host mass and distance is shown in Figure 7 , as well as the limits on θ E , π E , and the lens flux. We infer that the host is a dwarf, likely in the Galactic bulge at kpc. Using the mass ratio and scaled instantaneous projected separation from the model, we find that the planet mass is , and it orbits its host beyond the snowline at a projected separation of au. The estimated physical properties are summarized in Table 2 . Figure 7. Posterior probability distributions of the host mass and distance derived from the Bayesian analysis. Also shown are the limits on the lens flux (blue), on θ E for either bulge or far disk sources (red), and on the microlens parallax for a bulge source (green). Table 2 Physical Properties One of the main uncertainties in our Galactic model is the source distance. However, we note the the posterior probability for the lens mass (and thus the planet mass) is weakly dependent on the source distance. For a source at 8 kpc the mass is only 10% lower than for a source at 15 kpc, well within the range of our uncertainty. The lens distance, and consequently the projected separation between the planet and its host, is more sensitive to the source distance, with a difference of 45% between a bulge source and a far disk source. Future observations can resolve this and give a better estimation of the source distance, as we discuss below in Section 7 . 6. Extinction Law Recent studies of dust properties in the inner bulge suggest deviations from the standard extinction law (Nataf et al. 2016 ; Alonso-García et al. 2017 ). The extinction coefficient measured in our field, , is lower than the value of 1.37 from Nishiyama et al. ( 2009 ). This offset is a disconcertingly large ∼0.4 mag in K S . The offset relative to the value of predicted by Fitzpatrick ( 1999 ) is even larger—about 1.4 mag in K s . The magnitude of the RC can also be predicted in the YJ bands by assuming the color–color relations from Nataf et al. ( 2016 ) and the extinction coefficients from Fitzpatrick ( 1999 ). These are Y RC ≈ 14.63 + 1.63 + (7.35–3.12) ≈ 20.49 and J RC ≈ 14.63 + 1.63 + (5.44–3.12) ≈ 18.58, where we add the corresponding intrinsic color offsets and reddening offsets, respectively, to the observed clump H -band magnitude. Given that the limiting magnitudes for the DECam and VVV data sets are, respectively, Y ∼ 21.2 and J ∼ 19.4 (see Section 4.2 above), the RC should be clearly detected in each of these bandpasses by a wide margin of 0.7–0.8 mag, respectively. However, the photometry in both cases barely covers the RC. Figure 8 shows the HK s CMD of our field (gray points), with the sources detected in Y shown as yellow circles in the left panel, and the points detected in J shown as purple in the right panel. We conclude that the extinction toward this field is steeper than standard (as defined by Fitzpatrick 1999 ). Figure 8. Detection limits of DECam Y -band data (left) and VVV J -band data (right). These limits are brighter than


ACKNOWLEDGEMENTS We thank the anonymous referee for the useful report which helped to improve the quality of the paper. We acknowledge Gerrit Schellenberger for the help and fruitful discussion. LL and THR acknowledge support from the German Research Association (DFG) through the Transregional Collaborative Research Centre TRR33 ‘The Dark Universe” (project B18) and support from the German Aerospace Agency (DLR) with funds from the Ministry of Economy and Technology (BMWi) through grant 50 OR 1514. LL also acknowledges support from NASA through contract NNX17AD83G.


This work was partially supported by FONDECYT trough grant No 1181158, the Advanced Center for Electrical and Electronic Engineering (AC3E, Proyecto Basal FB0008), Núcleo Milenio de Formación Planetaria – NPF and Programa de Iniciación a la Investigación Científica (PIIC) de la Dirección de Postgrado UTFSM convenio No 015/2018. Michel Curé thanks the support from the Centro de Astrofísica de Valparaíso and the Centro Interdisciplinario de Estudios Atmosféricos y Astroestadística.


of their stellar mass at the present was already in place by z ∼ 2 via star formation (see Fig. 8 ). Figure 9. The average stellar mass assembly history of central galaxies as a function of redshift for haloes of different present-day masses, predicted by various empirical models as indicated. For Milky Way sized haloes, however, the differences between the model predictions are milder. All the models predict that about half of stellar mass was in place by z ∼ 1. There is a significant difference between L15 -U and other models at high z . For example, L15 -U predicts that about 15 per cent of the final stellar mass was assembled by z ∼ 2, while less than 10 per cent was predicted by the other models. For central galaxies in present-day massive haloes with M h (0) > 10 14 M ⊙ , the predictions of different models again become very different. M13 predicts a much later assembly for these galaxies than any other models. The predictions of B13 look similar to L15 -U, but the increase in stellar mass with time is due to different reasons. While L15 -U predicts that the increase at z 2 is dominated by accretion of stars from satellites, B13 predicts that a significant fraction of the increase at z 2 is actually due to in situ star formation (see Fig. 8 ). This difference is again due to the boost of star formation in low-mass haloes at high z in the L15 -U model. The increased amount of stars formed in progenitors at high z makes the accretion of stars more important in the growth of stellar mass in a massive galaxy, and the fraction of stars formed in situ has to be decreased proportionally in order to match the final stellar mass of the galaxy. The results demonstrate the importance of properly modelling the star formation in low-mass progenitors at high z in order to understand the star formation and stellar mass assembly histories of massive galaxies at the present day. 4.4 Stellar mass functions of high-redshift galaxies Fig. 10 shows the predictions of the empirical models for the SMFs of galaxies at a number of redshifts. The predictions of B13 and M13 are similar in both slopes and amplitudes at the low-mass ends, but B13 predicts many more massive galaxies than M13 , particularly at high redshifts. Y12 predicts significantly flatter slopes at the low-mass ends, and more galaxies in the intermediate mass range, than the other three models. The SMFs predicted by the L15 -U model match the predictions of B13 at M * > 10 10 M ⊙ , but are significantly steeper at the low-mass ends. Figure 10. The model predictions for the field SMFs at high redshifts (solid) in comparison with observations. We select some observational SMFs of high-redshift galaxies from the literature to compare with the model predictions. Specifically, we use the SMFs at 1.3 z 3.5 given by Pérez-González et al. ( 2008 ) and Marchesini et al. ( 2009 ). Pérez-González et al. used a sample combining data in three different fields with a total area of 664 arcmin 2 that have a total


by Spite (1967) and regularly improved since then. The package allows us to compute synthetic spectra by interpolating over a large grid of hydrostatic, LTE, and plane-parallel or spherical stellar atmospheres models (MARCS, Gustafsson et al. 2008 ). For all the elements studied here, we assumed the standard solar abundances provided by Grevesse et al. (1996) , namely (Fe), (Y), (La), (Ce), (Nd), and (Eu). Recent spectroscopic estimates of solar abundances by Scott et al. (2015) and by Grevesse et al. (2015) indicate very similar abundances. Indeed, the difference in dex ranges from +0.01 for Eu to 0.03 for Fe and Y, to 0.06 for La, and to 0.08 for Nd, while the new Ce abundance is identical to the old one. 2.2.1. Hyperfine structure and isotopic splitting Several lines used in our abundance analysis are affected by hyperfine structure (HFS) and/or isotopic splitting in the line profile. We searched in the literature for atomic data required to compute the fine-structure components that form these lines. We found that several of them are already available and for those that are not available we adopted the same approach discussed in M13 to compute the HFS. The atomic data required to compute the HFS of La and Eu were taken from Lawler et al. (2001a , b) , respectively. For the La line at 6774.27 Å we adopted the HFS already computed by M13. The same outcome applies to Y: we adopted the HFS data given by M13, but they are only available for three (out of six) lines of this element ( = 5119.12, 5289.81, 5728.89 Å). No atomic data have been found in the literature for our lines of Ce and Nd. Only the odd isotopes Nd and Nd have HFS, but their effects can be safely ignored – in the solar system these isotopes constitute only 20.5% of the total Nd abundance, their lines are very narrow, and recent laboratory transition probabilities by Den Hartog et al. (2003) indicate no evident HFS structure for more than 700 lines of Nd II. 2.2.2. Abundances corrected from HFS We derived the abundances of the current sample of 73 Cepheids by accounting for the HFS of the elements and lines mentioned in the previous section. In order to quantify the effects of the HFS on the derived abundances and their dependence on other parameters, we performed a comparison between our abundance results before and after performing the HFS analysis. The mean differences in abundance are summarized in Table 1 . The differences are much larger for some of the La lines, but they are very close to zero for both Y and Eu lines. For the possible dependences on other parameters, we found that these differences and their dispersions become smaller with increasing (specially for > 5500 K), with increasing surface gravity (specially for > 0.5), and with decreasing pulsation period ( 1.0). No clear correlation with metallicity is observed. Finally, we note that the lines selected to measure n-capture elements are typically weak (smaller than 180 mÅ) and unsaturated. This means that


model when adding the centimeter data emission with = 2.60 and with parameters of m ⋆ = 16 M ⊙ , a core of mass M c = 480 M ⊙ , and a Σ cl = 0.1 g cm −2 clump. The source detected at the Inner scale (or source 8a in Beltrán et al. 2016 ) is slightly extended at 6 cm, and it is blended with other components; however, it is a point source at 1.3 cm and 0.7 cm. Thus, it is possible that we are missing part of the flux at 6 cm, and this may explain why the models seem to overestimate the expected free–free emission at 6 cm. Our resulting protostellar mass of 16 M ⊙ is consistent with the value of of 18 M ⊙ estimated by Sánchez-Monge et al. ( 2013 ) when fitting the velocity field of the core with a rotating Keplerian disk, although they argue that this value corresponds to the total mass of a binary system in core B. Our results disfavor the models with protostellar mass and with relatively low core masses and high Σ cl clumps, because the expected free–free emission for these models is many orders of magnitude lower than the observed one. G45.47+00.05: The observed centimeter continuum emission from G45.47+00.05S (which is the dominant source in the region) is significantly higher (by a few orders of magnitude) than the predicted free–free emission from any of our IR-derived ZT models. This is despite the fact that the best two models from the ZT results already predict a rather high-mass protostar in the range of 30–50 M ⊙ embedded in a massive core, a relatively high Σ clump, and low accretion rates in the range of ∼10 −5 M ⊙ yr −1 . Among the eight sources presented in this study, this is the only case where none of the free–free models can describe the centimeter emission observed in the source. This region is also the most luminous and likely the most evolved one in this sample, which leads us to think that the radio emission from this source may be boosted by the process of photoevaporation. Photoevaporation is not yet accounted for in the TTZ model, which only considered photoionization of the magnetocentrifugally driven wind. However, as the protostar increases its mass above ∼20 M ⊙ , the ionizing radiation becomes dramatically stronger, creating a photoevaporation flow from the disk and infall envelope that is exposed by the outflow cavity wall, i.e., a wind driven by ionized gas-pressure. By enhancing the mass of ionized gas, the centimeter continuum emission is expected to be much higher than that predicted by the TTZ model without such a photoevaporation flow. IRAS 20126+4104: Including the centimeter continuum fluxes to better sample the SED, we can see in Figure 3 that the best five models seem to be overestimating the expected free–free emission. Therefore, after exploring other resulting models from the DLT17 study and the ZT models, we found a matching model with = 3.38 and = 5.42, which yields protostellar parameters of , core mass M c = 240 M ⊙ , and Σ cl = 0.1 g cm −2 . This protostellar mass is consistent with the value of


HEMT receiver followed by an autocorrelation spectrometer configured to record 4096 channels over 4 MHz for each polarization, yielding a spectral resolution of 0.09 km s −1 after Hanning smoothing. The system temperature varied between approximately 30 and 50 K during the observations. The telescope has a half power beam-width of 5.8 arcmin and rms pointing errors of 25arcsec. The spectra were obtained in a frequency-switching mode as a series of 30 s integrations. Most sources had an on-source integration of approximately 30 min. The system was regularly calibrated by observing the continuum source 3C123 (Ott et al. 1994 ) and daily checked using the source G32.744−0.076. This methanol maser source was reported as non-variable within ∼5 per cent (Caswell et al. 1995b ). Indeed, high-cadence observations reveal that some spectral features of the source have not shown variability higher than 8 per cent on time-scales from weeks to a few years. Detailed analysis of the light curves of five main spectral features of the source was reported in Szymczak et al. ( 2014 ). The features at 30.3, 38.5 and 39.1 km s −1 were non-variable during the monitoring period and the standard deviation of the flux density of these features indicates that the resulting accuracy of the absolute flux density is better than 10 per cent (Szymczak et al. 2014 ). The system temperature for the observations was measured by a continuously switched noise diode. Standard procedures were used to reduce the spectra. The typical rms noise level in the final spectra was 0.20-0.35 Jy at 0.09 km s −1 velocity resolution after averaging over polarization and time. There was no detectable interference on the spectra of our targets with the exception of G27.286+0.151. 2.3 Effect of offset observation and confusion For several sites in the sample (Table 1 ), accurate positions have recently been published (e.g. Bartkiewicz et al. 2014 , 2016 ; Breen et al. 2015 ), which show that for a majority of the targets the observed positions deviate from the interferometric positions by less than 0.4 arcmin. These differences (the fourth column of Table 1 ) are much less or comparable to the telescope pointing errors and have no significant effect on the flux density measurements. The measured profile of the telescope beam (Fig. 2 ) implies that our observations at an offset higher than 1.3 arcmin systematically underestimate the flux density by 9 per cent and its uncertainty increases by more than 10 per cent due to the pointing errors. There are 10 sites (11 distinct sources) in the sample for which the flux density measurements are significantly influenced by these effects and all of them are discarded in the following statistical analysis. Furthermore, the spectra of 18 sources (12 star-forming sites) are contaminated by confusion introduced by other sources in the telescope beam or side-lobe response from nearby bright sources and these sources are also excluded from the statistical


This work was supported by grant HST-AR-12123.001-A from the Space Telescope Science Institute. W.A.F. participated in the Research Experiences for Undergraduates (REU) Program of the National Science Foundation under award number 1262810. A. M. R. acknowledges support from the Kenilworth Fund of the New York Community Trust. The HST observations presented here were obtained from the Mikulski Archive for Space Telescopes (MAST). STSci is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555.


i and inclination do not affect the derived ephemeris in this paper. 4. PROSPECTS FOR GWS DETECTABILITY 4.1. Precision Estimates Throughout Advanced LIGO (aLIGO)/VIRGO (AdV) Observations Observing runs for GW searches are planned to commence in 2015, and will run through to 2022+. Estimated run duration for 2015, 2016–17 and 2017–18, are 3, 6, and 9 months, respectively. Starting from 2019, more extended runs are expected take place with aLIGO and AdV per every year. In addition, LIGO India is also expected be included in the observing schedule from 2022 onwards (LIGO Scientific Collaboration et al. 2013 ). The inferior conjunction value for the future GW searches ( T n ) will depend on the uncertainties on both T 0 and P orb when aLIGO/VIRGO observations commence. We consider here how the error on T 0 may be projected in time to any given epoch. Given the epoch for inferior conjunction, T n = nP orb + T 0 , following Galloway et al. ( 2014 ), we derive the projected uncertainty for T n aswhere and are the uncertainties in the P orb and T 0 , respectively, and is the cross-term of the covariance matrix. Substituting the values from Table 2 , we express the error on T n as follows: Here, is the uncertainty in the epoch of inferior conjunction at the time of t in days since T 0 . The evolution of is shown as a function of time in Figure 3 . In the absence of additional epochs of observations, term will grow linearly due to the factor, 4.1× day. The effective uncertainty will grow approximately as a function of time, 2× in 2018 and 3× or greater in/after 2022 compared the current error level. Figure 3. Projected uncertainty throughout the aLIGO observing period (shaded regions) is plotted as a function of time. The solid line denotes the approximately linear growth of the effective error throughout the aLIGO observing period (from 2015 onwards) in the absence of the additional epochs of optical data. The open squares describe the approximate effect on the additional observing epochs and the T 0 uncertainty is represented by the dotted curves. The estimated orbital period uncertainty ( σP orb ) is denoted by a dashed line. The above estimated values show that additional epochs of RV measurements will be needed to further refine the system parameters in order to improve the future GW searches. Based on the following assumptions, we also carried out simulations to estimate the effect of additional epochs of RV measurements. First, that the uncertainty on T 0 decreases as the total number of observations increases. Second, that the uncertainty on P orb decreases as the total span of the observations increases. We generated 6 epochs of 100 simulated radial velocity measurements in total. We considered each observing epoch to consist of 15 random observations of LT between 2015 and 2025. We have estimated the parameter uncertainties by combining all existing data with the simulated 100 RV measurements. Simulations allow us to improve the uncertainty


dispersion by scaling the dynamical mass to the Einstein radius: A key ingredient in all measurements concerning strong lensing systems is the observed velocity dispersion, which is a projected, luminosity-weighted average of the radially dependent velocity dispersion profile of the lensing galaxy. In order to predict this value based on a set of galaxy parameters, we start with equation ( 6 ). Note that this equation is valid when the relationship between stellar number density and stellar luminosity density is spatially constant, an assumption unlikely to be violated appreciably within the effective radius of the early-type lens galaxies under consideration. Furthermore, the actual observed velocity dispersion is measured over the spectrometer aperture θ ap blurred with atmospheric seeing. In our analysis, we will apply the aperture weighting function provided by Schwab, Bolton Rappaport ( 2010 ) where χ = θ ap /σ atm and σ atm is the seeing recorded by the spectroscopic guide cameras during survey observations. Considering the effects of aperture with atmospheric blurring and luminosity-weighted averaging (see Schwab et al. 2010 for details), the observed velocity dispersion can be expressed as The observed stellar velocity dispersion is a luminosity-weighted average dispersion inside the fibre aperture. Spectroscopic data obtained in different surveys comprise luminosity-averaged line-of-sight velocity dispersions σ ap measured inside different apertures. According to Cao et al. ( 2015 ), whose compilation of lenses is used in this paper, SLACS and BELLS source papers reported effective circular apertures, while for the SL2S and LSD surveys they have been assessed from the sizes of the slit reported in source papers. In our analysis, we take for σ atm the median value recorded by the spectroscopic guide cameras during survey observations. More specifically, we have added 1.4 arcsec seeing for the SLACS spectroscopic observations according to Bolton et al. ( 2008 ) and 1.8 arcsec seeing for the Baryon Oscillation Spectroscopic Survey spectroscopic observations according to Bolton et al. ( 2012 ) and Shu et al. ( 2015 ). For the SL2S sample, the seeing for each individual lens was taken after Sonnenfeld et al. ( 2013b ). In the case of LSD systems: CFRS03-1077, HST14176 and HST15433, the seeing data were taken from Treu Koopmans ( 2004 ). Since they reported two seeing values per lens, we have taken the median. For Q0047−281, we took the seeing value after Koopmans Treu ( 2003 ), but for MG2016+112 we assumed 0.8 arcsec seeing because there was no seeing reported in the source paper. From the above equation, one can immediately observe that there are degeneracies among α, β and δ. More importantly, if we accurately knew the distance ratio D s / D ls , we could get more stringent constraints on the parameters α and δ describing the mass distribution of lensing galaxies. In our analysis, the angular diameter distance D 12 ( z ) between


Bildsten 2010 ), we obtain ejecta mass , kinetic energy erg, magnetic flux density , rotation period ms. Here we have assumed an expansion velocity at peak of 5600 km s from the Fe ii velocity, and opacity . This simple model cannot reproduce the early peak, and therefore we fit only the later epochs; however, including all the epochs provides almost identical parameters. To reproduce the early peak in the magnetar scenario as well, we consider the early light curve as possibly produced by a magnetar-induced shock breakout (SBO), as described by Kasen et al. (2016) . Using the above derived parameters from the fit of the main peak with the magnetar model, we can derive the time of the SBO peak and its luminosity ( Kasen et al. 2016 , their Eqs. (26) and (27)). Both these values are off from what we actually observe in PTF11mnb; we obtained a peak epoch for the SBO of 83 days, with luminosity erg. Therefore, we do not favor a magnetar as the energy source for the early peak. Since a magnetar-induced SBO model does not fit the early light curve of PTF11mnb, whereas the early peak was nicely fit by a Ni decay model (see Sect. 6.2 ), here we also consider a hybrid model in which the first peak is due to a regular SN explosion powered by radioactivity, while the main peak is powered by a magnetar. It is indeed possible to fit the bolometric light curve with a simple Arnett model ( Arnett 1982 ) representing the radioactive component, plus a magnetar model ( Kasen Bildsten 2010 ) that injects energy in the ejecta starting a few days after explosion. This is illustrated in Fig. 11 , where the Arnett model is plotted as a dotted blue line and the magnetar model by a dashed black line. The sum of the two models, shown as a dashed red line, fit the bolometric light curve well. The magnetar injection of energy would have to start after 18 days from the SN explosion, otherwise it is not possible to reproduce both peaks simultaneously. In such a scenario, the SN explosion is characterized by , erg, , and the magnetar by and ms. The magnetar parameters in the hybrid model are different from those of the magnetar-only model previously described. Despite the nice fit of this hybrid model to the bolometric light curve, we still favor a progenitor scenario with the explosion of a massive star and a double-peaked Ni distribution because such a scenario requires a single source of energy and fewer parameters and, in particular, it naturally fits the post main-peak light curve. 6.4. Early light-curve modeling The early -band light curve of PTF11mnb appears rather flat in the first three epochs, which occur merely 4 d after the last non-detection. To reproduce this early emission, we adopted the model by Piro Nakar (2013) for the shock breakout cooling tail, assuming and kinetic energy and ejecta mass from the hydrodynamical fit to the bolometric light curve. We solved for the explosion epoch and for the progenitor radius, obtaining a radius of 90–105 and an


outlines the global expansion of the FR. Later on, this prominence eruption was accompanied by a CME observed by the Large Angle and Spectrometric Coronagraph (LASCO, Brueckner et al. 1995 ) onboard the SOHO satellite and it is given in the SOHO/LASCO CME catalog 1 ( Yashiro et al. 2004 ). The CME first appears in the LASCO C2 field-of-view at ≈15:45 UT. The speed of the CME leading edge is 452 km s −1 so it is less than twice its velocity in the low corona (Fig. 3 b). At the distances observed by C2, the CME was already decelerating with a mean value of deceleration of 12 m s −2 . 2.2. Photospheric magnetic field We analyze the AR magnetic configuration with the HMI magnetic field data. Since the studied eruption was at the limb on 02 March 2015, the photospheric magnetic field is not available. Therefore, we follow its associated AR evolution when it was on the solar disk. AR NOAA 12290 was on the eastern limb on 18 February 2015. One day later, it started to show on the solar disk as a small bipolar AR with a negative leading and a positive following polarity. During its disk crossing, it progressively grew in size as its magnetic field got dispersed by convective motions. Then, AR 12290 was well in its decay phase when it reached the western limb. Figure 4 shows four magnetograms of the longitudinal field component with a time cadence of one day. The AR was located at ≈15° of longitude after the central meridian on 25 February 2015 in panel a, and at ≈55° of longitude on 28 February 2015 in panel d. The magnetograms are spatially de-rotated to the central meridian so that the spatial extension of the polarities could be compared between the different panels. However, we kept the longitudinal component so projection effects appeared as the AR approached the limb (mostly on the limb side of the leading polarity where fake positive polarities appear). The magnetic field mostly disperses with time in each polarity so that the polarities become more extended. Then, the global bipolar magnetic configuration is progressively larger in size, which is expected to induce a global expansion of the coronal loops. This dispersion also implies that the polarities of opposite signs become into contact at the PIL. This induces cancellations of the magnetic flux, for example, at the locations surrounded by a green ellipse in Figs. 4 a and c. These cancellations of flux are best seen in the movie associated with Fig. 4 . Such cancellations imply a progressive buildup of an FR above the PIL ( van Ballegooijen Martens 1989 ; Amari et al. 2003 ; Green et al. 2018 ). Finally, the FR becomes unstable leading to an eruption (Sect. 2.1 ). Fig. 4. Evolution of the AR magnetic field from 25 to 28 February 2015. The locations of the flux cancellation are shown by green ellipses. The location of the H α filament on 28 February 2015 is displayed in panel d with a red contour. A movie of magnetic field during 25−28 is available online . The above analysis shows that


ACKNOWLEDGEMENTS This research was conducted by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. The authors wish to thank E. Zackrisson for useful discussion in the early stages of this work, D. Nicholls for assistance in constructing photoionization models using Modeling And Prediction in PhotoIonized Nebulae and Gasdynamical Shocks (MAPPINGS) V, and the anonymous referee for their comments that have helped to improve the focus, clarity, and relevance of this work. JC acknowledges research support from Australian Research Council Future Fellowship grant FT130101219. TG is grateful to the LABEX Lyon Institute of Origins (ANR-10-LABX-0066) of the Université de Lyon for its financial support within the programme ‘Investissements d’Avenir’ (ANR-11-IDEX-0007) of the French government operated by the National Research Agency (ANR). (Some of) The data presented herein were obtained at the W. M. Keck Observatory (programs 2015A_W012LA, 2015A_W033M, and 2016A_W034LA), which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.


We thank Zara Scoville for proof reading the manuscript and Sue Madden for suggesting use of the SPIRE fluxes for calibration. This paper makes use of the following ALMA data: ADS/JAO.ALMA# 2013.1.00034.S. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00111.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. R.J.I. acknowledges support from ERC in the form of the Advanced Investigator Programme, 321302, COSMICISM.


This work was supported by the Young Investigators Program of the Helmholtz Association, by the Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis”, project C3 and by the European Commision through the ASTERICS Horizon2020 project (id 653477). We would like to thank the H.E.S.S., MAGIC, VERITAS, and FACT collaborations for releasing the data that were used. This work made use of astropy ( Astropy Collaboration 2013 ) and sherpa ( Freeman et al. 2001 ). The authors are indebted to Abelardo Moralejo and Hans Dembinski for their useful suggestions on the statistical and systematic uncertainty estimation. We are grateful to the anonymous referee for improving the paper with helpful comments.


The Submillimeter Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2011.0.00172.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. K.S. was supported by the grant NSC 102-2119-M-001-001-MY3. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities Inc. We thank the anonymous referee for thorough and constructive comments. Facilities : SMA , ALMA .


Acknowledgements We thank G. Ambrazevičiūtė, V. Dobrovolskas, and D. Prakapavičius for their input during the various stages of this project. We also thank V. Vansevičius for the useful comments and discussions. This research was funded by a grant (MIP-089/2015) from the Research Council of Lithuania. HGL and AK acknowledge financial support by the Sonderforschungsbereich SFB 881 “The Milky Way System” (subproject A4) of the German Research Foundation (DFG). HGL and MS acknowledge funding from the Research Council of Lithuania for the research visits to Vilnius.


at +1542d (Stritzinger et al. 2012 ), and SN 2010jl at +461 day and +573 day (Fransson et al. 2014 ). Other coronal lines detected in SN 2017ens are similar to those seen in SN 2010jl (Figure 3 , bottom panel): [Fe x ] λ 6374.5 is strong, as are [Fe xi ] λ 7891.8, [Ne v ] λλ 3345.8, 3425.9, [Ca v ] λ 6086.8, and [Ar x ] λ 5533.2. The presence of these lines is indicative of a highly ionized and dense CSM, although we do not detect the highest-ionization coronal lines such as [Fe xiv ] λ 5302.9 and [Ar xiv ] λ 4412.3, which were seen in SN 2005ip. The flux ratio of the [O iii ] λ 4363 to λ 5007 lines is a function of the CSM density and temperature. Following Fransson et al. ( 2014 , their Figure 26), we use our measured flux ratio, log( λ 4363/ λ 5007) = −0.22, to constrain the CSM electron density to lie between 10 6 and 10 8 cm −3 for T e = 50,000 to 10,000 K. This density range is consistent with that observed for SN 2010jl. From our mid-resolution X-Shooter data, we resolve narrow P-Cygni profiles on top of the ∼2000 km s −1 wide Balmer and Paschen lines. We measure the blueshifted wavelength from the absorption component of the H γ , H β , and H α P-Cygni profiles, which suggests that the unshocked CSM has a low velocity of ∼50 km s −1 . A similar velocity of ∼60 km s −1 is obtained from the P-Cygni profile of the He i λ 10,830 line. Moreover, we measure the FWHM intensity of the wide components, such as H α (2500 ± 700 km s −1 ), H β (2300 ± 400 km s −1 ), Pa γ (2000 ± 200 km s −1 ), and He i λ 10,830 (2200 ± 200 km s −1 ). We also detect narrow absorption lines from the Balmer series (no clear emission), spanning H to H33 (3659 Å). In addition, we see emission from the H ii region close to the host-galaxy center (see Figure 4 , marked B1), as part of a faint galaxy (SDSS J120409.47–015552.4) with g = 21.92 ± 0.24 mag ( M g ≈ −16.5 mag). These lines have a slightly different redshift ( z = 0.1084) than SN 2017ens. In particular, the (noisy) detection of the weak auroral [O iii ] λ 4363 line indicates a low host-galaxy metallicity of ∼0.04–0.4 Z ⊙ using the direct T e -based method. If we instead use the empirical N2 metallicity diagnostic (Pettini Pagel 2004 ), we measure Z = 0.3 ± 0.2 Z ⊙ 4. Bolometric Light Curve and Model Fitting Using all of our available UV-through-NIR photometry, we built a pseudobolometric light curve for SN 2017ens using the prescription from Inserra et al. ( 2018b ). The results are very similar to those derived when using a blackbody fit, as expected as our photometry covers a large wavelength range. From a polynomial fit to the bolometric data we obtain L bol = (5.86 ± 0.20) × 10 43 erg s −1 at peak and an integrated energy of (3.53 ± 1.42) × 10 50 erg. To fit our bolometric light curve, we used a two-component model consisting of a central heating and an interaction component. First, the centrally heated component uses the standard Arnett method (Arnett 1982 ; Inserra et al. 2013 ). We tested three possible


but relatively insensitive to temperature. From fig. 1 of Keenan et al. ( 1995 ), the observed λ1483.3/λ1486.5 ratio corresponds to an electron density of (2.0–3.6) × 10 5 cm −3 in the temperature range (1–2) × 10 4 K. We conclude that the CSM density should be safely lower than the critical densities of the semiforbidden lines. 3.2.4 Line profiles Fig. 7 compares the velocity profile of several UV and optical lines at ∼3100 d. The Hα line extends to ∼2600 km s −1 on the blue side and only ∼1000 km s −1 on the red. Hβ, He i λ5876, and Mg ii λλ2796, 2803 have similar line profiles. While the blue side can be well fitted with an electron-scattering wing, the line centroid is shifted to the blue, with a ‘shoulder’ at ∼−800 km s −1 . As shown by Taddia et al. ( 2020 ) and Dessart et al. ( 2015 ), this type of profile can be explained by a combination of emission from the shock wave and emission from the pre-shock CSM. The emission from the shock is responsible for the blueshifted shoulder, while the central component is coming from pre-ionized gas in the CSM. Both components are affected by electron scattering in the low-velocity CSM. This gives rise to the smooth, blue wing shortward of the ‘shoulder.’ Because of the smoothing by the electron scattering, the velocity of the ‘shoulder’ is lower than the shock velocity. Figure 7. Line profiles of Hα (black), Hβ (green), He i λ5876 (red), Lyα (magenta), and N iv ] λλ1483.3, 1486.5 (blue). Note the narrower line profile of the N iv ] lines, as well as the contribution of the semiforbidden λ1483.3 component on the blue side. Both Hβ and He i λ5876 have line profiles similar to that of Hα, as expected if these arise from similar regions (Fig. 7 ). The Lyα line has a profile broadly consistent with Hα, although strongly distorted by a central absorption feature from both the Galactic and host-galaxy ISM. The C iii ], N iii ], N iv ], and N v UV lines are considerably narrower than Hα, and consistent with being unresolved at the ∼300 km s −1 instrumental resolution of STIS. These lines do not show the velocity shift of the Balmer and He i lines, and most likely come from the same optically thin, highly ionized region of the CSM as the high-ionization optical lines. 3.2.5 Spectral evolution The optical spectra in this paper span one of the longest periods of any SN. Fig. 8 highlights only a very slow evolution of the spectrum over three epochs: 736, 3099, and 5105 d. Unlike most other SNe, there is no obvious transition to a nebular spectrum even at this extremely late stage. However, the line ratios do exhibit some interesting quantitative changes that are consistent with the shock wave propagating into a medium of decreasing density. Figure 8. Evolution of the optical spectrum from day 736 to 5105 with identification of the high-ionization lines at the top and colour coding and identification of the low-ionization lines for the day 3099 spectrum. We start with a look at some with the most


The centres of the two groups projected on the sky are not very different. Given the low density and extended nature of the foreground population, it is possible that it extends beyond our field of view. The statistical parameters obtained for each group distribution ( d N and σ N of Model 2, see Table 1 ) suggest that the two groups are spatially separate. We consider the larger population to be the main Cygnus OB2 association (Group 2) and consider the foreground population to be a separate group approximately ∼400 pc in the foreground (Group 1). The distance of the foreground group of ∼1350 pc puts it at a similar distance to Cygnus-X as a whole (see Rygl et al. 2012 ) suggesting that the main part of Cygnus OB2 is actually behind Cygnus-X by several hundred parsecs (though the line-of-sight depth of Cygnus-X is not well constrained). Consequently, the main group is more distant than previously thought, and therefore its stellar content will both be more luminous (approximately 1.5 times more luminous compared to the estimates in Wright et al. 2015 ) and more massive. Interestingly, this puts the distance to the main part of Cygnus OB2 closer to that originally derived by Massey Thompson ( 1991 ). 5.2 The foreground group We have identified 19 stars in the foreground group ( of the sample), seven of them classified as O-type stars. The bright BD + 40 4212 double system ( G = 9.39 mag) is included in this group, as well as the star HD 195213 ( G = 8.38 mag). This group includes approximately 10 of the total population of O-type stars in Cygnus OB2, and thus its total mass can be estimated as a similar fraction of the total mass of 16 500 M ⊙ estimated by Wright et al. ( 2015 ), i.e. 1650 M ⊙ , similar to that of the Orion Nebula Cluster (although according to our results, the estimation by Wright et al. 2015 will have to be corrected upwards). We note that the foreground group is appreciably more dispersed on the sky than the main group. The proper motions also suggest it to be more diffuse and less likely to be a bound group. This could suggest that it is a part of older foreground population that extends further outside our field of view. However, a detailed study of the physical properties of its stellar content is needed to establish the most probable scenario. 5.3 Potential contaminants Here, we discuss the sources identified as probable foreground or background contaminants (Group 3) and not part of either the main Cyg OB2 population or the foreground group. Foreground contaminants : HD 196305 is a very luminous star and has a parallax that places it at a distance of pc, in agreement with previous studies that suggest it to be a foreground contaminant (Chentsov et al. 2013 ). CCDMJ20323 + 4152AB has been reported as a visual double star by Gili Bonneau ( 2001 ) and therefore its binary nature could be affecting the parallax. MT91-426 and MT-170 also appear as foreground sources, despite the fact that Wright et al. ( 2015 ) proposed them


and a global alignment to an absolute reference frame, here defined by the Gaia DR2 catalogue (Gaia Collaboration 2018 ). As there are no telescope offsets between the paired F 814 W direct and G800L grism exposures, the alignment of the former is applied directly to the latter. Finally, we combine the F 814 W direct exposures into a rectified mosaic with astrodrizzle at a final sampling of arcsec pix −1 and generate a source catalogue and segmentation map from this mosaic using the sep software package. 9 The global contamination model for a given field is constructed in two passes. In the first, we assume that each object has a spectrum flat in units of f λ flux density with a normalization set by its observed flux in the F 814 W direct image (integrated over the object’s segmentation polygon). In the second pass, we step through objects in the catalogue sorted by increasing magnitude and compute a third-order polynomial fit to each spectrum after subtracting the contamination model from any neighbours (i.e. either the flat spectra of fainter sources or the polynomial models of brighter sources). This refinement is iterated three times. In both passes, the spatial template of the dispersed 2D model spectra is taken from the observed F 814 W cut-out within an object’s segmentation polygon. Note that this approach neglects morphological variation across the G800L bandpass; e.g. the PSF, continuum colour gradients, and morphological differences between line- and continuum-emitting regions. However, it provides a spatial model with much higher fidelity than any simple parametrized representation (e.g. Gaussian or Sérsic approximations). The above process automatically yields a source and contamination model for each galaxy detected in the reference image. These can be used to ‘decontaminate’ source spectra – i.e. remove overlapping light from neighbouring sources – to provide more accurate line and continuum characterizations. This was critical in GLASS’ crowded cluster G102 and G141 pointings, but the ACS parallels are sparse enough that source contamination is often negligible (Section 3 ). Note that no spectra are modelled or extracted for objects not detected in the reference frame. As such, the catalogue presented here is truncated at a F 814 W flux of m 814 = 26 with various subsamples defined by brighter flux limits (Section 3 ). There are two main differences between grizli and the previous GLASS pipeline. First, grizli automatically handles HST grism spectra taken at one or more telescope roll angles that translate into different spectral dispersion PAs. This is implemented in that the 2D spectral models are computed in the pixel space of each separate grism exposure. This is the space in which the field-dependent grism dispersion configuration is measured and defined (e.g. Kümmel et al. 2009 ). This approach allows for evaluating model fits in the space of the original detector pixels with a well-calibrated noise model. Recent works


) assigned both peaks to transpolyacetylene at grain boundaries and surfaces of nanodiamonds. The weak peak at ∼1020 cm −1 along with the broad feature centered at ∼1270 cm −1 are also associated with nanodiamonds (Veres et al. 2007 ; May et al. 2008 ), and the broad doublet at ∼1800 and 1900 cm −1 may be due to overtones and/or combinations. In the 2000–4000 cm −1 region (not shown), the Raman spectra show broad CH-stretching peaks centered at ∼2900 cm −1 in addition to the second-order peaks of nanodiamonds and graphitic/amorphous carbon. Figure 2. Reflected-light image from the KBr pellet of the Kapoeta carbonaceous-chondrite material showing a cluster of diamond particles. Figure 3. Raman spectra of the Kapoeta nanodiamonds (see the text for details). Thus, in the cluster of particles shown in Figure 2 , each particle may be considered as an aggregate of nanodiamonds. To estimate the sizes of these nanodiamonds from their Raman spectra, we consider the particle-size dependence of the position and FWHM of the 1333 cm −1 diamond peak (Yoshikawa et al. 1993 ). Based on the analyses of 26 Raman spectra, the average diamond peak position and FWHM are 1332.6 cm −1 ( σ = 0.3 cm −1 ) and 4.0 cm −1 ( σ = 0.6 cm −1 ), respectively, suggesting a particle size of ≥50 nm for the Kapoeta diamonds that show the Raman diamond peak in their spectra. However, some spectra do not show the Raman diamond peak at 1333 cm −1 , but they display all the other peaks shown in Figure 3 (a), including the peaks and features associated with nanodiamonds at ∼1020, 1150, 1450 cm −1 , and ∼1270 cm −1 (Figure 3 (b)). The spectrum shown in Figure 3 (b) is very similar to the Raman spectra of ultrananocrystalline diamonds having an average particle size of ∼3–5 nm, i.e., single-digit nanodiamonds, where the 1333 cm −1 diamond peak is absent and they show the D- and G-bands along with the 1150 and 1450 cm −1 peaks (Popov et al. 2004 ). One difference is that the G-band in their spectra occur at a lower wavenumber (1560 cm −1 ), possibly due to differences in sp 2 /sp 3 ratios in the amorphous carbon phase. Thus, our Raman spectroscopic data suggest the existence of two populations of nanodiamonds in the Kapoeta CC material: nanodiamonds that are ≥50 nm and single-digit nanodiamonds. 3.3. Micro-FTIR Spectroscopy Figure 4 (a) shows a typical micro-FTIR spectrum taken from the diamond-enriched region (spot size is ∼70 μ m). It is dramatically different from the KBr-pellet spectrum (Figure 1 ) both in the high- and low-frequency sides. The position of the absorption bands and their possible assignments are listed in Table 1 . Figure 4. (a) Micro-FTIR spectrum collected from the diamond-enriched region in the KBr pellet of the Kapoeta carbonaceous-chondrite material (see the text for details). (b) Expanded view of the C–H stretch region. Table 1 Assignment of IR Bands in FTIR Spectrum of the Diamond-enriched Region of the Kapoeta CC Material 3.3.1. C–H Stretching Bands The C–H


We thank the anonymous referee for a critical reading of the paper and valuable suggestions that helped improve the quality and clarity of this work. We also thank Charles Dermer, Tyce DeYoung, Markus Bötcher, and Bin Zhang for useful discussions.


f.u., is obtained from the 16th and 84th percentiles. The lognormal parameters, μ and σ , can be calculated from the recommended (median) rate ( x med = e μ ) and the factor uncertainty ( f.u . = e σ ; for a coverage probability of 68%). The rate factor uncertainty is 1.1% for both reactions at most temperatures. Figure 6. Reaction rate probability density of d(d, n) 3 He at different temperatures. The rate samples (red histograms) are computed using the S -factor samples obtained from the Bayesian analysis. Blue curves represent lognormal approximations, where the lognormal parameters μ (“mu”) and σ (“sig”) are directly calculated from the expectation value and variance of all rate samples, ln(), at a given temperature. T9 is the temperature in GK. Figure 7. Reaction rate probability density of d(d, p) 3 H at different temperatures. The rate samples (red histograms) are computed using the S -factor samples obtained from the Bayesian analysis. Blue curves represent lognormal approximations, where the lognormal parameters μ (“mu”) and σ (“sig”) are directly calculated from the expectation value and variance of all rate samples, ln(), at a given temperature. T9 is the temperature in GK. Table 3 Present Recommended Reaction Rates a Note. a Reaction rates in units of cm 3 mol −1 s −1 , corresponding to the 50th percentile of the rate probability density function. The rate factor uncertainty, f.u., is obtained from the 16th and 84th percentiles (see the text). The parameters μ and σ of the lognormal approximation to the reaction rate are given by x med = e μ and f.u . = e σ , respectively, where x med denotes the median rate. Values for T > 2 GK, shown in italics, are adopted from Coc et al. ( 2015 ). The present rates for d(d, n) 3 He and d(d, p) 3 H agree with the results of Coc et al. ( 2015 ) within 1% at most temperatures. However, our rates are more than 15% larger than those of Coc et al. ( 2015 ) at very low temperatures (near 1 MK). This is caused by a low-energy cutoff that is too high for the numerical integration of the rates in the previous analysis. The theoretical model of Arai et al. ( 2011 ) only applies to low energies, thus we can derive Bayesian reaction rates only up to a temperature of 2 GK. The results in Table 3 for higher temperatures, shown in italics, are adopted from Coc et al. ( 2015 ). The most important temperatures for BBN are near 1 GK, corresponding to an effective kinetic energy range of 250 keV for the d+d reactions. The last step is to calculate the effect of the new reaction rates on the predicted primordial D/H ratio. The Bayesian mean value for the scale factor is larger by 0.21% for d(d, n) 3 He and larger by 0.12% for d(d, p) 3 H compared to Coc et al. ( 2015 ). The discrepancies of both reaction rates (0.21% and 0.12%, respectively), weighted by the sensitivity of the D/H abundance ratio to each reaction rate variation (−0.54 and −0.46, respectively; Coc Vangioni 2010 ), result in a 0.113% and


with the SPT cluster cosmology analysis of Bocquet et al. ( 2015 ), when it is combined with the first release of the Planck primary CMB cosmology results (Planck Collaboration et al. 2014 ). The combination of the velocity-dispersion-based MOR normalization constraints of Bocquet et al. ( 2015 ) with CMB data leads to a shift in Ω m – σ 8 orthogonal to the cluster SPT cosmology constraints on these parameters. As a result the normalization of the mass–SZE MOR shifts accordingly, to account for the implied different cluster mass scale, leading to the difference seen in Fig. 9 . For a quantitative comparison, we follow Bocquet et al. ( 2015 ) to compute the significance of the difference of two distributions. We randomly draw points from two distributions and compute the difference δ between pairs of points. We use this to estimate the probability distribution P δ of these differences and compute the likelihood that zero is within this distribution. Assuming a normal distribution, this likelihood is then converted to a significance. The lower normalization parameter A SZ , corresponding to higher cluster masses, inferred from a joint cosmological analysis of the SPT cluster sample and Planck CMB data sets (Bocquet et al. 2015 ) disagrees with our result at the 2.6 σ level. We emphasize that the change in normalization in Bocquet et al. ( 2015 , yellow to cyan line in Fig. 9 ) when the underlying cosmology shifts is caused by the self-calibration of the scaling relations from cluster number counts. In this work, we adopt a cosmology with Ω m , σ 8 and H 0 close to the results of Bocquet et al. ( 2015 ) without the Planck data added. Because we use the cluster mass function only for Eddington bias correction and not for self-calibration of the MOR, small changes in the cosmological parameters do not have any big impact on our recovered normalization A SZ . In particular, changing the cosmological parameters to the ones obtained from SPT clusters with Planck data (Bocquet et al. 2015 ) changes our normalization by less than 1 per cent and does not bring it into better agreement with their lower A SZ value. Also used in the SPT–SZ cosmology analysis is the mass– Y X scaling relation. As for the mass– ζ relation, our marginalized posterior for the normalization is in very good agreement with the prior utilized in the cosmology analysis (de Haan et al. 2016 ). This is an important result, as the prior was based on an external calibration of the normalization of the mass– Y X scaling relation, namely the Vikhlinin et al. ( 2009 ) scaling relation updated with the weak lensing mass calibration of the Weighing the Giants (WtG) and CCCP projects (von der Linden et al. 2014 ; Applegate et al. 2014 ; Hoekstra et al. 2015 ). We are now able to confirm that these priors were appropriate for cosmology analysis based on an internal calibration . Fig. 10 shows a comparison of the marginalized and joint posterior probability distributions for the normalizations A


We thank Alessandro Manzotti, Ariel Goobar, Mike Hudson, WeiKang Zheng, Bill Januszewski, Robert Kirshner, Licia Verde, Liz Humphreys, Dan Shafer, the PHAT collaboration, and Peter Stetson for valuable discussions and contributions, as well as Doug Welch for providing the optimal temporal spacings used in the optical Cepheid searches. We are grateful to Melissa L. Graham for her assistance with the Keck observations. This research was supported by NASA/ HST grants GO-12879, 12880, 13334, 13335 13344 from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. L.M.M.'s group at Texas AM University acknowledges additional support from the Mitchell Institute for Fundamental Physics Astronomy. D.S. acknowledges support from the Kavli Institute for Cosmological Physics at the University of Chicago through grant NSF PHY-1125897 and an endowment from the Kavli Foundation and its founder, Fred Kavli. A.V.F.'s group at UC Berkeley is also grateful for financial assistance from NSF grant AST-1211916, the TABASGO Foundation, Gary and Cynthia Bengier, and the Christopher R. Redlich Fund. P.C. is supported by NSF grant AST1516854 to the Harvard College Observatory. J.M.S. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under under award AST-1302771. R.J.F. gratefully acknowledges support from the Alfred P. Sloan Foundation. Some of the results presented here are based on data obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and NASA; the observatory was made possible by the generous financial support of the W. M. Keck Foundation. We thank the Keck staff for their expert help.


a list of new associations in the Galaxy. Clustering depends on the choice of length-scale and groups begin to merge for larger length-scales. In this scenario, although the presence of different groups and associations is ascertained from the results of their cluster analysis using an independent method, their boundaries are not well defined. This, consequently, affects the membership assignment and identification of parent associations for those stars located at the association boundaries. Here, we investigate the properties of the well-known association Cyg OB3. We primarily focus on the distances, proper motions, and three-dimensional velocity components of the members. We study a particular case of a confirmed black hole X-ray binary Cyg X-1 (otherwise known as HD 226868; Bolton 1972 ; Webster Murdin 1972 ) located at the association boundary (Fig. 1 ). Cyg X-1 is one of the brightest X-ray binaries in the X-ray sky and is composed of a black hole of 14.8 ± 1.0 M ⊙ (Orosz et al. 2011 ) and a companion of 19.2 ± 1.9 M ⊙ (Orosz et al. 2011 ) of spectral type O9.7Iab (Walborn 1973 ). Cyg X-1 has been included in the catalogues of Humphreys ( 1978 ), Garmany Stencel ( 1992 ), Ruprecht et al. ( 1981 ), and Blaha Humphreys ( 1989 ) as a member of Cyg OB3. The cluster analysis algorithm implemented by Mel’Nik Efremov ( 1995 ) divided the association Cyg OB3 into two subgroups for a length-scale of 15 pc viz. Cyg 3A and Cyg 3B; however, Cyg X-1 was not included in either of the subgroups and remained as a field star. Figure 1. Image obtained with DSS2 in the R band shows the Cyg OB3 region. The positions of the O- and B-type stars studied in this work are encircled. Cyg X-1 is encircled in red. There are a total of 41 stars shown in the image and given in Table 1 . Mirabel Rodrigues ( 2003 ) studied the connection between Cyg X-1 and the association Cyg OB3 using the astrometry from the Hipparcos mission. The authors suggested that the parent association of Cyg X-1 is indeed Cyg OB3, and showed that the binary is located at the same distance as the other stars in Cyg OB3 and exhibits a proper motion similar to that of the association. The authors also showed that the peculiar velocity of Cyg X-1 is very small (9 ± 2 km s −1 ) with respect to the association, and is consistent with the expected random velocities of stars in expanding associations. These results indicate that Cyg X-1 is most likely a member of Cyg OB3. This was later confirmed with precise measurement of the trigonometric parallax and the proper motion of Cyg X-1 using VLBI radio observations (Reid et al. 2011 ). In this paper, we revisit the proposed connection between Cyg X-1 and Cyg OB3 using the astrometric data from Gaia DR2, which provides trigonometric parallaxes and proper motions for over 1 billion stars in the Galaxy with the best accuracy and precision available to date (Gaia Collaboration 2018 ). We define the sample of stars and their filtering criteria in the next


The development of HIPPI was funded by the Australian Research Council through Discovery Projects grant DP140100121 and by the UNSW Faculty of Science through its Faculty Research Grants program. JPM is supported by a UNSW Vice-Chancellor's Fellowship. The authors thank the Director and staff of the Australian Astronomical Observatory for their advice and support with interfacing HIPPI to the AAT and during the three observing runs on the telescope. The authors wish to thank referee Hans Martin Schmid for constructive criticism of the Letter.


we find κ = 2 − 835.6440/582.143 23 = 0.564 5388. With these values of κ for both stars and a representative range for the neutron star radius (see Section 2.1 ), we can solve for M in equation ( 1 ), including the κ corr term from equation ( 3 ), the rotational correction κ 2 as given by Fig. 2 and a differential rotation term κ 3 consistent with a realistic differential rotation with Δ f ≳ 1 Hz inside the star. In Fig. 3 , we present our results for uniformly and differentially rotating models, and compare with the fit given by equation ( 5 ) of Andersson et al. ( 2014 ). Our results for uniformly rotating stars are ≈ 15 per cent lower than the estimate given by Andersson et al. ( 2014 ), where the fully relativistic r-mode frequency for slowly rotating uniform density stars from Lockitch et al. ( 2003 ) was combined with the second-order rotational corrections for Newtonian polytropes from Lindblom et al. ( 1999 ). As we mentioned in Section 3.3 , the main factor for this difference is our improved calculation of the rotational correction κ 2 (see Fig. 2 ). Differential rotation increases our mass estimate, but the effect is rather small: Δ f = 10 Hz increases the total mass by only ≈0.02 M ⊙ . In the range 11–13 km for the stellar radius, we find from our results 1.41–1.71 M ⊙ for XTE J1751−305 and 1.53–1.87 M ⊙ for 4U 1636−536. We can use the transversal curves in Fig. 3 , which indicate M ( R ) for a choice of three realistic tabulated equations of state (APR4 Akmal, Pandharipande Ravenhall 1998 , SLy Douchin Haensel 2001 and H4 Lackey, Nayyar Owen 2006 ) to gain more insight. Each equation of state crosses our M ( r ) line only once, providing a unique prediction for M and R . However, we can see that H 4 requires R > 13 km for both stars and is therefore less favoured according to current expectations for the neutron star radius. Combining the constraints from both the r-mode frequencies and the APR4 and SLy equations of state, we find a more restricted range of values for M and R : R ≈ 11.5–12 km and M ≈ 1.48–1.56 M ⊙ for XTE J1751−305 and R ≈ 11.4–11.9 km and M ≈ 1.60–1.68 M ⊙ for 4U 1636−536. However, it must be stressed here that these tight constraints are only valid if the frequencies reported in Strohmayer Mahmoodifar ( 2014a , b ) are indeed core r-mode frequencies (see the discussion in Section 1 ). 5 CONCLUSIONS Here, we have explored the consequences for the mass and radius estimates of these two stars if the frequencies detected by Mahmoodifar and Strohmayer really correspond to their r-modes. We used a careful calculation of the r-mode frequency, taking into account relativistic, rotational, and differential rotation corrections, as well as an estimate for the error due to the Cowling approximation, to find improved constraints on the compactness M / R of XTE J1751−305 and 4U 1636−536. Our results yield, for a representative radius of 12 km, masses of 1.55 M ⊙ and 1.70 M ⊙ , respectively. For XTE J1751−305, our value is


We thank the referee for a careful reading of the manuscript and for suggesting the inclusion of Table 2 . I.D. is supported by the Harvard College Observatory Menzel Fellowship and the Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship (NSERC PDF-421224-2012). The Smithsonian Institution supports the research of M.J.G. H.J.Z. gratefully acknowledges the generous support of the Clay Fellowship.


We thank an anonymous referee for helpful comments that improve the presentation of this paper. This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2017R1D1A1B03035359). We thank Attila Juhasz for help with radmc3dPy and J.-Y. Seok for discussion. One of the authors (Q.L.N.) was supported by the U.S. Department of Energy under Nuclear Theory grant DE-FG02-95-ER40934 and in part by the National Science Foundation under grant no. PHY-1430152 (JINA Center for the Evolution of the Elements).


The first author is grateful to Prof. Lynne Hillenbrand (Caltech) and Ben Zuckerman (UCLA) for their advice and comments. T.J.D is supported by the National Science Foundation Graduate Research Fellowship under grant No. DGE1144469. M.B. is supported by a NASA Space Technology Research fellowship, grant No. NNX13AN42H. This publication makes use of data products from the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. Facility: Very Large Telescope.


5 per cent lower). This can be attributed to the same reason EPICal underperformed with the full sky model: EPICal only used a single pixel in the sky to form its solutions. When using the full sky model, it was under utilizing the information in the rest of the sky. But when the sky model only contains the bright point source, the single pixel used in EPICal is the location where this model is most accurate, effectively downweighting the pixels with incomplete sky model. In contrast, the visibility-based calibration method does not have any preference to a certain direction, and weights the sky equally (beyond the inherent response of the instrument). This results in a more harsh penalty for missing sources in the model. In the regime typical of HERA discussed above, EPICal's error floor was 10 per cent lower than that of visibility-based solutions. Any realistic sky model will lie somewhere between the two extremes explored here. In the case where the sky is modelled by a single source, as is often the case for an initial calibration, EPICal actually achieves smaller gain errors compared to the traditional visibility-based calibration. As the sky model improves, both visibility-based and EPICal gains improve, though the former quicker than the latter. In the limit of a perfect model, both calibration methods produce noise-like errors in their gains which scale down with more integration time. Comparing the two regimes of receiver noise levels (red versus black lines in Fig. 8 ), we see that the gains errors behave as one might expect given our above interpretations. In the case of a perfect sky model, the higher receiver noise results in larger gain errors for a given integration time, but follows the same t −1/2 noise trend. The imperfect model lines begin exhibit the same offset at low integration time because the errors are dominated by the thermal noise. But as integration time increases, they approach the same floor due to the same level of power from unmodelled sources. 5 APPLICATION TO LWA DATA We next demonstrate our calibration algorithm using an observation from the LWA station in New Mexico (Ellingson et al. 2013 ). The data are from the LWA narrow-band transient buffer, with time-domain voltage data from the 255 core antennas within a 110 m × 100 m ellipse. The central frequency is 74.03 MHz, with a total observing bandwidth of 100 kHz, analysed by a filter bank into 512 channels of bandwidth 195.3125 Hz each and with a post-channelization sampling period of 5.12 ms. For this demonstration, we limit ourselves to a single polarization. For the gridding step, we adopt a grid with extent 120 m to encompass the footprint of the array, and a grid spacing of half-wavelength (≈2 m). The result is a 64 × 64 pixel grid, making the MOFF computational complexity of N g log 2 N g on the same order as the N a 2 computations for a cross-correlator. Our gridding kernel is a square tophat function with sides of 3 m, corresponding to the size of


The authors thank K. Bowen, P.-M. Hillenbrand, J. Liévin, D. C. Lis, O. Novotný, I. R. Sims, and A. Viggiano for stimulating discussions. D.W.S. and R.G.B. were supported in part by the NASA Astrophysics Research and Analysis (APRA) program and the NSF Division of Astronomical Sciences Astronomy and Astrophysics Grants (AAG) program. S.V. was supported in part by a Barry Goldwater Scholarship and the USRA James B. Willett Educational Memorial Scholarship Award. X.U. is a Senior Research Associate of the Fonds de la Recherche Scientifique-FNRS.


ACKNOWLEDGEMENTS We thank Frank van den Bosch and the anonymous referee for constructive comments on the paper. The simulations analysed in this paper were run on the Harvard Odyssey and CfA/ITC clusters, the Ranger and Stampede supercomputers at the Texas Advanced Computing Center as part of XSEDE, the Kraken supercomputer at Oak Ridge National Laboratory as part of XSEDE, the CURIE supercomputer at CEA/France as part of PRACE project RA0844, and the SuperMUC computer at the Leibniz Computing Centre, Germany, as part of project pr85je. KTEC acknowledges support from the Singapore National Science Scholarship.


of the H 2 ortho to para spin ratio (OPR), the correlations and anticorrelations between D and 15 N found in primitive materials (van Kooten et al. 2017 ). In this paper, we report calculations that re-evaluate those of W12 in light of the revised R15 fractionation chemistry. This study is necessary because, apart from the rate coefficients adopted, there exist significant differences in the physics and chemistry of the models of W12 and R15 . The calculations of W12 considered densities similar to pre-stellar core conditions, about 10–100 times greater than those of R15 , which were based on a standard dark cloud similar to TMC-1 ( n H = 0.2–2× 10 5 cm −3 ). In contrast to W12 , R15 neither included the depletion of gas-phase species by freeze-out on to dust grains, nor the time-dependence in the H 2 OPR – both effects which have been shown to have substantial impact on the relative fractionation in nitriles and amines (here nitrogen hydrides and chemical derivatives thereof) over time ( W12 ). Furthermore, even when reactions ( 1 )–( 3 ) are not operating, there are alternative fractionation processes that could still become important in the physical conditions of the W12 model. For example, W12 identified as playing a role in the fractionation of N 2 and NH 3 , whereas Rodgers Charnley ( 2008a ) speculated that the neutral–neutral pathway could be viable and efficient if there is a barrier to the reaction However, the reaction appears to have no formation barrier, and the recommended reaction rate from the KIDA data base (Wakelam et al. 2013 , http://kida.obs.u-bordeaux1.fr ) of k (T) = 8.8 × 10 −11 (T/300) 0.42 cm 3 molecule −1 s −1 agrees with laboratory measurements down to 56 K (Daranlot et al. 2012 ). W12 assumed a 200 K barrier for reaction ( 6 ) (see Le Teuff, Millar Markwick 2000 ) and R15 found that isotopic exchange through ( 5 ) was indeed plausible, a process not considered in W12 . We have therefore updated the W12 model with respect to the revisions to nitrogen fractionation advocated by R15 , and specific reactions in the nitrogen chemistry from the KIDA data base (Wakelam et al. 2013 ). The paper is structured as follows. Revisions to the 14 N fractionation chemistry and the updates to specific reactions in the nitrogen chemistry are described in Section 2 . The chemical model is summarized in Section 3 . Results from the model calculations are given in Section 4 where they are compared to previous work. Conclusions from this study are in Section 5 . 2 REVISED CHEMICAL NETWORK 2.1 15N fractionation Table 1 lists the major reactions potentially leading to 15 N fractionation in cold, dark clouds, both ion–molecule and neutral–neutral reactions, with ZPE differences, Δ E , as determined by R15 . The resulting equilibrium coefficient, K ( T ) = k f / k r , the ratio between the forward and reverse reaction rate (e.g. Terzieva Herbst 2000 ), is listed for each reaction at 10 K, both for the new fractionation


photons using 30′′ and 18 radius circular apertures, respectively. Table 1 Log of X-Ray Timing Observations of 1E 1207.4−5209 Notes. a Exposure times for XMM-Newton EPIC-pn does not reflect the 29% deadtime in the SmallWindow (sw) mode. b Newly reported observations. The NICER data sets are denoted by the ObsID and date of the first of the concatenated set of observations (see Section 2 for details). Table 2 Single Glitch Ephemerides for 1E 1207.4−5209 Notes. Derived parameters ( B s , , τ c ) are based on the pre-glitch timing solution. a Uncertainties in the last digits are given in parentheses. b Epoch of the glitch estimated by matching the zero phase of the two timing solutions; this assumes a constant post-glitch . The NICER (Gendreau Arzoumanian 2017 ) is an X-ray telescope attached to the International Space Station that provides submicrosecond time resolution in the 0.2–12 keV band. The NICER telescope consists of a set of 52 operational non-imaging silicon drift detectors (Prigozhin et al. 2016 ), each at the focus of an X-ray concentrator (Okajima et al. 2016 ) that subtends a 4′ radius field of view. The nominal effective area of the telescope is 1900 cm 2 at 1.5 keV. The NICER data sets were reduced and analyzed using the NICERDAD software suite distributed in the FTOOLS package, version 24Jun2019_V6.26.1, and the most up-to-date calibration files. We generated cleaned event files using the nicerl2 script that applied the standard filtering criteria. The data were further reduced by excluding detectors with anomalous count rates >5 σ above the mean rate, computed using all available detectors, in the energy range of interest. Similarly, we iteratively excluded time intervals with high background rates by comparing the event rate in 10 s steps to the mean rate. Since its launch in 2017, NICER has observed 1E 1207.4−5209 a total of 148 times to-date. Each observation is defined by a unique ObsID number and typically comprises short exposures (50% are less than 1.2 ks) spread over multiple satellite orbits, and often containing multi-day gaps within, and between. These exposures are generally too short to generate a precise pulse-phase measurement needed for our timing analysis. However, by concatenating adjacent observations we obtained eight sufficiently compact NICER data sets that contained the minimum number of events required to measure an independent pulse phase, as described below. 3. Timing Analysis For each reprocessed, cleaned event file, we folded the extracted photon arrival times on the pulsar frequency to compute the time-of-arrival (ToA) of phase zero of the pulse. To attempt a phase-connected timing solution, we fit the set of ToAs using the TEMPO software (Hobbs et al. 2006 ) to a model for the rotation phase of the pulsar including one or two of its frequency derivatives,Initially, we obtained the fold frequency from a periodogram search for the maximum power around the expected frequency, determined from the


CO is determined from the CO (2 − 1) emission by assuming a luminosity ratio of and α CO . Using the and 3σ for non-detections from table 6 in Boogaard et al. ( 2020 ), we estimate the α [C i ](1 − 0) with both molecular gas masses, and present in Table 6 and Fig. 8 with the coloured diamonds. Figure 8. α [C i ](1 − 0) as a function of metallicity. The black and red symbols represent each detections and 0.1 bin values for our sample. The blue boxes represent samples of high-redshift ( z = 1.9−3.4) gamma-ray burst and quasar molecular gas absorbers from Heintz Watson ( 2020 ) with , and the black line shows their best-fitting linear relation of logα [C i ](1 − 0) = −1.13 × log(Z/Z ⊙ ) + 1.33. The orange and green diamonds are ASPECS galaxies from Boogaard et al. ( 2020 ) with molecular gas masses estimated based on CO luminosity and 1.2 mm dust-continuum emission on the Rayleigh–Jeans tail, respectively. Table 6. The adopted redshift, molecular gas mass, [C i ] (1 − 0) luminosity, metallicity, and derived α [C i ](1 − 0) for the ASPECS galaxy. Note . Columns (1)–(5) are adopted from Boogaard et al. ( 2020 ). (1) and (2) are ASPECS ID and redshift, respectively. Column (3) is molecular gas mass that estimates via the 1.2 mm dust-continuum emission on the Rayleigh–Jeans tail, and (4) is the molecular gas mass determined from the CO (2 − 1) emission assuming a luminosity ratio of and α CO (see table 5 in Boogaard et al. 2020 ). Both of the molecular gas masses from Boogaard et al. ( 2020 ) include a factor of 1.36 to account for heavy elements. Column (5) lists the with 3σ for the non-detections (see table 6 in Boogaard et al. 2020 ). The α [C i ](1 − 0) in columns (6) and (7) are estimated based on molecular gas masses of M mol, RJ and M mol, CO , respectively. The metallicity in column (8) is adopted from Boogaard et al. ( 2019 ). Though our derived α [C i ](1 − 0) shows almost flat with metallicity, they distribute next to the relation of Heintz Watson ( 2020 ) as seen in Fig. 8 , and similar with the α [C i ](1 − 0) result of ASPECS galaxies as well [the α [C i ](1 − 0) values in both Heintz Watson 2020 and Boogaard et al. 2020 include a factor of 1.36 to correct the helium and heavier elements]. The metallicity in our sample mainly focuses in the range of , whereas the metallicity of the sample in Heintz Watson ( 2020 ) covers two orders of magnitude in the ranges of , which is mostly smaller than our sample. Various studies suggest that α CO increases with decreasing metallicity, turning up sharply below metallicity of (Wolfire, Hollenbach McKee 2010 ; Bolatto, Wolfire Leroy 2013 ) where CO is easily photodissociated, whereas H 2 is self-shields or is shielded by dust from UV photodissociation, and becoming shallower near subsolar metallicity (Glover Mac 2011 ; Tacconi et al. 2018 ). The flat profile between α [C i ](1 − 0) with metallicity in our sample presents that α [C i ](1 − 0) might be similar to α CO and has a fairly shallow


Acknowledgments This research was supported by the DFG cluster of excellence Origin and Structure of the Universe (JED).


as [see equation ( 1 )], but at the same time the additional power would increase AGN feedback, which is proportional to the luminosity. If we instead increase the BH feedback efficiency, i.e. the constant of proportionality with luminosity, the gas surrounding the BH is less cold and dense, causing BH accretion to decrease. To quantify these effects on the dual-activity observability time-scales, we compare Runs 02, C3 and C4, which differ only by the initial BH mass or feedback efficiency. Increasing the BH mass (by a factor of 2.5) increases the dual-activity time, but only by a factor ≲2. Increasing the BH feedback efficiency (by a factor of 5) tends to decrease the normalized dual-activity time, although the factor widely depends on the filter one chooses (see Fig. B8 for more details). We note that different prescriptions for BH accretion (e.g. taking into account the angular momentum of the accreting gas; Debuhr et al. 2010 ; Hopkins Quataert 2011 ; Anglés-Alcázar, Özel Davé 2013 ; Anglés-Alcázar et al. 2015 , 2017 ) or AGN feedback (e.g. Newton Kay 2013 ; Wurster Thacker 2013 ) may also affect the results. 4.4 Comparison with other work In this section, we compare our dual-activity time-scales to a few select studies, both observational and theoretical, to validate our results. 4.4.1 Comparison with observations Most surveys provide the total number of AGN pairs out of the total number of AGN (e.g. Shen et al. 2011 ). These results are difficult to compare to ours since, by construction, we do not simulate isolated galaxies that could be harbouring a single AGN. Koss et al. ( 2012 ) study the fraction of dual AGN in the all-sky Swift Burst Alert Telescope (BAT) survey. For each of the 167 BAT AGN from the survey, they search for apparent companions, finding that 77 of them have at least a companion 8 within 1–100 kpc. Of these 77 companions, 15 have an AGN, making the fraction of dual AGN systems out of interacting systems (in which at least one system has an AGN) equal to 19.5 per cent (15/77). If we divide their sample in major and minor pairs (defined as pairs with mass ratios > and ≤0.25, respectively), the fraction changes to 37.1 per cent (13/35) for the major pairs and to 4.8 per cent (2/42) for the minor pairs. Since in this case only interacting systems are considered, we can compare the numbers from Koss et al. ( 2012 ) to our results. To do that, we simply consider, for each of our mergers, the dual-activity time when the BHs are at r > 1 kpc (our galaxies, by construction, are never above r ∼ 90 kpc) and divide it by the activity time at the same projected separations. 9 In Fig. 7 , we show the normalized dual-activity time above 1 kpc, 10 kpc and 150 km s −1 , for all our simulated mergers. We also show the results of Koss et al. ( 2012 ) for their full sample of BAT AGN and their subsamples of major and minor pairs. If we neglect the 1:2 merger with high BH feedback efficiency (Run C3), which obviously has a much lower


This work was supported by the Research Projects of the Polish Ministry of Sciences and Higher Education: 04/45/DSPB/0148 (JR and JD) and 06/65/DSPB/0516 (ME).


We thank Marc Metchnik and Philip Pinto for their contributions to the A bacus simulation code. D.J.E., L.H.G., and D.W.F. have been supported by grant AST-1313285 from the National Science Foundation, and D.J.E. is additionally supported as a Simons Foundation Investigator.


70–600 keV). Therefore, the observation covers a wide energy band simultaneously and with high sensitivity making it possible to do detailed spectroscopic studies of the source. The data were reduced and processed with the standard procedures and using the latest caldb version ’20140825’. The XIS was operated in the standard data mode with the window mode that provided a timing resolution of 2 s. The nominal pointing was set to XIS. The XIS data was examined for the presence of pile-up. A central region corresponding to a pile-up fraction >4 per cent was discarded from all further analysis. 3 TIMING ANALYSIS: PULSATION SEARCH AND LIGHT CURVES We applied barycentric corrections to the event files for the timing analysis, using the FTOOLS task ‘aebarycen’. Orbital correction was not required as the observation duration is much less than the decoherence time-scale due to the orbital motion of the system (Chakrabarty et al. 1997 , decoherence time-scale is ∼54 d, equation A9). Light curves were extracted with a time resolution of 2 s and 1 s, respectively, from the XISs (0.2–12 keV) and HXD/PIN (10–70 keV). To search for pulsations in the data, pulse folding and χ 2 maximization technique was applied. Pulsations were found in the light curve at 835.29 ± 0.29 s, which was subsequently used to bin the light curves and also to create corresponding pulse profiles. Fig. 1 shows the light curves in the low (XIS: 0.2–12 keV) and high (PIN: 10–70 keV) energy bands along with the hardness ratio (HR) curve, all binned at the spin period of the source. The ‘HR’ is defined as the ratio of the count rate in the XIS band (with the three XISs added together) to the PIN band. It is clear from the figure that the count rates in both the energy bands vary by a factor up to 5 during the observation. Moreover the HR shows a variation by a similar factor with the PIN count rate (see right of Fig. 1 ) that indicates spectral variability in the source. In order to investigate the true energy dependence of the pulse profiles in this source and to construct an average model of the representative X-ray spectrum of the source, it is important to choose a time interval free from intensity or spectral variations. Therefore, in order to construct the pulse profiles and the energy spectrum of X Persei, we choose a stretch of the observation where 0.02 HR 0.03 and 0.8 PINc/s 1.4, as not much spectral variation is expected in this narrow stretch. Figure 1. Left figure shows the light curves in low- and high-energy bands with the lowest panel showing the HR. The light curves are binned at the spin period of 835.29 s. Right figure shows the HR plotted against the PIN count rate to highlight the variation in HR with the count rate in the high-energy band. 3.1 Energy dependence of the pulse profiles To investigate any possible energy dependence of the pulse profiles, we created the energy resolved pulse profiles by folding the data in different energy bands at the obtained pulse


centred at , . Small-diameter (7′) WISE H II regions from Anderson et al. (2014) are marked by white squares and two extended WISE H II regions (diameter > 20′) are indicated by blue dashed circles. Three very extended radio sources in both λ 21 cm and λ 6 cm images are SNR G126.2+1.6, SNR G127.1+0.5 and H II region G127.9+1.7 (DU 65, Dubout-Crillon 1976 , red rectangle), which is missing in the WISE H II region catalogue. Lower left panel : its H α emission is shown overlaid by the λ 6 cm total-intensity emission. Lower middle and lower right panels : TT-plot results for DU 65 by using the Urumqi λ 6 cm against the Effelsberg λ 21 cm data and the Effelsberg λ 11 cm data. 4.1.1. New H II regions In addition to the missing known extended H II regions in the WISE H II region catalogue, a few new Galactic H II regions and H II region candidates are also discovered in the Urumqi λ 6 cm survey. They are G68.4+0.2, G76.7−1.2, G94.4+2.7, G139.2−3.0, G139.9−2.0, G172.3+1.9 (H II region candidate), G186.7−4.0 (H II region candidate), G210.8−2.6 and G212.9−3.7. We show them in Figs. 4 , 5 and 6 . For G68.4+0.2, G76.7−1.2 and G94.4+2.7 it is difficult to compare the λ 6 cm radio emission with the WISE 12 μ m infrared emission due to the large difference in angular resolution. We extracted the 1′ resolution CGPS λ 21 cm images and found that the associated infrared emission are not for the entire radio source but for partial shell- or ridge-like structures. The TT-plots confirmed their thermal origin. For the six remaining newly identified H II regions and H II region candidates, we found supporting evidence both in the TT-plot results and the associated H α emission, except for G172.3+1.9 and G186.7−4.0. For G172.3+1.9, we did not obtain a reliable TT-plot result and for G186.7−4.0, which is not fully covered by the Effelsberg λ 21 cm survey, the sensitivity of the Effelsberg λ 11 cm data is not enough for a clear determination. The H α image quality is not high in the south-east part of G172.3+1.9, but the data show a good correlation in the north-west part. Fig. 4. New H II regions G68.4+0.2, G76.7−1.2 and G94.4+2.7 identified in the Sino-German λ 6 cm survey. We show λ 6 cm images and WISE 12 μ m emission overlaid by CGPS λ 21 cm total intensity in the upper and middle panels . We present their TT-plot results in the lower panels . Fig. 5. Newly identified H II regions G139.2−3.0, G139.9−2.0 and G172.3+1.9. H α emission overlaid by Urumqi λ 6 cm total intensity contours are shown in the upper panel . The image quality of the south-east part of G172.3+1.9 is not good; however, correlation can be clearly recognised in the north-west part. TT-plot results are displayed in the lower panels . Fig. 6. Upper panels from left to right : λ 6 cm image, H α emission overlaid by Effelsberg λ 11 cm total intensity and the TT-plot result for the new H II region G212.9−3.7. Lower panels from left to middle : H α emission overlaid by Urumqi λ 6 cm total intensity


We thank the referee for the careful reading of the manuscript. This research is partially supported by the Spanish Ministerio de Ciencia e Innovación (MICINN) under grants AYA2012-39364-C02-02 and AYA2015-68012-C2-2-P, and by the Generalitat Valenciana (ACOMP/2014/129). AM acknowledges support from the Generalitat Valenciana through the grant BEST/2015/242 and from the Ministerio de Educación, Cultura y Deporte through the grant PRX15/00030. This work is based in part on the observations obtained with the Jacobus Kapteyn Telescope operated on the island of La Palma by the Isaac Newton Group, in the Spanish Observatorio Roque de los Muchachos of the Instituto de Astrofísica de Canarias. This work is based in part on the observations made with the Italian Telescopio Nazionale Galileo (TNG) operated on the island of La Palma by the Fundación Galileo Galilei of the INAF (Istituto Nazionale di Astrofisica) at the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. This research is also based on the observations made with the Nordic Optical Telescope, operated on the island of La Palma jointly by Denmark, Finland, Iceland, Norway and Sweden, in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. Some of the data presented here have been taken using ALFOSC, which is owned by the Instituto de Astrofisica de Andalucia (IAA) and operated at the Nordic Optical Telescope under agreement between IAA and the NBIfAFG of the Astronomical Observatory of Copenhagen. This work is based in part on the observations made at Observatoire de Haute Provence (CNRS), France. This research has made use of the SIMBAD data base, operated at CDS, Strasbourg (France) and of the WEBDA data base, operated at the Institute for Astronomy of the University of Vienna. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.


We thank T. J. Haworth, J. Lesur and C. McNally for the useful discussions. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00798.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. We acknowledge funding from ANR of France under contract number ANR-16-CE31-0013, from the Australian Research Council (ARC) under the Future Fellowship number FT170100040, from the National Centre for Competence in Research PlanetS supported by the Swiss National Science Foundation, and from the European Union Seventh Framework Programme FP7-2011 under grant agreement No. 284405.


so its disruption radius is closer to the BH (see Equation ( 5 )). The longer t acc in XT2 is likely due to a lower disk thickness ratio H / R because the latter carries the most sensitive parameter dependence in Equation ( 6 ). A lower H / R in turn might be caused by a lower Eddington-normalized accretion rate. This could suggest that XT2 was a tidal stripping event because the total bound mass Δ m can be much smaller than that of a full disruption event. The isotropic-equivalent peak luminosity of CDF-S XT2, ≈3 × 10 45 erg s −1 , is extremely high, which raises the possibility of a relativistically beamed emission. Three relativistic jetted TDE candidates have been discovered so far: Sw J1644+57 (Bloom et al. 2011 ; Burrows et al. 2011 ), Sw J2058+0516 (Cenko et al. 2012 ; Pasham et al. 2015 ), and Sw J1112−8238 (Brown et al. 2015 ). The 2D model of Dai et al. ( 2018 ) suggests that the X-ray emission of TDEs may be caused by jets through the Blandfold–Znajek process. Similarly, the WD–IMBH tidal disruption/stripping discussed here may produce a jet as well. Xue et al. ( 2019 ) estimated the CDF-S XT2–like event rate density to be Gpc −3 yr −1 . For CDF-S XT1, Bauer et al. ( 2017 ) estimated a large range of the event rate, ∼10 3 Gpc −3 yr −1 for z = 0.5 to ∼1 Gpc −3 yr −1 for z = 3. Theoretically, the rates of WD disruptions are very uncertain. For IMBHs in globular clusters (GCs), Baumgardt et al. ( 2004 ) estimated a total stellar TDE rate of ∼10 −7 yr −1 via N -body simulations of GCs with an initial central BH mass of 10 3 M ⊙ . Among the disrupted stars, ∼15% are WDs, giving a rate of ∼1.5 × 10 −8 yr −1 . Adopting a number density of 34 Mpc −3 of GCs, this gives a volumetric rate of R IMBH−WD ∼ 500 yr −1 Gpc −3 (Haas et al. 2012 ; Shcherbakov et al. 2013 ). Recently, Fragione et al. ( 2018 ) semianalytically calculated the evolution of a population of GCs in a galaxy and found a rate of WD TDE ∼10 −5 yr −1 per galaxy; combining this with GC population’s dependence on redshift and galaxy types, their results show a present-day volumetric rate of R IMBH−WD ∼ 10 yr −1 Gpc −3 . For IMBHs in dwarf galaxies, MacLeod et al. ( 2014 ) calculated a rate of ∼10 −6 yr −1 per IMBH for WD disruptions via the loss-cone dynamics for the BH mass range of 10 4 –10 5 M ⊙ . This rate is ∼30 times lower than that of main-sequence stellar TDEs by supermassive black holes (e.g., Stone Metzger 2016 ). Assuming a number density of dwarf galaxies ∼10 7 Gpc −3 (Shcherbakov et al. 2013 ; MacLeod et al. 2014 ) and an occupation fraction f IMBH of IMBHs in dwarf galaxies, then the volumetric rate is yr −1 Gpc −3 . Compared with WD–IMBH TDEs, a similar case that happens more commonly is the tidal disruption/stripping of MS stars by IMBHs. Indeed, as was shown in Fragione et al. ( 2018 ), the event rate of MS–IMBH TDEs is about 30 times higher than that of WD–IMBH TDEs in most galaxies. However, the timescale (∼years) of MS–IMBH TDEs is much longer than that of


This research was supported by NASA grants NNX13AF99G, NNX10AE46G, and NNX12AH44G, and by NSF grant AGS-0955700. X.G. was also supported by the 973 program 2012CB825602, NNSFC grant 41231067, and in part by the Specialized Research Fund for State Key Laboratories of China. We are grateful for the use of the Neutron Monitor Database at http://www.nmdb.eu , the Wilcox Solar Observatory, and the OMNI data obtained through the GSFC/SPDF OMNIWeb interface at http://omniweb.gsfc.nasa.gov .


keV. As these features are not commonly observed in Galactic XRBs, they imply a new spectral regime, which is usually referred to as the ‘ultraluminous state' (Roberts 2007 ; Gladstone et al. 2009 ), and may be associated with super-Eddington accretion. A few luminous ULXs have very recently been observed with NuSTAR , resulting in spectra which demonstrate that the high-energy curvature persists above 10 keV (Bachetti et al. 2013 ; Walton et al. 2013a ; Rana et al. 2015 ), confirming this result. The spectral curvature may arise either from Comptonization in a cold, optically thick corona lying above an accretion disc (e.g. Stobbart et al. 2006 ; Gladstone et al. 2009 ); from a modified accretion disc, i.e. a slim disc (Mizuno et al. 2007 ); or the hot inner regions of the disc, which are highly distorted by advection, turbulence, self-heating and spin (Beloborodov 1998 ; Suleimanov et al. 2002 ; Kawaguchi 2003 ). The soft component is thought to originate from the photosphere of a radiatively driven wind that is ejected from the spherization radius of the accretion disc down to its innermost regions, which is expected to occur at super-Eddington accretion rates (e.g. Shakura Sunyaev 1973 ; Ohsuga Mineshige 2007 ; Poutanen et al. 2007 ; Ohsuga et al. 2009 ). Partially inhomogeneous winds in ULXs may explain the puzzling short-term variability reported in some ULXs (Heil, Vaughan Roberts 2009 ), through a combination of inclination angle and accretion rate (e.g. Middleton, Sutton Roberts 2011 ; Takeuchi, Ohsuga Mineshige 2013 ; Middleton et al. 2015 ). These same factors also determine the observed energy spectrum (e.g. Sutton, Roberts Middleton 2013b ; Pintore et al. 2014 ). On the other hand, HLX-1 in ESO 243-49 (Farrell et al. 2009 ), which has a peak luminosity of ∼10 42 erg s −1 , is observed with a strikingly different set of properties than standard ULXs. It displays a spectral evolutionary pattern similar to that of the Galactic BH X-ray transients (e.g. McClintock Remillard 2006 ; Fender Belloni 2012 ), undergoing periodic outbursts (Godet et al. 2014 ; Webb et al. 2014 ) in which the source switches from a high-luminosity soft state to a fainter hard state (Servillat et al. 2011 ). Multiwavelength emission has been detected from HLX-1 as it undergoes this outburst cycle. In particular, radio emission has been detected, which is associated with discrete jet ejection events (Webb et al. 2012 ), similar to those that have been observed during hard -to- soft state transitions state in Galactic BHs. By assuming that HLX-1 was in a similar state, the authors of that study were able to refine the BH mass estimates to ∼10 3 –10 4 M ⊙ . Other good IMBH candidates include: M82 X-1 (Feng, Rao Kaaret 2010 ), for which a BH mass of ∼400 M ⊙ has been suggested based on the tentative identification of twin-peak quasi periodic oscillations (Pasham, Strohmayer Mushotzky 2014 ); and an X-ray source in the galaxy NGC 2276 (Mezcua et al. 2015 ).


We thank the anonymous referee for a detailed report that was very helpful in improving the manuscript. A.B. is grateful to the Austrian Science Fund (FWF) for funding (projects P19300-N16 and P23946-N16). The authors thank B. Bösch (Innsbruck) for providing a Python code of the Kelson ( 2003 ) method for improved sky subtraction in the FORS2/MXU spectra. This publication is supported by the Austrian Science Fund (FWF).