. We keep the velocity resolution fixed, to match that of the observations, because our main focus is the comparison of the simulation with the Outer Galaxy Survey. We will study the effect of varying both the spatial and the velocity resolution in a separate work. To look for possible trends related to the spatial resolution also in the observations, we divide the observed clouds into six slightly overlapping distance intervals, corresponding to the spatial resolution intervals [ x i /1.25, x i × 1.25], with x i = 0.5 × 2 i /2 pc, i = 0, ..., 5 (see Table 1 ). The number of observed clouds, N i , in the x i intervals is N 0, ...5 = 212, 584, 1936, 1816, 360, and 88. The third interval is the most populated one and has an average resolution of 1.04 pc, nearly identical to that of our fiducial model. This procedure of dividing the observed clouds into distance intervals is not exactly the same as reobserving with different resolutions clouds at a fixed distance, as in the synthetic observations. In the real observations, clouds are selected from maps containing objects with a rather large range of distances. Thus, the following comparison of the dependence on resolution of simulated and observed clouds should be considered of a qualitative nature. We first consider the dependence of the slopes of the mass and size distributions, β M and β R , respectively, on the CO abundance model and on the resolution, shown in Figures 8 and 9 . The squares show the mean spatial resolution and mean slope derived from the observations in each distance interval (the lowest-resolution bin is not shown because the power-law fit to the histograms has too large uncertainty due to the small number of clouds). The shaded areas give the 1 σ uncertainty in the estimated slope and the standard deviation of the spatial resolution values (the statistical uncertainty in the resolution, from the cloud distance uncertainty, may be even larger). Figure 8. Power-law exponents of the fits to the mass distributions vs. spatial resolution. Each plot shows a different model for the assumed relative abundance of CO, with the error bars giving the 1 σ uncertainty of the estimated slope. The square symbols are the values from the different distance intervals of the observational sample (see text), and the shaded areas show the standard deviation of the mean spatial resolution within each interval and the estimated 1 σ uncertainty of β M . The lowest-resolution interval of the observations and the lowest-resolution value of model V3 are not shown due to their excessive uncertainty. Figure 9. Power-law exponents of the fits to the size distributions vs. spatial resolution. As in the previous figure, each plot shows a different model for the assumed relative abundance of CO, with the error bars giving the 1 σ uncertainty of the estimated slope. The square symbols are the values from the different distance


O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O B-Model 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 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 B-Model O O O O O O O O O O O O O O O B-Model I-Model I-Model I-Model O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O O B-Model O B-Model I-Model I-Model I-Model I-Model O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O O

from PFS are expected to be less competitive against existing cosmological probes. Notice that, observations by SDSS galaxy surveys in the low to moderate redshift range 0.15 z 0.67 is already being used to set interesting constraints on any departure from GR, e.g. Bel et al. ( 2015 ) obtained at the 2σ level. We would also like to stress that our results are based on all-sky analysis and we use an f sky scaling to take into account the effect of partial sky coverage. Surveys that cover a small fraction of sky such as PFS will have to deal with strong mode–mode coupling that drives the covariance matrix to near singularity. A detailed analysis will be presented elsewhere. Our constraints are derived for a spectroscopic survey. Inclusion of photometric redshift error will degrade the discriminating power of the survey. The comparison of the linear and non-linear curves in Figs 12 and 13 suggests that the non-linear contributions can make a significant effect. However, in the non-linear regime the covariance matrix should include the bispectrum and the trispectrum of the density field, which would reduce the χ 2 . Therefore, we can expect that a fully non-linear analysis, with a better modelling of the covariance matrix (e.g. from numerical simulations) would lower down the non-linear curves and make them closer to the linear result. However, because the departures from the ΛCDM power spectrum increase on weakly non-linear scales, non-linearities should still improve the discriminatory power of the analysis of 3D clustering as compared with the linear result. Here we follow a conservative approach as we estimate the constraints on the MG parameters by using the linear-theory χ 2 . We have studied the constraints in both f ( R ) and Dilaton models with and without RSD. We find that RSD only lead to a small broadening of the constraints. Below galaxy number density of the surveys rapidly lose their ability to discriminate. On the other hand, increasing the number density beyond does not lead to drastic improvements of the results. Our constraints are based on lower order tangential modes l ≤ 25. We find that the information contents in different l are highly degenerate. In the range 0.01 ≤ k ≤ 0.2 h Mpc −1 , we find that for more than three to four (logarithmic) bins in k the covariance matrix can become singular. Finally, our results are based on a uniform distribution of noise (constant ) while real surveys may have more complicated variations in the average galaxy number density which may depend on the radial and angular coordinates in the sky. 6 DISCUSSION AND FUTURE PROSPECTS In this paper we have studied the possibility of constraining MG theories using galaxy clustering. We have studied two different MG models: f ( R ) theories and Dilaton theories. In both cases we have used a specific parametrization and computed the χ 2 as a criterion for constraining the departure from the ΛCDM models. We used the sFB transforms and 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Identifier O O B-Dataset I-Dataset I-Dataset B-Citation I-Citation I-Citation I-Citation 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 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 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 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 B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation 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 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 B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-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 B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation 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 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 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 B-Citation I-Citation I-Citation I-Citation 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 B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O B-CelestialObjectRegion I-CelestialObjectRegion O B-CelestialObject I-CelestialObject B-Citation I-Citation I-Citation O O O O O O O B-CelestialObjectRegion I-CelestialObjectRegion B-CelestialObject I-CelestialObject O B-CelestialObject

trials. There are nine pulsars in our sample that have had RMs measured for the first time. These are listed in Table 3 . PSR J1643−4505, once the high RM is taken into account, joins the ranks of pulsars with high and very high linear polarization fraction. In contrast, the linear polarization fraction for the high pulsar PSR J1055−6028 remains low even after the RM correction. We note the extremely high value of RM uncovered for PSR J1114−6100, described in more detail in the subsection below. We have found a substantial number of pulsars for which our value of RM is more than 10 rad m −2 away from the published values as given in v1.63 of the ATNF pulsar catalogue (Manchester et al. 2005 ). 1 These pulsars are listed in Table 4 . The published value of PSR J1413−6141 was seriously in error, applying the correct value reveals that this pulsar has only a moderate linear polarization fraction. For PSR J0857−4424, the published value from 20 yr ago of −75 rad m −2 is erroneous; we measure +162 rad m −2 , a difference of more than 200 units. Table 3. Pulsars without previous RM measurements. The uncertainty is given in brackets and refers to the last digit. Table 4. Pulsars with derived RMs that differ by more than 10 rad m −2 from the published values, RM cat . The uncertainties, σ and σ cat , are also listed. Note . References: (1) Crawford, Manchester Kaspi ( 2001 ), (2) Hamilton Lyne ( 1987 ), (3) Han et al. ( 2006 ), (4) Han et al. ( 2018 ), (5) Han, Manchester Qiao ( 1999 ), (6) Johnston Weisberg ( 2006 ), (7) Johnston et al. ( 2007 ), (8) Noutsos et al. ( 2008 ), (9) O’Brien et al. ( 2008 ), (10) Rand Lyne ( 1994 ), (11) Taylor, Manchester Lyne ( 1993 ), (12) Weltevrede Johnston ( 2008 ). We observe that there are sometimes significant differences between the RMs returned by the two methods as can be seen in Fig. 7 . Karastergiou ( 2009 ) pointed out that the effects of interstellar scattering can lead to erroneous measurements of RM, and that the method of Noutsos et al. ( 2008 ) was best to use in the case of scattered profiles. In addition, Ilie et al. ( 2019 ) showed the presence of (apparent) RM variations across the pulse profile that meant that different RMs can be obtained depending on the method used. The most extreme example of this is PSR J1600−5044 for which we obtain an RM of 90 and 135 rad m −2 using the two methods. At low frequencies, the pulsar is heavily scattered, at high frequencies the position angle of the linear polarization has a very steep swing across the pulse profile. This is the scenario pointed out by Karastergiou ( 2009 ) as being the most detrimental to measuring correct values of RM. 6.1 PSR J1114−6100 We measured a value of −6729 ± 2 rad m −2 for the RM of PSR J1114−6100. This is the fourth highest value of |RM| for any known pulsar, beaten only by the Galactic centre magnetar PSR J1745−2900 (Shannon Johnston 2013 ), and two other pulsars near the Galactic centre, PSRs J1746−2849 and J1746−2856


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We thank the referee for constructive comments which helped to improve the manuscript. This work was granted access to the PRACE Research Infrastructure resource Curie hosted at the TGCC (France). FR acknowledges support from the European Research Council through grant ERC-StG-335936 . BF acknowledges the financial support from the Programme Investissements d'Avenir (PIA) of the Initiative d'Excellence (IdEx) programme from Université de Strasbourg.


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massive star clusters (or OB associations) is a power-law with slope ξ ≃ −2 ± 0.5 (Rosolowsky 2005 ; Hopkins 2012 ): where, assuming ξ = −2 (Hopkins 2012 ), we find A = M *, gal /Λ, with . Assuming M ⊙ and M ⊙ , we estimate Λ ≈ 9.2. Therefore, assuming an escape fraction from the atomic phase of the ISM in the galaxy (defined excluding the absorption due to the molecular cloud) that is constant as a function of the cluster mass, we find: Therefore, as anticipated before in Section 3.3 , in the local Universe (fiducial clouds) the escaping ionizing radiation from a galaxy is produced by roughly equal contribution from small and large mass star clusters, and the number of escaping photons is ∼10 59 per unit solar mass in stars. Therefore, the total escaping radiation is quite insensitive to the upper and lower mass limits of the mass distribution of OB associations. Compact star clusters are similar but with ∼10 times more ionizing photons per mass in stars. For very compact clouds (100 times denser than the fiducial clouds) the escaping ionizing radiation is dominated by the few most massive star clusters in the galaxy, and the number of escaping photons per units star mass is about 40 times higher than for the fiducial clouds. Also, if we make the simple assumption that the mass of the most massive star cluster is related to the total stellar mass M *, gal of the galaxy, by setting , we find . Hence, if star clusters in high-redshift galaxies form in very compact molecular clouds, massive galaxies would be more efficient contributor to propel reionization than dwarf galaxies. Of course the discussion above is only valid if is constant not only as a function of the star cluster mass but also as a function of the mass of the galaxy. Similar to S esc, tot , we can estimate the total emitted ionizing radiation by OB association: and the mean escape fraction from a galaxy by taking the ratio : This last equation confirms that from galaxies in the local Universe (fiducial clouds) is extremely small , and only assuming that molecular clouds at redshift z > 6 were 100× denser than in the local Universe is possible to propel reionization with UV radiation from massive stars in galaxies. 5 SUMMARY AND CONCLUSIONS In this paper, the second of a series, we calculate the hydrogen- and helium-ionizing radiation escaping realistic young star cluster forming in turbulent molecular clouds. To the best of our knowledge this is the first work in which is calculated by self-consistently simulating the formation, UV radiation feedback, and contribution to the escaping ionizing radiation from individual massive stars producing the observed IMF slope and normalization. We used a set of high-resolution radiation-magnetohydrodynamic simulations of star formation in self-gravitating, turbulent molecular clouds presented in He et al. ( 2019 ), in which we vary the mass of the star-forming molecular clouds between m gas = 10 3 M ⊙ and 3 × 10 5 M ⊙ and adopt gas


B-Person O B-Person I-Person O O O O O O B-Instrument O O O O O O O B-Person I-Person O O O O O O B-Instrument O B-Person O O O O O O O O O O O O O O B-Person O O O O B-Organization I-Organization O O B-Fellowship I-Fellowship I-Fellowship I-Fellowship I-Fellowship B-Person O B-Person O O B-Organization I-Organization I-Organization I-Organization O O O O O O O O B-Grant B-Person O B-Person O O O O O B-Organization B-Organization I-Organization I-Organization O O B-Grant I-Grant I-Grant I-Grant I-Grant O B-Person O O O O O B-Organization I-Organization I-Organization O O B-Organization O B-Grant B-Person O O O O B-Organization B-Grant I-Grant I-Grant B-Person O B-Person O O O O O B-Organization O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant B-Grant I-Grant O B-Grant I-Grant O O O O O O O B-Dataset I-Dataset I-Dataset I-Dataset O O O B-Organization I-Organization I-Organization O O O O O O B-Survey I-Survey I-Survey I-Survey I-Survey O O O O O O O B-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization O O O O O O O O O B-Organization I-Organization I-Organization B-Organization O B-Telescope O B-URL O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization B-URL O O O O B-Organization O O O O O O O O O O O O O B-Collaboration I-Collaboration I-Collaboration O O O O O B-Software O B-URL O O O O B-Software O O O O B-Citation I-Citation I-Citation O B-Citation O


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the spacecraft to the magnetic null (see Fu et al. 2015 for details). The PI method significantly depends upon the size of SC tetrahedron. Usually, a large tetrahedron (large SC separation) is favorable for null detection, while a small tetrahedron (small SC separation) is not. In contrast to the PI method, the FOTE method can detect a null both inside and outside of the tetrahedron, and can avoid limitations of the PI method such as data resolution, instrument uncertainty ( offset), and tetrahedron size (Fu et al. 2015 ). Taking the advantages and disadvantages of these two methods into account, in this study we will use the FOTE method to find magnetic nulls. In addition to null detection, the FOTE method can also be used to identify magnetic-null types based on the three eigenvalues () of the matrix and reconstruct magnetic-null topology by tracing magnetic field lines around the null (Fu et al. 2015 , 2016 ). In theory, the sum of the three eigenvalues is zero , meaning that either all of the three eigenvalues are real, or one is real while the other two are conjugate complex. If all of the eigenvalues are real, the null is of radial type, which includes A- and B-type. If one eigenvalue is real while the two others are conjugate complex, the null is of spiral type, which includes - and -type. A, B, and , are all null types in the 3D regime (Lau Finn 1990 ). They can, however, degenerate into 2D structures in certain conditions. Following Fu et al. ( 2015 ), we simplify the A- and B-type nulls to 2D X-line when , and simplify the - and -type nulls to 2D O-line when . If the relationship among the three eigenvalues belongs to none of the above six categories, we classify the null type as “unknown.” Such identification of magnetic-null types is summarized in Table 1 . Table 1 Null Type Identification Using the Three Eigenvalues () of Matrix δ , which is Derived from Four-point Measurements 3. Observations and Analyses 3.1. Event Overview The Earth's magnetosheath is a region downstream of the bow shock where the magnetosphere interacts with the solar-wind flow. Due to this interaction, usually strong turbulence of magnetic field and its resultant thin current sheet, where magnetic reconnection quite possibly occurs, can be observed (Burgess et al. 2005 ). Therefore, the magnetosheath is an ideal place to investigate magnetic reconnection driven by turbulence or reconnection in turbulent plasmas. In this study, the reconnection event we consider was measured by Cluster (Escoubet et al. 2001 ) on 2002 March 27. This event has been reported by Retinò et al. ( 2007 ) and further investigated by others (e.g., Sundkvist et al. 2007 ; Chasapis et al. 2015 ). In this event, Cluster was located in the magnetosheath at [9.6, −8.0, −8.1] RE in Geocentric Solar Ecliptic (GSE) coordinates; it forms a regular tetrahedron, with inter-spacecraft separation of ∼100 km. Such separation is quite small, so that the measurements of magnetic field by four Cluster


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KEKC thanks the Science and Technology Facilities Council of the United Kingdom (STFC) and JEG thanks the Royal Society for a University Research Fellowship. JSD acknowledges the support of the European Research Council via the award of an Advanced Grant, the support of the Royal Society via a Wolfson Research Merit Award, and both JSD and NB acknowledge the contribution of the EC FP7 SPACE project ASTRODEEP (Ref. No: 312725). AK acknowledges support by the Collaborative Research Council 956, subproject A1, funded by the Deutsche Forschungsgemeinschaft (DFG). We would like to thank Alexandra Pope for useful discussions regarding stacking of clustered populations and Martin Hardcastle for suggesting to perform the radio stack. This work is based on observations carried out with SCUBA-2 on the James Clerk Maxwell Telescope (JCMT). The JCMT is operated by the Joint Astronomy Centre on behalf of the STFC, the National Research Council of Canada, and (until 2013 March 31) the Netherlands Organisation for Scientific Research. Additional funds for the construction of SCUBA-2 were provided by the Canada Foundation for Innovation.


B-Wavelength I-Wavelength O B-Wavelength I-Wavelength O B-Telescope O O O O O O O O O O O O O B-Telescope O O B-Wavelength 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 B-Wavelength O B-Wavelength O O O O O O O O O O O O O O O O O O O O O B-Mission O B-Mission O O O O O B-Observatory O B-Telescope O O O O O O O O O B-Telescope 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-Wavelength O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O B-Wavelength B-Wavelength O B-Telescope 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 B-Mission B-Wavelength O O O O O O O O O O O O O O B-Wavelength B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O B-Wavelength O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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-Telescope O O O O O O B-Wavelength O O O O O O O O O B-Telescope O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O B-Telescope O B-Telescope O O O O O O O B-Wavelength B-Wavelength O B-Telescope O O O O O B-Model I-Model I-Model B-Model O O O O O O O O O B-Observatory O O O O O B-Telescope O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O

211 A and 171 A from AIA, and the bottom panels are the line-of-sight (LOS) magnetogram and intensity map from HMI. For the EUV images, our study utilizes bandpass filter images, and as such examines only periodicity in the EUV bandpass brightnesses, and not in other properties such as line Doppler velocity or Doppler width. The EUV and radio data were examined for periodicities utilizing a method that identified peaks and valleys above a threshold value in the normalized power (PSP and Wind) or normalized detrended light curve (SDO/AIA and HMI). The detrending time periods and intervals for analysis of AIA data were selected to avoid introducing artificial periods ( Auchère et al. 2016 ; Dominique et al. 2018 ). Periodicities for the radio data were determined both using the measured values and using data interpolated to the cadence of the AIA data. Figure 4 shows an example of the “peak” approach for the radio, EUV and HMI data sets. Panels a and b show the normalized power above average for the two frequency bands (6.2 and 18.4 MHz) from the PSP radio data versus time. Panel c plots the resulting average periods versus frequency for the HFR (>5 MHz) band, which was determined using a threshold of one for the normalized power. Panels d and e plot the detrended normalized intensity for two AIA bands, 171 and 211Å in Region 2; and panel f plots the average period versus temperature (based on the peak of the temperatureresponses of each bandpass for the six coronal lines and the one photospheric line) for Region 2, where the periodicitieswere most prominent. The AIA temperature responses are overplotted; colors identify the same lines for both periodicity and temperature response. It should be noted that all of the AIA bandpass filters have broad temperatureresponses and some have bimodal responses. We cannot use these data to measure a strict temperature without differential emission measure analysis, but the peak temperature response gives a rough estimate of which temperaturerange we are likely to be observing. For the filters that have a doubly peaked response, we plotted the temperatures of both peaks for reference. Although some studies of quasi-periodicities in EUV lines have identified a temperaturedependence, the periods we observe are independent of temperature to within the error bars. Panels g and h plot the time series of the HMI intensity for two regions within the EUV subregion 0, and the periods determined for all three HMI regions are plotted in panel i. It is clear that the periodicity in the radio waves (~4–5 min) is comparable to those in the AIA and HMI data. We note that periodicities in the radio, EUV and HMI data were also determined using fast Fourier transforms (FFTs), with similar results. Fig. 3 Regions analyzed overlaid on SDO images. Top row : two AIA filters that have sensitivity to quiescent coronal temperatures, and bottom row : HMI line-of-sight magnetogram and intensity map. Images shown are at a time near the


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J.R. acknowledges funding support from the French Research National Agency: CHAOS project ANR-12-BS05-0009 ( http://www.chaos-project.fr ). J.R. also acknowledges financial support from the UnivEarthS Labex program of Sorbonne Paris Cité (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02).


O O O O O O 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-TextGarbage I-TextGarbage I-TextGarbage O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O B-Citation I-Citation O O O O O O B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O B-CelestialObject O B-CelestialObject O O O O O O O O B-CelestialObject O B-CelestialObject 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 B-CelestialObject O O O O B-CelestialObject O O O O O O O O O O B-Model O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O O O O B-CelestialObject O O O B-CelestialObject O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O B-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O O O O O O O B-Model I-Model O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O B-Model B-Model O O B-Citation I-Citation I-Citation I-Citation O O O B-Database I-Database I-Database I-Database B-Database 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 B-CelestialObject O O O O O O O O O O O O O O B-Model B-Model O B-Database O O O O O O O O O O O O O O O B-CelestialObject O O B-Model O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

width measurements of several unblended Fe i lines. Projected rotation velocities () were determined by fitting the profiles of the Fe i lines after convolving with the instrumental resolution ( = 75 000) and a macroturbulent velocity adopted from the calibration of Doyle et al. (2014) . We also estimated from the CCF using the procedure of Boisse et al. (2010) and found consistent results: km s and km s for WASP-113 and WASP-114, respectively. Furthermore, we estimated the spectral type using Teff and Table B1 of Gray (2008) . The derived stellar parameters for WASP-113 and WASP-114 are given in Table 4 . We find that the stellar parameters of WASP-113 and WASP-114 are very similar. Table 4 Stellar parameters of WASP-113 and WASP114 from spectroscopic analysis. Notes. Spectral type estimated from and Table B1 of Gray (2008) . 3.1. Stellar age Fig. 6 Modified H-R diagrams for WASP-113 on the left and WASP-114 on the right . We display the stellar models of YY ( Demarque et al. 2004 ). The isochrones are shown solid black line (for Gyr old stars from left to right ) and and the mass tracks in red ( from left to right we show 1.3, 1.2, and 1.1 ) for the corresponding metallicity given in Table 4 . Combining the sin with the stellar radius we derive an upper limit to the stellar rotation period of days and days for WASP-113 and WASP-114, respectively. These allow us to derive an upper limit to the gyrochronological age of Gyr for WASP-113 and Gyr for WASP-114 using Barnes Kim (2010) . However, the lithium abundance suggests an age of Gyr for both stars ( Sestito Randich 2005 ). A discrepancy between the age indicators is not unusual ( Brown 2014 ; Maxted et al. 2015 ); however, the reason is still not clear. A more precise constraint on the age can be obtained by using theoretical stellar models. We follow the procedure of Brown (2014) using three sets of stellar models: the Padova models of Marigo et al. (2008) and Bressan et al. (2012) , the Yonsei-Yale (YY) isochrones of Demarque et al. (2004) , and the Dartmouth Stellar Evolution Database (DSED) models of Dotter et al. (2008) . We use the values of and [Fe/H] derived from the spectroscopic analyses and the transit derived stellar density whose derivation is explained in the next section. For WASP-113 we found an age of Gyr, Gyr, and Gyr respectively for the models of Padova, YY, and DSED. In the left panel of Fig. 6 we show the isochrones and mass tracks for WASP-113 for the YY models. According to these models WASP-113 has a mass of , which is very close to the critical mass for the stellar cores to become convective. Owing to the convective cores, the evolution and consequently the shape of the evolutionary tracks for higher mass stars is significantly different from lower mass stars, as can be seen in this figure. In this region of the parameter space details on the treatment of convective overshooting become important and the uncertainty in the stellar age and mass are higher. We


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

the effect of granular convection. Small coloured particles were put on the bottom layer, and the entire apparatus was shaken at Γ = 4.0 for around 12 000 cycles. After the oscillations, it was confirmed that the coloured small particles either remained at the bottom or were pushed to the cylinder wall, but none of them moved to the centre and rose upwards. This shows that granular convection was only slightly induced beside the cylinder wall, but it was not the dominant mechanism for the BNE in our experimental set-up. In this sense, both our numerical simulations and hardware simulations suggest the existence of ‘less-convective’ conditions. 4.2 Results and discussion Variation of the vibration amplitude The necessary conditions to induce the BNE were first investigated by focusing on the vibration amplitude. The amplitude was varied between 2.5, 5.0, and 10.0 mm, and the frequency was also varied to vary the parameter Γ for each amplitude. Fig. 12 shows the results of the evaluation of the BNE and the RBNE. For each amplitude value, the RBNE was induced over Γ ≈ 3.5, which is consistent with the evaluation by the one-dimensional model. Fig. 13 (upper panel) shows the number of cycles it took for the intruder to rise to the surface. The numbers of cycles required to rise to the surface was much larger than for the nominal cases of the simulations as a whole, especially under the conditions of case (b) for the one-dimensional model. This is because of the size difference of the small particles or the fact that both the particle–particle and particle–wall friction constants were lower in the laboratory experiments. It is clear that the BNE is more likely to occur as Γ increases. The decrease in the number of cycles plateaued at Γ ≈ 1.9 and this indicates that the mechanism for inducing the BNE changed at this point. This result is interpreted as the boundary between the cases (a) and (b) for the one-dimensional model with the coefficient of restitution being 0.4 e 0.6, which was estimated from Fig. 3 . This is also fairly consistent with the assumptions of the numerical simulations. The estimated coefficient of restitution is an ‘empirical’ value, which may be different from the value obtained in the bouncing experiments. It can be stated that the cases (a) and (b) are, respectively, the ‘slow BNE’ case and the ‘fast BNE’ case. For the cases categorized as (a) in the one-dimensional model where Γ 1.9, it was evaluated by Δ h = Af (Γ, e ) (Fig. 2 ) as is shown in Fig. 13 (lower panel), where e = 0.6. Even if the amplitude was different, the number of cycles was similar for the same value of Δ h , which is compatible with our hypothesis. Variation of the density of the intruder The density of the intruder was varied and the vibration amplitude was fixed at 5.0 mm. Three kinds of material were prepared for the intruder: acryl (1.2 g cm −3 ), glass (2.4 g cm −3 ), and high-carbon chromium steel (7.8 g cm −3 ), which were all identical to the


O O O O O O O O O O O O O O O O O O O O B-Wavelength 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 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 B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O O O O O B-CelestialObject O B-CelestialObject O O O O O 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 B-Telescope O O B-Identifier O B-Identifier O O O O O O B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialRegion B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O O O O O B-CelestialRegion B-CelestialObject O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialRegion B-CelestialObject O B-CelestialObject 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 B-Wavelength O O O O O O O O O O O O O O O O O B-Telescope 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

in Column 6. The uncertainties are at the 1σ level and are calculated with behr (Section 3.2 ). (9) Logarithmic 0.5–7 keV peak flux converted from the peak count rate in Fig. 6 with the method in Section 2.2.1 . (10) The Method(s) responsible for identifying the transient candidate. In Fig. 6 (left), we show the light curves of the transient candidates during the observation when the transient happens. The light curves are derived from the X-ray events extracted in Section 3.2 , and are binned by 5-count intervals. The data points in these light curves indicate total count rates, including contributions from the source and background. The estimated average background count rate is marked as the dashed line in each panel of Fig. 6 (left). The durations of XT1 and XT2 tend to be shorter than for other transient candidates (Fig. 6 left). The T 90 values of XT1 and XT2 are and ks, respectively (see Bauer et al. 2017 and Xue et al. 2019 for details). We do not derive T 90 for other sources, because T 90 cannot be derived for many transients that extend beyond the Chandra exposures (e.g. ID3 and ID9 in Fig. 6 left). Also, unlike XT1 and XT2, many of the other transient candidates have non-zero fluxes in the quiescent states, and thus their T 90 calculation requires careful subtraction of the quiescent fluxes, which is beyond the scope of this work. Figure 6. Light curves for each transient candidate. The left-hand panels are light curves for the observation with the transient, with each bin including 5 counts. The horizontal dashed lines indicate the estimated average background count rates. The right-hand panels are long-term light curves with each data point representing a Chandra observation. The transient observation is highlighted in red colour. The horizontal dashed lines indicate a net count rate of zero. We plot the long-term light curves in Fig. 6 (right), where each Chandra observation is represented by a data point. These data points indicate net count rates, which are background-subtracted. As expected, the transient observation generally has a count rate much higher than other observations. However, unlike the CDF-S XT1 and XT2 events, most of the other transient candidates have detectable signals in some of the non-transient observations. Also, CDF-S XT1 and XT2 tend to have higher hardness ratios than the rest of the selected transient candidates (Fig. 5 ). These differences indicate that most of the new transient candidates are physically distinct from CDF-S XT1 and XT2 (see Section 3.3 ). 3.3 Optical/NIR counterparts We have compiled the likelihood counterpart matching results from the survey catalogues (Table 1 ). All the transient candidates have optical/NIR counterparts. The counterpart properties are presented in Table 3 . We also match the counterparts with the Gaia catalogue (Gaia Collaboration 2018 ) using a 1 arcsec matching radius, and mark the sources with non-zero parallax and/or proper motion as ‘star’ in Table


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Acknowledgements This work was supported by the University of Zurich Candocreseach grant (Forschungskredit Candoc; FK-14-081 and FK-15-087).


O O O O O O O O O O O B-CelestialObject O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O O B-CelestialObjectRegion I-CelestialObjectRegion O O O O B-CelestialObject O O O O O O B-CelestialObject I-CelestialObject O O O O O O O B-CelestialObjectRegion O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O 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 O O O O O O O O O O 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 B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-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 B-CelestialObject O O O O O O O O O O O O O O B-Mission O O B-CelestialObject B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O

distance and shows a greater emission of gas and dust. Here, solar gravity and solar radiation pressure are shaping the morphology of the dust coma, and its tail is in fact obvious and oriented in nearly antisolar direction. Dust emission in comet 2I/Borisov seems to be less collimated than C/2014 B1, with the dust particles widely scattered in antisolar direction, possibly due to the combination of a lower ejection speed and the effect of a greater radiation pressure. Our estimate of the size of dust particles in the range of 30–100 μ m radius is consistent with that recently reported by Jewitt Luu ( 2019 ). However, it should be considered that in both cases the estimates apply to particles displaced to the end of the visible tail in order to reproduce its apparent length. In fact, using higher β values in the Finson–Probstein diagram, in the region close to the nucleus the simulation approaches the wide scattering of the emitted dust observed in the HST images, which might be suggestive of the presence also of dust particles with a smaller size. 4.2 An emissive rotating structure Assuming that the nucleus of comet 2I/Borisov is rotating and that the geometric position of the spin axis is such to favour a prolonged insolation, the dust emission from a single source located on the sunlit hemisphere should be modulated by the insolation and the resulting features of the inner coma should provide indications on the orientation of the spin axis. In the case of two emissive structures with similar morphology that are observed in approximately opposite directions, it could be supposed that both originated from the same active source on a nucleus that is rotating with a spin axis coinciding with the axis of the angle between the two structures and, if so, that they would be more evident when their position corresponds to the projection on the plane of the sky for the greater condensation of material on the perpendicular to the view (Sekanina 1987 ). The finding of a jet at PA 180 ○ and of its counterpart at PA 10 ○ suggests that the active area originating the structures is located in a near-equatorial position, and that the nucleus is probably rotating with a spin axis lying near the plane of the sky and geometrically oriented at PA ∼100 ○ or PA ∼100 ○ + 180 ○ (Fig. 3 ). Unfortunately, from the HST images it is not possible to determine the direction of rotation of the nucleus. To further verify our findings, we run a computer model of the inner coma using a proprietary software (P. Pellissier, 2009) specifically designed to reproduce Earth-based observations of the dust coma structures and of the development of the tail. The parameters needed to run the model are the properties of the dust particles, those of the nucleus, and the geometric conditions of the Sun–Earth–comet system. The following physical parameters of the dust particles, compatible with the observations of Rosetta on comet 67P (Fulle et al. 2015 ; Rotundi et al. 2015 ;


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

and the profiles of total velocity dispersion, we finally derive the reconstructed profiles of pseudo-entropy to be compared with the true profiles. Furthermore, in order to verify the accuracy of this procedure based on the Jeans equation to recover pseudo-entropy profiles as a function of the dynamical state of a cluster, we divided our set of simulated haloes into relaxed and disturbed. To carry out this classification, we followed the prescription in Biffi et al. ( 2016 ) and described here in Section 2 . In Fig. B1 , we compare pseudo-entropy profiles recovered from the Jeans equation procedure with the true intrinsic profiles for both relaxed and disturbed systems from the Hydro-1x simulation. In the right-hand panels, we show their density maps in logarithmic scale traced by the DM particles in two clusters within the 0.5 r vir spheres (in white). In each of the two panels on the left, we compare recovered and intrinsic profiles of S ( r ) for both the selected cluster (solid lines), whose density map is reported on the right, and the entire cluster sample (dashed lines). Upper panels display the profiles for relaxed clusters, while lower panels are for disturbed systems. The solid lines are specific to the single cluster, being in cyan the true profile and in orange the one produced by the Jeans equation procedure while the dotted lines refer to the median values of all 29 clusters in the simulation set. In the upper panel, we see that true and Jeans equation profiles overlap at almost all radii, showing that the Jeans equation procedure has correctly reproduced the true profile, which is also in line with the median result. In the lower panel, we show the same profiles but when obtained for a cluster labelled as ‘disturbed’, since a major merger is occurring as shown by its density map. In this cluster, the true profile consistently deviates from the median and the Jeans equation profiles. The latter, in turn, suffers a shift in normalization but not in the slope with respect to the median profiles. Figure B1. On the left: pseudo-entropy profiles (DM particles) for a relaxed and a disturbed cluster in the Hydro-1x simulation at z = 0. The dashed lines are obtained by stacking the true profiles (in cyan) or those obtained by the Jeans equation procedure (in orange), while the solid lines are for the profiles of the specific cluster. On the right: density maps in logarithmic scale traced by the DM particles in the same objects. The brightest colours indicate the areas at higher density. The white circles identify the 0.5 r vir spherical region centred on the cluster centre as computed by SubFind. To quantitatively describe the differences between recovered and true pseudo-entropy profiles, we follow the same procedure described in Section 3 to fit a power-law profile, which depends on two parameters, and concentrate our interest on the accuracy in recovering the slope α. In the first two lines of Table B1 , we report the true and the


O O O O O O O O O O O O O O O O O B-Person I-Person O O O O B-Person I-Person O O O O B-Instrument O O O O B-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility B-ComputingFacility B-ComputingFacility I-ComputingFacility I-ComputingFacility O B-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization I-Organization I-Organization B-Organization B-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility O B-URL O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Organization B-ComputingFacility I-ComputingFacility O O B-Grant B-Organization O O B-Grant O B-Grant B-Organization B-ComputingFacility B-Grant I-Grant I-Grant O B-Organization I-Organization B-ComputingFacility O O O O B-ComputingFacility I-ComputingFacility B-Person O O B-Organization I-Organization B-Fellowship I-Fellowship I-Fellowship O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O O O O O O B-ComputingFacility O O B-Location O B-Location I-Location I-Location I-Location I-Location O O O O O O B-Location B-Organization I-Organization I-Organization I-Organization B-Organization O O O O O O O O O O O B-Location B-Organization I-Organization I-Organization I-Organization B-Organization O O O O O B-Software O O O O B-Software O O O B-Citation I-Citation I-Citation O O O O O O O O O B-Software B-Citation I-Citation O

ACKNOWLEDGEMENTS We are grateful to the anonymous referee for improving the quality of this work. We thank Madusha Gunawardhana for helpful discussions and Themiya Nanayakkara for providing data from ZFIRE. This work used the Distributed Research utilizing Advanced Computing (DiRAC) Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the Science Technology Facilities Council (STFC) DiRAC High Performance Computing Facility ( www.dirac.ac.uk ). This equipment was funded by the Department for Business Innovation Skills (BIS) National E-infrastructure capital grant ST/K00042X/1, STFC capital grants ST/H008519/1 and ST/K00087X/1, STFC DiRAC Operations grant ST/K003267/1, and Durham University. DiRAC is part of the National E-Infrastructure. RAC is a Royal Society University Research Fellows. We also gratefully acknowledge the Partnership for Advanced Computing in Europe (PRACE) for awarding us access to the resource Curie based in France at Trs Grand Centre de Calcul. This work was sponsored by the Dutch National Computing Facilities Foundation (NCF) for the use of supercomputer facilities, with financial support from the Netherlands Organization for Scientific Research (NWO). This research made use of astropy , a community-developed core python package for Astronomy (Astropy Collaboration 2013 ). This work has benefited from the use of Py-SPHViewer (Benítez-Llambay 2017 ).


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

We thank Kristian Finlator, Brian Siana, Rychard Bouwens, Pascal Oesch, Dan Jaffe, and Jon Trump for useful conversations. S.L.F. acknowledges support from the University of Texas at Austin College of Natural Sciences. M.S. was supported by a NASA Astrophysics and Data Analysis Program award issued by JPL/Caltech. R.J.M. acknowledges ERC funding via the award of a consolidator grant. This work is based on observations made with the NASA/ESA Hubble Space Telescope , obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program #12060. This work is also based in part on observations made with the Spitzer Space Telescope , which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Support for this work was provided by NASA through an award issued by JPL/Caltech.


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We thank Matthias Gritschneder and the whole CAST group for helpful comments and discussions. We also thank the anonymous referee for improving the structure of the paper. SH wants to thank Alexander Beck for useful comments that clarified the presentation of the paper. AB and SH are supported by the priority programme 1573 ‘ Physics of the Interstellar Medium ’ of the German Science Foundation and the Cluster of Excellence ‘ Origin and Structure of the Universe ’.


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

We thank the anonymous referee for their insightful review. We thank E. Fomalont and J. Williams for useful discussions and suggestions during the analysis of the data. This paper makes use of the following ALMA data: ADS/JAO.ALMA.2015.1.00200.S . ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This research made use of astropy , a community-developed core python package for astronomy (Astropy Collaboration et al. 2013 ). This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. S.P. and L.C. acknowledge support from the Millennium Science Initiative (Chilean Ministry of Economy) through grant Nucleus RC130007. S.P. acknowledges CONICYT-Gemini grant 32130007. L.C. acknowledges support from CONICYT-FONDECYT grant 1171246. This paper used the Brelka cluster, financed by Fondequip project EQM140101. C.P. acknowledges funding from the Australian Research Council (ARC) under the Future Fellowship no. FT170100040. This work was undertaken with the SOLA (Soul of Lupus with ALMA) collaboration centered at the Joint ALMA Observatory. Facility: Atacama Large Millimeter/Submillimeter Array. Software: Common Astronomy Software Applications (McMullin et al. 2007 ), radmc-3d (Dullemond et al. 2012 ), MCFOST (Pinte et al. 2006 , 2009 ), Astropy (Astropy Collaboration et al. 2013 ).


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J.N. is funded by PUT246 grant from the Estonian Research Council. We acknowledge funding from the ESF grants IUT26-2, IUT40-2 and TK120 (the European Structural Funds grant for the Centre of Excellence “Dark Matter in (Astro) particle Physics and Cosmology”). Thanks to F. Pace for providing mapping routines. Thanks to P. Teenjes and M. Einasto for help. E.B. acknowledges the financial support provided by MIUR PRIN 2011 “The dark Universe and the cosmic evolution of baryons: from current surveys to Euclid” and Agenzia Spaziale Italiana for financial support from the agreement ASI/INAF/I/023/12/0. We thank S. Borgani and G. Murante for giving us access to the simulations analysed in this paper. C.G. thanks CNES for financial support.


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

This project is financed by the SoMoPro II programme (3SGA5916). The research leading to these results has acquired a financial grant from the People Programme (Marie Curie action) of the Seventh Framework Programme of the EU according to the REA Grant Agreement No. 291782. The research is further co-financed by the South-Moravian Region. This work reflects only the authors’ views, and the European Union is not liable for any use that may be made of the information contained therein. It was also supported by the grant 7AMB14AT015 and the financial contributions of the Austrian Agency for International Cooperation in Education and Research (BG-03/2013 and CZ-09/2014). This research has made use of the WEBDA database, operated at the Department of Theoretical Physics and Astrophysics of the Masaryk University. This paper is dedicated to Roswitha Müller who died during its preparation.


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et al. 2011 ; Ramos Almeida et al. 2011 ; Ichikawa et al. 2015 ; Audibert et al. 2017 ), i.e., N c ≃ 3–15, we find that at 86 GHz becomes ≃0.2–1.2, which is of the order of unity and agrees with observational properties of N1. To constrain the number density of the clump, getting the size of the clump ( r c ) should be required. The size of the self-gravitating clump in the context of AGNs has been estimated in the literature (e.g., Krolik Begelman 1988 ; Hönig Beckert 2007 ; Kawaguchi Mori 2011 ). According to the authors of these works, we have r c ≤ 0.05 pc at 1 pc from the central SMBH with M BH = 8 × 10 8 M ⊙ (Scharwächter et al. 2013 ). On the other hand, the size of clumps can be constrained by the observations of transient X-ray absorption events in nearby AGNs, i.e., the typical size is 0.002 pc (e.g., Markowitz et al. 2014 ; Tanimoto et al. 2019 ). By adopting N c and r c , taking into account their uncertainties of 3 ≤ N c ≤ 15 and , the lower limit of number density of each ionized cloud can be given byIn the case of ionized gas clumps, the typical Thomson scattering opacity of each cloud can be estimated as since the optical depth is given by , where σ T is the Thomson cross section. If we adopt the opacity ratio of the dusty gas and the ionized gas is ≃10 3 at the UV band for the AGN radiation (e.g., Umemura et al. 1998 ; Ohsuga Umemura 2001 ; Wada 2012 ), the optical depth of each cloud is , which is consistent with the lower value for nearby Seyfert galaxies (e.g., Table 10 in Ramos Almeida et al. 2011 ). This might indicate that the ionized gas is also clumpy within the 1 pc region of 3C 84. Wada et al. ( 2018 ) examine properties of the ionized gas irradiated by less luminous AGN such as Seyfert galaxies based on their “radiation-driven fountain” model (Wada 2012 ). They found that the ionized region show nonuniform internal structures, corresponding to the clumpy fountain flows caused by the radiation pressure on dusty gas, although the typical density () is smaller than our estimate. In addition, by the optical/NIR observations, the existence of dense clumps with cm −3 has been reported from the detection of coronal lines within NLR (e.g., Murayama Taniguchi 1998 ). These high density clouds in NLRs might contribute the absorption feature of N1. Alternatively, since the density is comparable to number density n c ≃ 10 3–5 cm −3 , based on the momentum balance between the jet thrust and the ram pressure from the clump (Nagai et al. 2017 ; Kino et al. 2018 ), these ionized clumps may contribute not only the absorption of counter jet but also the feedback on jets. Since the dust sublimation radius of 3C 84 is (e.g., Kino et al. 2018 ), the dust components in these dense clumps could be survived. If this is the case, the clumpy clouds might be related to the polar elongation in MIR continuum emission distributions revealed by high-resolution observations in nearby Seyfert galaxies (e.g., Tristram et al. 2014 ; Asmus et