listed in Cols. 2 and 3, respectively. Column 4 shows the isotopic ratio, while Col. 5 gives the references for the different elements. Table 2 Elemental abundances for individual lines in the solar spectrum. Notes. Columns 26 list the single abundances for the different instruments and resolutions. Columns 7 and 8 show the median abundance values for MIKE spectra for the different observation runs and the one dispersion, respectively. The mean abundances are used as solar abundance for MIKE spectra observed in the run with (see Table 1 in Bensby et al. 2014 ) since no solar spectra is available in this setting. Col. 9 lists the solar abundances values from Asplund et al. (2009) . 2.2. Spectral line synthesis The spectral lines from the heavy neutron-capture elements are usually located in the blue regions of the visible spectrum. The blue region is very crowded with spectral lines and is especially so for metal-rich disk stars, meaning that blends from other species are frequent. The crowdedness of the blue spectral regions also makes the placement of the continuum difficult. In addition, heavy elements usually consist of several isotopes, and their spectral lines can be affected by hyperfine splitting (hfs). Therefore, to determine abundances from these lines, one has to use spectral line synthesis in order to accurately account for wavelength shifts of the different isotopes, hyperfine components, and other blending features. Table 1 lists the spectral lines that we use in this study. It shows the analyzed lines with the isotopic ratios of the elements, ionisation stages, wavelengths, and excitation potential, along with reference work where these details were gathered. For Sm and Nd, which suffer in large part from isotopic shifts and also hfs, we use data from Roederer et al. (2008) , for La we used hfs-only components from Ivans et al. (2006) since La has only one naturally-occurring isotope, and for Eu we used shift correction for isotopic and hfs substructures from Lawler et al. (2001) . However, some of the lines of the elements affected by hfs are treated as single lines because the wavelength splitting is small enough to be ignored. In our linelists the lines treated as single lines are La ii at 4748 Å, Nd ii at 5130 Å, and at 5319 Å, and Sm ii at 4523 Å, 4577 Å, and 4669 Å. All lines from Sr ii , Zr ii, and Ce ii lines are treated as single lines since there is no information available on their hyperfine structure and isotopic shifts or because they do not suffer from any of these problems. Atomic data for blending lines and other nearby lines were taken from the compilation available in the Vienna Atomic Line Database (VALD, Piskunov et al. 1995 ; Ryabchikova et al. 1997 ; Kupka et al. 1999 , 2000 ). The complete linelists with hyperfine components can be found in Appendix A . 2.3. Abundance determination The methodology for abundance determination is the same as in Battistini Bensby (2015) where we analyzed Sc, V, Mn, and Co for


and mid-IR emission by Li Draine ( 2001b ). Figure 8. Schematic of allowable parameters for silicate nanoparticles for which the rotational emission and polarization do not violate observational constraints (white area). The orange shaded region represents the parameter space where the polarization violates the observed AME polarization, and the blue shaded area depicts the space where rotational emission violates the AME and the polarization by nanosilicate violates the UV polarization of starlight for the ISM. 6.4. Comparison with Previous Spinning Dust Studies Previous spinning dust models ( DL98 ; Ali-Haïmoud et al. 2009 ; Silsbee et al. 2011 ; HDL10 ; HLD11 ) dealt with spinning PAHs, because they are a well-established dust population in the ISM (see Tielens 2008 ). The last spinning dust model ( HDL10 ; HLD11 ) treats all known important physical effects, including grain wobbling due to thermal fluctuations within the grain, transient spin-up by single-ion collisions. In particular, the model can be applied to spinning dust grains of arbitrary shape and arbitrary temperature ( HLD11 ). Since the physics of spinning silicate (iron) is essentially analogous to that of spinning PAHs, it is natural to use our advanced spinning dust model for modeling emission from spinning non-PAHs. Finally, the spinning dust model developed in HDL 10 and HLD11 is the unique model that can predict self-consistently the polarization level of spinning dust emission (see also Hoang et al. 2013 , 2014 ); the spinning models from other groups only provide rotational emissivity. Therefore, our spinning dust model can be used to constrain the dipole moment and magnetic properties of nanoparticles using the AME polarization data. 7. SUMMARY In this paper, we have obtained the principal results as follows: 1. We have quantified rotational emission from spinning ultra-small silicate grains for different grain size distribution parameters, various fractions of total Si abundance contained in ultra-small grains , and a wide range of the dipole moment per atom β . 2. The polarization fraction of spinning silicate emission is found to increase with decreasing value of β . The polarization fraction is below for the dipole moments , but it can reach for assuming K. 3. We have identified that the presence of silicate nanoparticles with and the dipole moment does not violate the UV polarization of starlight and the observed polarization of the AME. With this realization (), rotational emission from spinning nanosilicate can adequately reproduce the observed AME from the ISM. 4. Our quantitative results suggest that rotational emission from spinning silicate nanoparticles may be an important source of AME. Future observations searching for signatures of nanosilicates from the regions with prominent AME would be valuable in understanding the carrier of AME. There are several outstanding questions on the problem of spinning nanosilicates, including the dipole moment and


the Small and Large Magellanic Clouds ( Crawford et al. 2001 ; Ridley et al. 2013 ); but also non-detections towards, for example, dwarf spheroidal galaxies ( Rubio-Herrera Maccarone 2013 ). In addition, several efforts have been made to capture periodic as well as single pulse signals from nearby galaxies, with different telescopes. Unfortunately none of these searches discovered new sources. For the deepest of such past surveys, we list the frequencies and sensitivities in Table 1 . These frequencies range from 328–1440 MHz. Based on the facts that at lower frequencies pulsar beams get wider and pulsar fluxes get higher ( Stappers et al. 2011 ), there is a possibility that deep, lower-frequency surveys could find pulsars that these past efforts have missed. Table 2 LOFAR Observations of nearby galaxies M 33, M 81, and M 82. Notes. For tied-array rings (TARs) only the coordinates of the central tied-array beam (TAB) are provided. The LOw Frequency Array (LOFAR, van Haarlem et al. 2013 ) is a radio telescope capable of tracking several nearby galaxies, at long wavelengths. The LOFAR high-band antenna’s (HBA, 120–240 MHz) frequency range approximately coincides with the peak of pulsar flux density distribution (100–200 MHz, Stappers et al. 2011 ), which is an advantage for a pulsar search. Compared to previous low-frequency pulsar searches, further improvements such as the much increased bandwidth (), the high core gain (), and the relatively low antenna temperature (, see Eq. (3) from van Leeuwen Stappers 2010 ) mean that LOFAR can, on the one hand, find many new interesting pulsars (see Coenen et al. 2014 ), and on the other hand, better investigate their fundamental emission peculiarities (see Stappers et al. 2011 ). In general, pulsar beams are thought to be wider at lower frequencies, giving LOFAR the advantage in capturing radio pulses over higher-frequency surveys. Finally, the LOFAR multi-beaming allows for efficient, deep integrations at a sensitivity and field of view unparalleled in the world. The main downside of searching for pulsars with LOFAR is that dispersion and scattering degrade the inherently sharp pulsar peaks. In this paper, we describe the searches we have performed with LOFAR for pulsars in spiral Triangulum Galaxy M 33 (distance ), Bode’s Galaxy M 81 (), and starburst Cigar Galaxy M 82 (). Given their northern positions (see Table 2 ), these three galaxies can be well targeted with LOFAR, as the projected effective area of LOFAR’s fixed dipoles, and thus the sensitivity, is the highest close to zenith (see van Leeuwen Stappers 2010 ). The origin and current environment of a galaxy inevitably affect its star-formation rate and mass-energy distribution. As a result, the pulsar population in nearby galaxies can be different from that of our Milky Way, and a comparison between this population and that of our Milky Way is interesting. Furthermore, measuring a dispersion measure (DM) toward an extragalactic pulsar


This work was supported by NASA grant NNX10AD78G. 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. We thank the referee, Tim Kallman, for his careful review of the paper and constructive comments. We thank A. Maeder, M. Catalan, and E. Behar for useful suggestions. We also thank G. Ferland and associates for their continuing maintenance of cloudy .


We thank the anonymous referee for helpful comments and suggestions. Support for A.E.R. was provided by NASA through Hubble Fellowship grant HST-HF2-51347.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS 5-26555. A.E.R., J.E.G., and E.G. thank the Kavli Institute for Theoretical Physics for hosting the 2013 workshop “A Universe of Black Holes,” during which this project began. This research was supported in part by the National Science Foundation under Grant No. NSF PHY11-25915. This research has made use of data obtained from the Chandra Data Archive and the Chandra Source Catalog, and software provided by the Chandra X-Ray Center (CXC) in the application packages CIAO, ChIPS, and Sherpa. This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by NASA. This work has also used observations made with the NASA Galaxy Evolution Explorer. GALEX is operated for NASA by the California Institute of Technology under NASA contract NAS5–98034. This study 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 NASA. We are grateful to Michael Blanton and all who helped create the NASA-Sloan Atlas. Funding for the NASA-Sloan Atlas has been provided by the NASA Astrophysics Data Analysis Program (08-ADP08-0072) and the NSF (AST-1211644).


of XMM - Newton and NuSTAR simultaneous data. As a first attempt, we simply adopted this absorbed PLCUT model as a pure phenomenological and statistically acceptable description of the continuum emission. The results from fitting the eight time-resolved 3–78 keV spectra in this way are reported in Table 2 . Table 2 Time-resolved Spectroscopy (3–78 keV), Fitting Only NuSTAR Data of the Flare Peaks Note. a Fluxes (corrected for the absorption) and luminosities are in the energy range 1–100 keV. In some of these spectra, negative residuals appeared at energies between 15 and 40 keV. To account for these absorption features, we modified the PLCUT continuum multiplying it with a cyclotron absorption model ( cyclabs in xspec package). At first, we calculated the line significance by increasing the Δ χ 2 value until the confidence region boundary of the depth (Depth cycl in Table 3 ) of the cyclotron line crosses 0. The significance of these lines was greater than 2 σ in five (of eight) spectra (flare numbers 1, 2, 3, 4, and 6), so we have reported absorption line parameters only for these spectra in Table 3 for clarity. The absorption lines were measured with a significance ranging from σ = 2.3 to σ = 4.4 (with cyclabs model), with the best value obtained during the first flare observed by NuSTAR . The centroid energy was different: in spectra 1, 4, and 6 the feature was in a narrow range around 17–18 keV, while during spectrum 3 it was at ∼33 keV. In spectrum 3, the line at ∼17 keV was undetected: we could place an upper limit (Depth cycl ≲ 0.18) to the depth of the cyclabs line with the centroid energy fixed at 17 keV (and line width of 2 keV). Table 3 Same as Table 2 ( NuSTAR Only), Considering Only Flare Peaks 1, 2, 3, 4, and 6, Where the Inclusion of an Absorption Feature Resulted in a Line Significance Larger than 2 σ Notes. a cyclabs model in xspec . b This line significance was calculated by increasing the Δ χ 2 value until the confidence region boundary of the depth (Depth cycl ) of the cyclotron line crosses 0. In order to test the significance of the absorption line, since the F-test could bring unreliable results (Protassov et al. 2002 ), we used Monte Carlo simulations to confirm the presence of the cyclotron line in spectrum 1 (our best case, Figure 6 ), following the approach described by several authors (Barrière et al. 2015 ; Bhalerao et al. 2015 ; Lotti et al. 2016 ). We used the simftest routine in xspec with 100,000 simulations, 6 6 simftest is a script that tests for the presence of an additional component simulating N fake spectra based on the model without such component. It then fits the spectra with both the models with and without the additional component registering the Δ χ 2 and comparing it with the Δ χ 2 obtained with the real data to obtain the probability of a false detection. fixing the N H to the values reported in Table 3 to speed up the process, and found that the probability of such a line to be required by


measurement. 4. We claim that VGT–PCA as a modification of the VGT is advantageous in studying the magnetic field morphology and predicting the contribution from the Galactic foreground. Appendix A GALFA-H <sc>i</sc> Data Selection In this work, the ensemble GALFA-H i data is separated into three data sets: East (A), North pole (B), and West (C). Each data set is divided into five individual subregions. The velocity range of each data set is selected, such that the majority of the Galactic neutral hydrogen gas is included. Table 1 gives the description of the GALFA-H i data set used in this work. The uncertainty on the AM is given by the standard error of the mean, i.e., the standard deviation divided by the square root of the sample size. Figures 11 – 16 show the morphology of the POS magnetic fields inferred from Planck polarization and VGT–PCA with resolution ≃1°. Figure 11. The morphology of the POS magnetic fields on the sky patch with (a): R.A. from 240 to 480 and decl. from −12 to 371, and (b): R.A. from 480 to 720 and decl. from −12 to 371. Left: the magnetic field predicted from VGT–PCA (red segments) with resolution ≃1°. Middle: the magnetic fields inferred from Planck polarization (blue segments). Right top: the histogram of the relative orientation between the magnetic field predicted by VGT–PCA and the one inferred from Planck polarization. Right bottom: the variation of the AM with respect to the polarization percentage. Figure 12. The morphology of the POS magnetic fields on the sky patch with (a): R.A. from 720 to 960 and decl. from −12 to 371, and (b): R.A. from 960 to 1200 and decl. from −12 to 371. Left: the magnetic field predicted from VGT–PCA (red segments) with resolution ≃1°. Middle: the magnetic fields inferred from Planck polarization (blue segments). Right top: the histogram of the relative orientation between the magnetic field predicted by VGT–PCA and the one inferred from Planck polarization. Right bottom: the variation of the AM with respect to the polarization percentage. Figure 13. The morphology of the POS magnetic fields on the sky patch with (a): R.A. from 2640 to 2880 and decl. from −12 to 371, and (b): R.A. from 2880 to 3120 and decl. from −12 to 371. Left: the magnetic field predicted from VGT–PCA (red segments) with resolution ≃1°. Middle: the magnetic fields inferred from Planck polarization (blue segments). Right top: the histogram of the relative orientation between the magnetic field predicted by VGT–PCA and the one inferred from Planck polarization. Right bottom: the variation of the AM with respect to the polarization percentage. Figure 14. The morphology of the POS magnetic fields on the sky patch with (a): R.A. from 3120 to 3360 and decl. from −12 to 371, and (b): R.A. from 3360 to 3600 and decl. from −12 to 371. Left: the magnetic field predicted from VGT–PCA (red segments) with resolution ≃1°. Middle: the magnetic fields inferred from Planck polarization (blue segments). Right top: the


Virial equilibrium (PVE), instead of the usual SVE. The implication, being a single magnitude of the external pressure, P ext , may be insufficient to explain the variations reported by Heyer etal. ( 2009 ). Instead, their data could be reconciled only if different clouds in the sample experienced different magnitudes of pressure in the range 10 4 10 7 K cm 3 . Equivalently, it meant that the sizelinewidth relation would be modified by the magnitude of external pressure, P ext , experienced by a GMC. In an earlier work, Ballesteros-Paredes ( 2006 ) strongly argued against GMCs obeying the SVE because: (i) clouds being the consequence of turbulent fragmentation in the interstellar medium (ISM), the fragmentation process must necessarily induce a flux of mass, momentum and energy between clouds and the ISM, and (ii) clouds often exhibit asymmetries in their respective line-profiles, which is inconsistent with clouds obeying the SVE. Instead, the Larsons scaling relations should, at best, be viewed as evidence for energy-equipartition. The possibility of bound, turbulence-supported clouds as suggested by Krumholz McKee ( 2005 ) is difficult to reconcile, for it is unlikely that there will be no redistribution of mass within such clouds over their lifetime (Ballesteros-Paredes 2006 ). Gas dynamics within a typical GMC is likely governed by collisions between smaller clumps within them (e.g. Anathpindika 2009a , b and references therein), and/or feedback from existing stellar populations (see e.g. Mac Low Klessen 2004 and references therein). In a more recent work, Ballesteros-Paredes etal. ( 2011 ) argued that the variations in the sizelinewidth relation reported by Heyer etal. ( 2009 ) is actually consistent with the scenario of hierarchical fragmentation in which a cloud does not collapse as a whole, but only isolated pockets in it collapse to form stars. In this paradigm, although turbulence in the ISM possibly plays a role in assembling the cloud, post its formation, when the contribution due to self-gravity becomes significant, the cloud no longer needs to be in equilibrium with the external medium. It is therefore unnecessary to resort to mechanisms that can actually hold clouds with complex geometries and density distributions in approximate equilibrium (see also Burkert Hartmann 2004 ). Simulations of unbound clouds leading to inefficient star formation were discussed by e.g. Clark etal. ( 2005 ) and Clark, Bonnell Klessen ( 2008 ). The virial state of GMCs has also been interrogated numerically. Dobbs etal. ( 2008 ) and Tasker Tan ( 2009 ), for instance, reported that clouds forming in a galactic disc typically have a virial coefficient between 0.2 and 10. Furthermore, Dobbs etal. ( 2011 ) argued that intercloud collisions in the disc of a galaxy was a viable mechanism to render clouds unbound. Alternatively, several other authors examined the formation of clouds out of collisions between large-scale flows (e.g. Vzquez-Semadeni, Passot


at a distance of 1 arcsec (140 au) from the star. 5.6 Orientation in space The envelope thickness at y = 0, measured by an FWHM of ∼0.55 in λ (Fig. 10 , left) meaning ∼0.7 arcsec, is consistent with the value quoted in Section 3.1 from which an upper limit of ∼22° was placed on a possible tilt θ 2 between the disc plane and the ( y , z ) plane about the y -axis. The observed ∼10° tilt θ 1 about the z -axis of the map of 〈 V z 〉 that was mentioned in Section 3.2 and illustrated in Fig. 4 deserves some comments. As remarked then, such a tilt is also seen in the integrated flux for distances from the star exceeding ∼3 arcsec, not only in the present data (Figs 1 and 3 , left), but also in infrared observations (Tobin et al. 2008 ). The difference of inclination of the central part of the envelope, as measured from the integrated flux, and of the outer part, including the outflow, is not due to some trivial geometric effect but is real. Many factors may play a role in this context, such as magnetic field (see for example the review of Li et al. 2014 ) or binarity (Loinard et al. 2002 ). 6 SUMMARY In summary, the main original contribution of the present analysis to our understanding of the formation process of L1527 is the presentation of a simple 3D parametrization of the morphology and kinematics of the gas envelope based solely on the observation of the C 18 O (2–1) line emission from regions that are not dominated by absorption. In the explored range, the model, which uses eight relevant parameters, reproduces observations significantly better than earlier attempts. This was made possible by assuming central symmetry and axisymmetry about the x (west–east) axis and by excluding from the analysis Doppler velocities between 0 and 0.32 km s −1 and a band of the sky map of ±0.675 arcsec about the x -axis. Moreover we have reduced the radial dependences of the temperature and density to a single quantity, the effective emissivity. We have presented arguments showing that such assumptions are indeed reasonable. The main conclusions of the analysis are as follows. Rotation : the model uses successfully trajectories spiralling down towards the disc on paraboloids coaxial with it. We give evidence for the dominance of rotation over the explored range and confirm that the rotation curve is as expected from a transition from an inverse distance law to a Keplerian regime. The rotation velocity decreases from ∼1.66 to ∼0.34 km s −1 when r increases from 1 to 5 arcsec. However, the range explored is too distant from the star to allow direct observation of the disc. In-fall velocity : we measure a mean in-fall velocity of 0.43 ± 0.10 km s −1 over the explored range, smaller than free-fall velocity, with no evidence for a significant r -dependence, in particular no evidence for the sudden jump to ∼1.5 km s −1 required by Ohashi et al. ( 2014 ) to describe the absorption-dominated central region. Central mass : using simple arguments of energy conservation, we


Acknowledgements This work was supported by CONICYT through its grants CATA Basal PFB06, QUIMAL project 140002, ALMA 31150012 and Fondecyt 11140428, ESO-Chile Joint Committee for Development of Astronomy, and NL NOVA ALMA RD project. We thank Xilinx Inc. for the donation of FPGA chips and software licenses.


The author wishes to thank the anonymous reviewer for useful comments that lead to significant improvement of the paper.


ACKNOWLEDGEMENTS The authors express thanks to Aniruddha Kembhavi for a careful reading of the manuscript and comments. S.A. acknowledge the use of IUCAA cluster Sarathi for the computational work. A.K.A. would like to thank the Square Kilometre Array South Africa (SKA SA) for funding the research project. This research has been conducted using resources provided by the Science and Technology Facilities Council (STFC) through the Newton Fund and the SKA Africa. Funding for the SDSS IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is www.sdss.org . SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional / MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.


LL would like to thank Robert Beswick for fruitful discussion and helpful comments. We thank an anonymous referee for constructive comments, which helped improve the manuscript. KKK acknowledges support from the Swedish Research Council.


J.V. would like to acknowledge the availability of computing resources on the Occigen super computer at CINES (France) to perform the computations required for modeling the theoretical meteoroid streams. Special acknowledgement also goes to all members of the Croatian Meteor Network for their devoted work on this project.


assumption that BBHs trace the location of their parent star particles. The prospects for detecting BHs without stellar companions remain limited. Still, with a refined model of the star formation history in the MW, non-detections with X-ray, radio and microlensing surveys will put constraints on binary evolution models. Conversely, a much higher number of detected systems will be equally constraining, and may inform us of additional BBH formation channels (Rodriguez et al. 2015 ; Marchant et al. 2016 ). Our understanding of massive binary evolution is still very uncertain, especially the modelling of stellar mass-loss through different evolutionary phases, the properties of common-envelope mass transfer, and supernova kicks. Specifically, we have assumed that common envelope interactions during the Hertzprung gap result in stellar mergers, which may underestimate the BBH production rate. Conversely, we assume the BH natal kicks are reduced with respect to NS natal kicks, which may overestimate the number of systems surviving both supernova explosions. As our work highlights the importance of improved galactic models, we do not explore the wide parameter space of current binary models. The different BBH merger rates predicted by Mapelli et al. ( 2017 ) show how different binary evolution models will eventually be ruled in or out by observational data, provided appropriate models are used for the star formation. Based on our assumptions for binary evolution, we predict that LISA will detect about 25 BBHs within the MW halo. The frequency of these systems will not vary over the duration of the LISA mission, which means that the measured chirp mass will be degenerate with the distance of the system (see equation 4 ) and discerning it from a closer NS binary may not be possible. While certain aspects of binary evolution are likely to be revised, we emphasize that the BBH predictions of our model are somewhat on the high end (see a discussion in Lamberts et al. 2016 ) and that fine-tunning it in order to produce more galactic BBHs may overproduce the observed BH merger rate (Abbott et al. 2017c ). Unless a mechanism allows for the formation of BBHs at close to solar metallicity, BBHs are strongly biased towards the galactic halo, which strongly reduces their GW signal on Earth relative to disc populations. Even a single detection with accurate localization will provide information on the binary evolution mechanism and the conditions of its formation. We find that less than a million BBHs have merged in the MW by the present day. Focusing on the mergers within the last 2 Gyr, we find a merger rate of ≃10 −5 yr −1 . Our model, which assumes a binary fraction of unity, is on the higher end of the measured rate. We find the mean mass of the systems to be 29, which is in line with the announced detections (Abbott et al. 2016a ; 2017a , b , c ). We find that sources with represent 8 per cent of the merged systems, meaning there could be roughly 40 000


sets with each set obtained from weighted average over 100 voxels. This yields an RMS of for clean modes in the range k ≃ 0.02–1 h Mpc −1 . The RMS estimate can be further improved by averaging over all the data for a fixed τ (this leaves the mean unchanged for reasons outlined in the foregoing) and then computing the RMS using bootstrapping (e.g. Kolopanis et al. 2019 ). We plot the power spectra for both the XX and YY polarizations in all the figures to emphasize the long-term system stability in a drift scan. Our calibration does not involve a polarized source so both the polarizations are assigned equal weight in the beginning of the scan. Our results show that no significant deviation emerges at the end of the scan. As the H i signal is unpolarized, the power spectra for the two polarizations can be added in quadrature to yield a further improvement in RMS by nearly a factor of . 5.5.1 Comparison with other H <sc>i</sc> power spectrum measurements Our results can be directly compared to PAPER H i power spectra upper limits as it was also a drift scan experiment. PAPER was located in Karoo desert, South Africa at geo-coordinates 30°43′17′′S, 21°25′42′′E. Its 2-m antenna had a primary beam (FWHM) of nearly 50°. PAPER’s 64 antennas were placed in a 8 × 8 pattern. For the final results presented in Kolopanis et al. ( 2019 ), six of the antennas were flagged and only a subset of baselines were used for the power spectrum estimations. This subset consisted of 30-m East–West baselines between adjacent columns and those staggered by one row (see fig. 2 of Kolopanis et al. 2019 ). The system temperature for the observational runs varied between 400 and 800 K in the frequency range. These data sets had a total bandwidth of 10 MHz with a channel width of 493 kHz, centred around six frequencies in the range 120–167 MHz. The power spectrum estimate was based on 135 d of observations, but only the data from 124 d was used for LST binning. The LST spanned the range 00 h 30 m 00 s to 08 h 36 m 00 s . The data sets were initially binned with a time resolution 43 s. After the LST binning, they were further averaged with a top-hat filter in time which yielded a resolution of 938 s. With these instrumental parameters, the PAPER collaboration recently published the analysis of its final data products. Their upper limits on the H i power spectra at z ≃ 8.13 are: Δ 2 ( k ) ≃ (1000 mK) 2 at k = 0.2 h Mpc −1 and (380 mK) 2 at k ≃ 0.32 h Mpc −1 (Kolopanis et al. 2019 ). Our upper limit at a comparable wavenumber and redshift is ≃(1000 mK) 2 , based on nearly 35 h of MWA drift scan data. While a more direct comparison between the performance of MWA and PAPER is harder, this allows us to note the following salient differences between the two interferometers and analysis pipelines: (i) the collecting area of MWA is nearly five times large, (ii) PAPER baselines have a higher level of redundancy (Section 3 ), (iii) the time over which the visibilities are averaged is


The present work is supported by the Xinjiang Natural Science Foundation grant No. 2014211A069. This work is also funded by the CAS Key Laboratory of Solar Activity, National Astronomical Observatories, the Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education, China Scholarship Council (CSC), XAO Director Foundation, and NSFC (No.11673056 and 11403087). The authors also thank the support from the Supercomputer Center of University of Arizona. In addition, the authors appreciate Prof. J. R. Jokipii and his group in the University of Arizona, Profs. Hongbo Hu and Hong Lu at IHEP, Prof. Xueshang Feng and his group at NSSC, Profs. W. Gan and Haisheng Ji at PMO, Prof Jun Lin at YNAO, Profs. Quanming Lu and Chenglong Shen at USTC of CAS, and Prof. Liuguan Ding at Nanjing University of Information Science and Technology for their many helpful discussions.


We are grateful for all of the referee’s suggestions and comments, which greatly improved the scientific quality and information flow of this work. K.C. would like to recognize and thank the Royal Thai Government Scholarship in the section of the Ministry of Sciences Technologies for the National Astronomical Research Institute of Thailand (NARIT) and the University of Florida’s Graduate School Doctoral Dissertation Award for guidance and financial support during the phase of completing this work and his thesis. E.S.S. acknowledges funding and support from the Spanish MINECO under the projects MDM-2014-0369 of ICCUB (Unidad de Excelencia “María de Maeztu”), AYA2015-70498-C02-2-R and AYA2017-88085-R (both cofunded with FEDER funds), and the Catalan DEC grant 2017SGR643. R.C. and J.M.R.E. acknowledge support from the Spanish Ministry of Science under grant AYA2015-70498-C2-1. J.M.R.E. also acknowledges support from the Spanish Ministry of Science through grant AYA2017-84061-P. R.C., J.M.R.E., and A.H. acknowledge the Spanish Ministry of Science’s support through the Severo Ochoa grant SEV2015-0548. A.H. acknowledges support by the Spanish Ministry of Science through grant AYA2015-68012-C2-1 and by the Gobierno de Canarias through grant ProID2017010115. J.G. acknowledges support by the Spanish Ministry of Science through grant AYA2016-75808-R. J.M.M.H. is partially funded by Spanish State Research Agency grants ESP2017-87676-C5-1-R and MDM-2017-0737 (Unidad de Excelencia ‘María de Maeztu’ CAB).


We are grateful to the referee for the constructive comments and substantial improvements to this work. We thank Dr. Chang Liu for providing ideas to explain the observation, and Dr. Xin Chen for providing the tracing method. The observations presented in this paper were mainly obtained with NST at the Big Bear Solar Observatory, which is operated by New Jersey Institute of Technology. The data are courtesy of the BBSO and NASA/ SDO teams. This work was partially supported by NASA grants NNX13AF76G, NNX16AF72G, NNX13AG13G, and NNX14AC12G, and by NSF grants AGS 1348513, 1408703 and 1620875. BBSO operation is supported by NJIT, US NSF AGS-1250818 and NASA NNX13AG14G, and NST operation is partly supported by the Korea Astronomy and Space Science Institute and Seoul National University, and by strategic priority research program of CAS with grant No. XDB09000000.


consequence of the release of the S-species from the icy grain mantles because of thermal and nonthermal desorption and sputtering. Contrary to dark clouds and prestellar cores, in B1b the S depletion is comparable to that of C and O, which explains the high abundances of S-bearing species in this core. Table 4 Elemental abundances relative to H. Notes. (a) From the compilation by Agúndez Wakelam (2013) ; (b) this work; (c) elemental abundance in the gas phase; (d) depletion factor = (X/H)/(X/H). The abundance of sulfur in grains is very uncertain. Thus far, the only S-bearing molecule unambiguously detected in ice mantles is OCS, because of its large band strength in the infrared ( Geballe et al. 1985 ; Palumbo et al. 1995 ), and maybe SO ( Boogert et al. 1997 ), but there are only upper limits of the solid HS abundance ( Jiménez-Escobar Muñoz Caro 2011 ), which is thought to be the main sulfur reservoir in the ice. Measurements of the sulfur depletion are derived by comparing the predictions of gas-phase chemical models with the observed abundances of the main S-bearing species. As reported above, chemical models are limited by the uncertainty in the reaction rates and the poorly known [O]/[O] ratio and the exact value of the sulfur depletion is uncertain. Our results suggest, however, that the sulfur depletion in Barnard 1b is not as extreme as in dark clouds where the abundance of S-bearing species is lower. This moderate sulfur depletion could be the consequence of the star formation activity in the region. B1b is irradiated on its eastern side by UV photons from the nearby Herbig Ae stars LkH 327 and LkH 328 ( Walawender et al. 2009 ; Young et al. 2015 ). On its western side, B1b is impacted by the outflows associated with B1a and B1d ( Hatchell et al. 2007 ). Not unrelated, the level of turbulence in B1 (0.4 km s) is higher than in cold cores such as L1544, TMC-1 or L134N (0.1 km s). Sputtering induced by collisions may be efficient in this turbulent environment to erode the icy mantles and release the sulfuretted species to the gas phase. Sputtering could also explain the high abundances of complex molecules observed in this core ( Marcelino et al. 2009 ; Cernicharo et al. 2012 ). The main drawback of this interpretation is that it would also imply a low depletion of CO that is not observed. Another possibility is that the moderate sulfur depletion is a consequence of the rapid collapse of B1b. Aikawa et al. (2005) investigated the impact of different collapse timescales on the gas chemistry. The initial conditions of their first model lie close to the critical Bonnor-Ebert sphere with a central density of 10 cm. When the central density of the core reaches 10 cm, the S-bearing species were totally depleted at AU. In the second case, internal gravity overwhelms pressure and the collapse is much faster (about 0.1 Myr). When the central density of the core reaches 10 cm, molecules such as CS, SO, and CS have abundances of 10 in the


provide the best overall fits in terms of visual inspection of the residuals and through comparison of likelihood ratios between models. Thus, we adopt the BaSTI models for this Letter. We adopt a metallicity grid that ranges from −2.3 ≤ [M/H] ≤ −0.5 with a resolution of 0.1 dex and an age grid that ranges from with a resolution of dex. We find that including ages younger than did not change the SFHs (as these galaxies have no young populations) but increased the computational time. Thus, we simply exclude ages with from our CMD modeling. Finally, given that our CMDs do not reach the oldest MSTO, we follow Weisz et al. ( 2011 , 2014 ) and adopt a prior on the age–metallicity relationship that requires the metallicity to increase monotonically with time, with a modest dispersion allowed at each age. This choice helps to mitigate some of the age–metallicity degeneracy on the RGB and RC. We compute random (which accounts for the finite number of stars on the CMD) and systematic uncertainties (which are a proxy for uncertainties in the physics of the underlying stellar models) as described in Weisz et al. ( 2014 ). Finally, as detailed in Section 3.6 and Figure 6 of Weisz et al. ( 2014 ), SFHs measured from CMDs that include the HB but do not include the oldest MSTO have larger systematic uncertainties, but are consistent with SFHs measured from the oldest MSTO-depth CMDs. Figure 1 illustrates the CMD modeling process. Panel (a) shows the observed Hess diagram of And XXI, and panel (b) shows the best-fit model for the BaSTI stellar library. A visual comparison of the model and data indicates good overall agreement. Panel (c) is a residual significance diagram, i.e., (model–data)/model, which quantifies the level of (dis)agreement. The majority of populated pixels are consistent within ∼1 σ , while only a handful of pixels are highly discrepant (i.e., 3 σ ). There are no signs of poorly fit regions of the CMD (e.g., large swaths of only black or white pixels), which indicates that the model is a good fit to the data. Panel (d) shows the cumulative SFH of And XXI, i.e., the fraction of total stellar mass formed prior to a given epoch. The solid black line is the best-fit BaSTI solution, and the gray-shaded band reflects the 68% confidence interval of the total (i.e., random plus systematic) uncertainties. The random uncertainties are negligibly small compared to the systematic uncertainties. The thin colored lines in panel (d) are the best-fit SFHs from the other four stellar libraries. These SFHs are similar in shape to the BaSTI solution, though particular features can be shifted by up to ∼2 Gyr, which is due to differences in the underlying stellar physics (e.g., Gallart et al. 2005 ). As intended, the scatter in the SFHs from different models is well captured by the gray-shaded error envelope. The small amount of apparent star formation ∼1–3 Gyr ago is likely due to a handful of blue stragglers that occupy similar portions of the CMD as


The authors thank Catherine Espaillat and Lynne Hillenbrand for providing the optical spectra and physical parameters of a few objects, and John Kwan for providing the mid-IR hydrogen line ratios. E.R. is supported by the NASA's Astrophysics Data Analysis Program research grant to I.P. (ID: NNX11AG60G).


to be around 2.5 throughout the whole envelope in good agreement with previous measures. All these conclusions lead us to think that Y CVn is a quite active star that could have developed structure in its envelope. Due to the physical mechanisms that are producing the suggested asymmetries close to the star, the high temperatures, gas densities below ~ 10 8 –10 10 cm −3 , the influence of the Galactic UV radiation field in the chemistry at different distances from the star, and its proximity, Y CVn is a good candidate to better understand the physical and chemical evolution of the envelopes of low mass-loss rate C-rich stars. Appendix A Brief description of HCN Hydrogen cyanide is a linear triatomic molecule that belongs to the C ∞ group and displays a permanent dipole moment of 2.985 D ( Ebenstein Muenter 1984 ). The vibration of its atoms can be described with the aid of four normal modes, two of which are degenerate and are combined into the bending mode ν 2 ( π ). These two degenerate normal modes involve atomic oscillations out of the molecular axis that allow the molecule to spin around it. This results in the creation of a vibrational angular momentum that splits the rotational levels in sublevels with different e − f parity ( Brown et al. 1975 ). The other two normal modes, ν 1 ( σ + ) and ν 3 ( σ + ), are stretching modes that can be essentially described as the HC–N and H–C vibrations along the molecular axis, respectively (e.g., Herzberg 1956 ). Fig. A.1 Vibrational energy diagram of HCN below 5000 K. The lines that connect the vibrational states represent the strongest allowed vibrational transitions. The transitions in blue lie around 13.5 μm, in red around 7.5 μm, and in orange at other wavelengths (3.0, 3.6, 4.8, and 8.2 μm, depending on the transition). Those plotted with ticker lines are detected in the spectra presented in the current work ( Figs. 1 and 2 ). In the current work, we adopted two different notations commonly used in Molecular Spectroscopy and Astrophysics to label a vibrational state: v 1 ν 1 + v 2 ν 2 + v 3 ν 3 ( l ± ) and , where i = 1, 2, 3. The v i is the vibrational quantum number related to the i -th normal mode, l the quantum number for the vibrational angular momentum, which can be expressed with the Greek letters σ , π , δ , φ , γ , ... for l = 0, 1, 2, 3, 4, ..., ± is the total parity of the molecule in this vibrational state (usually given only for the σ states), and p is the e − f parity. The v = 0 means the vibrational ground state (G.S.). The change of the rotational number in a transition is given by B p ( J l ) for the ro-vibrational lines, where J u and J l are the upper and lower rotational levels, respectively, B is the branch (P: Δ J = J u − J l = −1, Q: Δ J = 0, and R: Δ J = +1), and J = J u − J l for the pure rotational ones. More information about the selection rules and the line intensities of linear molecules can be found, for example, in Fonfría et al. (2008) . Figure A.1


WASP-South is hosted by the South African Astronomical Observatory and we are grateful for their ongoing support and assistance. Funding for WASP comes from consortium universities and from the UK’s Science and Technology Facilities Council. J.A.K.-K. and D.F.E are also funded by the UK’s Science and Technology Facilities Council. A.M.S. acknowledges support from grants SGR1458 (Generalitat de Catalunya), ESP2014-56003-R and ESP2015-66134-R (MINECO). This research has made use of “Aladin sky atlas” developed at CDS, Strasbourg Observatory, France and NASA’s Astrophysics Data System.


from the SDSS DR11 quasars at z = 2.34, we reconstruct the evolution of the dark energy EoS using the polynomial expansion, PCA, and the correlated prior method, respectively. In this paper, we pay attention to the high-redshift BAO measurement of BOSS DR11 quasars, the Ly α FB data, which have been widely used to constrain cosmological parameters (Lee et al. 2015 ; Delabrouille et al. 2015 ), especially the EoS of dark energy. However, we note that this measurement slightly prefers a negative dark energy density, so the second main purpose of this paper is to study the possible influence of this Ly α FB data on the constraints of the EoS of dark energy. In the next section, we show the numerical method and measurements used in this paper. Then, we use different reconstruction methods to explore the evolution of the dark energy EoS. In particular, we carefully check the impact of Ly α FB data on the results. The final section presents our conclusions and a discussion. 2. Method and Data We perform a global fitting analysis of cosmological parameters, using the CosmoMC package, 3 3 http://cosmologist.info/cosmomc/ a Markov Chain Monte Carlo (MCMC) code. We assume purely adiabatic initial conditions and a flat universe. Our parameterization is thus:the and are the baryon and cold dark matter physical densities, Θ s is the angular size of the sound horizon at decoupling, τ is the optical depth, n s and A s are the spectral index and the primordial power spectrum, and P w will be the parameters that describe dark energy. In order to understand the nature of dark energy well, we consider the following data sets. 2.1. SN As standard candles, the SN measurements provide a luminosity distance as a function of redshift z , making them a powerful probe for cosmology, particularly for the EoS of dark energy. In this paper, we use 740 samples from JLA (Betoule et al. 2014 ), in which the redshift spans 0 z 1.299, and there are only eight high-redshift samples ( z > 1). The absolute magnitude comes from the logarithm of the luminosity of an astronomical object seen from a 10 parsec distance, which can also be used to give its brightness. We use the distance modulus μ th to relate the expansion history of the universe to the apparent magnitude of an SN at a given redshift. It is defined as the difference between the apparent magnitude m and the absolute magnitude M of the SN and relates to the distance throughwith μ 0 = 42.38 and dimensionless D L = H 0 d L . The luminosity distance is given by d L = (1 + z ) r ( z ), with r ( z ) standing for the comoving distance: The observation of distance modulus μ obs is given by an empirical linear relation:where α and β are set to nuisance parameters in our numerical calculations, , x 1 , and are parameters describing the SN, and M B is related to the host stellar mass M stellar by a simple step function (Betoule et al. 2014 ):so we can calculate the χ 2 of SN following:where σ i is the error of i th SN sample.


ACKNOWLEDGEMENTS This publication was made possible by grants NPRP-X-019-1-006 and NPRP-09-476-1-78 from the Qatar National Research Fund (a member of Qatar Foundation). GD acknowledges Regione Campania for support from POR-FSE Campania 2014-2020. MPGH acknowledges support from the Villum Foundation. Work by C.H. was supported by the grant (2017R1A4A1015178) of National Research Foundation of Korea. This work makes use of observations from the LCOGT network, which includes three ‘SUPAscopes’ owned by the University of St Andrews. The RoboNet programme is an LCOGT Key Project using time allocations from the University of St Andrews, LCOGT and the University of Heidelberg together with time on the Liverpool Telescope through the Science and Technology Facilities Council (STFC), UK. This research has made use of the LCOGT Archive, which is operated by the California Institute of Technology, under contract with the LCO. OGLE Team thanks Profs. M. Kubiak and G. Pietrzyński, former members of the OGLE team, for their contribution to the collection of the OGLE photometric data over the past years. The OGLE project has received funding from the National Science Centre, Poland, grant MAESTRO 2014/14/A/ST9/00121 to AU. LM acknowledges support from the Italian Ministry of Instruction, University and Research (MIUR) through FFABR 2017 fund. LM acknowledges support from the University of Rome Tor Vergata through ‘Mission: Sustainability 2016’ fund. KH acknowledges support from STFC grant ST/R000824/1.


We are grateful to Ewine van Dishoeck for suggesting this study and we thank her, Simon Bruderer, and François Lique for valuable discussions. We thank the computer and communication department (CCZ) of the Faculty of Science of the Radboud University for computer resources and technical support. The work is supported by The Netherlands Organisation for Scientific Research, NWO, through the Dutch Astrochemistry Network, and in part by the National Science Foundation under grant No. NSF PHY11-25915. N.B. is supported in part by NSF grant PHY-1505557. K.M.W. and P.C.S. acknowledge support from NASA grants NNX12AF42G and NNX13AF42G. I.K. and W.F.T. acknowledge funding from the European Union Seventh Framework Programme FP7-2011 under grant agreement no. 284405.


in an unbroken sequence lasting for 9.45 h which is slightly longer than Ceres rotational period of 9.074 h. On August 26, we took a second sequence of 29 exposures of 900 s each. The observations started on August 26 at 23:36 ut at the beginning of the night and ended at the set of Ceres on August 27 at 06:49 UT ut (MJD 2457260.9878–2457261.2840) in an uninterrupted sequence lasting 7.25 h, i.e. a bit shorter than a complete rotation. Five exposures were also taken at the end of the night on August 27, when we could shortly open the dome during a pause of bad weather. The journal of the observations together with the relevant quantities are given in Table 1 which is available online. Table 1. Journal of July observations. 1 st column is the time of the start of the exposure in UT. 2 nd column is the time in MJD. 3 rd column is the radial velocity computed by the HARPS pipeline. 4 th column is the radial velocity corrected from the instrumental drift. 5 th column is the BERV of the HARPS pipeline. 6 th column is the exposure time in seconds. 7 th column is the flux weighted mid exposure fraction. 8 th column is the effective mid exposure time. 9 th column is the radial velocity of Ceres with respect to the Sun. 10 th is the radial velocity of the observer relative to Ceres. The full table is available online. Ceres had an APmag of ≈7.5 mag, an angular dimension of 0.69 arcsec and was observable during most of the night. The observations were performed at phase angles of 4.4 and 12.3 in July and August, respectively. At opposition, Ceres becomes slightly brighter due to the Opposition Surge effect, but no radial velocity variations are expected within a single rotational period (Molaro et al. 2015 ). Inspection of the solar activity through the Solar Dynamic Observatory revealed no significant change in the solar activity to induce an important radial velocity change. The solar activity measured from the total sunspot area changed slightly from ≈500 to ≈1250 millionths of solar hemisphere between July 31 July and August 27. However, note that these will introduce a radial velocity offset between the two nights but not a change within one epoch. We used HARPS pipeline to obtain the radial velocities from sunlight spectra which are reported in the third column of online Table 1 . In the HARPS spectrograph, a second fibre supplies simultaneous ThAr spectra that are used to correct for instrumental radial velocity drifts occurring over the night due to residual temperature, pressure or mechanical changes. The radial velocity differences with respect to the previous calibration provide the instrumental drifts of the spectrograph. We are considering the values RV c corrected for instrumental drift and reported in the column 4 of online Table 1 but note that corrections are always below 0.5 m s −1 and do not affect the results. Single observations have S/N ≥ 100 and the error in the radial velocity measurement is lower than 1 m s −1 .


Acknowledgments The work of A.F. Seleznev and the science leave of G. Carraro in Ekaterinburg was supported by Act 211 Government of the Russian Federation, contract No. 02.A03.21.0006 and by the ESO DGDF programme. G. Baume acknowledges support from CONICET (PIP 112-201101-00301) and financial support from the ESO visitor programme that allowed a visit to ESO premises in Chile, where part of this work was done. This publication also made use of data from the Two-Micron All-Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. We thank the Centre de Donnes Astronomiques de Strasbourg (CDS), the US Naval Observatory and NASA for the use of their electronic facilities, especially SIMBAD, ViZier and ADS, and the WEBDA data base. This work was based on observations obtained at the Cerro Tololo Inter-American Observatory, Chile, and at ESO Paranal Observatory under programme 292.D-5043.


period for the outermost planet of 192 d, i.e. one third of the period found by Burke et al. (2014) . They reported planetary masses for the two giant objects of and . We secured five observations of KOI-1574 with SOPHIE HE. The data have a rms of 12 m s which is compatible with the uncertainties. The bisector and FWHM have rms of 44 m s and 38 m s, respectively. We fitted four circular orbits to the data fixing the orbital ephemeris to the transit ones. For KOI-1574.02, we choose the period of 572 days. We detect a hint of variation for the EGP at 114 days, with an amplitude of m s, and an upper limit of m s within the 99% confidence interval. For the other planets, we find upper limits at the 99% level on their amplitudes of m s, m s, and m s. Assuming a stellar host mass of ( Ofir et al. 2014 ), it gives a mass constraint for the EGP in our sample of ( at the 99% level). This value is fully compatible with the one derived by the transit timing analysis of Ofir et al. (2014) . For the other planet candidates, we find upper limits on the mass at the 99% level of , , and . Assuming that KOI-1574.02 orbits with a period of 192 d, as stated by Ofir et al. (2014) , does not change significantly our constraints on the mass of KOI-1574.01. For the other planets, the upper limits at 99% change to , , and . We conclude that none of these transiting candidates is an eclipsing brown dwarf or low-mass star eclipsing the target star. We confirm they have a mass within the planetary range. Appendix A.35: KOI-1645.01 The giant-planet candidate KOI-1645.01 was found by Burke et al. (2014) with an orbital period of 41 days. We observed it twice with SOPHIE HE and find a clear SB2. We fitted the cross-correlation function with a two-Gaussian profile to derive the RV of both stars. We call star A the one with the deepest line profile and star B the one with the shallowest line profile. The variation of both stars is anti-correlated, which gives a mass ratio of 87.6%. The star A exhibits a RV variation in anti-phase with the Kepler ephemeris, revealing that the secondary eclipse was detected in the light-curve. This candidate is not a transiting planet but a secondary-only EB. Appendix A.36: KOI-1783.01 A giant-planet candidate was found to transit the host star KOI-1783 with a period of 134d by Batalha et al. (2013) . We observed it twice with SOPHIE HE and find no significant RV variation. The bisector and FWHM variation are also compatible with their photon noise. By fitting a circular orbit with no drift at the transit ephemeris, we find an upper limit at the 99% level on the RV amplitude of m s. We derived the stellar atmospheric parameters that are reported in Table B.2 . We find a host mass of , a radius of , and an age of Gyr. Combining the two analyses, we derive an upper limit on the mass of this candidate of , within the 99% confidence interval. We can therefore exclude a star or brown dwarf eclipsing the target star, but we can not firmly


(2013) and Jiménez-Serra et al. (2010) have introduced the possibility of low-velocity shocks, which are most likely an effect of large converging flows, such as cloud collision or interaction between two filaments. Jiménez-Serra et al. (2010) consider this possibility for emissions apart from the high-density structures. This could also be a valid possibility for our region, since the spectra from W43 ( Nguyen-Lu’o’ng et al. 2013 ) are, in view of line width and the velocity shift of the SiO line, comparable to those from IRAS 19410 2336. Additionally, the shift in the line peak in the present data lies with a maximum of 6.1 km s (see Table B.1 ) far below the predicted shock velocity for high-velocity shocks ( km s, Gusdorf et al. 2008a , b ). This could be a hint of low-velocity shocks, but it could, as mentioned, also be an effect of the projection in the observed plane and the acceleration of environmental gas. The main reason for Nguyen-Lu’o’ng et al. (2013) considering low-velocity shocks due to large colliding flows was the fact that they observed large SiO structures with relatively uniform emission over the whole area. Again, this is in contrast to our observation that the whole southern part of IRAS 19410 and 2336 lacks SiO emission. Therefore, a collision of flows could again explain the SiO emission in the middle of the region. Still, it cannot explain the only partially present SiO emission in the south or the differences between SiO and CO data. Furthermore, the detection of H emission as a jet shock indicator in the south without any SiO counterpart could not be explained by this. 5. Conclusion After combining the different results, we found that the SiO emission is most likely caused by high-velocity C-type or CJ-type shocks probably from protostellar jets. This explains the emission in the northern part, as well as the observed line widths. The emission in the middle of the region could be a result of decelerated postshock gas due to two or more colliding outflows. The only part of the observation that does not fit this explanation is the missing SiO emission in the southern region. The observed H emission clearly shows shock activity, and the CO data exhibit several bipolar structures. The most valid explanation for us is that there is ongoing jet and outflow activity in the southern part, which is not visible in the SiO data. The missing SiO emission could be a result of the different excitation properties of the molecules and the shock geometry. When the shocks break out of the densest area, as the H data suggest, the density could be too low for SiO to get excited. However, the opposite explanation that SiO is also not visible owing to densities that are too high in the core has to be considered here. Our final conclusion from this observation is that SiO emission has to be used with some caution, because a lot of effects can have an influence on the emission. This is nicely shown by the observed region, since it can be


different dependencies on the three stellar parameters. We chose the molecular TiO bands around and Å (- and - electronic transitions, respectively) since these oxide bands are very sensitive to effective temperature, but almost insensitive to surface gravity (Fig. 1 ). For cooler stars, as shown here, a dependence on surface gravity is introduced by the increasing micro-turbulent velocities, which enhance the line width towards lower values (see Wende et al. (2009) for FeH as an example). More sensitive to surface gravity are alkali lines (Fig. 1 ), which show large alterations of their line wings that are due to pressure broadening (in case of the K lines it is more pronounced towards cooler temperatures). We chose the K- and Na-line pairs at around Å and Å, respectively. To determine the metallicity, we can use all these regions as well because the -TiO band and the alkali lines are strongly dependent on this quantity (see Fig. 2 ). Rajpurohit et al. (2014) also used the K- and Na-line pairs because of their sensitivity to gravity and metallicity, as well as several TiO-bands for temperature determination. They also illustrated the dependency on the different stellar parameters. The chosen spectral regions are also affected by lines of the Earth’s atmosphere. Especially the K- and Na-line pairs are contaminated by O2 and H2O bands, respectively. We used masks to exclude the atmospheric lines from the fit. The contamination of the TiO bands around and Å is very weak and can be neglected. The degree of sensitivity on a certain parameter for a particular line (band) also varies. In Fig. 3 we show how the width of a -level in a -map changes with varying effective temperature (i.e. the of the synthetic spectrum from which the -map is produced is varied) for a fixed surface gravity of cgs and metallicity of cgs. The change is also measured in terms of discretion elements in the -maps, since this determines the given resolution (left axis). All investigated regions become less gravity sensitive towards higher , which is probably due to the increasing thermal broadening, which becomes stronger than the pressure broadening (see also Sect. 3.3 ). At this point we have to keep in mind that the sensitivity curves only represent the behaviour for the chosen parameter values. They look different for other metallicities or surface gravities, which are not shown in detail here. Nevertheless, it becomes clear that the combined -map of all regions is the best choice for most temperatures and provides the highest sensitivity. Even though the sensitivity of the K lines is better for most temperatures than the combined one, we would not suggest to use only these lines because the results would then only depend on two lines. Experience in fitting real observations showed that the results deduced from the K lines alone can differ from the combined solution where the latter matches the actual parameters to a higher accuracy. 3.2. <inline-formula


Acknowledgements The authors are grateful to the anonymous referee for careful and critical comments, which helped to improve the manuscript substantially. HVŞ acknowledges support from the Scientific and Technological Research Council of Turkey (TÜBİTAK) through the project grant 1001 - 115F033. EI acknowledges support from the Young Scientist Award Programme BAGEP-2016 of the Science Academy, Turkey, and is grateful to T. Reinhold and R. H. Cameron for useful discussions. GAJH would also like to acknowledge the IDEX initiative at Université Fédérale Toulouse Midi-Pyrénées (UFTMiP) for funding the STEPS programme that enabled regular research visits. This work has made use of the VALD data base, operated at Uppsala University, the Institute of Astronomy RAS in Moscow and the University of Vienna.


We would like to thank the reviewer for comments that improved the clarity of the presentation. DF was supported by the DoD SMART Scholarship. TP was partially supported by the NSF grant AST-1109113 and the DOE grant DE-FG52-09NA29548. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231, and the Air Force Research Laboratory. The software used in this work was in part developed by the DOE Flash Center at the University of Chicago. The data analysis was performed in part using the yt Project (Turk et al. 2011 ), and VisIt (Childs et al. 2012 ). This research has made use of NASA's Astrophysics Data System Bibliographic Services.


I thank Long Wang for his interest. The status of the initial conditions in the N -body model emerged after a check instigated by an enquiry from him.


We thank Drs D. Koester and R. D. Rohrmann for the enlightening discussions and useful suggestions throughout this work. IP and SO Kepler are supported by CNPq and FAPERGS-Pronex-Brazil.


We acknowledge the anonymous referee for helpful comments and suggestions. This work made use of data supplied by the UK Swift Science Data Centre at the University of Leicester. Our study was supported by the National Basic Research Program of China with Grant No. 2014CB845800 and by the National Natural Science Foundation of China with Grants No. 11473012, No. 11475085, and No. 11275097.


ACKNOWLEDGEMENTS We thank the anonymous referee for insightful comments and constructive suggestions. This research has made use of data, software, and/or web tools obtained from NASAs High Energy Astrophysics Science Archive Research Center ( heasarc ), a service of the Goddard Space Flight Center and the Smithsonian Astrophysical Observatory. This research has made use of the XRT Data Analysis Software ( xrtdas ) and the NuSTAR Data Analysis Software ( nustar-das ) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (Caltech, USA). PG would like to thank IUCAA and BARC for their hospitality. RG would like to thank the IUCAA associateship programme and CSIR, New Delhi (03(1412)/17/EMR-II) for the financial support.


We are grateful to Daisuke Nagai and Fang Peng for giving us a copy of their Fortran code that we used to reproduce the data in their sedimentation model. We are grateful to the anonymous referee for a thoughtful and detailed report. The suggestion to make a more comprehensive comparison between a homogeneous and a heterogeneous ICM inspired us to produce several new figures and helped us to significantly improve the final version of this manuscript. We acknowledge useful discussions with Daisuke Nagai, Matthew Kunz, Prateek Sharma, Ellen Zweibel, and Ian Parrish during the 3rd ICM Theory and Computation Workshopheld at the Niels Bohr Institute in 2014. We also thank Sagar Chakraborty and Henrik Latter for valuable comments. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) under ERC grant agreement 306614. T.B. also acknowledges support provided by a Lørup Scholar Stipend and M.E.P. also acknowledges support from the Young Investigator Programme of the Villum Foundation.


Acknowledgements OSIRIS was built by a consortium led by the Max-Planck-Institut für Sonnensystemforschung, Göttingen, Germany, in collaboration with Centro di Ateneo di Studi ed Attivit'a Spaziali ‘Giuseppe Colombo’ (CISAS), University of Padova, Italy; the Laboratoire d’Astrophysique de Marseille, France; the Instituto de Astrofísica de Andalucia, Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain; the Science Support Office of the European Space Agency, Noordwijk, the Netherlands; the Instituto Nacional de Técnica Aeroespacial, Madrid, Spain; the Universidad Politéchnica de Madrid, Spain; the Department of Physics and Astronomy of Uppsala University, Sweden and the Institut für Datentechnik und Kommunikationsnetze der Technischen Universität Braunschweig, Germany. Rosetta is an ESA mission with contributions from its member states and NASA. The support of the national funding agencies of Germany (DLR), France (CNES), Italy (ASI), Spain (MEC), Sweden (SNSB) and the ESA Technical Directorate is gratefully acknowledged. We thank the Rosetta Science Ground Segment at ESAC, the Rosetta Mission Operations Centre at ESOC and the Rosetta Project at ESTEC for their outstanding work enabling the science return of the Rosetta Mission.


distance between the points on a zenith angle circle being ∼ R / N μ ) and equally spaced points on the dayside from terminator to disc edge (Fig. 1 ) with the same approximate separation. Our computed disc-averaged radiance was thus calculated as where μ i are the Gaussian quadrature points, Δμ i are the Gaussian weights, φ ij are the azimuth angles (which are different for different μ i ), and ij are the azimuth angle trapezium-rule integration weights. To ensure correct normalization, ∑ j ij = 1.0 and (i.e. ). In addition, we assumed that the exoplanet had a transit with low impact parameter such that northern and southern hemispheres were observed at the same emission angle and we also assumed north/south symmetry such that we only had to integrate over half the disc. Figure 1. Example of disc-averaging quadrature points chosen for N μ = 2, 3, 4, and 5 and phase angle ζ = 45° (where 0° is nightside and 180° is dayside). The daylight side of the planet is coloured light grey, while the nightside is dark grey. The zenith angles of the chosen Gauss–Lobatto quadrature scheme are shown by the dotted white lines. The azimuth points along each zenith angle line (indicated by the asterisk symbols) are chosen to coincide with the terminator for zenith angles that intersect the terminator and be roughly equally spaced with a fractional disc distance of 1.0/ N μ . As we assume north–south symmetry, only points on or above the equator are needed for disc-integration. An example of the chosen quadrature points for our scheme using N μ = 2, 3, 4, or 5 zenith angles is shown in Fig. 1 for a phase of 0.125 (i.e. a phase angle ζ = 45°). Here we can see that as we add more zenith angles, the number of sampled points on the disc is greatly increased. In our scheme if a given zenith angle circle intersects the day–night terminator, one azimuth quadrature point is placed on that terminator and the remaining azimuth points are chosen to be roughly equally spaced on the day and nightside, as noted above. The reason for doing this is that if we were to observe a planet where the reflected starlight is strong, we would expect that contribution to fall rapidly towards the terminator. By making sure we place a quadrature point at the terminator, we can thus ensure a better estimate of the azimuthal average of the radiance at a given zenith angle. Although in this study we have not included reflected starlight in our models, the disc-integration scheme we have defined means that we well be able to seamlessly and accurately incorporate this in future. 2.1 Computation of contribution of reflected starlight Assuming a Lambertian reflecting surface, the scattered radiance at any point on the disc is equal to α F star /π, where α is the reflectivity and F star is the local stellar irradiance (i.e. W m −2 ), calculated as F star = P cos θ/(4π d 2 ), where P is the total power of the star, d is the distance of the planet from the star, and θ is the local stellar zenith


C.C. and A.K. were supported in part by the Kavli Institute for Cosmological Physics at the University of Chicago through grant NSF PHY-1125897 and an endowment from the Kavli Foundation and its founder, Fred Kavli. E.B. and B.J. are partially supported by US Department of Energy grant DE-SC0007901. T.N.V. was supported by the SFB-Transregio 33 The Dark Universe by the Deutsche Forschungsgemeinschaft (DFG) and the DFG Cluster of Excellence “Origin and Structure of the Universe.” The weak lensing boost factors were calculated and calibrated using the computing facilities of the Computational Center for Particle and Astrophysics (C2PAP). D.R. is supported by a NASA Postdoctoral Program Senior Fellowship at the NASA Ames Research Center, administered by the Universities Space Research Association under contract with NASA. The CosmoSim database used in this paper is a service by the Leibniz-Institute for Astrophysics Potsdam (AIP). The MultiDark database was developed in cooperation with the Spanish MultiDark Consolider Project CSD2009-00064. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. ( www.gauss-centre.eu ) and the Partnership for Advanced Supercomputing in Europe (PRACE; www.prace-ri.eu ) for funding the MultiDark simulation project by providing computing time on the GCS Supercomputer SuperMUC at the Leibniz Supercomputing Centre (LRZ; www.lrz.de ). The Bolshoi simulations have been performed within the Bolshoi project of the University of California High-Performance AstroComputing Center (UC-HiPACC) and were run at the NASA Ames Research Center. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas AM University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory, 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. The DES data management system is supported by the National Science Foundation under grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. The IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007–2013), including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) through project number CE110001020. This manuscript has been authored by the Fermi Research Alliance, LLC, under contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. This paper has gone through internal review by the DES collaboration.


This paper makes use of the following ALMA data: ADS/JAO.ALMA#2012.1.00453.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. S.A. acknowledges support from the Swedish National Science Council grant 621-2011-4143. K.S. was supported by MOST grant 102-2119-M-001-011-MY3. E.G.-A. is a Research Associate at the Harvard-Smithsonian Center for Astrophysics, and thanks the Spanish Ministerio de Economía y Competitividad for support under project FIS2012-39162-C06-01 and NASA grant ADAP NNX15AE56G. F.C. acknowledges support from the Swedish National Science Council grant 637-2013-7261. J.M.-P. acknowledges partial support by the MINECO under grants AYA2010-2169-C04-01, FIS2012-39162-C06-01, ESP2013-47809-C03-01 and ESP2015-65597-C4-1.


selected from an ensemble of similar objects. Light curves were constructed by taking one of the observable parameters from the formal solutions and computing its response to the driving in depth from equation ( 8 ). In detail, this was carried out by fitting a cubic natural spline through the parameter values at the known depths. 4.4 Variable background radiation The operations for variation of the background radiation follow a broadly similar approach to those used in Section 4.3 . However, there are important differences. First, we apply only the default sinusoid and function D 6 as driving functions, since only D 6 is based on a model where the background, rather than the pump, is varied. The process is easier in the sense that the parameter that is varying in time is simply a parameter in equation ( 4 ), and no complicated justification is required for its use. A significant complication in the variable background case, however, is that the driver in the background analogue of equation ( 8 ) now demands a response from a number of different models, each computed with a different value of i BG . Therefore, modelling flares from background variation is computationally considerably more expensive. 5 VARIABLE PUMPING RESULTS We plot, in Fig. 3 , the flare light curves, over one period, that result from driving with pump functions D 0 – D 5 for a prolate cloud with Γ = 0.6, with the observer placed in the optimum position (viewing the cloud along the z -axis close to the long axis of the cloud). The cloud style is uniform in unsaturated inversion. The parameters of equation ( 8 ) in this case are τ min = 10.0 and Δτ = 5.0, corresponding to a regime that is already characteristic of quite strong saturation. Throughout Section 5 , the y -axes of graphs are shown in flux density, based on a background specific intensity that is scaled to the saturation intensity of the maser transition. Flux density is likely the best parameter to plot in view of the prominent role of single-dish monitoring in repetitive flare studies. An explanation of the flux-density scale appears in Appendix A . Owing to the nature of the saturation intensity, and particularly the fact that this quantity may itself depend on the energy density of pumping radiation, a conversion formula to units of Jy at a typical source distance, and for a particular maser transition, for example, is not trivial. Such a conversion is also discussed in detail in Appendix A . The variability index of the maser light curve, defined as in the Introduction as F pk / F qui is 1.74 in Fig. 3 . The same parameter for the driving functions, replacing flux densities with values of the depth multiplier, is 1.5. Although the variability index of the maser is significantly larger here, the light curve in Fig. 3 demonstrates the smoothing effect of saturation: as exponential growth gives way to a functional form that is closer to linear at high flux density, the maser responds more weakly to the driving


to study the reactions of carbon atoms with anthracene and coronene. In our computational study, we were mainly interested in the investigation of the pathways found previously for the reaction of carbon with benzene (Krasnokutski Huisken 2014c ) and naphthalene. Hence, we focused on the formation of seven-membered carbon rings. Our computations predict the barrierless reaction of carbon atoms with anthracene molecules and the possibility of a ring opening for all three aromatic cycles, i.e., the formation of seven-membered carbon rings in the center as well as on the sides of the molecule. Thus, the calculations reveal the possibility of the same reaction pathways as observed for benzene and naphthalene. This is in line with the experimental results obtained for benzene, naphthalene, and anthracene molecules. However, similar to the case of naphthalene, the carbon atom cannot be inserted into the CC bond shared by two aromatic rings. Figure 8 shows the molecular structures of the initial associative complexes between coronene and the carbon atom. It can be seen from this figure that, similar to the cases of the other PAHs studied, the carbon atom lands on the top of the CC bonds. The binding of the carbon atom to the bonds of the central ring is the weakest, and increases when the carbon atom moves to the side of the coronene molecule. Similar to the previously studied PAH molecules, the carbon atom cannot be inserted into the CC bond shared by the two aromatic rings. Therefore, the formation of a seven-membered ring in the center of coronene is not possible. Such a structure was found to be a transition state, with negative binding energy of the carbon atom. The binding of the carbon atom to the coronene molecule was enhanced going from the initial triplet state to the singlet state of the system. The highest binding energy (186.2 kJ mol −1 ) was found for the carbon atom on top of the peripheral CC bonds in the singlet state. With a relatively small energy barrier, the carbon atom can be inserted into the peripheral CC bonds of the coronene molecule. In this case, the reaction pathway is the same as for the other molecules studied. However, in the case of coronene, there is a considerable increase in the number of CC bonds, which cannot be opened. Therefore, landing of carbon atoms on these sites would not lead to the formation of a seven-membered ring. Two of the four complexes shown in Figure 8 , namely complexes 1 and 2, have rather low binding energy. Moreover, the energy barrier for the formation of a seven-membered carbon ring was computed to be a little higher compared to the case of benzene and naphthalene molecules. Therefore, there is a higher chance that the initial associative complexes 3 and 4 could also be frozen inside the liquid helium droplets. This explains the results of the calorimetric measurements showing only low energy released in the reaction of carbon atoms with coronene molecules. Figure 8. Molecular


of higher frequency as well as others due to nonlinear interactions of the flows. Our present simulations indeed show complex timing properties of the radiation as well as the flow dynamics. For this, we chose the flow to have an accretion rate () below the Eddington limit for all of the cases we studied. The exponent α in the cooling rate was varied from 0.5 (bremsstrahlung) to 0.6 to observe the effects of the strength of cooling on the oscillation of shocks. The specific angular momentum λ was chosen based on the recent study by Deb et al. ( 2017 ), where an onset of vertical oscillation was seen between the two values chosen here. The variation of the radius of the neutron star had a more complex effect as it controlled the effects of radiation pressure on the hydrodynamics through the parameter when a self-consistent variation is chosen. Note also that for the timescales of simulations C1 to C5, the mass and momentum deposited on the star were negligible as compared to the star’s mass and spin. This accumulation of mass and momentum would become significant for timescales of the order of years or more, which is outside the purview of the current paper. However, the energy release at the surface was found to be significant, resulting in a measurable change of T NS . We show, among other things, that the simulations produce both low- and high-frequency QPOs, and the oscillations last during the whole simulation period (more than 200 dynamical timescales measured at the injected flow radius, i.e., 30 r S ). This suggests that the QPOs are formed from a part of the flow dynamics and are not a transient effect, as inferred by others (e.g., Barret et al. 2005b ). We measure the QPO frequencies and find that both the centroid frequencies and Q factors match well with the observed results of neutron stars such as GX17+2, 4U 1728-34, and Cir X-1. We believe that the advective flow suggested in the literature, while explaining the behavior of the source Cir X-1 (Boutloukos et al. 2006 ), may be the same as the dynamic transonic flow solution we discuss here. We showed that the presence of angular momentum itself can generate multiple modes of oscillation in CENBOL and NBOL, manifesting as QPOs in the PDS, in the presence of cooling. In Figures 1 (b), 2 (c)–(d), and 3 (a), we see different types of shocks are being formed. The outer shock was found to be vertical near the equatorial plane and oblique away from the plane, very similar to what was seen for simulations around black holes ( MLC94 ). The bending instabilities reported in M01a are also found here and correspond to the hectohertz oscillations found in the PDS. So far, in the literature, a model that appears to be capable of phenomenologically addressing both timing and spectral properties is the TL model of TLM98 , who assumed the disk to be Keplerian to begin with. The viscosity was also assumed to be high enough to maintain a Keplerian distribution. The QPOs are then explained as the


g is a binary loss function and s j the scorefor learner j . The predicted class for x 0 is associated with the index k for which the vector Δ is minimal. Appendix C Description of classification algorithms: SVM – Ensemble methods Support-machine vectors The SVM method classifies the data by finding the best hyperplane separating the datapoints of one class from those of another category. Given a training set of M datapoints , where x i refers to the P-dimensional feature vector and y i the associated label that indicates whether the datapoint belongs to the positive class ( y i = +1) or the negative class ( y i = −1), the objective of SVM is to separate the data into distinct classes using a separating rule in form of a parametrized function f ( x ). For linearly separable data, the equation of the hyperplane is: where the scalar product w . x is equivalentto w ⊤ x . An infinity of hyperplanes verify Eq. ( C.1 ), but only one hyperplane maximizing the margins between the observationsand the hyperplane exists. This optimal hyperplane verify: To find the“best” linear hyperplane minimizing the margins , the SVM algorithm consists in solving a quadratic problem: For non-linearly separable data, the use of a kernel φ trick enables to map the distribution of the datapoints x into a projected space where φ ( x ) can be linearly separable, and defines, in the same approach as in Eq. ( C.2 ), a quadratic problem: where f ( x ) = f W , B ( x ) = W . φ ( x ) + B = 0. The selection of an adequate kernel K is determined by a list of criteria. By definition, a kernel must be symmetric, definite positive, square integrable and satisfy: Among commonly used kernels: Linear: K ( x i , x j ) = x i . x j Power: Gaussian (rbf): Further details about the SVMs are available in Vapnik (2000) and Cristianini Shawe-Taylor (2000) . C.2: Ensemble classifiers The principle of ensemble methodology is to combine a set of predictions from different learners in order to improve the accuracy of a single learner. Among the ensemble methods, two distinct approaches are identified: 1. Averaging methods: Bagging/ Random forests... 2. Boosting methods: AdaBoost/GentleBoost/RSBoost/... – AdaBoost AdaBoost, known also as “Adaptive Boosting” refers to a specific algorithm of boosted classifier defined as the sum of individual predictions from T weak learners. The algorithm aims to minimize the (weighted) classification error at each iteration: where: x i is the feature vector of the i th observation. y i is the true label of the i th observation. h t is the prediction of learner t . 1 is the indicator function. is the weight of the i th observation at step t . t the iteration step from 1 to T . At the first iteration, the weights are initialized (e.g., ) and the weak learner h t is obtained by minimizing the error ε t . For the next iteration, the weights of the learner ( t + 1) are adjusted according to the performance of the previous one ( t ): whether increase for misclassified


of C 6+ . However, the Ecker–Kröll model ( Ecker Kröll 1963 ) predicts a much higher population fraction of C 6+ compared with that obtained using our model. This differs from the conclusion drawn using the solid-density Mg, Al, and Si plasma ( Ciricosta et al. 2012 , 2016 ; Zeng et al. 2020 ), where the IPDs determined by the Ecker–Kröll model were in much better agreement with the experimental results than those of the SP model. Fig. 4. Charge-state distribution of C plasma as a function of the plasma temperature at mass densities of 2.25 g cm −3 ( a ), 3.75 g cm −3 ( b ), and 5.0 g cm −3 ( c ). Our results are compared with the Debye–Hückel (DH; Debye Hückel 1923 ), Stewart–Pyatt (SP; Stewart Pyatt 1966 ), ion-sphere (IS; Rozsnyai 1972 ), and Ecker–Kröll (EK; Ecker Kröll 1963 ) analytical models. We obtained the best agreement with the Ecker–Kröll model ( Ecker Kröll 1963 ) for the CSDs at a mass density of 3.75 g cm −3 . The SP ( Stewart Pyatt 1966 ), IS ( Rozsnyai 1972 ), and Debye-Hückel ( Debye Hückel 1923 ) models predicted a higher population fraction for C 5+ and a lower fraction for C 6+ than those predicted by our model and the Ecker–Kröll model. With the increasing mass density up to 5.0 g cm −3 , our theoretical IPDs and those of the IS and Ecker–Kröll models exhibited good agreement. The SP and Debye–Hückel models predicted a higher population fraction for C 5+ and a lower fraction for C 6+ . The aforementioned results can be seen in Fig. 5 , which shows the average ionization degree of C plasma as a function of the plasma temperature (100–500 eV) at mass densities of 2.25, 3.75, and 5.0 g cm −3 . Our predicted average ionization degree is consistent with those of the Ecker–Kröll model ( Ecker Kröll 1963 ) at a density of 3.75 g cm −3 and the IS and Ecker–Kröll models at a density of 5.0 g cm −3 , which is completely in accordance with the features observed with respect to CSDs (Fig. 4 ). Fig. 5. Average ionization degree of C plasma as a function of plasma temperature at mass densities of 2.25 g cm −3 ( a ), 3.75 g cm −3 ( b ), and 5.0 g cm −3 ( c ). Our results are compared with the Debye–Hückel ( Debye Hückel 1923 ), Stewart–Pyatt ( Stewart Pyatt 1966 ), ion-sphere ( Rozsnyai 1972 ), and Ecker–Kröll ( Ecker Kröll 1963 ) analytical models. 3.3. Analysis and explanation of a recent experiment Here, we verify the validity of our proposed model of screening potential by comparing its predictions with the results of a recent experimental measurement of the average ionization degree of C in a CH-mixture plasma ( Kraus et al. 2016 ). In this experiment, a highly compressed CH-mixture plasma (polystyrene) was created via spherically convergent shocks at the National Ignition Facility. Then, the spectrally resolved X-ray scattering of line radiation at 9.0 keV was recorded. The experiment measured an average ionization degree of 4.92 ± 0.15 for C at an average temperature of 86 ± 20 eV (radiation–hydrodynamics simulations show that


to first order with b – which is degenerate with the function of σ n e – and the only way to break the degeneracy is to have enough variation in the secant of the zenith angle. In a sparse and noisy observation of ΔTEC, the secant variation is poor and it is expected that this degeneracy exists. Therefore we also show the product b σ n e , and we see that this compound value discrepancy is smaller by approximately 35%. Table 2. Average and standard deviation, over all experimental conditions, of the difference between the learned physical hyperparameters and the true hyperparameters. In Table 3 we summarise the performance of the physical kernel against each competitor kernel. We display the mean of ΔLPO C ( η ), and ΔLPH C ( η ) over all experimental conditions, as well as their values at the nominal experimental conditions of 30 directions per 12.6 deg 2 , and ΔTEC noise of 1 mTECU, which is indicated with η nom . We use bold font in Table 3 to indicate the best competitor model. Table 3. Probability ratio FOMs (see text) averaged over experimental conditions and at nominal conditions. Notes. Larger values indicate that the physical model is more probable. Bold face indicates the best performing competitor model (lower number). We first consider the ability of each model to represent the observed data. For the dawn ionosphere, the M52 competitor kernel has the best (lowest) ⟨ΔLPO C ⟩ η = 1.55 and , implying that the M52 kernel model is 55% and 46% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. We note that the M32 kernel produced similar results. For the dusk ionosphere, the EQ kernel model is likewise the best among all competitors, being only 73% and 54% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. In all experimental conditions, the physical model provides a significantly more probable explanation of the observed data. We now consider the ability of each model to infer the held-out data. For the dawn ionosphere, the M52 competitor kernel has the best (lowest) ⟨ΔLPH C ⟩ η = 1.49 and = 1.31, implying that the M52 kernel prediction is 49% and 31% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. We note that the M32 kernel produced similar results. For the dusk ionosphere, the EQ kernel model is likewise the best among all competitors, with predictions only 16% and 12% less probable than the physical kernel model on average over all experimental conditions, and at nominal conditions, respectively. In the case of the rougher dawn ionosphere, the physical model provides a significantly more probable prediction of the held-out data in all experimental conditions. However, for the smoother dusk ionosphere at nominal conditions, the physical model is only 12% more probable than the EQ kernel model,


et al. 2013 ). Moreover, Cleeves et al. ( 2015 ) show that reduced cosmic ray ionization (a consequence of exclusion by disc winds) produce disc chemistry features in agreement with TW Hya observations. Our population results showing that X-ray ionized models produce populations that better reproduce observational features are in agreement with these results. One key difference in our super Earth populations and the observations is that our model produces fewer close-in super Earths. When comparing the direct population outputs (Fig. 10 ) with the populations that account for observational biases (Fig. 9 ), we see that a portion of the zone 5 planets in our model are filtered out as they have low observation probabilities. Additionally, our model does produce a large number of failed cores, but a significant fraction of these orbit just outside the defined outer observable limit of 2 au, and are situated on orbits out to 5 au. The range of radii between 1 and 5au is the most common region for super Earths to form in our model as this is the region where the ice line, heat transition, and cosmic ray dead zone traps converge to. Thus, our model predicts there to be a significant population of super Earths with orbital radii of 1–5 au. Our planet formation model does not produce low mass super Earths that migrate into short-period orbits. We noted this several times throughout Section 4 and refer to this as a zone of avoidance of our model within zone 5 of the mass-semimajor axis diagram. The lowest mass super Earths formed in our model that populate zone 5 are ∼3 M ⊕ and orbit outside of 1 au. This feature of our populations is tied to the evolution of the traps that evolve from orbital radii outside ∼8 au to within ∼1 au on disc evolution time-scales. This prevents ∼Earth-mass planets from migrating to short period orbits in our model as they evolve with the trap they are forming within. Planets can, however, become liberated from their host planet traps at the onset of type II migration. In this case, planets must exceed their gap opening mass (equation 17 ) of ∼10 M ⊕ to type II migrate separately from their trap. Initially, type II migration takes place on shorter time-scales than trapped type I migration, so these planets can, in some cases, migrate to short period orbits during their slow gas accretion phase before their accretion is terminated at the disc lifetime. Thus, the lowest mass zone 5 planets orbiting between 0.01 and 0.1au formed in our model have masses ≳10 M ⊕ . Hasegawa ( 2016 ) also found that a core accretion model involving trapped type I migration could not form a failed core population consistent with observations. In particular, this model could only form super Earths more massive than ∼4–5 M ⊕ , similar to the results found in this work. Based on the results of our constant dust-to-gas ratio model, one might suggest that formation scenarios in addition to the failed core scenario are necessary to explain the observed


Members of the CAGE team (Cosmochimie, astrophysique et géophysique expérimentale) at IPGP are thanked for insightful discussions. G.A. acknowledges funding from the Région Ile-de-France through the DIM-ACAV+ program. J.D.G. acknowledges funding from the Science and Technology Facilities Council (STFC) grant No. ST/R000751/1. This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. This is IPGP contribution #4092.