We thus expect an homogeneous envelope to become transparent after a timewhich is frustratingly well above the duration of the Eddington luminosity phase. Here we assume κ ≈ 0.1 cm 2 g −1 , which is a reasonable value for ordinary supernova material. At lower X-ray energies ( x ≲ 3 keV), photoionization may delay the time at which the envelope becomes transparent by an additional factor of . Despite this, the youngest neutron star we have uncovered has an age of about 340 yr (Fesen et al. 2006 ). In a few instances, a surviving binary companion has been detected in postexplosion deep optical imaging of extragalactic supernova (Maund Smartt 2009 ; Folatelli et al. 2014 ). In the case of 1993J, for example, the brightness of the transient dimmed sufficiently after about a decade so that its spectrum showed the features of a massive star superimposed on the supernova (Maund et al. 2004 ). It is perhaps a stinging fact that despite the expected manifestations of neo-neutron stars, one of the main issues in the field has been that most Galactic supernova remnants have no detectable central source. Figure 11 shows the current detections and upper limits of thermal emission in nearby neutron stars with model predictions. Observational selection effects are clearly at play when uncovering young objects yet there is the possibility that a sizable fraction of massive star collapses might produce black holes rather than neutron stars, with the clearest example being W49B (Lopez et al. 2013 ) and the other four examples, from Kaplan et al. ( 2004 , 2006 ), all plotted in Figure 11 . Figure 11. Plot of luminosity as a function of the age of the remnant for nearby neutron stars together with the predictions from neutron star cooling models. The (green) dots are measurements and the (blue) squares are upper limits from detected neutron stars (data taken from Beznogov Yakovlev 2015 ) while the (red) dotted error bars are upper limits on the compact objects, black holes or neutron stars, expected to be presents in five core-collapse supernova remnants. These remnants are, in order of increasing age: G043.3–0.2 (a.k.a. W49B, marked by a diamond) from Lopez et al. ( 2013 ), and G127.1+0.5, G084.2+0.8, G074.0–8.5, and G065.3+5.7 from Kaplan et al. ( 2004 , 2006 ). Shaded areas show model predictions of Page et al. ( 2004 , 2009 ) for the minimal cooling of neutron stars that cover uncertainties on the chemical composition of the envelope and nucleon pairing at high densities. In contradistinction, the dashed-pentadotted curve, Z , exemplifies the effect of fast neutrino emission from the direct Urca process expected to occur in massive neutron stars (Boguta 1981 ; Lattimer et al. 1991 ) resulting in very cold stars (Page Applegate 1992 ). Also plotted are the three different models (A, C, D) shown in Figure 9 . 6.2. Neutron Star Mergers, Short Gamma-Ray Bursts, and LIGO Events The merger of binary neutron stars and the subsequent production of a beamed,


We thank an anonymous referee for insightful comments. We are grateful to B. Burkhart, A. Lazarian and Z. Manchester for insightful comments on the manuscript. T.H. thanks B.T. Draine for stimulating discussions on spacecraft charging. This work was supported in part by a grant from the Breakthrough Prize Foundation.


NLRs and in the NLRs, effects of the dusts on the observed values (marked by the suffix of obs) should be where D (BLRsextNLRs) , D (extNLRs) and D NLRs represent effects of dusts in all regions, effects of dusts in the regions from the BLRs to the observer and the effects of dusts in the NLRs, respectively. Effects of dust extinctions can be considered as the role factor to determine the scatters of the correlations shown in Fig. 5 . There are more similar dust extinctions on L (5100 ,obs) and on , but more different dust extinctions on the L (5100 ,obs) and on the . Therefore, there are tighter correlations between L (5100 ,obs) and than the correlation between L (5100 ,obs) and . Moreover, with considerations of dependence of Balmer decrements (which can be used to trace the dust extinctions) on [O iii ]luminosity, the steeper correlation of L (5100 ,obs) and can also be simply explained as follows. Based on the observed and the Balmer decrements from common NLRs BD obs, NLRs for our objects with both narrow H and narrow H, we can find that there is a moderately negative correlation shown in Fig. 7 , with Spearman rank correlation coefficient of about 0.32 with P null 510 8 . And the anticorrelation can be simply described by with narrow 0.045. Then, combining with the empirical equation to estimate colour excess with 3.1 as the intrinsic flux ratio of narrow H to narrow H, the intrinsic luminosity can be estimated by where k and E ( B V ) represent the reddening curve and colour excess, respectively, and narrow 0.8 k narrow . Meanwhile, similar equations to estimate L (5100 ,int)) and can be described by where ext 0.8 k ext and have probably different values of ext and 5100 from the value of narrow , because of different dust obscurations for observed continuum luminosity and for observed luminosity from the extended components of [O iii ]lines. And, the values of must be smaller than 1, otherwise an unreasonable result can be expected that intrinsic luminosity after considerations of dust extinctions could be smaller than the observed luminosity. Then, we have the following sub-equations Based on the third sub-equation in equation ( 10 ) combining with the shown results in the top panel of Fig. 6 , we can find that . Therefore, the correlation of L (5100 ,obs) versus is steeper, because of its larger slope of being about 1.2times larger than the slope of for the correlation of L (5100 ,obs) versus . Certainly, the results above are highly idealized, and seriously depend on physical properties of dusts in central regions of AGN; however, the results can be applied to find qualitative explanations to the results shown in Fig. 5 . Figure 7. Correlation between observed Balmer decrements(flux ratio of narrow H to narrow H) and the luminosities of the core components of the [O iii ]emission lines. Before the end of this section, we show our simple discussions on which component is well applied to trace central AGN intrinsic luminosity in type-2


We highly appreciate the referee for his/her valuable comments and suggestions that significantly improved our work. J.F.Z. thanks support from the National Natural Science Foundation of China (grant Nos. 11233006 and 11363003). A.L. acknowledges the NSF grant AST 1212096 and the Center for Magnetic Self Organization (CMSO). H.L. and J.C. acknowledge support by the National R D Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2064475).


show the rest of the sources detected; in the case of sources 32, 35, and 38 the K = 0, 1 lines were detected, but no valid solution was found for the simultaneous Gaussian fitting of all four components. Integrated line fluxes obtained from the Gaussian fitting are reported in Table 1 and were used to compute Boltzmann plots (i.e., the rotational diagram) relating the column density of the emitters normalized by the level degeneracy and the energy of the levels. Using standard procedures (e.g., Fontani et al. 2002 ), assuming LTE and optically thin conditions that are appropriate for this line and class of sources (e.g., Bergin et al. 1994 ) the temperature and total column density of CH 3 C 2 H can be derived from a linear fit to the rotational diagram (Goldsmith Langer 1999 ). Because our observations are single-pointed toward the 70 μ m peak of the sources, we could only derive beam-averaged column densities and we chose to focus on the analysis of the CH 3 C 2 H rotational temperatures (see Table 1 ). 3. RESULTS 3.1. CH3C2H Detection Statistics The sources listed in Table 1 are ordered according to their L/M value computed from the Herschel fluxes, augmented with ancillary data at mid-IR wavelengths (D. Elia et al. 2016, in preparation), and it can be immediately seen that the detection rate in CH 3 C 2 H( J = 12–11) is strongly dependent on the L/M values. We can distinguish three ranges of L/M : the rate is 0 (none out of 23 sources) for the sources with L/M 1, 58% (seven out of 12) for sources with 1 L/M 10 and 90% (16 out of 18 sources) for sources with L/M > 10. In Table 1, in sources # 32, 35, and 38, at least the K = 0 line is detected, but the four-component Gaussian fit to the four lines did not converge and so no further analysis could be carried out for these sources. This finding is preliminary confirmation of the working hypothesis that higher values of L/M should be related with a more advanced evolutionary stage. As CH 3 C 2 H is a temperature probe for gas with densities in excess of 10 5 cm −3 (Bergin et al. 1994 ), its values are influenced more by the inner clump regions and relatively less by the outer envelope. Looking at Figure 3 , the range of L/M ≲ 1 is characteristic of “pre-stellar” clumps (in which, contrary to the sources of the present study, 70 μ m emission is not detected); the internal input energy producing the detectable 70 μ m flux would not seem sufficient to drive up considerably the bolometric luminosity as well as the internal clump temperature, resulting in non-detections in CH 3 C 2 H( J = 12–11) for any of the sources with L/M 1. This suggests that in these clumps intermediate or high-mass star formation has not yet started. Above the L/M = 1 threshold, corresponding to the upper envelope of the “pre-stellar” clumps distribution in Figure 3 , the CH 3 C 2 H( J = 12–11) detection rate increases sharply, implying that the internal energy produced by star formation is rising to


the power-law slope α , the dust temperature , and the spectral slope of the dust emissivity as fitting parameters, using the observed-frame 500 μ m flux density as a normalization factor. We also fit “general” models that allow for wavelength-dependent changes in optical depth, adding the wavelength , where the optical depth reaches unity as an additional fitting parameter. The optically thin fitting procedure yields statistical mean values of K, , and . 19 19 α is only poorly constrained by the data. The general fit yields mean values of μ m, K, , and . The fit also implies rest-frame infrared (8–1000 μ m) and FIR (42.5–122.5 μ m) luminosities of L ⊙ and L ⊙ , respectively. 20 20 The measured agrees to within ∼2% with independent estimates based on integrating a normalized MAGPHYS-based SED template based on the z = 6.34 starburst HFLS3 (Cooray et al. 2014 ), showing that the adopted power-law approximation of the short wavelength emission has a minor impact on the measured quantities. Assuming a dust absorption coefficient of m 2 kg −1 at 125 μ m (e.g., Dunne et al. 2003 ), we also find a dust mass of M ⊙ . 21 21 Given the limited photometry, the uncertainties may be somewhat underestimated. Assuming a Chabrier ( 2003 ) stellar initial mass function, these parameters suggest a total SFR of ∼2400 M ⊙ yr −1 . Given the limited SED constraints in the rest-frame optical, we obtain an estimate for the stellar mass M ⋆ of ADFS-27 by normalizing the MAGPHYS-based SED template of HFLS3 in Figure 4 to the observed-frame 4.5 μ m limit. This yields M ⋆ 1.2 × 10 11 M ⊙ . 4.2. Molecular Gas Mass, Gas-to-dust Ratio, and Gas Depletion Time The value of ADFS-27 (based on the adopted r 51 = 0.39) implies a total molecular gas mass of M ⊙ . 22 22 We here adopt a conversion factor of M ⊙ (K km s −1 pc 2 ) −1 for nearby ultra-luminous infrared galaxies and SMGs (e.g., Downes Solomon 1998 ; Tacconi et al. 2008 ). Taken at face value, this yields a gas-to-dust ratio of /, which is comparable to that in the z = 6.34 starburst HFLS3 and within the range of values found for nearby infrared-luminous galaxies (Wilson et al. 2008 ; Riechers et al. 2013 ), but ∼4× lower than that for the z = 5.30 starburst AzTEC-3 (Riechers et al. 2014 ). At its current SFR, this implies a gas depletion time of /SFR ≃ 100 Myr, consistent with the general SMG population (e.g., Carilli Walter 2013 ). 4.3. SFR and Gas Surface Densities, Gas Dynamics, and Conversion Factor The apparent 870 μ m continuum sizes of ADFS-27N (mal) and ADFS-27S (yong) imply physical sizes of (1.83 ± 0.18) × (1.28 ± 0.16) and (2.05 ± 0.18) × (0.87 ± 0.15) kpc 2 at z = 5.655, which are comparable to the ∼2.5 kpc diameters found for other z > 4 dusty starbursts like AzTEC-3, HFLS3, and SGP-38326 at similar wavelengths (Riechers et al. 2013 , 2014 ; Oteo et al. 2016 ). Assuming that their flux ratios at 870 μ m are representative at the peak of the SED, this implies surface densities of and 7.5 × 10 12 L ⊙ kpc −2


Acknowledgements The Submillimeter Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. Swift /BAT transient monitor results are provided by the Swift /BAT team. This research has made use of MAXI data provided by RIKEN, JAXA, and the MAXI team. TSA acknowledges support through the Russian Government Program of Competitive Growth of Kazan Federal University. We thank Sebastian Heinz and Chris Fragile for useful discussions.


This work required the use and integration of a number of Python software packages for science, including AstroPy (Astropy Collaboration et al. 2013 ), 5 5 http://www.astropy.org Matplotlib (Hunter 2007 ), 6 6 http://matplotlib.org NumPy, 7 7 http://www.numpy.org SciPy, 8 8 http://www.scipy.org/scipylib/ and yt (Turk et al. 2011 ). 9 9 http://yt-project.org We are thankful to the developers of these packages. We also thank Volker Springel for the use of the AREPO code. J.A.Z. thanks David Barnes, Rahul Kannan, Federico Marinacci, and Hui Li for useful discussions. He also thanks Grant Tremblay for assistance with a last-minute fix to a figure. J.A.Z. acknowledges support through Chandra Award Number G04-15088X issued by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. J.Z. acknowledges support by a Grant of Excellence from the Icelandic Research Fund (grant No. 173929−051). The numerical simulations were performed using the computational resources of the Advanced Supercomputing Division at NASA/Ames Research Center.


It is a pleasure to thank Eve Ostriker for stimulating discussions and insightful suggestions, Anne Decourchelle and Jean Ballet for enlightening elements regarding the observations of supernovae remnants and Matthieu Gounelle for triggering our interest in supernova explosions. We thank the anonymous referee for a careful reading of the manuscript which has significantly improved the paper. This work was granted access to HPC resources of CINES under the allocation x2014047023 made by GENCI (Grand Equipement National de Calcul Intensif). P.H. acknowledges the financial support of the Agence Nationale pour la Recherche through the COSMIS project. This research has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013 Grant Agreement No. 306483 and No. 291294).


; Taddia et al. 2018 ; Prentice et al. 2019 ). Indeed, the previous studies on 56 Ni mixing in stripped-envelope SNe indicate that 56 Ni mixing can affect the photospheric velocity evolution in stripped-envelope SNe (e.g. Dessart et al. 2012 ). In this paper, we perform a systematic study of the effect of 56 Ni mixing on the photospheric velocity evolution in stripped-envelope SNe. The rest of this paper is organized as follows. We summarize how we get progenitor models, how we mix 56 Ni, and how we conduct radiation transfer simulations in Section 2 . Our results are presented in Section 3 . We discuss the results and conclude this paper in Section 4 . 2 METHODS 2.1 Progenitor models We obtain hydrogen-free progenitor models by using the public stellar evolution code mesa (Paxton et al. 2011 , 2013 , 2015 , 2018 , 2019 ). We assume a metallicity of Z = 0.02. We adopt the standard mixing-length theory for convection with a mixing-length parameter of 2.0 with the Schwarzschild criteria. Our focus in this paper is on hydrogen-free SNe. There are many mechanisms to remove the hydrogen-rich envelope from SN progenitors and the mass-loss mechanisms themselves are a well investigated field of research (Smith 2014 ). In this work, we artificially remove hydrogen-rich envelopes to obtain hydrogen-free SN progenitors. We first evolve massive stars from zero-age main sequence (ZAMS) to the end of helium burning without mass loss. The helium core mass does not change much after the helium burning. At this point, we stop the stellar evolution calculation and gradually reduce the mass from the surface through wind by using the relax_mass option in mesa . The maximum mass-loss rate allowed in this process is 10 −4 M ⊙ yr −1 ( lg_max_abs_mdot = -4 in mesa ). When the stellar mass reaches the desired mass, we resume the stellar evolution calculation without mass loss. The stellar evolution calculations are performed at least until the end of oxygen burning. The subsequent evolution would not affect the envelope structure of the progenitors significantly. Using the method described above, we obtain three hydrogen-free progenitor models: He3.85, CO3.85, and He4.90 (Table 1 ). The density structure of the progenitors is shown in Fig. 1 . He3.85 and CO3.85 have the same mass (3.85 M ⊙ ), but He3.85 is a helium star while CO3.85 is a carbon+oxygen star without helium. The ZAMS masses of He3.85 and CO3.85 are 14 M ⊙ and 20 M ⊙ , respectively. He4.90 is a 4.90 M ⊙ helium star progenitor with a ZAMS mass of 17 M ⊙ . These masses are determined to have the typical ejecta mass (≃2 M ⊙ ) in stripped-envelope SNe (e.g. Lyman et al. 2016 ). Figure 1. Density structure of the progenitor models in the mass coordinate (top) and radius coordinate (bottom). Table 1. Progenitor properties. When we explode these progenitors, we set a mass cut M cut below which is assumed to form a compact remnant that is not ejected. The mass cut is set at 1.35 M ⊙ (He3.85 and CO3.85) and 1.40


The author wishes to thank Derek Ward-Thompson for valuable discussions, the anonymous referee for a detailed and constructive review which greatly improved the content of this paper, and Chris Powles for proof-reading the derivations. The author wishes to thank the Science and Technology Facilities Council for studentship support under grant number ST/K501943/1 and post-doctoral support under grant number ST/K002023/1 while this research was carried out. This research has made use of the NASA Astrophysics Data System.


ACKNOWLEDGEMENTS I appreciate discussions with Hanno Rein, Dan Tamayo, Scott Tremaine, Ed Bertschinger, Walter Dehnen, Matt Payne, and Marie-Claude Arnaud. I appreciate feedback from the anonymous referee.


et al. 2014 )—have positions on the IR CMD consistent with the field locus (e.g., Figure 14 ). Finally, the AB Dor member SDSS J1110+0116 (T5.5; Gagné et al. 2015a ) also coincides with the field locus. Thus, for L/T objects as young as ≈100 Myr, while the temperatures for a given spectral type may be age dependent, the IR absolute magnitudes across the L/T transition do not seem to strongly depend on age. Figure 25. Absolute magnitudes vs. spectral type for our sample with an expanded range of spectral types, in order to highlight the latest-type objects. The L/T objects are shown as green squares, with the measurement errors comparable to the symbol size. Normal field objects (small gray triangles) are the same as in Figure 12 . The thick black lines are the polynomial fits from Dupuy Liu ( 2012 ), and the gray region shows the rms about the fits. See Figure 6 caption for further details. The two latest-type red objects in our sample, WISE J0206+2640 (L9 (red)) and WISE J0754+7909 (T2p (red)), may be illuminating landmarks for the transition. Their spectra are unusual compared to most field objects of similar type, especially WISE J0754+7909 (see details in Appendix ). And yet their absolute magnitudes are largely consistent with field objects, with the largest discrepancy being WISE J0206+2640 having ≈0.5 mag fainter magnitudes at Y and J bands compared to the field. Even given its very peculiar spectrum, WISE J0754+7909 has absolute magnitudes and color–magnitude positions in accord with the field. Thus, while the spectral peculiarities of these two objects may not be due to low gravity (indeed both have old field kinematics; Table 15 ), their consistency with field objects may be circumstantial evidence that the IR magnitudes of the L/T transition are robust over a range of physical properties. One interesting question that we can address is whether the J -band flux reversal that is a hallmark of L/T transition binaries in the field population (e.g., Liu et al. 2006 ) would appear different at lower gravities. The most extreme such flux-reversal binaries known are 2MASS J1404−3159AB (Δ J = −0.54 ± 0.08 mag, Δ K = 1.33 ± 0.05 mag; Looper et al. 2008a ) and SDSS J1052+4422AB (Δ J = −0.45 ± 0.09 mag, Δ K = 0.52 ± 0.05 mag; Dupuy et al. 2015 ). SDSS J1052+4422AB has a very precise evolutionary-model-based age derived from its dynamical masses and luminosities, clearly demonstrating that it is field age and gravity ( Gyr and = 5.0–5.2 dex; Dupuy et al. 2015 ). Now that we have parallaxes for members of YMGs that span the L/T transition, we can imagine hypothetical L/T transition binaries composed of these coeval objects. For example, if the AB Dor members WISEP J0047+6803 (L7 int-g ) and SDSS J1110+0116 (T5.5) were paired together in a binary, they would have Δ J = −0.37 ± 0.11 mag, Δ K = 2.04 ± 0.09 mag. If the primary were instead 2MASS J2244+2043 (L6 vl-g ), the flux ratios would be Δ J = −0.47 ± 0.09 mag, Δ K = 1.89 ±0.09 mag. Thus, it


ACKNOWLEDGEMENTS This work has been funded by a French Doctoral Contract. The authors are also grateful to Google for the unrestricted gift given to the University of Santa Cruz to carry out the project: “Deep Learning for Galaxies”.


We thank Seshadri Nadathur, Shaun Hotchkiss, David Alonso, Airam Marcos-Caballero, Fabio Finelli, Francesco Paci, István Szapudi, and the anonymous referee for useful comments that improved the paper. We also thank Krishna Naidoo, Ofer Lahav, and Aurelien Benoit-Lévy for informative discussions. AK thanks Márton Vargyas, and JGB the Theory Division at CERN, for their hospitality, where the main ideas of the project were discussed. Funding for this project was provided by the Spanish Ministerio de Economía y Competitividad (MINECO) under projects FPA2012-39684, FPA2013-47986, and Centro de Excelencia Severo Ochoa SEV-2012-0234 and SEV-2012-0249.


model assumes a temperature of 10 K for the gas, whichis up to 40% higher than the values found for the central parts of the cores (6–7 K). However, further calculations have shown that lowering the temperature by 2–3 K does not produce significant differences in the results (Roueff, priv. comm.). Visser et al. (2018) highlighted how isotope-selective photodissociation is a key mechanism to understand the nitrogen isotopic chemistry in protoplanetary disks. We can speculate that different levels of selective photodissociation in different environments could reproduce the variety of N-isotopic ratios that are observed. Further investigation of this point from both an observational and a theoretical point of view is merited. From the L1544 and L694-2 results, there seems to be tentative evidence that N 15 NH + is more abundant than 15 NNH + . This can be explained by the theory according to which the proton transfer reaction, tends to shift the relative abundance of the two isotopologues, slightly favouring N 15 NH + due to the fact that N 15 NH + zero-point energy is lower than that of 15 NNH + by 8.1 K (see reaction RF2 in Wirström Charnley 2018 ). It is interesting that the same trend is found also in a very different environment such as OMC-2 FIR4, a young protocluster hosting several protostellar objects. In this source, Kahane et al. (2018) measured lower values for the isotopic ratio, but in agreement with our result found that 15 NNH + is less abundant than N 15 NH + . However, we emphasise that higher-quality observations and those with better statistics are needed to confirm this point. 6. Conclusions We have analysed the diazenylium isotopologues’ spectra in three prestellar cores, L183, L429 and L694-2 in order to derive nitrogen isotopic ratios. Since LTE conditions are not fulfilled, especially for the N 2 H + (1–0) transition, we have used a fully non-LTE radiative transfer approach, implemented in the numerical code MOLLIE. We have carefully derived the physical models of the sources, computing their volume density and dust temperature profiles. With these, we were able to produce synthetic spectra to be compared with our observations, in order to derive the best-fit values for the molecular abundances and column densities. Using the same method, we have also recomputed the isotopic ratio of L1544. The difference with the literature value of Bizzocchi et al. (2013) , due to changes in the molecular collisional rates, is well within the uncertainties. In our sample of four cores, we derived 14 N/ 15 N values in the range 580–1000. Within the confidence range given by our uncertainties estimation, all our results are inconsistent with the value ≈400, predicted by the current theoretical models. L1544 still presents higher depletion levels than the other sources, but in general all the cores are anti-fractionated. The theoretical bases of such a trend are at the moment not understood. A deep revision of our knowledge of the


only the two-point statistic is insufficient to probe all the effects of different cosmological parameters on the evolution of the overall cosmological field. This motivates the need for considering other summary statistics in the analysis of clustering on smaller scales. Since the total information available in a survey scales with the number of independent modes (Tegmark 1997 ), and given that there are many more independent modes on small scales, it is important to develop these new statistical methods, that better extract information from small scales, to make optimal use of data from ongoing and future cosmological surveys. Various approaches towards harnessing information beyond that contained in the two-point statistics have been explored in the literature. One method is consider the higher N -point functions in the analysis – the three-point function and its Fourier transform, the bispectrum (e.g. Scoccimarro et al. 1998 ; Takada Jain 2004 ; Sefusatti et al. 2006 ), or the four-point function and its Fourier transform, the trispectrum (e.g. Verde Heavens 2001 ). While these additional statistics can be highly informative about small-scale clustering, and promise to yield significantly tighter constraints on some cosmological parameters (Coulton et al. 2019 ; Hahn et al. 2020 ), one drawback is that they generally entail much higher computational costs to measure either from simulations, or from data. The computational complexity rises with N – the order of the highest connected correlation function considered, while keeping the data size fixed. Further, the noise properties for these higher order estimators often make it difficult to obtain good signal-to-noise ratio (SNR) over a wide range of scales. In spite of these issues, analyses including higher order correlations, especially the bispectrum, have successfully been applied to certain cosmological data sets (Gil-Marín et al. 2015 ; Slepian et al. 2017 ; Gualdi et al. 2019 ). In this context, there also exist approaches which attempt to undo the non-linear effects of gravitational clustering on various cosmological fields. Once this procedure is applied, the analysis proceeds with the measurement of the two-point function of the linearized field. This approach has already been applied specifically to the reconstruction of the Baryon Acoustic Oscillation (BAO) signal (Eisenstein et al. 2007 ; Padmanabhan, White Cohn 2009 ; Padmanabhan et al. 2012 ). Schmittfull et al. ( 2015 ) showed that the reconstruction process can be interpreted as the transfer of information from the higher connected N -point functions in the observed field into the reconstructed two-point function. More recently, methods have been proposed for the full reconstruction of the initial linear modes from non-linear observables at low redshift (Baldauf et al. 2010 ; Seljak et al. 2017 ; Modi, Feng Seljak 2018 ; Horowitz, Seljak Aslanyan 2019 ). There is also a large body of literature investigating the one point


ACKNOWLEDGEMENTS EZ and AV acknowledge funding from the Swedish National Space Board. AKI and TH acknowledge the financial support from the National Astronomical Observatory of Japan (NAOJ) ALMA Scientific Research Grant number 2016-01 A. AKI also acknowledges the support from the Japan Society for the Promotion of Science (JSPS) KAKENHI 17H01114. TH acknowledges the JSPS Grant-inAid for Scientific Research 19J01620. Numerical simulations of S16 have been performed with Cray XC30, XC50 at the Center for Computational Astrophysics at NAOJ and with Cray XC40 at the Yukawa Institute for Theoretical Physics at Kyoto University. DC is a DAWN fellow. This research has made use of NASAs Astrophysics Data System.


We gratefully acknowledge support by the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics (MAS), by Fondecyt Regular 1150345 and by the BASAL-CATA Center for Astrophysics and Associated Technologies PFB-06. This work was finalized at the Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1066293. M.Z. and D.M. were partially supported by a grant from the Simons Foundation, during their stay in Aspen.


ACKNOWLEDGEMENTS This material is based upon work supported by the National Science Foundation under Grant No. 1411943. Part of the work presented herein is supported by NASA Grant No. NNX15AK92G. P.F.W. acknowledges funding through the Horizon 2020 European Research Council Consolidator Grant No. 683184. We thank the Stanislav S. Shabala as the referee for a constructive report that greatly improved the paper. This study is based on data taken with the Karl G. Jansky VLA that is operated by the National Radio Astronomy Observatory. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This work is based, in part, on data collected at the Subaru Telescope and obtained from the SMOKA, which is operated by the Astronomy Data centre, National Astronomical Observatory of Japan; observations made with the Spitzer Space Telescope , which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA; and data collected at UKIRT that is supported by NASA and operated under an agreement among the University of Hawaii, the University of Arizona, and Lockheed Martin Advanced Technology centre; operations are enabled through the cooperation of the East Asian Observatory. When the data reported here were acquired, UKIRT was operated by the Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the UK. This study is also based, in part, on observations obtained with WIRCam, a joint project of CFHT, Taiwan, Korea, Canada, France, and the Canada–France–Hawaii Telescope that is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique of France, and the University of Hawai’i. The scientific results reported in this article are based in part on observations made by the Chandra X-ray Observatory and data obtained from the Chandra Data Archive. The spectrographic data presented herein were obtained at the W.M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W.M. Keck Foundation. We wish to thank the indigenous Hawaiian community for allowing us to be guests on their sacred mountain, a privilege, without with, this work would not have been possible. We are most fortunate to be able to conduct observations from this site.


thick discs. We observed (see Table 1 ) the mid-plane of NGC 4111 with the SCORPIO in the wavelength range 4800–5600 Å with the spectral resolution ∼2.2 Å =55 km s −1 . The remaining five data sets were obtained using SCORPIO-2 at slightly lower spectral resolution (∼3.8 Å =95 km s −1 at 5100 Å) at the broader wavelength range (3600–7070 Å). Table 1. Long-slit spectroscopy of the sample galaxies. We reduced our spectroscopic observations with our own idl -based reduction pipeline. We estimated the night sky background from outer slit regions not covered by our target galaxies and an optimized sky subtraction technique that takes into account spectral resolution variations along the slit (Katkov Chilingarian 2011 ; Katkov, Sil'chenko Afanasiev 2014 ). All three galaxies were observed with the Infrared Array Camera (IRAC) at Spitzer Space Telescope in the imaging mode at wavelengths 3.6 μm and 4.5 μm. We fetched fully reduced 3.6 μm images from the Spitzer Heritage Archive. 1 2.3 Data analysis Van der Kruit Searle ( 1981 ) have shown that in case an isothermal disc in the equilibrium state the vertical disc density profiles are described by the law I = I 0 sech 2 ( z / z 0 ), where I 0 is the central intensity and z 0 is the disc scale-height. We fitted vertical profiles obtained by averaging Spitzer IRAC images along the radius using models including one and two components. We set central positions of both components to be the same in the case of two-component fitting but left the position itself a free parameter. Our results for NGC 4111 and NGC 5422 quantitatively agree with those presented in the S4G survey (Salo et al. 2015 ), however the results for NGC 4710 decomposition were not presented there. To derive internal kinematics and stellar population properties (mean ages and metallicities [Fe/H]) of thick and thin discs we first binned our long slit spectra in the spatial direction with the adaptive binning algorithm in order to reach the minimal signal-to-noise ratio S / N = 30 per bin per spectral pixel in the middle of the wavelength range. Then, in every bin we applied the nbursts full spectral fitting technique (Chilingarian et al. 2007a , b ) with a grid of high resolution stellar pegase.hr (Le Borgne et al. 2004 ) simple stellar population (SSP) models based on the ELODIE3.1 empirical stellar library (Prugniel et al. 2007 ). The nbursts technique implements a pixel space χ 2 minimization algorithm where observed spectrum is approximated by a stellar population model broadened with parametric line-of-sight velocity distribution (LOSVD) and multiplied by polynomial continuum (10th degree in our case) to take into account dust attenuation and/or possible flux calibration imperfections in both observations and models. We used Gaussian parametrization of the stellar LOSVD, except our higher resolution mid-plane spectra of NGC 4111 where we exploited the standard Gaussian–Hermite parametrization (van der Marel Franx 1993 ). The [Mg/Fe]


with high stellar mass surface densities (see next section). Figure 5. Comparison of circularized half-light radii at ALMA/870 μ m (red circles) with those at HST / H 160 band (cyan squares) for our sample of 12 galaxies with size measurements at 870 μ m. Gray circles indicate the rest-optical sizes for star-forming galaxies around the main sequence at z = 1.9–2.7, which are drawn from the 3D- HST survey. A dashed line shows their fitting function, . Table 2 Galaxy Properties for 12 SFGs with 870 μ m Size Measurements Notes. a Sérsic indices and half-light radii at 1.6 μ m. We do not use U4-16795 because the best-fit Sérsic index reaches the constrained limit of n = 8. b Half-light radii at 870 μ m. c Stellar mass surface density within a central 1 kpc calculated in stellar mass maps. d SFR surface density within a central 1 kpc calculated from the best-fit exponential models at 870 μ m and total SFRs. e Bulge formation timescales to reach the stellar mass surface density of log(Σ M bulge / M ⊙ kpc −2 ) = 10 (Equation ( 3 )). f Gas depletion timescales by star formation and outflows (Equation ( 4 )). In the analysis of size measurements, we do not include two massive star-forming galaxies with log( M * / M ⊙ ) > 11 in the parent sample of galaxies identified by the narrowband survey. One is not observed with ALMA, and the other one (U4-36568) is not detected in the high-resolution map (Figure 1 ). Given the high completeness of 86% (12/14) in the stellar mass range, our results are not significantly affected by the sample selection. Therefore, we find massive galaxies to commonly form stars in the extremely compact central region as at least 64% (9/14) have small 870 μ m sizes of R 1/2,870 μ m 1.5 kpc. This result is in excellent agreement with similar and independent evidence coming from an ALMA/870 μ m study of six massive star-forming galaxies at z ∼ 2 (Barro et al. 2016 ). Barro et al. ( 2016 ) find that the mean half-SFR radius is ∼30% smaller than the mean half-mass radius. The main difference between our work and Barro et al. ( 2016 ) is that they preselect only optically compact star-forming galaxies while our study almost completely selects main-sequence galaxies. 4. BULGE FORMATION IN EXTENDED, ROTATING DISKS Given that our ALMA sample is already massive, , they are likely to soon thereafter quench the active star formation and to be observed as quiescent galaxies in the local universe. Quiescent galaxies are always smaller than star-forming galaxies at any redshift and any stellar mass and have a cusp profile ( n > 2) unlike star-forming galaxies with exponential disks (Wuyts et al. 2011b ; van der Wel et al. 2014b ). A spatial distribution of stars within galaxies would not be changed unless a violent process like major mergers happens. However, centrally concentrated star formation reduces the half-light or half-stellar-mass radii of galaxies, and their Sérsic index would increase by central bulge formation. We quantitatively


We would like to thank John O’Meara for providing HIRES spectra and Nigel Mathes for reducing UVES spectra. We thank Nicolas Lehner for discussions on the UV ionizing background. We also thank Neil Crighton for the MCMC analysis software and Cloudy ionization training. Support for this research was provided by NASA through grants HST GO-13398 from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. S.K.P. acknowledges support through the Australian Government Research Training Program Scholarship. G.G.K., N.M.N., and M.T.M. acknowledge the support of the Australian Research Council through the Discovery Project DP170103470. C.W.C. and J.C.C. acknowledge support by the National Science Foundation under grant No. AST-1517816. Parts of this research were supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100012. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. Observations were supported by Swinburne Keck programs 2014A_W178E, 2014B_W018E, 2015_W018E and 2016A_W056E. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programs listed in Table 2 .


We thank Tsang-Keung Chan, Xiangcheng Ma, Daniel Anglés-Alcázar, Freeke van de Voort, Robert Feldmann, Chris Hayward, and Michael Anderson for various constructive suggestions. We thank Romeel Davé for providing details on wind implementation for our simulation comparison section. We thank the anonymous referee for providing suggestions that improved this work. DK was supported by a Hellman Fellowship and NSF grant AST-1412153, and by funds from the University of California San Diego. Support for PFH was provided by the Gordon and Betty Moore Foundation through Grant 776 to the Caltech Moore Center for Theoretical Cosmology and Physics, by the Alfred P. Sloan Foundation through Sloan Research Fellowship BR2014-022, and by NSF through grant AST-1411920. C-AF-G was supported by NSF through grant AST-1412836, by NASA through grant NNX15AB22G, and by Northwestern University funds. EQ was supported by NASA ATP grant 12-APT12-0183, a Simons Investigator award from the Simons Foundation, the David and Lucile Packard Foundation, and the Thomas Alison Schneider Chair in Physics at UC Berkeley. The simulations analysed in this paper were run on XSEDE computational resources (allocations TG-AST120025, TG-AST130039, and TG-AST140023). We would like to thank the Simons Foundation and the participants of the Galactic Super Winds symposium for stimulating discussions. We would also like to thank the Kavli Institute for Theoretical Physics and the participants of the Physics of Star Formation Feedback program for interactions that improved this work.


Other channels may account for the most carbon-rich early-R stars. 4. Conclusion and Discussion The merger of an HeWD with an RGB star in a CE provides a possible model for the origin of lithium- and carbon-rich stars. By adopting a grid of approximate initial conditions for such mergers informed by previous SPH simulations, we have made one-dimensional mesa calculations of the post-merger evolution of a wide range of possible systems. Using BSPS we have computed the frequency and distribution of HeWD + RGB binary-star systems that are likely to merge. Analysis of the post-merger evolution models show that the final surface abundances depend on the masses of the progenitor HeWDs. The Li-rich giants form from low-mass HeWDs, i.e., ; the C-rich giants form from low-mass HeWDs, i.e., . The pathway to either of these categories is related to the temperature of the helium-core-burning shell and the balance of nuclear products during initial shell burning. Analysis of the distribution of post-merger systems, as given by BSPS and mesa evolution tracks, shows that predictions for HeWD+RGB mergers are consistent with observations of most Li-rich giants in terms of surface effective temperature ( T eff ), surface gravity (log g ), surface luminosity (log L ), surface abundance ( A (Li) and 12 C/ 13 C), and the Galaxy disk space density of . They are also consistent with the properties of some early-R carbon stars, in terms of T eff , log g , C/O, A (Li), 12 C/ 13 C, and a space density of (3.5−5.9) × 10 −7 pc −3 . According to the models, the post-merger objects are helium-core-burning stars, which means that they are all in the red clump stage of evolution, i.e., they are red giants with nondegenerate helium cores. However, only a few Li-rich giants have had their precise evolutionary stage determined by asteroseismology. Those stars that have been analyzed in this way correspond well with our results. Hence, we strongly argue that the Li-rich red clump stars and the majority of early-R carbon stars most likely form following the merger of an HeWD with an RGB star. A few problems still need to be addressed: (i) Post-merger evolution calculations depend on evolution during the CE phase that is determined by the progenitors of the merger, the amount of mass loss during the CE phase, and the structure of the CE. However, our knowledge of CE phase in binary-star evolution is still poor. Future theoretical studies and simulations will improve this deficiency. Also, as the products of post-CE mergers, further observations of Li-rich red clump stars will provide additional data for studies of the CE phase. (ii) Some of the observed Li-rich giants have measured rotation velocities, but we have not calculated rotation speeds for post-merger models immediately following CE ejection. Our model will be improved with more knowledge of angular momentum transport during the merger. (iii) The HeWD+RGB merger model provides a successful explanation for Li-rich red


high-redshift probe of the first stars. Figure 2. Confronting structure formation theory with observations. Shown is the critical (minimum) DM halo mass required for the runaway collapse of primordial gas versus redshift for different cosmological models (see Fig. 1 ). The three coloured lines represent the critical halo properties for CDM ( blue ) as well as FDM models with ( orange ) and ( green ). The symbols indicate 2σ, 3σ, and 4σ peaks, respectively. The magenta region summarizes estimates for the DM halo mass, hosting UFD galaxies in the Local Group. Complementary to this, the light-grey region delineates the redshift range inferred from the observed EDGES spectral trough in the 21-cm absorption profile (Bowman et al. 2018 ). The dark-grey region shows the epoch of reionization. As is evident, the combined empirical constraints disfavour FDM models with the lightest axion masses. 3.1 Local Group dwarf galaxies The first constraint is the mass limit for the first star-forming haloes, M crit , such that at a given redshift runaway cooling can only occur in sufficiently massive hosts. Observations of UFD galaxies provide non-trivial tests for any model of structure formation, as the model must allow star formation in low-mass host haloes, sufficiently early in cosmic history. Possibly, masses of the UFD progenitors may be as small as at a redshift of z ≈ 10 (Bland-Hawthorn, Sutherland Webster 2015 ). However, observations of individual UFDs in the present-day Universe give relatively uncertain virial masses of the order of (e.g. Wolf et al. 2010 ; Simon et al. 2011 ; Boylan-Kolchin, Bullock Kaplinghat 2012 ), and a recent statistical analysis of local UFDs provides a conservative upper limit of the minimum UFD-hosting halo mass of (Jethwa, Erkal Belokurov 2018 ). We illustrate the plausible range of UFD halo masses in Fig. 2 . Structure formation models represented by curves that lie beneath the upper limit of this region are consistent with the Local Group constraints. In the ΛCDM cosmology, M crit is always below the region constrained by the UFD observations, regardless of overdensity νσ. This is in accordance with the standard picture of hierarchical structure formation. For the FDM model with the lighter axion, , on the other hand, M crit lies above the allowed UFD region for 2σ peaks. In this case, the most frequently occurring haloes would form with masses larger than what is inferred for the UFD hosts. The resulting tension implies that existing UFD observations disfavour the lighter-axion FDM model. Conversely, this tension does not exist in the model with heavier-axion FDM (). Here, values of M crit for reasonable, not too unlikely, σ peaks lie within or below the allowed UFD region. Though it remains unclear whether UFDs alone can rule out FDM entirely, further restrictions for the allowed models are anticipated through observations of lower-mass UFDs with upcoming large-scale surveys, carried out, e.g., with the Large Synoptic


derived from CfA3 in red. 5.3 Comparison of light-curve fitter results To make any cosmological statements based on the results in the previous section is beyond the scope of this paper, as this would require a detailed study and justification of the utilized light-curve fitters and their parameters, among many other considerations. It is interesting and possible, however, to compare results from the two light-curve fitters we employ to check for consistency. As the principal quantity of interest when fitting the light curves of SNe Ia is distance, we will focus our comparison on the derived distance moduli. The left-hand plot in Fig. 12 compares the distance moduli from snoopy and mlcs2k2 after correcting to put the measurements on the same scale (so that relative distance moduli are compared, independent from assumptions about the Hubble constant). This correction consists of adding an offset to the distance moduli from each fitter such that the value of H 0 measured from each set of results yields 65 km s −1 Mpc −1 . We perform this comparison only for spectroscopically normal SNe Ia in our sample for which snoopy finds Δ m 15 1.7 mag and for which z > 0.01. Of course, further restrictions should be placed when selecting a sample for cosmological purposes, but our selection is reasonable for performing a general comparison. We find strong agreement between the two sets of corrected distance moduli – a Kolmogorov–Smirnoff test gives a P -value of 1.000. The median residual is −0.026 mag with a statistical dispersion of 0.135 mag. Figure 12. Comparison of the (scaled) distance modulus and host-galaxy reddening results from both light-curve fitters for the selected subset of our data set. If we were to ensure consistency in choosing the parameters for each light-curve fitter, the residuals would almost certainly decrease. In particular, when fitting with mlcs2k2 , we place an exponentially decaying prior on A V , but no such prior was imposed with snoopy . This difference may well manifest in statistically and systematically different results for host-galaxy reddening and distance moduli between the two fitters. We compare host-galaxy reddening results in the right-hand panel of Fig. 12 , where for mlcs2k2 we have converted to reddening using E ( B − V ) host = A V / R V , with R V = 1.7. The agreement is reasonable, with a median residual of −0.056 mag and statistical uncertainty of 0.055 mag. Furthermore, the facts that the median residual ( snoopy minus mlcs2k2 ) is negative and that the disagreement is most severe for small E ( B − V ) host are consistent with what one might expect given the prior imposed by mlcs2k2 . 6 CONCLUSION In this paper, we present BVRI (along with some unfiltered) light curves of 93 SNe Ia derived from images collected by the LOSS follow-up program primarily over the interval from 2009 to 2018, but with several instances as early as 2005. Careful and consistent observational and processing techniques ensure


gravitational instabilities (GI) operating in the outer disc, driving material towards the protostar, and the magneto-rotational instability (MRI) operating in the inner disc region around the young protostar when the conditions are appropriate for a sufficient ionization fraction (Armitage et al. 2001 ; Zhu et al. 2009a , b , 2010a ; Zhu et al. 2010b ; Stamatellos et al. 2011 ; Mercer Stamatellos 2017 ). As the disc increases in mass by accreting infalling gas from the envelope it becomes gravitationally unstable, as suggested by the formation of the spiral structures seen in the quiescent phase snapshots in the left column of Fig. 1 . Thus, angular momentum is redistributed outwards efficiently resulting in inward gas flow. The inner disc is hotter and gravitationally stable such that there is no mechanism available to redistribute angular momentum, and material accumulates around the protostar (∼1 au). As more gas flows into this region, compressive, viscous, and protostellar heating raise the local temperature further. Once a threshold temperature of 1400 K is reached, this region is sufficiently ionized for the MRI to be activated (Zhu et al. 2009a , b , 2010a ; Zhu et al. 2010b ). The MRI increases the viscosity (the effective viscosity parameter becomes α MRI = 0.1; Shakura Sunyaev 1973 ), providing a way to redistribute angular momentum and resulting in rapid gas accretion onto the protostar. During the outburst phase, the accretion luminosity increases dramatically (∼10 3 L ⊙ ), heating the disc and disrupting the spiral structure (outbursting phase snapshots in the left column of Fig. 1 ; see also MacFarlane Stamatellos 2017 ). Once the gas from the inner region has been accreted, the protostar returns to the quiescent phase. The process of quiescent to outbursting phase repeats multiple times during the evolution of the YSO (Stamatellos et al. 2012 ). Outbursts happen every ∼1000 yr initially, whereas at later stages (but still during the Class O/I stage) every ∼10 000 yr (see Stamatellos et al. 2011 , 2012 ). This is similar to what is inferred from observations (e.g. Scholz, Froebrich Wood 2013 ; Hillenbrand Findeisen 2015 ; Fischer, Safron Megeath 2019 ; see also Peña, Naylor Morrell 2019 , for longer outburst time-scale estimates for older, Class I/II, YSOs). It is assumed that in the quiescent phase gas accretes onto the protostar at a rate of (this is a free parameter in our calculation) and the protostellar luminosity is then on the order of ∼1 L ⊙ . As a comparison, at the same time the accretion rate onto the outer disc is on the order of ∼10 −5 −10 −4 M ⊙ yr −1 . During the outburst phase the accretion onto the protostar is significantly enhanced. This model is different than the models of Vorobyov Basu ( 2005 , 2006 , 2010 ). In their models the GIs alone are responsible for the observed outbursts: GIs create clumps in the disc of a young stellar object that eventually fall onto the protostar, resulting in outbursts


time variations appear for all radii, with typical amplitude changes in η R / η max of ∼0.2, except for the innermost radial bin. The thick solid grey curve indicates the mean bar half-length variations (black curve from Fig. 5 ), showing a very good agreement with the amplitude fluctuations. Note that as the bar decreases in length, so does its amplitude. Very similar time variations appear for all radii, with typical amplitude changes in η R / η max of ∼0.2, except for the innermost radius considered. The thick solid grey curve in Fig. 7 represents the mean bar half-length variations (black curve from Fig. 5 ), which can be seen to agree very well with the amplitude fluctuations, including the short and long periods. The fluctuations seen in the bar amplitude at fixed radii (Fig. 7 ) are more similar to the bar length time variations than the η max , which can vary with radius (see Fig. A6 ). This can generally be seen for Model2 as well in Fig. 8 . One key difference between the two simulations is the fact that for Model1 all radii peak at nearly the same time, while in Model2, they are delayed with the lowest radius of 3 kpc always peaking last. Such a pattern suggests a spiral arm contribution. Indeed, we established in Section 3.4 that the Model2 bar true length is about 3 kpc; therefore, the region examined in Fig. 8 lies at or outside the true bar, yet in the regime where our three measurement methods detect a bar. The solid grey curve in Fig. 7 shows the bar length time variations, seen to follow the overall trend in η R / η max , in best agreement with the two outermost radii. Figure 8. As Fig. 7 , but for Model2. The solid grey thick curve indicates the mean bar half-length variations (black curve from Fig. 6 ), showing an overall good agreement with the amplitude fluctuations. Since the true bar length is ∼3 kpc, the outermost three radial bins for which η R was estimated correspond to the spiral arms, as evident from the systematic time offset among different curves. 4.4 Bar pattern speed We estimated the instantaneous bar pattern speed using the modified TW method by Sanders et al. ( 2019 ), who applied it to both MW data and N -body simulations. The top left-hand panel of Fig. 9 shows the configuration we used to measure Ω p , over the stellar density of Model1. As done by Sanders et al. ( 2019 ), we assumed a bar angle of 33°, a Galactic latitude range | b | 5°, and a solar Galactocentric distance of R 0 = 8.12 kpc. The rotation is clockwise. Estimates are done in bins of Galactic longitude dl = 2° in the radial range indicated by the two arches centred on the Sun (at distances of 4.12 and 8.12 kpc), which is sitting at ( x, y ) = (8.12, 0). The straight black line over the bar has a half-length of 3.2 kpc, which corresponds to l ∼ 17° for our bar angle. The bar angle is kept the same at each time output. The white circles show the bar time-median CR, 4:1 OLR, and 2:1 OLR at R CR = 3.25, R 4: 1OLR = 4.2, and R 2: 1OLR =


We thank an anonymous referee for the constructive report. L.F.R. and L.L. acknowledge the support of DGAPA, UNAM, and CONACyT, México.


confined the jet to propagate along high-density walls (similar to model B simulations in BTG17 ). Our simulations, performed in both 2D and 3D, follow jets from the compact object to a scale 10 3 larger. These large scale simulations allow us to follow the jet acceleration through conversion of its magnetic energy into kinetic energy. In the context of short GRB jets, as the jet breaks out from the confining medium, it essentially travels through vacuum (or at least very low ambient gas). This is quite advantageous since BTG17 have shown that these type of jets are almost identical in 2D and 3D runs and that they are fairly axisymmetric (see also Bromberg Tchekhovskoy 2016 ). For this reason, we focus our efforts on axisymmetric 2D simulations, which are considerably less computationally expensive and can be better resolved numerically. We mimicked the boundary of the dynamical ejecta by setting the ambient gas density of our previous simulations (see BTG17 model B-2D-vhr for details on jet and ambient gas parameters and numerical resolution) to be essentially zero beyond 100times the size of the compact object as displayed in Fig. 2 , which shows both density and velocity (Lorentz factor) contours. After the jet breaks out from the dynamical ejecta, a rarefaction wave crosses the jet and it spreads sideways and accelerates further (Komissarov etal. 2010 ; Tchekhovskoy etal. 2010 ). For this work, we simulate steady state jets that will maintain the conditions at the central engine constant. This means that the rotation of the black hole and the magnetic field strength is kept constant, so that the jet has a constant power throughout the duration of the simulation. Future simulations will explore short-lived jets. Figure 2. Numerical simulation of a jet that is collimated by and breaks out from the dynamical ejecta. We show 2D cuts of density (left-hand panel) and Lorentz factor (right-hand panel), where r 0 stands for a few times the radius of the central compact object. The jet accelerates as it breaks out from the dynamical ejecta and spread sideways. At large distance the jet turns conical and its lateral structure is fixed. The distance where the radiation from the jet takes place is uncertain; the prompt emission may originate at the transparency radius of the jets or further out, at optically thin conditions (see the next section). The distance at which the jet produces the -ray radiation lies beyond our simulated region. However, we argue that the dynamical range of our current simulations is sufficient for the objectives of our estimates. This is due to the fact that after the phase of lateral expansion and the crossing of the rarefaction wave, the jet becomes approximately conical and its properties freeze out as a function of angle (see Fig. 3 ). Therefore, the lateral structure of the jet, which is the key aspect of our study, is set at this distance. It is no longer necessary to follow the jet beyond this point since its


code. We adopt the high- z edition, which includes the modeling in the radio bands, as well as the Lyman absorptions in the rest-frame UV from the intergalactic medium ( da Cunha et al. 2015 ). We plotted the best-fit models in Fig. 6 , along with the photometric measurements, and the fitting results of HYPERZ and those using the infrared templates. Fig. 6. UV-to-radio SEDs for each of our sample SMGs except ALESS66.1 for which the UV-to-NIR photometric measurements are contaminated by a foreground quasar Simpson et al. (2014) . Sources that are identified to host an AGN are marked with an asterisk after the ID. The measurements are plotted as black points and the best-fit models from MAGPHYS , HYPERZ , and the IR templates compiled by Swinbank et al. (2014) are plotted as gray, blue, and red curves, respectively. For clarity, we do not show the uncertainty, however, it is discussed in the text, including the fact that the IR luminosities that were estimated by the two methods are in agreement with each other within the uncertainty. The insets are zoom-ins in the OIR regime from 0.3 to 10 μ m in log–log scale. We find significant differences in higher L IR sources between the best-fit models of MAGPHYS and HYPERZ ; also, in all of these discrepant cases, the HYPERZ modeling provides a better fit with a lower χ 2 . We first compare the infrared luminosities estimated from the SED templates 8 and those from MAGPHYS . We confirm that both values are statistically consistent with each other in all sources. We then turn to A V in which we find significant differences in some sources. As seen in Fig. 6 , ALESS67.1, ALESS75.1, and AS2UDS412.0 appear to have different best-fit models in the UV-to-NIR regime, although the fit from HYPERZ shows better agreement with the data. Interestingly, in these three cases where the A V differ, the values obtained from MAGPHYS are all higher. We note that given the different assumptions of the attenuation curves in these two codes, the A V values from the UV-to-NIR regime should also ideally be deduced from MAGPHYS by turning off the fitting of the MIR-to-radio part. Unfortunately, the public version of MAGPHYS does not support this option. However, the fact that the A V values are in excellent agreement with each other in ALESS17.1 and AS2UDS292.0, which is as expected from a good match shown in Fig. 6 , suggests that the systematic difference between the A V values can be neglected. We also tried to homogenize the two codes by running the HYPERZ fitting that only uses the τ -decay SFHs that are similarly adopted by MAGPHYS , thus definitively removing the bursting SFH in our adopted method for HYPERZ . We find that the output values vary within uncertainties and the conclusions remain unchanged. Since stellar mass is not one of the direct outputs from HYPERZ, we estimated the stellar masses by converting the absolute magnitude, which is one of the outputs from HYPERZ based on some estimates of mass-to-light


Acknowledgements It is a pleasure to thank A. Beresnyak, B. Burkhart and A. Schekochihin, as well as the anonymous Referee, for helpful comments and discussion. JS was funded in part by the Gordon and Betty Moore Foundation through Grant GBMF5076 to Lars Bildsten, Eliot Quataert and E. Sterl Phinney. Support for PFH was provided by NASA Astrophysics Theory Grant NNX14AH35G National Science Foundation Collaborative Research Grant #1411920 and CAREER grant #1455342. Numerical calculations were run on Caltech cluster ‘Zwicky’ (NSF MRI award #PHY-0960291) and XSEDE allocation TG-AST130039 supported by the NSF.


Acknowledgements OSIRIS was built by a consortium of the Max-Planck- Institut für Sonnensystemforschung, Göttingen, Germany, CISAS University of Padova, Italy, the Laboratoire d’Astrophysique de Marseille, France, the Institutode Astrofísica de Andalucia, CSIC, Granada, Spain,the Research and Scientific Support Department of the European Space Agency, Noordwijk, The Netherlands, the Instituto Nacionalde 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. 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. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.


spectrum and 27 have multiple archival spectra. As we are refining our CLAGN candidates by selecting candidates where the archival spectra already show some signs of change, there is the potential to miss CLAGNs where the spectra have changed after the last archival spectra was taken and this will decrease our completeness. Of the AGNs that were not identified by our CLAGN candidate selection methods and have multiple archival spectra, we identified AGNs that appeared to have some small variations in the spectra, however, these variations were not significant enough to require further investigation (i.e. changes in spectral class were 0.1 or less). Thus, while our three candidate selection criteria select many AGNs that are not CLAGNs, our selection criteria are not missing a large number of nearby CLAGNs. 3 SPECTROSCOPIC FOLLOW-UP Once candidates were identified using the colour and flux criteria discussed in Sections 2.2– 2.4 , we inspected the archival spectra of these objects in order to identify potential CLAGNs without obtaining new spectra. It should be noted that we did not follow up candidates where archival spectra from the past two decades showed no variability (irrespective of the selection criteria). We used the Wide Field Spectrograph (WiFeS, Dopita et al. 2010 ) integral field unit (IFU) on the Australian National University’s 2.3-m telescope at Siding Spring to obtain new spectra of our candidates to confirm that they are indeed CLAGN. WiFeS has a field of view of 25 × 38 arcsec 2 , divided into 950 spaxels. The advantage of using an IFU for follow-up observations of candidates is that extraction aperture size can be matched to previous observations (e.g. the 7-arcsec fibre of 6dF, Jones et al. 2009 ). The wavelength coverage is 3800–9200 Å, which spans the H α , H β , and O iii lines at z 0.04, and we expect H α to show the most clear signs of change in CLAGNs. We observed in nod-and-shuffle mode taking at least three frames with 60s on object, 60s on sky, and 10 cycles per frame. This results in 40 min on object, 40 min on sky, and ∼15 min on overheads including telescope nod time, guide star re-acquisition and CCD readout. In total we allow ∼100 min per galaxy. Our observations were taken between 2018 July and 2019 March. We reduced our data with PyWiFeS, Python-based pipeline (Childress et al. 2014 ). We observed 15 CLAGN candidates over multiple observing sessions, the AGNs are presented in Table 4 . Table 4. CLAGN candidates that were observed with WiFeS between 2018 July and 2019 March. a New CLAGN that we have identified. b Known CLAGN. 4 SPECTRAL VARIABILITY AND NEW CLAGNS We next present the spectra of the new CLAGNs that we have identified using the selection criteria mentioned in Sections 2.2 – 2.4 , where we compare archival spectra of the galaxies with spectra taken using WiFeS. We plot archival spectra, where available, with the spectra taken using WiFeS to display the change. Multiplicative scaling has


J.L. thanks O. Venot, T. Cavalie, S. Guerlet, A. Spiga, and T. Fouchet for helpful discussions. J.L. is also indebted to S. Guerlet for making the radiative data for Saturn available. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 679030/WHIPLASH)


time-series, but all data points available in the original time-series are published in DR2. Consequently, all measurements are shown in the figures displaying Gaia light curves in this paper, and the disregarded measurements are identified by magenta crosses. Fig. 3 Skydistribution in Galactic coordinates of all LPV candidates published in Gaia DR2. Each star is drawn with a purple–red–yellow colour-grading scale according to the median ( G BP ) −median ( G RP e ) colour scale shown on the right of the figure. 3 Overview of the <italic>Gaia</italic> all-sky LPV survey We present in this section an overview of the all-sky Gaia DR2 catalogue of LPV candidates, together with light-curve examples. Validations on catalogue subsets according to distance to the Sun or stellar populations are presented in the next sections. 3.1 Sky distribution overview The sky distribution of all DR2 LPV candidates is shown in Fig. 3 , colour-coded with the G BP − G RP e colours 1 of the stars according to the colour-scale on the right of the figure. The Galactic disc, bulge, and halo are clearly visible, as well as the extra-galactic populations of the Magellanic Clouds (the two concentration areas below the Galactic plane at Galactic longitudes of about 285° for the Large Magellanic Cloud (LMC) and 303° for the Small Magellanic Cloud (SMC)) and of the Sagittarius dwarf galaxy (elongated tail below the Galactic bulge). The reddest LPV candidates (yellowish colour in the figure) are seen to be distributed in the Galactic plane, where dust leads to high extinctions. Fig. 4 Histogram of G time-series durations, in days, of all Gaia DR2 LPV candidates. The dashed blue histogram represents the subset of the catalogue published with periods for the sources. 3.2 Light-curve overview The histogram of the G -band time-series durations of all LPV candidates is shown in Fig. 4 , with the subset of sources with published periods shown by the dashed blue histogram. Only 27 sources have durations shorter than one year, while 90% of the sources have observationsover more than 1.5 yr. The time coverage of the measurements over the observation duration is equally important. To compute the time coverage, we divided the time-series into 20 equally spaced sub-time intervals, and computed the fraction of sub-time intervals that have non-zero measurements. The histogram of the resulting time coverage is shown in Fig. 5 . It shows a distribution of the number of stars with time coverages between 35% and 90% that is about equal. Fig. 5 Histogram of the time coverage (see Sect. 3.2 ), in percentage, of measurements in the cleaned G time-series of all Gaia DR2 LPV candidates (grey bars) and of the subset with published periods (blue bars). Fig. 6 Sky distribution of the G -band time-series durations of each DR2 LPV candidate, according to the colour scale shown on the right of the figure. Time-series with durations shorter than 500 d are plotted in yellow (they are mostly located in the


and low column densities, which appear to support that the hot core is externally heated. The strong bursting observed in the water masers located in the direction of the HMC in Orion-KL could be caused by the interaction between the explosive outflow and the ambient quiescent gas (Hirota et al. 2011 , 2014 ). In this study, using the tremendous sensitivities, a better uv-plane coverage, and a very broad covering of frequency range at (sub)millimeter wavelengths offered by the recent operational Atacama Large Millimeter/Submillimeter Array observatory (ALMA), we carried out a search for compact and faint continuum sources (in five different continuum bands of ALMA) that are probably associated with (proto)stellar objects and hot molecular gas within the HMC in Orion-KL. We report, in addition to Orion Source I and Orion BN, the detection at submillimeter wavelengths of three new compact continuum sources (ALMA1-3) that are located in the vicinities of the HMC, but not with associated hot molecular gas. Additionally, one of these compact sources seem to be associated with high-mass stars, but it does not account for the internal heating of the HMC. We thus conclude that the hot molecular core in Orion-KL is indeed heated externally, as suggested by many observational works. 2. Observations The observations were carried out with ALMA between 2014 and 2015 as part of the ALMA programs: 2013.1.01034.S (Band 4), 2012.1.00146.S (Band 6), 2013.1.00048.S (Band 8), and 2012.1.00123.S (Bands 7 and 9). The total bandwidth used to estimate the continuum emission in the ALMA observations was more than 2 GHz (see Table 1 ). However, as there are many lines detected in the spectral windows, and there is probably some contamination from very faint lines. In order to search for compact millimeter sources within the hot molecular core in Orion-KL, we constrain the uv-range of the observations (see Table 1 and Figures 1 – 3 ). We chose the uv-range based on a trade-off between removing as much extended emission as possible and keeping enough visibilities to obtain a good map. The resulting synthesized beams are presented in Table 1 . The number of antennas used during the observations varied between 31 and 40 (see Table 1 ). Weather conditions were very good and stable, with an average precipitable water vapor of 6 mm (Band 3), 1 mm (Band 6), 0.3–0.7 mm (Band 7), 0.3 mm (Band 8), and 0.15 mm (Band 9). Figure 1. ALMA continuum band 4 (3.0 mm) contour and color-scale images of the HMC located in Orion-KL. Upper panel: continuum image using the full uv-range from the ALMA observations. The contours start at 15% and go up to 90% in steps of 5% of the intensity peak. The intensity peak is 0.150 Jy Beam −1 . The half-power contour of the synthesized beam of the image is shown in the bottom-left corner. Lower panel: continuum image using only part of the uv-range (>150 kλ ) from the continuum observations. The contours start at 15% and go up to 90% in steps of 5% of


manuscript, we report on the results of the monitoring of MAXI J1631-472 with the MeerKAT interferometer, complemented with X-ray data from Swift and MAXI . We show the evolution of the radio and X-ray emission throughout its outburst and provide a physical interpretation of the data set. We also compare the behaviour observed with other known outbursting BH systems. The paper is structured as follows: in Section 2 , we describe the observations and details of the data reduction. In Section 3 , we present the results of the two wavebands and highlight some of the interesting behaviour. In Section 4 , we summarize our conclusions. 2 OBSERVATIONS 2.1 MeerKAT The MeerKAT monitoring of MAXI J1631-472 was performed weekly, with the observations carried out at a central frequency of 1.28 GHz and bandwidth of 856 MHz. These observations were taken as part of the ThunderKAT Large Survey Programme on MeerKAT (Fender et al. 2016 ). Each observation consists of a 15-min scan of the target, and a 2-min scan of the phase calibrator, in this case J1726-5529. The primary calibrator used was J1939-6342, which was observed at the start of each observation for 5 min. We used casa (McMullin et al. 2007 ) to perform the data reduction. We performed flagging of radio frequency interference, where we removed the first and last 150 channels (channel width ∼ 209 kHz) of the band. The autoflagging algorithms, rflag and tfcrop , were used for subsequent flagging. We then performed calibration, where the flux density on the primary calibrator was set using the known model. The phase-only and antenna-based delay corrections on the primary calibrator were then solved for, then the bandpass corrections for the primary were then applied. We solved for the complex gains on the primary and secondary, then proceeded to scale the gain corrections from the primary to the secondary and target source. Finally, the imaging was performed using wsclean (Offringa et al. 2014 ). Flux densities for MAXI J1631-472 were extracted from the images using pybdsf (Mohan Rafferty 2015 ); these are presented in Table 1 . We were able to extract an in-band spectral index (α, with the flux density S ν scaling with frequency ν as S ν ∝ ν α ) at the four brightest epochs using a fit to eight uniformly spaced channels across the 856 MHz bandpass. To determine the spectral index, we performed a least-squares fit using the scipy and curve-fit module and function, respectively, in python to calculate the slope and its associated error bar. The results from these fits are presented in Table 2 and indicate that the source was optically thin during the times of strongest radio flux density. Table 1. MeerKAT radio (1.28 GHz) and Swift X-ray (1–10 keV) fluxes of MAXI J1631-472. The spectral states of each of the observations are indicated in the final column: Soft (S), intermediate (I) and Hard (H). Table 2. Spectral indices during the strongest radio flux: S ν ∝ ν α . 2.2 MAXI We used X-ray data from the


are from MERLIN 5 GHz observations of the radio jet (Gallimore et al. 2004 ); the MERLIN beam is 4.2 pc FWHM. Upper right: spectral map from a simulation of a bipolar outflow with the axis tilted 5° from the plane of the sky and rotated to PA 33° (see the text for details). Colors are as for the left panel. The outflow axis of ionized gas traced by optical emission lines in the narrow-line region is inclined 5° out of the plane of the sky (Crenshaw Kraemer 2000 ), and we assume the same for the CO outflow. Can such a small inclination produce the red–blue spatial separation observed in CO? To test this possibility we conducted a numerical simulation of a bipolar outflow. We employ a truncated cone geometry with a disk base diameter of 1 pc (comparable to the H 2 O maser disk), 4.4 pc total bicone height (based on the spread of CO features in Figure 2 ), and half-opening angle of 30°. To model the outflow, we randomly placed a total of 10 4 clumps inside the cone. Clumps were evenly distributed in cylindrical radius ρ (i.e., radius projected onto the disk plane) and distance along the polar axis. The direction of motion for a clump is vertical for clumps directly above and below the disk (i.e., clumps with ρ ≤ 0.5 pc) and radial from the edge of the disk for clumps with ρ > 0.5 pc. We further assume uniform acceleration from 30 km s −1 at the molecular disk to 800 km s −1 at the cone height; these values were selected from interactive trials to give a reasonable match to the observations. Finally, all clumps are equally luminous point sources of CO emission, added as beam-shaped, two-dimensional Gaussians to the planes of a simulated data cube. The cone was then inclined by 5°, and we repeated the cube analyses on the simulated data. To simulate the effects of measurement uncertainty, each measured peak position was displaced randomly, assuming 0.5 pc errors, comparable to the relative, observed position uncertainties. The simulation results shown in the right panel of Figure 2 broadly reproduce a parsec-scale asymmetry of redshifted and blueshifted peaks similar to that observed (left panel). This simple simulation was not meant to replicate any physical model or explain every aspect of the data; in particular, the systemic-velocity emission is concentrated to the east, at odds with the random distribution we use. Our simulation is only meant to demonstrate that the small 5° tilt detected in narrow-line observations is sufficient to produce the red–blue asymmetry of the excited CO emission. In addition to the outflow component described above, we also find evidence for rotation consistent with the compact H 2 O maser disk. Figure 3 shows the position–velocity ( p – v ) diagram constructed by slicing the CO spectral line cube along the axis tracing the extended systemic emission. Surface brightnesses were averaged within the three-pixel extraction width (green dashed lines in Figure 1 ). The data were smoothed using a Savitzky–Golay


ACKNOWLEDGEMENTS We thank Prof. W. A. Coles and the anonymous referee for very helpful discussions on the interpretation of curvature variation. This work was supported by the National Natural Science Foundation of China (U15311243), the National Basic Research Program of China and the 973 Program (2015CB857101, XDB23010200, 11690024, 11373011, 11303093, U1431113, 11178001 and 11573008). KJL is supported by the Max-Planck Partner Group.


but not part of the interacting pair (UGC 10549, z = 0.009, 50 kpc projected separation). The integrated spectrum shows a somewhat complex profile, with the velocity of the peak emission at ≳50 km s −1 lower velocity than the redshifts of the two components of dm1647+21. This peak corresponds to the redshift of UGC 10549, suggesting a substantial amount of the single-dish H i flux is associated with that galaxy. In support of this interpretation of the single-dish profile, follow-up interferometric observations with the Very Large Array (VLA) reveal a clumpy H i distribution in dm1647+21 and emission associated with UGC 10549 (S. Stierwalt et al. 2017, in preparation). Unfortunately, the VLA observations (taken in the B-array) do not recover the total single-dish H i flux, so we cannot reliably separate the single-dish H i mass into contributions from dm1647+21 and UGC 10549. Considering the uncertain distribution of the H i mass, the single-dish measurement implies an upper limit to the gas depletion time of 2 Gyr when considering only the SFR of dm1647+21. The true gas depletion time for dm1647+21 is likely shorter than this estimate. 3.3. ISM Properties We use the line images created from the MUSE data to construct optical line ratio maps, based on classic optical diagnostic plots (e.g., Baldwin et al. 1981 ; Veilleux Osterbrock 1987 ; Kewley et al. 2006 ). Maps of [O iii ]/H β , [N ii ]/H α , and [S ii ]/H α are shown in Figure 2 . The system-integrated values for these ratios are: log 10 ([O iii ]/H β ) = 0.51, log 10 ([N ii ]/H α ) = −1.8, and log 10 ([S ii ]/H α ) = −1.0. In Figure 3 , we show the line diagnostic diagrams with the distribution of values from Figure 2 and the source separation lines from Kewley et al. ( 2006 ). The line ratios in the ratio maps and diagnostic diagrams are all consistent with excitation from star formation, and we see no evidence for LINER-like or Seyfert emission. Figure 2. Optical line ratio images. First row, from left to right: [O iii ]/H β , [N ii ]/H α , [S ii ]/H α . Second row, from left to right: A H α and [S ii ] 6718/[S ii ] 6732. The ratio maps were all masked to the fainter of the two lines (Figure 1 ). No extinction corrections have been applied. Figure 3. Optical line diagnostic diagrams based on the narrow-band images in Figure 2 . Left: [O iii ]/H β vs. [N ii ]/H α . Right: [O iii ]/H β vs. [S ii ]/H α . The colored contours and small points show the distribution of values for individual 02 × 02 spaxels in the data cube; the number in the bottom left gives the number of spaxels shown in each panel. The plotted spaxels are those with a ≥5 σ detection in [N ii ] 6585 (left) or [S ii ] 6718 + 6732 (right). The cyan crosses mark the locations of the three brightest clumps identified from the H α map (Section 3.2 , 15 diameter apertures). The colored lines are the Kewley et al. ( 2006 ) separations between star-forming galaxies, Seyferts, and LINERs. The line ratios are consistent with


events for both the pn and MOS. The events detected in the two MOS cameras were combined into a single data set, and we used averaged response files. Since with the ML analysis it is possible to derive an accurate estimate of the background at the source position, it was not necessary to remove time intervals of high background. We applied the ML in a circular region positioned to avoid the gaps between the CCD chips (radius of 60, center at R.A. = 11 h 54 m 232, decl. = −62°49′47′′). For the spectral extraction we fixed the source position at the coordinates found by the ML in the pn+MOS image in the 0.4–2 keV range (R.A. = 11 h 54 m 204, decl. = −62°50′044). This position is consistent with that found with the SAS pipeline. In the energy range 0.4–2 keV the source was detected with 113 ± 21 counts (pn) and 66 ± 15 counts (MOS), which correspond to a total count rate of (3.4 ± 0.5) × 10 −3 count s −1 . The energy bins for the spectral analysis were chosen from the requirement to have a signal-to-noise ratio greater than 2.5 σ in each spectral channel. This resulted in four energy bins for the pn and three for the MOS. Below 0.4 keV and above 2 keV, the source detection was below our significance threshold; therefore we derived upper limits on the source counts in these energy bins. 3. Possible Optical Counterparts In the Gaia database there are no stars closer than 4′′ to the corrected X-ray position; see Figure 1 . The closest object, at 406, is Gaia DR 2 5334588220496359424 which has g = 17.48. A slightly brighter star, Gaia DR2 5334588151811060992 with g = 14.15, is located at 429. It has B = 16.4, I = 13.11, J = 11.937, and K = 10.951, while the fainter source has B = 19.6, I = 16.08, J = 14.07, and K = 12.86 (Epchtein et al. 1999 ). Based on the color differences I − J and J − K , we identify the brighter star as a main sequence star of spectral type K5 and the fainter one as an M5 star. We fitted the spectra of our X-ray source with thermal plasma models ( apec by Smith et al. 2001 ; mekal by Mewe et al. 1985 ), as expected in the case of coronal emission from normal stars. Acceptable fits could be obtained with temperatures in the range kT = 0.23–0.26 keV, absorption N H ∼ 10 22 cm −2 , and flux ∼3 × 10 −15 erg cm −2 s −1 . The resulting X-ray-to-optical flux ratios (Agüeros et al. 2009 ) were −3.34 and −2.6 for the K and M star, respectively. These values are compatible with X-ray emission from late-type stars and therefore we cannot exclude these objects as potential counterparts of the X-ray source based on spectral and/or flux arguments. However, we note that both stars are outside the 3 σ confidence level error circle of the X-ray source and therefore we consider that PSR J1154–6250 is much more likely responsible for the detected X-ray emission. 4. Results The results of the spectral analysis obtained with the two methods are summarized in Table 3 . The absorption column density was poorly constrained due to the small


for such an effect. We point out that these X-ray reprocessing models cannot simultaneously explain several other aspects of AGN UV/optical variability, including the timescale dependent color variations (i.e., the lamp-post SSD can explain the variable spectra, but cannot account for the fact that the color variations are timescale dependent; see, e.g., Zhu et al. ( 2018 ) and the anti-correlations between variability amplitude and L (e.g., MacLeod et al. 2010 ; Sun et al. 2018b ). Below, we discuss the alternative disk-corona magnetic coupling scenario (Sun et al. 2020a ), which has shown to be successful in reproducing the color variations and the dependences of the variability amplitude upon L (Sun et al. 2020b ), to understand our results in Figure 1 . 3.2. Implications for the Magnetohydrodynamic Accretion Physics Theoretically speaking, disk temperatures ( T ) should vary in response to the time-dependent magnetohydrodynamic (MHD) turbulent heating with a response on the thermal timescale ( τ TH ). For an SSD, , where α , G , and R are the dimensionless viscosity parameter, the gravitational constant, and the distance to the central black hole, respectively. Therefore, the short-wavelength emission, which is produced by the inner hotter plasma, has a smaller response thermal timescale than the long-wavelength emission. The differences in the response thermal timescale would add an additional inter-band time lag ( τ add ), beyond the light-travel time lags. On short timescales ( t dur ), the observed UV/optical variations are mainly produced in the similar small- R regions with τ TH ≤ t dur as the gas in these regions can vary its temperature significantly, and τ add is small and negligible (and vice versa). For an SSD, at the characteristic radius R λ (with k B T ( R λ ) = hc / λ , where k B and h are the Boltzmann and Planck constants, respectively; and ), τ TH ( R λ ) scales as α −1 L 0.5 λ 2 . Therefore, τ add would only be prominent for AGNs with small L (i.e., short τ TH ( R λ )) or large t dur . Below, we consider a specific thermal-fluctuation model to qualitatively reproduce the observed result. In earlier work, we proposed the corona-heated accretion-disk reprocessing model (hereafter the CHAR model) by assuming the accretion disk and the extremely compact X-ray corona are magnetically coupled (Sun et al. 2020a ). Magnetic fluctuations in the corona can alter the disk surface magnetic field, drive variations in the disk MHD turbulent dissipation, and eventually induce coherent multi-band variations. We have predicted that τ dif increases with decreasing L for sources with similar monitoring durations (see Figures 8 and 16 of Sun et al. 2020a ). The first two model parameters, M BH and , are fixed according to the observational constraints. The only free parameter, α , might be 0.1–0.4 according to studies of the outbursts of dwarf nova or soft X-ray transients (e.g., King et al. 2007 ). We fix 10 10 Note that the CHAR model


4 . The two small circles in the NUV spectral window mark the downflow components in the Mg ii h and k lines. Figure 4. Average IRIS spectra at specific locations of two slits as marked in Figures 2 and 3 . The black lines represent the spectra of the typical plage region, whereas the green lines represent the spectra of a typical umbral region (marked on the right side of each panel of Figure 2 ). The red and blue lines are the average spectra of two regions where supersonic downflows are observed. These two regions are marked at the right side of each panel of Figures 2 and 3 , respectively. The red spectra show a downflow only in TR lines, whereas the blue spectra show a downflow in TR lines as well as in the chromospheric C ii and Mg ii h and k lines. Two arrows in the NUV spectral window mark the downflow components seen in the Mg ii h and k lines (spectra shown in blue). For the identification of different lines, we refer to Tian ( 2017 ). 3.2. Detection of Supersonic Downflows and Multi-Gaussian Fitting to Their Spectra To derive spectroscopic parameters of the supersonic downflows, we have selected a small spectral window of 1399.31–1403.89 Å, which contains the O iv 1400/1401 Å doublet and Si iv 1403 Å spectral lines. The primary reason for selecting this wavelength range is that Si iv 1403 Å is one of the strongest TR lines in the IRIS spectral range. Also, the O iv 1400/1401 Å doublet lines are useful to determine the electron density of the TR plasma. Figure 5 presents some examples of the spectra in the selected window. A total of six isolated components corresponding to the three spectral lines O iv 1400 Å, O iv 1401 Å, and Si iv 1403 Å and their satellite lines (due to supersonic downflows) can be seen at some locations. To increase the signal-to-noise ratio), we have made a three-point running smoothing along the wavelength and also along the slit dimensions. Figure 5. IRIS spectra containing two O iv lines and one Si iv line acquired along the vertical solid lines marked in Figure 1 . The spectral line profiles at the location indicated by the dashed line (also shown with the red dots in Figure 1 ) of each left panel are shown in the corresponding right panel. A six-component Gaussian fit to the spectra of the regular and downflow components of the O iv 1400/1401 Å and Si iv 1403 Å lines is shown as the overplotted blue line in each right panel. The fitting parameters (i—peak intensity, v—Doppler shift, w—line width) are also printed. The spectra within the red square box contain the Si iv line and its downflow component, which are used for the initial detection of the supersonic downflows (see the text for details). The detection of supersonic downflows is sometimes difficult, as the signal within sunspots is very weak (though it also depends on the exposure, binning of the spectra, etc.). Also, the TR is very dynamic and often shows broad line profiles due to different activities. To ensure that we primarily detect


allows for the simultaneous determination of Z g and Z ⋆ , tracing the ratio of oxygen to iron (O/Fe) in young stars and the surrounding ISM (Steidel et al. 2016 ). The O/Fe ratio is of interest because it is a sensitive probe of the star formation and chemical enrichment history of galaxies. Typical O/Fe ratios at high redshift are expected to be enhanced relative to the solar value due to the fact that the element abundance ratios in relatively young star-forming systems will be dominated by core-collapse supernova (CCSNe) yields (Maiolino Mannucci 2019 ). Indeed, this result has already been reported in the literature. Based on a simultaneous rest-frame FUV+optical analysis of 30 star-forming galaxies at z ≃ 2.4, Steidel et al. ( 2016 ) found O/Fe ≈ 4−5 × O/Fe ⊙ . Similar levels of O/Fe-enhancement have been reported for individual galaxies at z ≃ 2.3 in more recent studies (Topping et al. 2020a , b ). The observed O/Fe-enhancement (also referred to as α-enhancement) has a number of important implications. First, it places robust constraints on the typical star-formation histories at early cosmic epochs, confirming results from previous photometric analyses that the stellar populations at high redshift are typically 1 Gyr old (Reddy et al. 2012 ). Secondly, a deficit of Fe relative to O will mean that the stellar ionizing spectrum of typical high-redshift galaxies is harder at fixed oxygen abundance compared to galaxies in the local Universe. This relative hardening of the ionizing continuum at fixed O/H offers a natural explanation for the observed offset of z > 2 star-forming galaxies relative to z ≃ 0 galaxies in the common line ratio diagrams (e.g. the BPT diagram; Topping et al. 2020a ; Runco et al. 2021 ). Non-solar α/Fe ratios will also force us to re-think current stellar population techniques when applied to high-redshift galaxies. Almost universally, current stellar population synthesis models assume solar abundance ratios; accurate analyses of FUV-optical spectra at z > 6 with JWST will require new models allowing for non-solar abundance ratios. In this paper, we expand upon a number of previous works at z ≃ 2.5 (Steidel et al. 2016 ; Topping et al. 2020a , b ) and present a simultaneous analysis of FUV and optical spectra for a sample of 33 star-forming galaxies drawn from the VANDELS survey at z ≃ 3.4. Combining ultra-deep optical VIMOS/VANDELS spectroscopic observations (tracing the rest-frame FUV) with MOSFIRE H - and K -band near-IR follow-up (tracing the rest-frame optical), our analysis provides the first investigation of Z g and Z ⋆ for galaxies at z > 3, and we present the first estimates of O/Fe at these redshifts. In addition, we present both the stellar and gas-phase MZRs for our sample, tracing O/Fe as a function of stellar mass. The structure of this paper is as follows. In Section 2 , we discuss our combined VANDELS+MOSFIRE spectroscopic data set and describe our final z ≃ 3.4 galaxy sample (referred to


We thank J. T. Gosling for many helpful discussions. This work is supported by the Science and Technology Development Fund of Macao SAR (080/2012/A3), the National Natural Science Foundation of China (NSFC) under grants 41204123, 41374174, 41231068, and 41331070, and the Science Foundation of Jiangxi Province under grant 20144BAB2110005. We acknowledge NASA contract NAS5-02099 and V. Angelopoulos for use of data from the THEMIS Mission. Specifically, we thank C. W. Carlson and J. P. McFadden for use of ESA, K. H. Glassmeier, U. Auster, and W. Baumjohann for use of FGM data, and D. Larson and R. P. Lin for use of SST data. We also thank ACE and Wind teams and NASA CDAWeb for providing the data.


ACKNOWLEDGEMENTS We thank useful discussions with Phillip Busch, Neal Dalal, Benedikt Diemer, Bhuvnesh Jain, Andrey Kravtsov, Yen-ting Lin, Hironao Miyatake, Ryoma Murata, Masamune Oguri, Masahiro Takada, Simon White, Ying Zu, and the organizers and participants of the KITP program: The Galaxy–Halo Connection. KITP is supported by the National Science Foundation under Grant No. NSF PHY17-48958. TS is supported by Kakenhi grants 15H05893 and 15K21733. SM is supported by Kakenhi grant 16H01089.


This work used the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the National E-Infrastructure. We also acknowledge PRACE for access to the resource Curie at Trés Grand Centre de Calcul. This work received financial support from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement 278594-GasAroundGalaxies, from the UK STFC (grant numbers ST/F001166/1 and ST/I000976/1), and from the Belgian Science Policy Office ([AP P7/08 CHARM]). RAC is a Royal Society University Research Fellow.