could hinder follow-up observations. 6 CONCLUSION The ASKAP Early Science data set presented in this paper verifies that the array and its associated processing pipeline askapsoft – both in moderately preliminary states – are successfully producing scientifically useful data. Overall, within the 12-beam image cube centred on the NGC 7232 group, we detect 17 H i sources. Six of these detections are well-known H i -rich galaxies including one fully interacting pair. Five of these detection are newly resolved H i galaxies with identifiable stellar counterparts. The remaining six H i detections are likely tidal debris associated with the NGC 7232/3 triplet. The triplet is a complex system. The H i components of NGC 7232 and NGC 7233 appear to be fully intertwined. NGC 7232B still retains most of its gas but shows evidence that it is beginning to interact with the other two galaxies. H i clouds C5 and C6 are likely tidal clumps that have been produced by the triplet system and possibly indicate the projected spatial location of two tidally formed tails of H i . If H i cloud C6 is moving away from its parent galaxies, it has sufficient mass to eventually decouple from the tidal tail and possibly develop into a long-lived TDG. H i clouds C1–C4 might have been produced by the triplet system, or these clouds could indicate an earlier interaction event involving another group member, such as IC 5181. A portion of the H i within this region, which was originally identified as belonging to HIPASS J2214−45 is likely diffusely distributed and lies below the detection threshold of our current ASKAP observations. Accordingly, it is difficult to ascertain the origins of the H i bridge that appears to be connecting NGC 7232/3 and IC 5181. Future work would include adding short-spacing/single-dish observations to the ASKAP data in order to recover more of the diffuse gas in and around the tidal features, in order to further understand the interaction processes that are taking place. The high-resolution capabilities of ASKAP produced moderately well-resolved moment maps of four galaxies (i.e. IC 5171, ESO 288−G049, ESO 298−G010, and ESO 289−G011) that can be used for further kinematic analysis. The newly detected H i counterparts of five stellar galaxies indicate their membership to the group. These nine galaxies provide a more complete picture of the group environment surrounding the NGC 7232/3 triplet. There are many areas where the data quality and processing procedure can be greatly improved for ASKAP. The addition of more antennas – to provide more complete sky coverage and achieve higher sensitivity with less integration time – along with the use of the on-dish calibration system and an overall improved observing method will show vast improvements to the data quality. With expanded computing systems and a more robust askapsoft pipeline, larger data sets can be processed more efficiently. The observations and results presented in this paper serve as one


We acknowledge financial contribution from the agreement ASI-INAF I/037/12/0. NLP and LS acknowledge the grant from PRIN-INAF 2014 ‘Towards a unified picture of accretion in HMXRBs’.


(Shu, Adams Susana 1987 ) do not also play a role in bringing ε SF down. We note, however, that if clouds form stars preferentially in one short burst with ε SF ≈ ε min , this presents a challenge to the survivability of these star clusters because star clusters with ε SF ≲ 10 per cent tend to dissolve after the natal gas has been removed (Baumgardt Kroupa 2007 ; Shukirgaliyev et al. 2017 ). We speculate that studies of star clusters with N -body simulations that include a centrally peaked star formation profile as in the model by Parmentier Pfalzner ( 2013 ) together with slow gas removal with velocities of approximately 10 km s −1 as predicted by our warpfield2 simulations will result in a higher survival rate even for low star formation efficiencies. 4.3.1 Giant clumps Giant clumps at redshift ∼ 1−3 with gas masses of have typical surface densities of pc −2 and are consistent with isothermal spheres, i.e. α = −2 (Ceverino et al. 2012 ). In this paper we probe the lower mass end of such clumps. Our results indicate that they are more resilient against destruction by stellar feedback than clouds with shallower density profiles. As noted above, this is due to the larger binding energy of the giant clump and in particular of the inner region when α = −2 (see equation 28 ). As more of the gas mass is accumulated in the inner regions, an expanding shell in a cloud with such a steep density profile decelerates faster, making it more susceptible to energy loss via leakage of hot gas after gravitational fragmentation and via strong cooling. Our results indicate that giant clumps at the low-mass end () can be destroyed by a single starburst with ε SF ≥ 4 per cent for pc −2 ( per cent for pc −2 ). Were the star formation spread out over the lifetime of the clump (∼30 Myr), this would correspond to ε ff = 0.5 per cent ( per cent for pc −2 ). This result is in line with simulations by Oklopčić et al. ( 2017 ) but puts analytic models by Krumholz Dekel ( 2010 ) into question who argue that star formation efficiencies per free-fall time of a few per cent are insufficient to disrupt a giant molecular clump (they focus on clumps with though). Our result does not yet take into account that the metallicity in galaxies at ∼ 2 is approximately a factor 2 lower than in present-day galaxies (Yuan, Kewley Richard 2012 ), which will affect the amount of radiative cooling, the strength of metal-line-driven winds of massive stars, and the coupling of radiation to the ISM. We plan to revisit giant clumps with warpfield in the future. 5 CONCLUSION In this paper we have presented improvements to the 1D stellar feedback code warpfield (Rahner et al. 2017 ). warpfield is a fast, publicly available code which models the formation of a shell around a massive cluster and how this shell is affected by stellar winds, radiation, and SNe, as well as gravity. The improvements which are part of a new code release, warpfield2 , include, but are not limited to, a better treatment of


This work is based in part on observations from Spitzer Space Telescope and was supported by RSA 1415602 and RSA 1346842, issued by JPL/Caltech. Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência, Tecnologia e Inovação (Brazil) and Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina). This work is also partially based on observations made with ESO-NTT Telescope at the La Silla under programme ID 184.D-1140 (PI Benetti). We would like to thank the anonymous referee for their insightful comments during the revision of this paper.


We thank Roger Griffin for radial-velocity monitoring and calculation of the orbital elements for HIP 99725. We acknowledge support from the Research Council of the Lithuania under grant No. MIP-85/2012, from the Latvian Council of Science under grant No. 09.6190, and from the Universidad Nacional Autónoma de México by way of project IN103014 of the PAPIIT/DGAPA. This research has made use of the Simbad database operated at CDS, Strasbourg, France. We greatly thank the referee for his constructive and valuable discussions and comments which have improved this paper.


smaller than the symbols. Right: Similar to the left panel, but showing the distribution of . All spectra were visually inspected for the presence of Ca ii triplet emission. The spread in EW values is extremely large at low S/N values due to noise (compare the left and right panels of Fig. 3 ), but rapidly tightens as the S/N increases, making it trivial to identify genuine emission features such as SDSS J1228+1040. Closer to the bulk EW distribution, it becomes difficult to detect Ca ii triplet emission such as that of SDSS J0738+1835 and SDSS J1043+0855 (see Fig. A1 for the EW values). We therefore chose to limit the sample used for the following statistical analysis to spectra with S/N ≥ 5, wherein if there were any additional emission features with an EW similar to the gaseous debris discs in our sample we should have detected them. Assuming that we can only detect gaseous emission in the spectra of systems above S/N = 5, the number of white dwarfs in our sample reduces to 7705, which we use for the calculations in the next section. 2.1 The occurrence rate of gaseous discs at white dwarfs We find no new gaseous discs in our magnitude and S/N limited Gaia /SDSS sample, resulting in a total of five detected gaseous components to debris discs out of 7705 systems. Using Bayes’ theorem we calculate the probability distribution, p ( f | n, t ), of an occurrence rate, f , of a gaseous debris disc at a white dwarf, given n detections out of t systems as where p ( n | f, t ) = f n (1 − f ) t − n is the binomial distribution, p ( f ) is the prior distribution of f , and p ( n, t ) is the probability distribution of detecting n gaseous disc hosts out of t systems which is a constant. We assume an uninformative prior distribution known as the Jeffreys prior (Jeffreys 1946 ), which for the binomial distribution is where B is the beta function. Substituting this prior into equation ( 1 ) results in Taking the median and 1σ intervals of this probability distribution (which is independent of the proportionality constant) with n = 5, and t = 7705, we find that 0.067 ± per cent of white dwarfs host a debris disc with a detectable gaseous component (Fig. 4A ). This is significantly lower than the occurrence rate of detectable dust discs around white dwarfs, which has been estimated by various studies to be 1–3 per cent (Farihi et al. 2009 ; Girven et al. 2011 ; Rocchetto et al. 2015 ; Rebassa-Mansergas et al. 2019 ; Wilson et al. 2019 ). For further discussion, we adopt the estimate of Wilson et al. ( 2019 ), 1.5 ± per cent, calculated using an unbiased sample of 195 white dwarfs observed with Spitzer containing three dusty debris discs. We note, however, that using equation ( 3 ) with n = 3 and t = 195, we obtain a slightly different occurrence rate of 1.6 ± per cent, and the probability distribution of this occurrence rate is shown in Fig. 4(B) . These two occurrence rates of dusty debris discs at white dwarfs are in agreement, and the difference results


of any model for distributions of the properties of the clouds in the BLR. In fact, in the current analysis, our only constraint is to suppose that all the hydrogen lines are emitted from the same clouds with certain density and excitation conditions, which is a reasonable assumption considering the similarity of the profile of the various H i lines we analysed for each object. Additionally, if more than one grid point is a good match to the data, we adopt a simple weighting scheme where we calculate median Φ H , n H and line ratios from these point. A detailed description of our photoionization modelling scheme is given in Section 3.3 . 3.3 Extinction measurements The extinction affecting a set of line ratios may be determined by comparing the measured ratios with their theoretically predicted intrinsic values. In the case of the He ii lines, considering the He ii λ4686/He ii λ10 126.4 line ratio is expected to be similar to the case B prediction even in the BLR spectrum (Bottorff et al. 2002 ), we estimate the extinction to both the NLR and BLR assuming an intrinsic He ii λ4686/He ii λ10 126.4 line ratio of 3.77, corresponding to the case B prediction for a temperature of 10 000 K and a density of 10 6 cm −3 . If broad He ii λ4686 Å is not observed, we estimate a lower limit to A V (BLR) based on the broad He ii λ10 126.4 Å flux. This was done decreasing the observed broad He ii λ4686/He ii λ10 126.4 ratio in steps of 0.1, starting from the intrinsic value of 3.77, until the broad He ii λ4686 Å line becomes indistinguishable from the continuum. The lower limit to the extinction is then derived from this ratio. In Table 2 , we list A V (NLR) and A V (BLR) as estimated from the He ii lines for each object. The errors in these estimates were derived assuming an uncertainty of 20 per cent in the observed line ratios. Table 2. Extinction to the narrow-line region, A V (NLR), and extinction to the broad-line region, A V (BLR), derived from Balmer and Paschen H i line ratios and the He ii λ4686/He ii λ10 126.4 line ratio as described in Section 3.3 . In objects where broad He ii λ4686 is not detected, a lower limit to A V (BLR) was determined from broad He ii λ10 126.4 Å. These lower limits are marked with an asterisk. Abbreviated name: MCG–05-14-012, MCG–05-23-16 and MCG–06-30-015. In the case of the H i lines, determining A V (NLR), is a straightforward process since, as outlined in Section 3.1.3 , a fixed set of intrinsic line ratios can be assumed. Deriving the extinction A V ( NLR ) to the NLR was done during the step of fitting and subtracting the narrow lines as described in that section. In Table 2 , we list A V (NLR) as estimated from the H i lines for each object. The errors in A V estimates were derived assuming an uncertainty of 20 per cent in the observed line ratios. The extinction to the BLR is derived in a different way, since the H i intrinsic line ratios are not known. To do so, we followed these steps. We compared the


TT is grateful to K. Nitadori and J. Makino for the tuning of our code. The computations in this research were performed on the K computer of the RIKEN AICS through the HPCI System Research project (Project ID:hp120304) together with XC30 of CfCA in NAOJ. YS was supported by JSPS postdoctoral fellowships for research abroad. This study was supported in part by the Grants-in-Aid for the Scientific Research from the Ministry of Education, Science and Culture of Japan (Nos. 24103006, 24244036, 26104001, 26707013, 26870823, 15H01039 and 15H00789) and by HPCI Strategic Program of Japanese MEXT.


CV thanks Andreas Floers, Stefan Lietzau and Markus Kromer for stimulating discussions during various stages of this project. The authors gratefully acknowledge Stefan Taubenberger for sharing his enormous expertise in the field of supernovae and for being a key member of the supernova cosmology project that motivated this work. CV would also like to thank Michael Klauser for providing help and guidance during the start of this project. The authors thank the anonymous reviewer for valuable comments. This work has been supported by the Transregional Collaborative Research Center TRR33 “The Dark Universe” of the Deutsche Forschungsgemeinschaft and by the Cluster of Excellence “Origin and Structure of the Universe” at Munich Technical University. SAS acknowledges support from STFC through grant, ST/P000312/1. Data analysis and visualization was performed using M ATPLOTLIB ( Hunter 2007 ), N UMPY ( Oliphant 2006 ) and S CIPY ( Jones et al. 2001 ).


This work was supported by the cluster of excellence “Origin and Structure of the Universe.” Thanks to the referee for valuable comments that substantially improved the paper. I thank my CAST group at the University Observatory, Munich for inspiring discussions and suggestions. Special thanks also go to Edvige Corbelli, Avishai Dekel, Ken Freeman, Pavel Kroupa, Go Ogiya, Jerry Ostriker, Paolo Salucci, and David Spergel for interesting discussions and a careful reading of the manuscript. Finally, I would like to thank the Harvard Center for Astrophysics for support, lively discussions, and an infinite supply of coffee that was essential and the driver for finishing this paper.


It is a pleasure to thank Seppo Mattila, Rubina Kotak, Nancy Elias-Rosa, Stephen Smartt, Anders Jerkstrand, John Eldridge and Justyn Maund for useful discussions and comments on this work, and Gerry Gilmore for ongoing support. We thank the anonymous referee for a careful reading of the Letter. This Letter is based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 094.D-0351. This work was partly supported by the European Union FP7 programme through ERC grant number 320360.


midplane values n e0 and n CR0 for the electron densities. For (corresponding to ), , and , we have . This is large compared to the typical Galactic magnetic field strength of a few , so Faraday rotation effects are weak. We perform the line-of-sight integration by computing on a ( θ , φ ) mesh with 36 × 72 meshpoints for distances s from the observer going up to in steps of . 4.2.3. Results from the Mean-field Model In Figure 3 we show the results for Model B of Brandenburg Furuya ( 2020 ). In spite of the parameters being unrealistic for the Galaxy, there are characteristic features that are similar to what is seen in Figure 1 for the Galaxy: positive Q at φ = 0 and 180°, along with negative E at φ = 90° and 270°. The results for U and B are not immediately obvious because there are two nearly equally big patches of opposite sign in each hemisphere. Only after φ -averaging do we recognize a latitudinal dependence that is similar to that of the Galaxy; see Figure 4 . The observed sign of emerges only when we place the observer at a distance sufficiently far away from the Galactic center ( μ = A), while for smaller distances ( μ = B), the sign changes and the profile becomes similar to that shown in the top right panel of Figure 1 of Brandenburg ( 2019 ). Faraday rotation is weak, but it contributes a profile that is symmetric about the equator. This is because the global magnetic field has quadrupolar symmetry; see Brandenburg ( 2019 ) for a corresponding result for a dipolar field. Changing the sign of α 0 results in a qualitatively different (oscillatory) dynamo, but, to our surprise, it does not change the sign of . The sign only changes when the differential rotation changes and thereby the global Galactic spiral. Changing the signs of α 0 and Ω 0 simultaneously has the advantage of leaving the dynamo properties unchanged. Figure 3. Polarization results for the mean-field model with the Faraday rotation term in Equation ( 6 ) included. Top: and . Bottom: and . Figure 4. Azimuthally averaged polarization results for the mean-field model with Faraday rotation using (solid lines) and without Faraday rotation (dashed lines). Top: and . Bottom: and . The statistical error of the mean and the standard deviation are indicated by dark and light shades, respectively, except for panels (b) and (d), where only the former is shown. An important difference between model and observation is the fact that in our model, the amplitude of is several hundred times smaller than that of , while for the Galaxy, it is only about 10 times smaller; see Table 1 . We recall that the quadratic EE correlation for dust polarization was found to be about twice that of the BB correlation (Planck Collaboration 2016 ). This came as a major surprise (Kandel et al. 2017 ) and triggered numerous investigations trying to explain this (Kritsuk et al. 2018 ). In the present Letter, however, we have not studied quadratic correlations, but the signed functions E and B themselves.


sets in some specific cases – in particular in the second outermost shell when using projct . We speculate that these discrepancies may be due to a combination of remaining calibration issues and artifacts propagated when projct is used. Nevertheless, in both cases the deprojected EPIC spectra clearly reveal the presence of central drops for the Fe, Si, S, Mg and Ca abundances. Despite the formal consistency reported above, we note that the uncertainties of the XMM-Newton abundances of Ar and Ca are much larger than the uncertainties of the Chandra measurements. This point is discussed in more detail in Sect. 3.5 . 3.5. Broad-band versus narrow-band fits Whereas fitting the spectra using the full energy band (i.e., typically ∼0.5–7 keV for Chandra ACIS) is the standard procedure for deriving the best-fit parameters, this approach may bias the metal abundance measurements. In fact, since the abundance of a given element is directly proportional to the equivalent width of its line (namely, the ratio between the total line flux and the continuum flux at the centroid energy of the line), incorrect determination of the continuum in the vicinity of the line would lead to incorrect measurement of the corresponding elemental abundance. Because no instrument is calibrated with infinite precision, some slight imperfections of the effective area may produce these local over- or underestimates of the continuum. In the case of very deep observations, that is, when statistical uncertainties become smaller than the above systematic effect, such a bias may become highly significant (e.g., Mernier et al. 2015 ; Simionescu et al. 2019 ). A good way to minimize such biases is to refit the spectra between several local energy bands, each of them including the K-shell emission line(s) used to derive the abundance of a given element as well as part of the surrounding continuum. In these “narrow-band” fits (by contrast to the above “broad-band” approach), the only parameters that were kept free were the (local) normalization and the abundance of the considered element. All the other parameters were fixed to the best-fit value of the broad-band fit. A comparison of the resulting narrow-band fit profiles with the ones obtained using broad-band fits (assuming a vgadem model and using dsdeproj ) is shown in Table 2 and Fig. 4 . We used the 1.5–2.8 keV energy range for Si and S, 2.9–3.5 keV for Ar, 3.3–4.3 keV for Ca, and 5.0–7.0 keV for Fe. Fig. 4. Deprojected profiles of Fe, Si, S, Ca, and Ar abundances, and the Ar/Fe (Fe taken from the broad-band fits) ratio, using broad- (red) and narrow-band (blue) fits, obtained using the vgadem model and the dsdeproj deprojection method. All errors are quoted at the 68.27% (1 σ ) confidence level based on . The blue data points have been shifted slightly on the X-axis for clarity. The narrow-band Fe profile in Fig. 4 is consistent with the broad-band profile and also shows a drop in the innermost two bins (at r ≲10 kpc). The


between observed and calculated intensities expressed as a percentage of the observed intensity for all the spectral lines used in the emission measure analysis, except the those beyond 1000 Å. The data are plotted at the wavelength of the relevant line. We show the full line-list with wavelengths for cross-checking in Table 1 . Results are shown for all daily spectra analyzed (1103 observations). 3.2. Comments on the Accuracy of the Li-like Spectral Lines There is a caveat to our threshold condition. While the atomic structure of Li-like ions is very simple (three electrons only) and lines emitted from these ions are therefore expected to be spectroscopically accurate, they are well known to sometimes show anomalously large intensities (Dupree 1972 ; Judge et al. 1995 ). Based on off-limb observations from Hinode /EIS, where the solar corona is close to isothermal and so atomic data discrepancies are readily apparent, Warren et al. ( 2016 ) found that the contribution functions for some O vi lines need to be adjusted upward by a factor of 3.4 to bring them into agreement with other lines. Other work has found that these discrepancies are wavelength dependent (Muglach et al. 2010 ), and several studies have successfully used the longer wavelength O vi lines we use here for composition studies (Feldman et al. 1998 ; Landi Testa 2015 ). In our analysis, we also found that the Li-like O vi 1031.93 Å, O vi 1037.64 Å, Ne viii 770.42 Å, and Ne viii 780.38 Å observed line intensities were several factors brighter than the intensities calculated from the EM distribution. Excluding the Ne viii lines and adjusting the G ( T , n ) functions for O vi 1031.93 Å and O vi 1037.64 Å upward by the factor of 3.4 determined from EIS off-limb spectra by Warren et al. ( 2016 ) improves the minimization of the EM solution, and helps to anchor the distribution around their formation temperatures. We therefore make the adjustment solely for the purpose of determining the best EM solution. We make no adjustment for the subsequent abundance ratio correction since Ne viii is also Li-like and it is necessary to treat them consistently. Note also that the Warren et al. ( 2016 ) adjustment factor was computed using off-limb EIS spectra taken near solar minimum in 2007, November. Our analysis suggests that this factor is also representative of the rest of the solar cycle, since the computed O vi line intensities are maintained within 25% of the observed intensities in all the daily spectra. If there was a cycle dependent variation greater than 25% in this adjustment factor, perhaps due to the presence or absence of the high temperature component in the emission measure found by Morgan Taroyan ( 2017 ), then it would be detected in our analysis as a discrepancy between the calculated and observed intensities. It is interesting that the same adjustment factor works well for both our EVE analysis and the independent EIS off-limb spectral analysis. For a further cross-check of


synchrotron emission. The XMM–Newton upper limit (black arrow) and the Fermi -LAT data (red squares) are from this work. The other data are taken from Aliu et al. ( 2014 ) (VERITAS – green diamonds) and Abdo et al. ( 2007 ) (MILAGRO – pink hexagons). The blue and grey butterflies are the HAWC models from Abeysekara et al. ( 2020 ) and from Abeysekara et al. ( 2017 ), plotted for comparison, but not used in the fit. We performed a binned analysis with five bins per energy decade and spatial pixel size of . In the maximum likelihood fitting, the normalization parameter of all the sources within 3° of the ROI centre, as well as the diffuse emission components, was left free to vary. Instead the parameters of all the other sources at more than 3° were fixed to the values given in the 4FGL catalogue (Abdollahi et al. 2020 ). To describe the spatial morphology of MGRO J1908+06, we used the VERITAS emission region at 3σ level (i.e. the outermost contour in Fig. 1 ), while for the spectral model we assumed a power law with photon index Γ = 1.6. This leads to a detection significance in the energy band considered. The γ-ray flux was obtained by binning the γ-ray data in the range from 10 to 1000 GeV into four energy intervals, and performing a binned likelihood analysis in each energy bin. The resulting Fermi -LAT SED is plotted in Fig. 4 . 3.3 X-ray analysis To study the X-ray emission in the vicinity of PSR J1907+0602, we used a 52 ks long observation carried out on 2010 April 26 with the XMM–Newton satellite. We analysed the data of the European Photon Imaging Camera (EPIC)-Metal Oxide Semiconductor (MOS) instrument that was operated in full frame imaging mode and with the medium thickness optical filter. We excluded time intervals with high background, resulting in net exposure times of 36 and 38 ks for the two MOS cameras. Using the Extended Source Analysis Software ( esas 3 ), we extracted the spectra from a circular region of 5 arcmin radius centred at the position of PSR J1907+0602 (excluding a circle of 30 arcsec radius around the source) and from a concentric annular region with radii 5 and 12.5 arcmin. The latter was used to estimate the X-ray background (which in this sky region is dominated by the Galactic ridge diffuse emission). Comparison of the two spectra showed no evidence for diffuse emission associated with PSR J1907+0602, with an upper limit (at 95 per cent confidence level) of 1.2 × 10 −15 erg cm −2 s −1 arcmin −2 on the surface brightness in the 1–10 keV energy range. 4 ORIGIN OF THE γ-RAY EMISSION Emission at TeV energies indicates the presence of ultrarelativistic particles that, in principle, can produce it through inverse Compton (IC) scattering of the cosmic microwave background (CMB), infrared (IR), and/or starlight seed photons by electrons, or through the decay of neutral pions resulting from proton–proton (and/or other nuclei) interactions. In this section, we first explore the possibility that a single mechanism is


We thank the anonymous referee for constructive comments that helped to improve the paper. This work is partially supported by the Bundesministerium für Wirtschaft und Technologie through the Deutsches Zentrum für Luft und Raumfahrt (grant FKZ 50 OG 1301). V.D. and A.S. thank the Deutsches Zentrum für Luft- und Raumfahrt (DLR) and Deutsche Forschungsgemeinschaft (DFG) for financial support (grant DLR 50 OR 0702). Partial support comes from NewCompStar, COST Action MP1304. Part of this work is based on the publicly available “GBM Accreting Pulsar Histories” provided by the Fermi team.


) but in the range of the H α -excess galaxies at redshifts ∼4−5 that were recently identified in the Spitzer UltraVISTA ultra-deep stripes survey (SMUVS survey; Caputi et al. 2017 ). An important fraction (15%) of the SMUVS galaxies in the 9.4 ≤ log( M stellar [ M ⊙ ]) ≤ 11.0 mass range appear to be in a starburst phase with SFR of hundreds to thousands M ⊙ yr −1 and sSFR well above 24 Gyr −1 , and up to 120 Gyr −1 . The SMUVS galaxies in the starburst phase account for more than 50% of the cosmic SFR density at redshifts ∼3.9−4.9. Therefore, extreme UV luminous galaxies such as BOSS-EUVLG1 could represent the short lived phase of this class of H α -excess galaxies, and could be important contributors to the SFR density at intermediate and high redshifts. BOSS-EUVLG1, therefore, brings on the opportunity to investigate the presence and properties of ionized gas outflows for the first time in this class of extreme star-forming galaxies. In this article, we report the detection of the ionized phase of the outflow in BOSS-EUVLG1 using the H α emission line. Section 2 describes the observations and calibrations of the H α spectrum. Section 3 presents the analysis of H α emission and rest-frame UV absorption lines. The results of the analysis and interpretation as outflowing material are presented in Sect. 4 , including the overall kinematic and mass (Sect. 4.1 ), the outflowing mass rate and mass-loading factor (Sect. 4.2 ), the nature of the energy source (Sect. 4.3 ), and the mass escape fraction into the intergalactic medium (Sect. 4.4 ). The results are discussed in Sect. 5 , focusing in the comparison with low- and high- z SFGs (Sect. 5.1 ), the multi-phase nature of the outflows (Sect. 5.2 ), and the possibility of detecting these outflows with the future X-ray observatory Advanced Telescope for High ENergy Astrophysics (ATHENA; Sect. 5.3 ). Finally, Sect. 6 present the summary and outlook. We adopted H 0 = 67.7 km s −1 Mpc −1 and Ω m = 0.307 ( Planck Collaboration XIII 2016 ) as cosmological parameters and the Salpeter initial mass function (IMF; Salpeter 1955 ) throughout this paper. A factor of 1.66 ( Newman et al. 2012 ) is used to convert the SFRs and stellar masses ( M stellar ) from a Chabrier (2003) to a Salpeter (1955) IMF when using some published data. 2. Observations Near-infrared (Near-IR) long-slit spectroscopy of BOSS-EUVLG1 was obtained with the Espectrógrafo Multiobjeto Infra-Rojo (EMIR) instrument on the 10.4 m Gran Telescopio Canarias (GTC). Observations were taken in two service mode nights – 2020 February 10 and May 8 – in bright Moon and subarcsec seeing (≃0.8′′ full width half maximum) conditions, as part of the GTC program GTCMULTIPLE2E-19A (PI: R. Marques-Chaves). The observations used the K g r i s m with a dispersion of 1.71 Å pix −1 and a 0.8′′-wide long-slit, providing a spectral resolution of 82 km s −1 at ∼2.25 μm. The slit was centered on a bright ( K = 17.43 ± 0.02, AB) reference star (SDSS


, and replace the bulge with a rotating bar using the analytic model from Long Murali ( 1992 ). For the bar parameters, we take a mass of 5 × 10 9 M ⊙ , a length of a = 3 kpc, a width of b = 1 kpc, and a present-day orientation of θ 0 = −30° (as in Hattori et al. 2016 ; Erkal et al. 2017 ). We vary the pattern speed between 0 and −100 km s −1 kpc −1 in steps of 0.1 km s −1 kpc −1 (where negative pattern speeds are prograde with the MW disc), more than encompassing the observed constraints on the pattern speed (e.g. Bland-Hawthorn Gerhard 2016 ). We find that the bar has a small effect on the stream, giving slightly different stream tracks depending on the pattern speed of the bar. Thus, the properties of the bar will also need to be marginalized over in order to measure the LMC mass from the misaligned proper motions. 5.4 Effect of LMC on other streams in the Milky Way The predicted misalignment between the stream track and proper motions should also be seen in any streams which receive a large tidal force from the LMC. Given the orbit of the LMC, we expect that the most affected streams will likely be in the southern Galactic hemisphere, e.g. the 11 streams newly discovered in Shipp et al. ( 2018 ), the Phoenix stream (Balbinot et al. 2016 ), the Jet stream (Jethwa et al. 2017 ), or the atlas stream (Koposov et al. 2014 ). In order to determine which are the most affected, each stream will need to be evolved in the presence of the LMC. Interestingly, some works have proposed assuming that stream tracks are aligned with their proper motions to measure the Sun’s proper motion (Majewski et al. 2006 ; Malhan Ibata 2017 ). If the significant misalignment predicted for the Tuc III stream is verified, this means that these methods must account for the perturbation of the LMC or only use streams which have received a negligible perturbation (possibly those in the northern Galactic hemisphere). 5.5 Effect of Tuc III on the Milky Way disc Our analysis in Section 3 found that Tuc III is on a very eccentric orbit with a pericentre of ∼1.8 kpc. This close passage of Tuc III with the MW could induce perturbations in the inner disc of our Galaxy (Feldmann Spolyar 2015 ). For these perturbations to be significant, Tuc III would have needed a substantial mass of ∼10 8 −10 9 M ⊙ and hence would have to be a dwarf galaxy. Given the current data, it is difficult to determine whether Tuc III is a globular cluster or a dwarf (see Li et al. in preparation). However, assuming that Tuc III is a dwarf, if Tuc III’s recent pericentre was its first approach to the MW (and thus Tuc III still retained the bulk of its dark matter halo) there could be detectable perturbations in the MW centre. We note that although we have assumed a disruption age of 3 Gyr, during which our best-fitting orbits have experienced multiple pericentric passages (e.g. Fig. 3 ), our model is not designed to determine when Tuc III began disrupting. Instead, it is designed to match the observed


Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under program 87.C-0040. We thank the anonymous reviewer for helpful comments and suggestions that helped us to improve our manuscript.


Acknowledgments The SILCC project details are available at http://www.astro.uni-koeln.de/silcc . All simulations have been performed on the Odin and Hydra clusters hosted by the Max Planck Computing Data Facility ( http://www.mpcdf.mpg.de/ ). We thank M. Anderson, C. Federrath, J. Mackey, M. M. Mac Low, E. Pellegrini, and X. Shi for useful discussions and Leroy etal. ( 2008 ) and Ekstrm etal. ( 2012 ) for making their data publicly available. AG, SW, TN, PG, SCOG, RSK, and TP acknowledge the Deutsche Forschungsgemeinschaft (DFG) for funding through the SPP 1573 The Physics of the Interstellar Medium. SW acknowledges funding by the Bonn-Cologne-Graduate School, by SFB 956 The conditions and impact of star formation, and from the European Research Council under the European Communitys Framework Programme FP8 via the ERC Starting Grant RADFEEDBACK (project number 679852). TN acknowledges support by the DFG cluster of excellence Origin and Structure of the Universe. RW acknowledges support by the Czech Science Foundation project 15-06012S and by the institutional project RVO: 67985815. RSK and SCOG acknowledge support from the DFG via SFB 881 The Milky Way System (sub-projects B1, B2, and B8). RSK acknowledges support from the European Research Council under the European Communitys Seventh Framework Programme (FP7/2007-2013) via the ERC Advanced Grant STARLIGHT (project number 339177). The software used in this work was in part developed by the DOE NNSA-ASC OASCR Flash Center at the University of Chicago. We thank C. Karch for the program package fy and M. Turk and the yt community for the yt project (Turk etal. 2011 ).


agrees well with the assumption. In addition to the reactions presented above, the following unimolecular hydrogen molecule elimination (UHME) process may occur during the photolysis of methanol at low temperatures (Gerakines et al. 1996 ; Hama et al. 2009 ): The reaction products of UHME process may be incorporated into AAs. If these reactions occur in a statistical manner, the frequencies of reactions ( 4 b ) and ( 5 a ) are twice as large as those of their counterparts (reactions ( 4 a ) and ( 5 b ), respectively) because the number of C–H bonds in CH 2 DOH is larger than that of the C–D bond, which may affect the D/H ratio of the reaction products. The production of C–H bonds is also possible from other pathways that do not use methanol as a reactant. Briggs et al. ( 1992 ) reported the formation of various COMs such as glycolic acid (HOCH 2 COOH), glycerol (HOCH 2 CH(OH)CH 2 OH), and Gly after the photolysis of solid mixtures (CO:H 2 O:NH 3 = 5:5:1) at 10 K. Although their gas composition is different from that in our study, similar products can be produced in the present experiment, which has the potential to decrease the deuteration level of Gly. Although the formation of other AAs was not reported by Briggs et al., others reported the formation of various AAs upon the hydrolysis of the organic residues even in the absence of methanol (Muñoz Caro et al. 2002 ), which should have the potential to drastically decrease their deuteration levels. 4.2. Effect of Hydrolysis on Deuteration Levels of Amino Acids We demonstrated that the acid hydrolysis of the organic residues generally results in significant increases in AA abundances, which is consistent with that demonstrated in previous studies (Nuevo et al. 2008 ; Evans et al. 2012 ). On the other hand, there has been no report on the variation in the deuteration levels of AAs after the acid hydrolysis of the organic residues. The present result suggests that the five AAs can be divided into two groups depending on whether the deuteration level decreases upon hydrolysis: those whose D/H is significantly decreased ( α -Ala and β -Ala; referred to as the Ala group) and those whose D/H did not change (Gly, Sar, and Ser; referred to as the Gly group). This indicates that the two groups have different formation mechanisms upon hydrolysis. We propose one possible scenario to explain the observed D/H ratio: we assume that these five AAs are produced by reactions of small radicals during warming of the irradiated ice and that most AAs formed are incorporated into macromolecules via relatively weak bonds such as peptide bonds. Upon hydrolysis, these weak bonds are broken and the unit AAs are released. If this is the only pathway for the formation of AAs, the deuteration levels of the AAs would not vary between the free and bound samples, as is the case for Gly-group AAs. On the other hand, for Ala-group AAs, if there are other formation pathways that do not use deuterated radicals and/or molecules


ACKNOWLEDGEMENTS The authors wish to acknowledge the DJEI/DES/SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and support. This work used the BATS-R-US tools developed at the University of Michigan Center for Space Environment Modeling and made available through the NASA Community Coordinated Modeling Center. DÓF wishes to acknowledge funding received from the Trinity College Postgraduate Award through the School of Physics. AAV acknowledges funding received from the Irish Research Council Laureate Awards 2017/2018. SJ acknowledges the support of the German Science Foundation (DFG) Research Unit FOR2544 ‘Blue Planets around Red Stars’, project JE 701/3-1 and DFG priority program SPP 1992 ‘Exploring the Diversity of Extrasolar Planets’ (RE 1664/18). We thank Jackie Villadsen and Joe Llama for their useful discussion on topics of stellar radio emission. The authors would like to thank our referee, Dr Jorge Zuluaga, for his valuable input on this work. Software: bats-r-us (Powell et al. 1999 ), radiowinds (Ó Fionnagáin 2018 ), scipy (Jones et al. 2001 ), matplotlib (Hunter 2007 ), and tecplot and seaborn (Waskom et al. 2018 ).


of the next shock after one pulsation period. Any dust formed at 1 may thus not accumulate and grow over several pulsation periods in the shocked photosphere. The situation is different at larger radii, where the post-shock gas temperature drastically decreases. 3.2.1. Alumina The alumina clusters chiefly form in the post-shock gas at 1 , and their abundances in the first oscillation are illustrated in Fig. 6 . The formation of AlO involves the dimerisation of AlO through termolecular recombination while AlO forms from the reaction of Al with OH in the first place. The dimerisation of AlO is also described by termolecular recombination to form the AlO dimer, (AlO), which has a binding energy of eV ( Li Cheng 2012 ), and can thus withstand the high temperatures of the post-shock gas. This dimer is considered as the seed that will further condense into alumina grains. Over one oscillation resulting from the first shock at 1 , only a small quantity of alumina dimers forms, reaching an abundance of at . A large fraction of Al stays in the form of molecular rings (e.g., AlO and AlO). Because the dimerisation of AlO and AlO is termolecular and thus dependent on the background pressure, cluster formation occurs over a reduced range of radii in the inner wind and the formation of AlO cluster ceases after 2 . The grain size distributions for alumina in one oscillation at 1 is presented in Fig. 7 as a function of pulsation phase. Because of the high post-shock gas temperatures after the shock passage and the extremely low abundance of alumina dimers, no grains form at . Very small grains start forming after , but coagulation starts to be efficient for dust growth in the phase range , for which gas temperatures span the range K and the alumina cluster abundance reaches a maximum. The distribution peaks at grains in the size range Å, despite the small fraction of AlO clusters available. However, the post-shock gas densities are very high at 1 , as seen from Table 2 , and the clusters coagulate and condense with high efficiency and lead to the formation of AlO grains of small size over one oscillation and pulsation period. Under the assumption that small alumina grains may not survive the passage of periodic shocks, the results show that there exists a steady population of small size (m) alumina grains located in the shocked photosphere at 1 . If we consider that grain growth may also proceed through surface deposition when the grain radius has reached several tens of Å (at and after), the grain size will increase, but the growth will be time-limited and proceed only between . We now consider the extreme case where alumina grains survive the periodic shocks at each shock passage, while molecules are destroyed and reform. We may then investigate grain growth over several pulsation periods. For a gas drift velocity of 0.5 km s, the gas layer at 1 takes 12 pulsations to reach 2 . We thus consider the build-up of dust grains over a number of 12 pulsations for


detail. As mentioned before, we identified two mild-Am stars, HD 33204 and HD 46304. We show abundance distributions of these stars in Fig. 10 . A typical Am star exhibits overabundances of iron-peak elements and some heavy elements (Zn, Sr, Zr and Ba), but Ca and Sc abundances of these stars are underabundant (Gray Corbally 2009 ). As can be seen in Fig. 10 , the mild-Am stars in our study have nearly solar abundances of Ca and Sc elements. Only HD 33204 shows overabundances in some heavy elements typical for Am star. In the case of HD 46304, most of lines are blended due to high rotation velocity of the star. These blended lines cause difficulties in abundance calculations. The abundance differences between HD 46304 and a typical Am star can be caused by this effect. In the spectral classification process some stars were defined as metal-poor, mostly taking into account Mg, Mn and Fe lines (see Table 2 ). For these stars, the average abundance of Mg (7.57 dex) was found to be close to the solar abundance. However, the average abundances of Mn (5.13 dex) and Fe (7.27 dex) are slightly lower than the solar abundances. For some of our targets (HD 26298, HD 33204, HD 106103, HD 110379, and HD 126516) chemical abundances were already obtained before the present study. We compared our atmospheric parameters and abundance results with the literature values. Abundances of HD 33204 and HD 10613 were derived by Varenne Monier ( 1999 ) and Fossati et al. ( 2008 ), respectively. In their work, similar methods for abundance analysis were used but different atomic data bases were adopted. For HD 33204 higher abundances of Sc, Mg and Y were obtained in the present study. Our result is consistent with the Am: type peculiarity of this star. For HD 106103 only the Y abundance is different. Abundances of HD 26298, HD 110379 and HD 126516 were derived by Bruntt et al. ( 2008 ) using the equivalent width method and the different atomic data base. In the case of these three stars, the marked differences were detected only for V and Ba abundances. Figure 10. Chemical abundances of Am: stars compared with the solar values (Asplund et al. 2009 ). HD 33204 is represented by stars and HD 46304 by triangles. Additionally, we checked the possible correlations of the element abundances with atmospheric parameters, i.e. T eff , log g and ξ. Similar as in Niemczura et al. ( 2015 ), no correlations were found. Moreover, the element abundances do not depend on v sin i . The same result was found by Fossati et al. ( 2008 ). However, Takeda et al. ( 2008 ) reported negative correlations between v sin i and C, O and Ca elements. Finally, we checked the relations between abundances of iron and other elements. Strong positive correlations were found for Mg, Si, Ca, Sc, Ti Cr, Ni, Y and Ba. Similar correlations between Fe abundances and iron-peak elements were found by Niemczura et al. ( 2015 ). On the other hand, a negative correlation was obtained between Fe and O abundances. 6


NA and SV thank Denis Erkal for stimulating discussions. We thank the referee, Ray Carlberg, for a constructive report.


ACKNOWLEDGEMENTS This work was supported by the Science Technology Facilities Council (STFC) (Research Grants ST/L000628/1 and ST/M503599/1). We thank S. Falle for the use of the mg hydrodynamics code used to calculate the simulations in this work and S. van Loo for adding SILO output to it. The calculations used in this paper were performed on the DiRAC1 Facility at Leeds, which is jointly funded by STFC, the Large Facilities Capital Fund of the Department of Business, Innovation and Skills, and the University of Leeds. The 3D volumetric renderings were created using the VisIt visualisation and data analysation software (Childs et al. 2012 ). The data associated with this paper are available from the University of Leeds data repository ( https://doi.org/10.5518/573 ).


; Tiwari et al. 2019 ), and the hottest loops can live up to multiple hours (López Fuentes et al. 2007 ; Klimchuk et al. 2010 ). Most of the longest lived loops are probably a result of interaction of multiple loop strands, which sequentially heat making the loop bundle live much longer than a loop strand (e.g., Warren et al. 2002 ). Loop 23 in Table 1 shows this behavior of sequential heating (see the animation of Figure 1 ). The coordinates of the center of the box outlining the two footpoints for each loop are also given in Table 1 . For easy identification, the coordinates in bold font indicate that the surrounding box has unipolar magnetic flux; all others have mixed-polarity magnetic flux. We quantified the distribution of magnetic field strength and polarity at each foot of each loop via histograms of LOS magnetograms within the selected footpoint region, in the way presented in Figure 5 for three examples. We use a 6′′ × 6′′ boxed area centered at each foot to obtain LOS magnetogram histograms at each foot. The 6′′ × 6′′ size of the box ensures that the loop foot is completely covered within the box. We have three categories of loops: (1) loops with unipolar magnetic flux in both feet, (2) loops with mixed-polarity magnetic flux in both feet, and (3) loops having one foot in mixed-polarity flux and the other foot in unipolar magnetic flux. Of the 23 loops that we examined, ∼40% (9/23) have both feet in unipolar magnetic flux, ∼4% of loops (1/23) have both feet in mixed-polarity flux, and ∼56% (13/23) have one foot in unipolar and one in mixed-polarity flux. We have considered the presence of mixed-polarity only when the LOS magnetogram at the loop foot contains values ≥20 G of positive or negative minority polarity flux. The random noise level in the LOS magnetograms is about 7 G (Couvidat et al. 2016 ), fairly well below our selected lower limit. We would have counted footpoint 2 of our only category 2 loop as unipolar if there was no minority-polarity pixel with a negative B z value larger than 20 G, i.e., if there were no pixel in the second bin (in 20–40 G range, bin size is 20 G) on the negative/minority polarity side of the zero line in this footpoint box’s histogram in Figure 5 . This case is the most marginal one in our sample—in all other mixed-polarity cases there are several to many minority-polarity pixels with the magnitude of B z larger than 20 G. Because there is only one loop in the category of both loop-feet having mixed-polarity flux, in our discussions we often count it in the category of loops having at least one foot in mixed-polarity flux region. In Figure 2 we show three example loops, one from each of the three categories. In the top row of Figure 2 , we show an example loop that has both feet in unipolar magnetic flux (category 1); the peak time in the hot 94 image comes at 05:02:59 and the associated LOS HMI magnetogram is obtained at 05:02:52. The light curve for this loop in Figure 3 (a) also shows another


[CII] redshift ( Carilli et al. 2013 ). The rest-frame frequencies of HF J = 1–0 and H 2 O (2 20 –2 11 ) are 1232.476 and 1228.789 GHz respectively. In contrast, the line width of the H 2 O (2 20 –2 11 ) in the QSO is, within the uncertainties, exactly the same as for the other lines. Neither the QSO or the SMG show clear evidence for an outflow in the H 2 O line, although there is a possible excess in the line profile of the QSO seen ∼400 km s −1 blueward of the systemic H 2 O redshift, consistent with the velocities of the weak [CII] outflow ( Carilli et al. 2013 ). 4. Discussion 4.1. HF: Molecular gas irradiated by AGN and young stars The detection of HF emission in the QSO is unusual. In most of the sources observed thus far, the HF J = 1–0 line is observed as an absorption line with little or no emission ( van der Werf et al. 2010 ). Only a handful of sources are known to have HF purely in emission, without any obvious associated absorption. These sources are the Orion Bar ( van der Tak et al. 2012 ; Kavak et al. 2019 ), Mrk 231 ( van der Werf et al. 2010 ), and a few nearby galaxies ( Pereira-Santaella et al. 2013 ; Lu et al. 2017b ). To increase the number of galaxies observed with HF in emission, we extracted the HF emission line fluxes from local galaxies observed with Herschel in the sample of Yang et al. (2013) , which all host AGN. We find that the galaxies that host AGN and the QSO in BR 1202-0725 have an approximately constant ratio of L HF / L IR irrespective of L IR (Fig. 3 ). We note, however, that the exact slope of the relation only considering the local AGN and QSO is dependent on the far-infrared luminosity used. Other estimates of the infrared luminosity of the QSO in the literature would lower L IR / L HF (cf. Salomé et al. 2012 ; Wagg et al. 2014 ; Lu et al. 2017a ). Fig. 3. Relationship between the infrared luminosity and the ratio of the HF luminosity and infrared luminosity of BR 1202-0725, a sample of local AGN ( Yang et al. 2013 ), and the Orion Bar ( Salgado et al. 2016 ; Nagy et al. 2017 ). All sources are labeled. We show a least-squares fit to the local AGN, QSO, and the Orion Bar (black line with the dotted line extension to the Orion Bar), which implies the ratio is constant. We note that fitting the AGN and QSO only yields a similar fit but with a slightly steeper slope. For comparison, we show the ratio L HF / L IR for the entire IR luminosity of the Orion Bar and the luminosity if only 2% of the total IR luminosity is within the Herschel beam used to measure the HF flux (see text for details). The Orion Bar is the only known galactic source or sight-line where HF appears purely in emission (although we note that the absorption of HF and the nearby H 2 O (2 20 –2 11 ) could mask any emission in other galactic and extra-galactic sources; see e.g., Monje et al. 2014 ; Neufeld et al. 2010 ; Sonnentrucker et al. 2015 ). If we compare L HF / L IR and L IR for the Orion Bar ( Salgado et al. 2016 ; Nagy et al.


afterglow emission takes place when the Lorentz factor of the core of the blast slows down to Γ ∼ 1/ θ v which falls into mildly relativistic domain if θ v is relatively large. Since we wish to follow the blast emission up to, and shortly after, its peak, a treatment that follows the blast deceleration down to a trans-relativistic speed is required. In this work we use a semi-analytical model following the shock jump conditions derived by an approximate trans-relativistic EoS (Uhm 2011 ). This model works well at both high and modest Lorentz factors and can smoothly transform to a Sedov–Taylor solution when the shock is no longer relativistic. Our model assumes one-dimensional radial solution for each blast segment. Therefore, we neglect sideways (lateral) expansion in our model. The sideways expansion of the blast is important only at later stages of the blast evolution and it affects the light curve predictions after its peak (Gill Granot 2018 ). However, the key factor to constrain inclination angle, of interest in this study, is the arising slope before the peak flux, where the jet is still relativistic. So the sideways expansion will not influence our result. To describe the physical quantities more clearly, here we consider two reference frames. One is burst rest frame (hereafter BR frame) which is at rest with the central engine. The other is the shock co-moving frame (hereafter CM frame) which is at rest with respect to the gas just downstream of the forward shock. Primed variables (i.e., denoted with ′) are measured in CM frame, while non-primed ones are measured in BR frame. Under the approximation of neglecting sideways expansion, we can regard the jet evolution at each radial direction as being independent. It is therefore convenient to describe the evolution at each direction by using isotropic equivalence quantities. Consider an isotropic burst of total energy E iso = Γ 0 M ej c 2 , where M ej is the mass of ejecta and Γ 0 is its initial Lorentz factor. The outflow spreads in a cold uniform environment of number density n and forms a relativistic shock. We assume that the blastwave is expanding adiabatically without further energy injection (for considerations of energy injection in the blast see Lamb et al. 2020 ). Energy loss due to radiation is negligible compared with E iso . We define the radius R of the shock by the distance from burst center to the forward shock. All swept-up mass is compressed in the downstream of forward shock, moving with Lorentz factor Γ measured in BR frame, and its isotropic kinetic energy is E k . We also assume that all ejecta are moving together with shocked ambient at the same Lorentz factor behind the contact discontinuity, and its total energy is E ej = Γ M ej c 2 . The trans-relativistic shock jump conditions is (Uhm 2011 ; Ryan et al. 2019 ) where , , and are the number density, internal energy, and pressure of shocked region, respectively. is the dimensionless speed. c is the speed of light.


enhanced, so Tri II does not follow the strange K–Mg anticorrelation in NGC 2419. Note that the halo sample here is different than in Figures 2 and 4 because our usual halo compilation does not have K abundances (Frebel 2010 ). Adapted from Venn et al. ( 2017 ). 4.4. Iron-peak Elements: Cr, Mn, Co, Ni, Zn The Fe-peak abundances were determined with equivalent widths, except for Mn, which is synthesized owing to hyperfine structure (Kurucz Bell 1995 ). In Gru I we can constrain Cr, Mn, Co, and Ni, finding that both stars have essentially identical abundances of these elements. Though the Cr and Mn abundances are similar to those in metal-poor stars in other UFDs or in the Milky Way halo, the Co and Ni abundances are somewhat higher. However, this difference is not very significant, especially for Co, which is derived from only a few bluer lines. One Zn line is marginally detected in GruI-032 with an abundance consistent with the halo trend, though with large uncertainty. In Tri II, we can detect Cr and Ni in TriII-40 and provide upper limits in TriII-46. Mn, Co, and Zn are unconstrained, as they only have strong lines blueward of 5000 Å. The upper limits for Cr and Ni in TriII-46 are uninteresting. For TriII-40, we detect a normal [Cr/Fe] ratio, but Ni appears significantly enhanced ([Ni/Fe] = 0.57 ± 0.16), in agreement with Kirby et al. ( 2017 ) and Venn et al. ( 2017 ). 4.5. Neutron-capture Elements: Sr, Ba, Eu Strontium is detected only in Gru I, as the strong Sr ii lines at 4077 and 4215 Å are out of the range of the Tri II (GRACES) spectra. The abundance of both lines is determined with spectrum synthesis. The Sr abundances in these two stars are very similar, [Sr/Fe] ≈ −2, which is much lower than what is found in most halo stars but similar to most UFDs (Figure 4 ). Barium is measured with four different lines in the Gru I stars including hyperfine structure and isotope splitting (McWilliam 1998 ). We use solar isotope ratios (Sneden et al. 2008 ), but given the low overall abundance, changing this to r - or s -process ratios does not significantly affect our abundances. GruI-032 has a low [Ba/Fe] ≈ −1.6, but GruI-038 has a much higher Ba abundance [Ba/Fe] ≈ −1.0. This is formally only 1.6 σ different, but differential comparison of the line strengths (e.g., the λ 6497 line in Figure 1 ) suggests that the difference is real. We discuss this more in Section 5.1 , but both Ba abundances are low and similar to those in most UFDs. Ba is not detected in either Tri II star, so instead we place 5 σ upper limits. The Ba limit for TriII-40 is [Ba/Fe] −1.25, suggesting a low Ba abundance similar to other UFDs. Kirby et al. ( 2017 ) determined [Sr/Fe] = −1.5 and [Ba/Fe] = −2.4 from their HIRES spectrum of this star, consistent with our upper limit and showing that TriII-40 clearly has very low neutron-capture element abundances. The Ba limit for TriII-46 is only [Ba/Fe] ≲ −0.2, but this is still at the lower envelope of the halo trend (Figure 4 ).


We thank the anonymous referee for insightful remarks that helped us to improve the paper. B.V. acknowledges the support from an Australian Research Council Discovery Early Career Researcher Award (PD0028506). This work was partially supported by grants ARC FT130101593, and HST /GO 13767, 12905, and 12572. Facilities : HST (ACS), HST (WFC3).


H β +[OIII] lines after the subtraction of the continuum. The red curve represents the best-fit retrieval model from MC fits, and the shaded area encompass 68% of the MC sample; the black curve shows the [OIII] best fit obtained by Shen (2016) . Table B.1. [OIII] best-fit parameters. Notes. For each component, we report the amplitude (in erg s −1 cm −2 Å −1 ), the centroid, and the width of the best-fit Gaussian profile. B.1. Modelling rest-frame optical spectra The H α +[NII]+[SII] and H β +[OIII]+FeII systems of J0149+1501, J1421+2241, J1431+0535, J1436+6336, and J1220+0004 are redshifted in the K and H bands, respectively. We simultaneously fitted the continuum and all emission lines to reduce the degeneracies between BLR, NLR, and FeII emission. In short, we fitted a (single) power-law continuum, an optical FeII template ( Kovačević et al. 2010 ), and a combination of Gaussian functions to model BLR and NLR emission lines. In particular, depending on the complexity of FeII emission, we considered one or two FeII templates (see e.g. Vietri et al. 2018 ). These were convolved with a Gaussian; the width of this Gaussian, the amplitude normalisations, and velocity offset of the FeII templates were free variables in our fit (see Perna et al. 2017a for further details). Gaussian components were used to reproduce the emission lines. Specifically, we used (1) a systemic narrow ( F W H M 700 km s −1 ) Gaussian component for the [OIII] doublet and for the H β , H α , [NII], and [SII] doublets associated with unperturbed NLR emission; (2) a broad ( F W H M > 2000 km s −1 ) Gaussian component to fit the BLR H β and H α emission; and (3) an outflow ( F W H M > 700 km s −1 ) Gaussian component for all forbidden and permitted emission lines to reproduce prominent and asymmetric wings associated with outflowing gas. One or more additional sets of Gaussian components were considered to reproduce more complex profiles, for instance, asymmetric BLR emission lines (see e.g. J1421+2241, Fig. B.1 ) and extremely broad [OIII] profiles (e.g. J0843+0750, Fig. B.2 ; see also Fig. 11 in Coatman et al. 2019 ). Fig. B.2. Parametrisation of the MgII+FeII+FeIII ( left ) and H β +[OIII]+FeII ( middle and right panels ) of z > 2.5 QSO1 from the sample of Shen (2016) . The red lines show the best-fit from multiple systemic, outflow, and broad Gaussian profiles to the line features, together with a two local power laws to the continuum and one (two) template(s) to the FeII features (see text). Each curve with a different colour represent a distinct Gaussian set (or a FeII template) with same kinematic properties, as labeled in the figure. In the right panel we show, for each target, the spectrum around the H β +[OIII] lines after the subtraction of the continuum. The red curve represents the best-fit retrieval model from MC fits, and the shaded area encompass 68% of the MC sample; the black curve shows the [OIII] best-fit obtained by Shen (2016) . For J2238−0921, we do not


We thank Joshua Bloom, Saavik Ford, Kohei Inayoshi, Barry McKernan, and Cole Miller for useful discussions. BDM gratefully acknowledges support from NASA Fermi grant NNX14AQ68G, NSF grant AST-1410950, and the Alfred P. Sloan Foundation. Financial support was provided to NCS by NASA through Einstein Postdoctoral Fellowship Award Number PF5-160145 and to ZH by NASA ATP grants NNX11AE05G and NNX15AB19G. ZH gratefully acknowledges support from a Simons Fellowship in Theoretical Physics.


Acknowledgements MS thanks Keiichi Umetsu and Peter Melchior for useful discussions. SE and MS acknowledge the financial contribution from contracts ASI-INAF I/009/10/0, PRIN-INAF 2012 ‘A unique dataset to address the most compelling open questions about X-Ray Galaxy Clusters’, and PRIN-INAF 2014 1.05.01.94.02 ‘Glittering Kaleidoscopes in the sky: the multifaceted nature and role of galaxy clusters’. SE acknowledges the financial contribution from contracts NARO15 ASI-INAF I/037/12/0 and ASI 2015-046-R.0. This research has made use of NASA's Astrophysics Data System (ADS) and of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This work is based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/IRFU, at the Canada–France–Hawaii Telescope (CFHT) which 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 (CNRS) of France, and the University of Hawaii. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. CFHTLenS and RCSLenS data processing was made possible, thanks to significant computing support from the NSERC Research Tools and Instruments grant program.


repeated for 10 shots. This plot indicates that for increasing bias voltage (which is proportional to the axial magnetic field), the wavelength increases. This phenomenon is consistent with the MRTI theory, since Equation ( 5 ) shows that the increase of magnetic field causes the corresponding fastest-growing wavelength to increase. For a sufficiently large magnetic field, the wavelength is larger than the loop dimensions and the MRTI cannot occur. This is most likely the reason for the absence of the MRTI in previous solar loop simulation experiments where a much larger axial magnetic field was used (e.g., Stenson Bellan 2012 ; Wongwaitayakornkul et al. 2017 ). In Equation ( 5 ), besides B 0 , the other two parameters, ρ , g , also influence the fastest-growing wavelength. In order to remove the influence of these parameters and verify that the increasing wavelength corresponds with the MRTI theory, we introduce the parameter s = λρg . Using Equation ( 5 ), the fastest-growing mode corresponds toTo determine s , the quantities λ and g are obtained from the image measurement, while n is determined as a relative density from the light intensity that scales as n 2 . Keeping other experimental parameters the same, we set V b = 30, 40, 50, 60 V for 10 shots at each voltage to obtain s . Figure 3 (c) shows s plotted versus . The error bars are determined by the shot-to-shot variation of measured data and the line shows the best fit. From this figure, it is clear that s scales as as predicted by Equation ( 10 ). As the geometry of the experiment is more like a cylinder, the experimental results can also be used to compare with the MRTI under cylindrical geometry derived by Zhai Bellan ( 2016 ). Using the notation in Zhai Bellan ( 2016 ), the growth rate iswhere , , , and a is the radius of the cylinder. Consider a system with certain values Φ 2 and α subject to a random perturbation that contains all possible x = ka . The component x that gives the largest is defined as x *, and the fastest growth rate is defined as γ *. Figure 4 (a) shows the dependence of the fastest-growing mode on α for fixed Φ 2 = 3.77. Note that α is proportional to the axial magnetic field and x corresponds to 1/ λ . Figure 4 (a) shows that with the increase of axial magnetic field, the wavelength increases and the growth rate decreases, the same trend as predicted for the MRTI in planar geometry. Figure 4. (a) x * and γ * vs. α with Φ 2 = 3.77. (b)–(f) Comparison between the observed growth rate and the calculated growth rate from the MRTI theory under cylindrical geometry (each data point is from 10 shots and the error bar represents the spread over these shots). We assumed all of the experiments have the same parameters except for axial field with , , I = 10 4 A, , and that the observed mode is the fastest-growing mode. The only free parameter in the determination of Φ 2 is a . From the experimental results, x * can be obtained and the corresponding α and γ * can be


may favour jet production. If a jet is launched, it is likely to dominate the power output of the event if viewed on-axis, where the radiation will likely be non-thermal, whereas off-axis emission is likely to be thermal. There is also the possibility of radiatively driven winds originating from the accretion disc, but we do not consider these here. Following MacLeod et al. ( 2014 ), if t visc > t fb , no longer traces , with viscous expansion of the disc modifying accretion time-scales according to (Cannizzo Gehrels 2009 ; Cannizzo, Troja Lodato 2011 ): where and α = 0.1 for thick discs. The form of during its rise is currently not well constrained; we model it crudely here using a half-Gaussian centred on t peak with σ = t peak /4, and normalize its peak to L Edd . In this work, we assume that the observable X-ray light curve will trace and plot examples of our simulated light curves following the above description in Fig. 1 . Figure 1. Simulated light curves for WTDEs based on the theory described in Section 2.1 . Higher M bh have faster rises to peak L , with fastest decay rates in light curve for BHs. For comparison of peak luminosities of these events, we normalize each light curve by L 5, peak , the Eddington luminosity of a BH. The WF experiences extreme compression perpendicular to the orbital plane, with this being maximized as it crosses pericenter. This is expected to trigger thermonuclear burning if its time-scale is much shorter than the dynamical time-scale (see eg. Luminet Pichon 1989a ; Brassart Luminet 2008 ; Rosswog et al. 2009 ; Haas et al. 2012 ). This burning originates in the tidal debris, with emission likely resembling type 1a SNe-like light curves in the optical band (Rosswog et al. 2008 , 2009 ; MacLeod et al. 2016 ; Kawana et al. 2018 ). Whilst thermonuclear burning may affect the amount of bound debris and subsequent accretion rate, no robust models of this currently exist and we neglect its impact in this work. 2.1.2 Spectral properties Following the modelling of emission from thick accretion discs (see Balbus Hawley 1998 for a review), the innermost sections of the disc, R 5 R S (as in Ulmer 1999 and Khabibullin et al. 2014 ), radiate as a blackbody at temperature: where L Edd is the Eddington luminosity of the BH. For IMBHs with masses of , this results in X-ray spectra with temperatures 0.2 keV. The soft emission from these events can be significantly affected by the interstellar medium’s (ISM’s) absorption along the line of sight (Auchettl, Guillochon Ramirez-Ruiz 2017 ). For each TDE, we set the Galactic neutral hydrogen column density, N H , to , close to the estimated extra-galactic median of N H (Esquej et al. 2008 ). We model the WTDE’s spectrum using the xspec model tbabs(zbbody) , but neglect modelling any spectral evolution. An optically thick extended envelope may form from stellar debris around the BH, reprocessing a fraction of the X-ray accretion flare into the UV and optical bands (eg. Loeb


We thank the anonymous reviewer for comments that improved this Letter. This work has been supported by Polish National Science Centre MAESTRO grant DEC-2012/06/A/ST9/00276. KG thanks the Poznań Supercomputer and Network Centre for computing grant no. 195. We acknowledge the use of the HPC cluster HAL9000 of the Computing Centre of the Faculty of Mathematics and Physics at the University of Szczecin.


We are grateful to the anonymous referee for her/his constructive critical comments and suggestions, which helped improving the paper.


ACKNOWLEDGEMENTS K. C-j has been supported by the Kim Il Sung University during this investigation, and would like to thank Jo Jong-won for providing Korean historical data and Mun Ui-ri for help with the English. The authors are grateful to an anonymous referee for detailed and helpful comments.


e.g., or Mm, the 1027 line reaches higher layers. We also included, as reference, an isothermal contour at 7000 K that seems to follow relatively closely the height where the optical depth is unity for these lines. In the case of Ca ii , the near-UV transition forms at heights similar to those covered by the O i lines, while the infrared spectral line scans lower layers in the low to mid chromosphere. In the region , magnetic loops are stratified and the TR shifts to higher layers. Following this trend, the spectral lines also form higher in the atmosphere, with the exception of Ca ii IR, which is sensitive to the low chromosphere. 3.3.3. Inferring the LOS Velocity One of the main targets of future spectroscopic observations (e.g., done by Solar-C (EUVST) and SO/SPICE) is to examine the dynamics of the solar phenomena from the chromosphere to the outer atmosphere. We aim to estimate the capabilities of the spectral lines of interest to tackle this task. We do this by computing the Doppler shift of the line core of Lyman β and the two O i 1027 and 1028 Å lines. We believe a single Gaussian fitting is not accurate enough for Lyman β because the line shows multiple lobes, similar to the Mg ii UV spectral lines. Therefore, using the analysis of the IRIS observations as a reference, we perform the following steps. First, we detect the extrema of each spectral profile. Among them, we select the extremum that is nearest to the line core rest wavelength, and we perform a parabola fitting around the extremum to obtain the corresponding line core wavelength. In the case of the O i transitions, we compute the line core Doppler shift using a single Gaussian fitting of the intensity profile. We tried more complex procedures than we do for Lyman β , but we did not achieve noticeable improvements. The inferred LOS velocities for the entire snapshot FOV are plotted in the upper row of Figure 9 . We include in the middle row the LOS velocity at the height where the optical depth is unity for the line core wavelength (similar to what we did for the temperature in Figure 6 ). Additionally, we show in the bottom row the joint probability density functions (JPDFs) of the velocity obtained by the fitting and that from the B ifrost atmosphere (top and middle rows). In some pixels, the derived velocities were larger than 20 km s −1 . We believe those large values correspond to a bad fit of our method. The pixels where the velocity is larger than 20 km s −1 were excluded from the JPDF plots and from the computation of the Pearson correlation rate (although they are shown in the top row panels). Examining the JPDF results, we can say that both methods provide a similar spatial distribution of the LOS velocity for the three spectral lines. We can trace the velocities along the loop-like structures as well as inside the magnetic field elements. The differences among these LOS velocities can be seen in the JPDF plots and the Pearson correlation between both cases. For the


This work was supported by the Science Development Foundation under the President of the Republic of Azerbaijan –Grant No EIF-2013-9(15)-46/14/11 .


This research was supported by the Recruitment Program of Global Experts of China, NSFC under grants 41374173 and 41774179, and the Specialized Research Fund for State Key Laboratories of China. We acknowledge the use of data from SOHO , Wind , ACE , Ulysses , and GOES .


signatures associated with relativistic protons. In this study, we have demonstrated how high-energy neutrino observations may be used to assess p at SN shocks propagating in dense CSM. In particular, the IceCubes measurement of the diffuse neutrino flux already constrains p 0.3(0.04/) or p 0.2(0.04/) when including the up-going muon neutrino sample from the Northern Sky (Section 3.1 ). Even more stringent constraints on the acceleration efficiency may be placed by direct observations of close-by SNe IIn with neutrino telescopes (Section 3.2 ). The neutrino flux from an SN IIn depends on its distance, shock velocity, mass-loading parameter and proton acceleration efficiency (Section 3.2 ). All parameters, except for the latter, can be, in principle, inferred from radio and optical observations of SNe IIn. For example, v sh is routinely inferred from the width of optical emission lines, while K w can be derived by the fitting of radio observations (Chevalier 1998 ; Chevalier, Fransson Nymark 2006 ; Chandra etal. 2015 ). Besides the intrinsic source parameters that affect the neutrino luminosity, as exemplified through scenarios S1 and S2, the actual distance of the source has the strongest impact on the IceCube expected neutrino rate (see equation 12 ). The IceCube event number expected within the first year in both scenarios discussed in Section 3.2 is plotted in Fig. 9 (bottom panel) as a function of the source distance. Different types of lines correspond to three values of the proton acceleration efficiency marked on the plot. The curves are obtained after scaling the total number of muon neutrinos obtained in S1 and S2 (see Table 2 ) with the source distance and p . The top panel in Fig. 9 shows the all-sky number of SNe IIn expected over 8years up to a distance D L (black thick line); the number is derived using the CC SN rate of HB06 and 0.04. For p values allowed by the diffuse flux measurements, i.e. p 0.2(0.04/), only S1-like SNe IIn at distances 18Mpc are strong enough neutrino emitters to constrain less efficient acceleration scenarios, in agreement with the predictions by Murase etal. ( 2011 ). If p 0.2, IceCube would be capable of claiming a neutrino detection from an S1-like SN IIn exploding at 10Mpc. For SNe IIn with lower mass-loading parameter K w , as discussed in S2, the neutrino rate decreases significantly, even for p 0.2. This restricts the accessible distance to no more than 4Mpc. Figure 9. Bottom panel: Expected IceCube event number as a function of luminosity distance D L for the two SN scenarios discussed in the text (S1: blue lines; S2: cyan lines). For the second scenario, the number of neutrinos expected only within the first year is shown. For both scenarios, the results for three different values of the proton acceleration efficiency are presented (see the legend). The horizontal black and red lines indicate the number of neutrinos required to reach IceCubes sensitivity and 5 discovery potential,


The THÉMIS observations were supported by the Programme National Soleil-Terre (PNST) of the French Centre National de la Recherche Scientifique (CNRS).


y ∈ [−50, 50] L 0 yieldswhich is in good agreement with the growth rate shown in Figure 16 . Evidently, the growth rate for this instability derived in the Boussinesq regime, which is admittedly inapplicable in our simulation, produces values that are in good agreement with our simulation results. Thus, both the fastest growing parallel and perpendicular wavenumbers and growth rates of the instability observed in our simulation are in reasonable agreement with theoretical predictions of these same quantities in the undular instability. 3.6. Interaction of the Lobes The emergence of magnetic lobes was found by Archontis et al. ( 2013 ) to result in significant reconnection and a jet-like disturbance in the corona. In our simulation, we see a similar event around t = 820 t 0 that may affect the linear evolution of the undular mode, which stops around the same time. Figure 17 shows the magnetic energyand kinetic energyin the corona as a function of time. The decrease in the magnetic energy that starts around t = 824 t 0 (green line) corresponds approximately to the same magnitude increase in the kinetic energy which peaks at t = 864 t 0 (blue line). In Figure 18 , we plot a series of field lines, seeded along a vertical line at ( x = y = 0, z > 0) in the corona on top of vertical cuts of the vertical velocity V z . The field lines are colored by B z . Early in the interaction, at t = 824 t 0 , the field lines only barely extend above the photosphere. They slowly rise, and it is clear that by t = 860 t 0 , the two primary lobes are interacting, and field lines with opposite signs of B z begin to reconnect. This creates a significant upflow, as the newly reconnected concave up field lines release their tension. This upflow is only seen low down in the corona before t = 860 t 0 , and with much smaller magnitude, since the field is not significantly expanding into the corona, but by t = 880 t 0 and t = 900 t 0 there is a clear upflow visible in the corona along with a concave-down arcade overlying the interacting loops. This type of event likely represents an Ellerman bomb, which is associated with this type of topology (Pariat et al. 2004 , 2006 , 2009 ; Isobe et al. 2007 ; Archontis Hood 2009 ; Danilovic 2017 ). Figure 17. Magnetic energy (black) and kinetic energy (red) in the corona as a function of time. The green (blue) line at t = 824 t 0 ( t = 864 t 0 ) shows the local maximum of the magnetic (kinetic) energy. Figure 18. Field lines colored by B z overplotted on a vertical plane of V z and a photospheric magnetogram at several times before and during the reconnection event. 4. Conclusions In this paper, we simulated the buoyant rise and emergence of an untwisted toroidal flux rope from deep in the convection zone. The flux rope is able to rise coherently through the convection zone, emerge through the photosphere, and form a relatively large (≈40 Mm across) active region. The emergence mechanism of our flux rope is identified to be the undular


We thank the US National Science Foundation for its financial support through grant NSF AST-1518057, and NASA for its financial support through the WFIRST Preparatory Science program, grant No. NNX15AJ79G. This work is based in part on observations made with the NASA/ESA Hubble Space Telescope , obtained [from the Data Archive] at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program #14201. Support for program #14201 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. T.J. thanks the LSSTC Data Science Fellowship Program; her time as a Fellow has benefited this work. We thank an anonymous referee for their helpful review, and Chun Ly for pointing out a possible systematic effect of recently revised atomic physics on our results. This work has made use of data from the Sloan Digital Sky Survey (SDSS). Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is www.sdss.org.SDSS-IV and is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.


ACKNOWLEDGEMENTS Judith Biernaux acknowledges the support of the F.R.I.A. fund of the F.N.R.S.


We acknowledge useful comments from an anonymous referee and discussions with Harry Warren, Jim Klimchuk, Karel Schrijver, Amy Winebarger, and Craig Deforest. Part of the work was supported by the NASA contracts NNG04EA00C of the SDO /AIA instrument and NNG09FA40C of the IRIS mission. Note added in proof. While this manuscript went into print, we were informed that work in progress shows that no clear evidence of observed features smaller than about 3.5–4.5 Hi-C pixels (≈250–330 km) was found in an ongoing study, using a (Fourier-based) noise-gating technique (Craig DeForest, private communication, 2017). In our view, this corroborates our result of a lower loop width cutoff w min below the peak (or most frequent) width of w p = 550 km.


of the observations. Notes .The airmass column contains the value at the start and end of the observations. The seeing value is corrected for the airmass and corresponds to the seeing at the zenith. Table 2. Comparison of the modelling parametrization of the telluric correction methods. Notes .Molecfit and TelFit packages allow for fitting the atmospheric transmission to the target spectrum. A summary of the capabilities and models employed by each package is shown in Table 2 . Appendix A presents the spectra of the telluric standard and the synthetic transmission spectra obtained with each of the packages. 3.1. TelFit The Python package TelFit ( Gullikson et al. 2014 ) models the atmospheric transmission and fits it to a spectrum with the Levenberg–Marquardt algorithm. TelFit can adjust the wavelength solution of the data to the telluric lines or vice versa with a polynomial function. The order of the polynomial can be set by the user, but the values of the coefficients have to be directly set in the code. TelFit offers a parametric model of the line shape through a Gaussian function and a non-parametric model with the singular value decomposition mode. For each observation, TelFit uses, by default, an average atmospheric profile for mid-latitudes. The website of the Global Data Assimilation System (GDAS) 6 enables the use of a custom atmospheric profile, adapted to the location and time of the observations. The GDAS profiles are available for observations taken since 2004 and are updated every three hours. We used the atmospheric profile that was closest to our observations, with a time difference inferior to 2 h, to first fit the spectrum of the standard star. We experimented with the parametric and non-parametric options for the line shape and attained the same level of correction. Since the other models studied in this work use parametric correction, and to provide a more meaningful comparison, we selected the parametric correction. To perform the fit of the standard star, we left the parameters of the wavelength solution, the continuum level, line shape, resolution, and the mixing ratio of the molecule relevant for the wavelength range considered (H 2 O or O 2 ) as free parameters. TelFit does not allow a different parametrization of spectral orders separated by wavelength gaps. Therefore, each of the three CRIRES detectors is treated independently because the parameters of the wavelength solution differ from one detector to the other. As such, for each fit we obtain a different value for the following parameters: molecule abundances, resolution, temperature, and pressure profiles. We homogenize these parameters by taking their mean values weighted by the inverse of the χ 2 calculated for each detector. Then, we use the homogenized values of the parameters to compute a final atmospheric transmission for each detector. We divide the stellar spectrum by the atmospheric transmission to correct for the telluric lines. After the


ACKNOWLEDGEMENTS DN would like to thank Kate Rubin, Gwen Rudie, and Alice Shapley for insightful discussions and suggestions, as well as the anonymous referee for a constructive report. SG, through the Flatiron Institute, is supported by the Simons Foundation. The primary TNG simulations were realized with compute time granted by the Gauss Centre for Supercomputing (GCS): TNG50 under GCS Large-Scale Project GCS-DWAR (2016; PIs Nelson/Pillepich), and TNG100 and TNG300 under GCS-ILLU (2014; PI Springel) on the GCS share of the supercomputer Hazel Hen at the High Performance Computing Center Stuttgart (HLRS). GCS is the alliance of the three national supercomputing centres HLRS (Universität Stuttgart), JSC (Forschungszentrum Jülich), and LRZ (Bayerische Akademie der Wissenschaften), funded by the German Federal Ministry of Education and Research (BMBF) and the German State Ministries for Research of Baden-Württemberg (MWK), Bayern (StMWFK), and Nordrhein-Westfalen (MIWF). Additional simulations were carried out on the Draco and Cobra supercomputers at the Max Planck Computing and Data Facility (MPCDF).


J.L.D. acknowledges support for this work provided by NASA through Hubble Fellowship grant HF-51303.01 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. B.D.S. acknowledges support from the National Aeronautics and Space Administration (NASA) through Einstein Postdoctoral Fellowship Award Number PF5-160143 issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. C.L. acknowledges support from NSF grant AST-1004583. Finally, we thank the anonymous referee for discussions and comments that improved the paper.


We wish to acknowledge the referee for his very fruitful comments that improved our work. We thank C. Brogan for supplying us with the VLA 330 MHz image. This research has been funded by Argentina grants ANPCYT-PICT 0795/08 and 0571/11. G.C. is Member of the Carrera of Investigador Científico of CONICET, Argentina. L.S. is a Ph.D. Fellow of CONICET, Argentina. The GOODS-Herschel data was accessed through the Herschel Database in Marseille (HeDaM – http://hedam.lam.fr ) operated by CeSAM and hosted by the Laboratoire d’Astrophysique de Marseille. We thank the staff of the GMRT, who have made these observations possible. GMRT is operated by the National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. This work made use of the High Performance Computing facility, funded by grant 12-RD-TFR-5.02-0711, at National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research.


ACKNOWLEDGEMENTS PMY thanks the University of Tasmania for a Dean’s Summer Research Studentship and an Australian Postgraduate Award. SSS and MGHK thank the Australian Research Council for an Early Career Fellowship (DE130101399). We also thank an anonymous referee for his useful comments. This research was carried out using the Tasmanian Partnership for Advanced Computing high-performance computing clusters.


given by We define z ph to be where the scattering optical depth s 1 (where subscript s denotes scattering) and set f cor / f cor,0 1. Near the photosphere, magnetic pressure begins to play a major role in hydrostatic equilibrium (e.g. Blaes, Hirose Krolik 2007 ), and near the effective photosphere, the gas temperature begins to diverge from the radiation temperature, so we acknowledge that the assumptions underlying our scheme do not reflect the detailed physics in this region. But since our goal is only to calculate spectra, for optical depths s 1, the accuracy of this scheme is not important. We can assess the validity of this scheme in the region s 1 by comparing the flux from spectral calculations with the intended flux given by equation ( 9 ), or, equivalently, by comparing the corresponding Eddington ratios. In Section 3.1 , we make this comparison for each set of scaling parameters we use and find that they generally agree to within 10percent. More importantly, we find that normalizing the spectra at different radii so that their corresponding Eddington ratios match has a negligible impact on the observed spectrum when contrasted with the discrepancies between spectral calculations with and without bulk velocities. In other words, because the potential error is significantly less than the effect we are measuring, our scaling scheme is adequate. These are the appropriate equations for scaling data to a different set of fundamental shearing box simulation parameters, in particular, z , x v y and . If we substitute in equation ( 9 ) for , we can alternatively regard F as a fundamental parameter instead of . Shearing box scalings in terms of F are given in Appendix C . This substitution is useful in order to scale to a different set of fundamental accretion disc parameters, since it is straightforward to express F in terms of accretion disc radius, mass and accretion rate. The scalings for z , x v y and F for both Newtonian and Kerr discs, allowing for a non-zero stress inner boundary condition, are given in Appendix D . The final scalings for , T g , v and v s in terms of fundamental accretion disc parameters are given in Appendix E . We only use Kerr scalings for our spectral calculations, but the Newtonian scalings are potentially useful for the purpose of comparing with other works in which Newtonian parameters are used and also for developing physical intuition. 2.3 Dependence of turbulent Comptonization on radius To characterize the contribution of turbulent Comptonization, we must model spectra at multiple radii. Our choice of radii is guided by the scaling of the ratio of bulk to thermal electron energies. We estimate this effect for a disc with no spin and a stress-free inner boundary condition with the Newtonian scalings in Appendix E . The bulk velocity scaling is The photosphere thermal velocity scaling is The scaling for the ratio of bulk velocity to thermal velocity at the photosphere is We also calculate the scaling for


observations of the six targets were obtained between 2016 and 2020 using a variety of instruments: X-shooter on the Very Large Telescope (VLT; Vernet et al. 2011 ) observatory at Cerro Paranal, Chile; the Intermediate-dispersion Spectrograph and Imaging System 1 (ISIS) on the William Herschel Telescope (WHT) at Roque de los Muchachos Observatory (ORM) in La Palma, Spain; the Optical System for Imaging and low-Intermediate-Resolution Integrated Spectroscopy (OSIRIS; Sánchez et al. 2012 ) on the Gran Telescopio Canarias (GTC) at the ORM; the High Resolution Echelle Spectrometer (HIRES; Vogt et al. 1994 ) on the 10-m Keck I telescope at Mauna Kea Observatory, Hawaii; the Magellan Echellette (MagE) spectrograph on the 6.5-m Baade Magellan telescope at Las Campanas Observatory, Chile; and the FAST spectrograph on the 1.5-m Fred Lawrence Whipple Observatory telescope on Mount Hopkins, Arizona (Fabricant et al. 1998 ). The observation log is reported in Table 1 , and sample spectra are displayed in Figs A1 and A2 in the online appendix. Table 1. Log of spectroscopic observations. X-shooter observations were carried out using a 1-arcsec slit aperture for the ultraviolet (UV)-blue (UVB) arm and 0.9 arcsec for the visible (VIS) arm. In all cases flux in the NIR arm was insufficient for any meaningful analysis. On average the resolving power of our X-shooter spectra is R ≃ 4000–5500 in the UVB arm and R ≃ 6500–8000 in the VIS arm. All data were reduced using the standard procedures within the reflex 2 reduction tool developed by ESO. Telluric lines removal was performed on the reduced spectra using molecfit (Kausch et al. 2015 ; Smette et al. 2015 ). ISIS observations were obtained using the R600B and R600R gratings, in the ISIS blue and red arms, respectively; two central wavelength settings were used for each arm, nominally 3930 and 4540 Å in the blue and 6562 and 8200 Å in the red, which were chosen to cover important atmospheric lines, such as the Balmer series and Ca HK, as well as the Ca ii emission triplet in addition to other potential emission features (e.g. Fe ii /Mg i 5175 Å). The slit width varied between 1 and 1.5 arcsec depending on the observing conditions and we employed a binning of 2 × 2, resulting in an average resolution of ≈2 Å. ISIS spectra were debiased and flat-fielded using the standard starlink 3 packages kappa, figaro , and convert . We carried out optimal spectral extraction using the package pamela . Wavelength and flux calibration were performed using the routines within the software molly 4 (Marsh 1989 ). The OSIRIS spectroscopy of WD J0846+5703 was obtained using the R2500I grism with a 0.6-arcsec slit, and binning the detector 2 × 2. This set-up provides wavelength coverage over Å at a resolution of R ≃ 2500. The data were bias and flat-field corrected using the standard tools within iraf . 5 We performed sky background subtraction and optimal spectral extraction (Horne 1986 ) using the pamela data reduction