O O O O O B-Wavelength O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject B-CelestialObject I-CelestialObject B-CelestialObject I-CelestialObject B-CelestialObject I-CelestialObject B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O B-CelestialObject I-CelestialObject O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O

has been observed. Detection of radio flare during the HIMS of GX 339−4 (2002 outburst) and the first and second flares in V404 Cyg, indicate similar characteristics. Since the source MAXI J1836−194 is not observed to have transited to a SIMS or HSS (Ferrigno et al. 2012 ; Russell, Soria Miller-Jones 2014 ), the system of jet ejection for such sources which have ‘failed outbursts’ seems to be different as compared to other BH sources. Although Russell et al. ( 2013 ) suggests the presence of a compact jet, the fact that QPOs are not observed suggests of a probable transient jet ejection event. A deeper look into the properties of the companions of these BHs suggests them to be of late F, G, K or M type (Sanchez-Fernandez et al. 1999 ; Hynes et al. 2003 ) which are in general magnetically active (Linsky 1985 ), with field strength around 1 kilogauss. This suggests that the models which specify the role of magnetic fields in formation of jets by evacuation of the Comptonized corona (see Section 4 and Nandi et al. 2001 ) are applicable for these sources, wherein the magnetic field from the companion flows along-with the matter into the accretion disc. This field probably gets amplified during the rising phase of the outburst (i.e. the LHS and HIMS) and finally collapses to push out the matter from the corona (Nandi et al. 2001 ; Fender et al. 2004 , 2009 ) forming jets/flares, as the source enters the SIMS. The time-scale required for amplification of the field is equivalent to the rising time of the source upto the end of the HIMS when the jet ejection occurs. A typical BH source with a fast rise and exponential decay light curve requires around 10 d to reach the transition point from HIMS to SIMS. For the sources studied in this paper which have a typical fast rise-time (XTE J1859+226, H 1743−322, XTE J1748−288, GRO J1655−40, XTE J1550−564), the time-scale required for the field amplification is found to vary from 5 to 15 d. In these sources, we find that the peak radio flux is higher and varies between 100 and 600 mJy, except the 2009 outburst of H 1743−322 and 2005 outburst of GRO J1655−40 which have very less radio flux during the moment of ejection (5.7 and 1.6 mJy, respectively) and also while in the peak. For the sources which have a slow rise towards the X-ray peak require more time to reach the transition point i.e. they take larger time-scale to amplify the magnetic field. As an example XTE J1752−223 requires 88 d, 48 d is taken by GX 339−4 during its 2002 outburst and 104 d for GX 339−4 during its 2010 outburst. For these sources, the peak radio flux is found to vary from 18 to 55 mJy. Fender et al. ( 2004 ) has performed detailed study on the estimation of the jet power based on the characteristics of the radio flare, like its rise time and flux density (see also Fender Munoz-Darias 2016 ). It is also possible to have a quantitative estimation of the kinetic jet power based on the X-ray characteristics of the source. Recently Aktar,


O O O O B-Person I-Person O O O O O B-CelestialObject B-Observatory B-Instrument O O O O O O O O O O B-Person O B-Person I-Person B-Person I-Person O B-Person I-Person O O B-Person O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O B-Fellowship I-Fellowship I-Fellowship I-Fellowship B-Person O O O O O B-Location B-Grant I-Grant I-Grant I-Grant I-Grant O O O O O B-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O O B-Proposal I-Proposal B-Proposal B-Proposal I-Proposal B-Proposal I-Proposal I-Proposal B-Proposal B-Proposal B-Proposal I-Proposal

We are thankful to Dan Harris for providing us with the M87 NRAO radio data, flux measurements and many insightful comments on the manuscript. AP acknowledges M. Krause, E. Churazov, and R. Sunyaev for discussions. SM is grateful to the University of Texas in Austin for its support, through a Tinsley Centennial Visiting Professorship. AP acknowledges financial support from the Spanish 2011 Severo Ochoa Program SEV-2011-0187, and the hospitality of the CAST group of the Ludwig Maximilians University of München (LMU) and the Max-Planck Institute für extraterrestrische Physik where most of this work was done. Based on VLT 4.B-0404(A), 76.B-0493(A), ALMA 2011.0.00754.5, NRAO AH 822, 862, 885, Chandra 3088.


O O O B-Collaboration I-Collaboration O O O O O O O O B-Database I-Database I-Database I-Database O B-URL O O O O O O O O B-Person O O O O B-Person I-Person O O O O B-Person O O O B-Organization I-Organization I-Organization O B-Grant O B-Grant I-Grant I-Grant B-Grant B-Person O O B-Grant I-Grant I-Grant O B-Organization I-Organization I-Organization B-Person O O O O B-Fellowship I-Fellowship I-Fellowship I-Fellowship I-Fellowship B-Person O O O O B-Organization I-Organization I-Organization B-Organization O B-Grant I-Grant I-Grant I-Grant O O O B-Organization I-Organization I-Organization I-Organization B-Grant I-Grant I-Grant I-Grant B-Grant I-Grant B-Person O B-Person O B-Organization O B-Grant I-Grant O O O O O O O B-Database I-Database O O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Organization O O B-Organization I-Organization I-Organization B-Organization O O O B-Organization O B-Organization

We thank the Planck collaboration for making available the calibration sample in the SZ cluster data base ( http://szcluster-db.ias.u-psud.fr ). We thank the referee for helpful comments. HI would like to thank D. Applegate for a helpful discussion. HI acknowledges support from European Research Council grant MIRG-CT-208994 and Philip Leverhulme Prize PLP-2011-003. JN acknowledges a PUT 246 grant from Estonian Research Council. RJM is supported by a Royal Society University Research Fellowship. THR acknowledges support from the German Research Association (DFG) through Heisenberg grant RE 1462/5 and through the Transregional Collaborative Research Centre TRR33 ‘The Dark Universe’ (project B18). GS and THR acknowledge DFG grant RE 1462/6. This research has made use of the SZ-Cluster Database operated by the Integrated Data and Operation Center (IDOC) at the Institut d'Astrophysique Spatiale (IAS) under contract with CNES and CNRS.


O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject I-CelestialObject I-CelestialObject O O O O O O O O O O O O O O B-Wavelength O O B-Telescope I-Telescope B-Telescope O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O B-Telescope B-Wavelength O O B-Formula I-Formula I-Formula B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O B-Telescope B-Wavelength O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

). Moreover, due to the compactness, compact radio sources trace sightlines towards central denser regions of radial density profiles of the H i disc (Pihlström et al. 2003 ). This could be the other reason for the low detection rate in extended radio sources. Available very-long-baseline interferometry (VLBI) images for some of the compact LERG radio sources with H i absorption detection suggest that these have sizes on the scale of pc. However, we lack complete information on all sources. In HERGs, in addition to clouds in the ISM, a dusty torus in an AGN could give rise to H i absorption if the compact radio source is of a scale similar to or smaller than the torus. Figure 5. Top: log sSFR versus W2−W3. Bottom: log total stellar mass versus log sSFR, for absorption detections (filled symbols) and non-detections (empty symbols). Since H i absorption detections as well as star-formation rates are higher for WISE ‘late-type’ galaxies, it is relevant to enquire about the possible origins of the gas or fuel. It could be the result of a normal process of galaxy evolution, in which star formation started in a dusty gas-rich environment and decreased at later stages due to scarcity of fuel, combined with possible feedback from a radio source. In addition, star formation or AGN activity may get a boost from dust-rich mergers or interactions. In the literature, suggested fuelling mechanisms vary from major mergers or secular processes for HERGs to accretion of halo gas, stellar remnants, winds from evolved stars or minor mergers for LERGs (Ellison et al. 2015 ). Also, LERGs are reported with suppressed star formation in the literature (Gürkan et al. 2015 ; Ellison et al. 2016 ; Pace Salim 2016 ). Although this may be true for a majority of the LERGs in our sample, it does not explain the higher H i gas detection rate and higher rate of star formation in WISE ‘late-type’ LERGs. There have been suggestions for higher rates of mergers for WISE IR-selected AGNs with W1−W2 > 0.8 (Satyapal et al. 2014 ; Ellison et al. 2016 ). However, as can be seen from Fig. 2 , our sample hardly has any AGNs with W1−W2 > 0.8. Also the optical host galaxies do not suggest any significant morphological differences, except for a few cases of disturbed morphology or evidence of interaction with a companion galaxy, e.g. J105327.2+205836. To understand these aspects and clarify any possible evolutionary scenario, we fit a first-order linear function in log sSFR versus WISE colour for LERG detections, non-detections and also for compact and extended LERGs separately. Using the Kendall tau test, we find that there exists a significant correlation (τ = 0.47, p = 4.37 × 10 −10 ) between log sSFR and WISE W2−W3 colour (Fig. 6 ). The left panel of Fig. 6 also shows that compact radio sources (slope: 0.81 ± 0.12) and detections (slope: 0.78 ± 0.16) have similar flatter slopes, while extended sources (slope: 0.98 ± 0.19) and non-detections (slope: 0.96 ± 0.14) have similar and


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset O O O O O B-Dataset I-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O

Figure 10 that their N/H ratios are also some of the lowest in the dwarf galaxy sample. However, as shown in Figure 9 , their N/O ratios cover the range of N/O ratio values of all the dwarf galaxies studied. This is consistent with the expectation that nitrogen behaves as a primary element for galaxies with low and moderate metallicities. Details of these 21 dwarf galaxies with extremely low metallicities are listed in Table 3 , including their gas-phase chemical abundances. 6. Conclusions The nucleosynthesis of nitrogen is a vital component of the chemical evolution of galaxies in our universe. We estimate the nitrogen abundance and N/O ratio of dwarf galaxies using the direct T e method and spectroscopic line flux measurements from the SDSS DR7 sample as reprocessed in the MPA-JHU catalog. The 135 galaxies analyzed suggest a slight large-scale environmental dependence of the N/O ratio, where void dwarf galaxies could have a lower N/O ratio than dwarf galaxies in denser environments. Thus, the large-scale () environment might influence the chemical evolution of dwarf galaxies. We find small, statistically insignificant shifts in the mean (or median) N/O ratio for galaxies between the void and denser regions across all blue, star-forming galaxies with . These shifts are somewhat more significant, however, when we look at the entire sample of galaxies. Each magnitude bin is shifted in the same direction, and they are potentially very interesting, as they might indicate delayed star formation histories, more constant SFRs, and larger ratios of dark matter halo mass to stellar mass in void galaxies, as discussed in Section 5 . A larger sample would help test these results. We look to increase the sample and probe a larger magnitude (and mass) range of dwarf galaxies in K. A. Douglass M. S. Vogeley (2017, in preparation). In addition, we look at the relationship between the N/O ratio and other physical characteristics of our dwarf galaxies. In the relation between N/O and O/H, our galaxies all reside on the so-called “nitrogen plateau,” where the N/O ratio is predicted to be constant for low and intermediate metallicities. However, instead of a constant value for these galaxies, we find a negative correlation between the N/O and O/H ratios. Our dwarf galaxies show a positive correlation between stellar mass and the N/O ratio. These dwarf galaxies have some of the lowest N/O ratios for both their color and (s)SFR. Beyond the suggestive large-scale environmental dependence of the N/O ratio, there is no clear large-scale environmental dependence in any of these relationships. The N/O ratios of the extremely metal-poor dwarf galaxies are no different than those of the remaining dwarf galaxy sample, though their N/H abundance is also extremely low. A more detailed study of these 21 extremely metal-poor dwarf galaxies is recommended to confirm their abundance values and discover any characteristics shared by the population. Although SDSS provides


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Density maps were created with splash , by Price ( 2007 ). We thank the anonymous referee for useful comments that greatly helped us to improve the presentation of the paper. JC acknowledges support from CONICYT-Chile through FONDECYT (1141175), Basal (PFB0609) and Anillo (ACT1101) grants, and the warm hospitality at Leicester. Theoretical astrophysics research in Leicester is supported by an STFC Rolling Grant. QDW acknowledges the support by NASA via the SAO/CXC grant TM3-14006X. This work was performed using the Geryon computers at the Centre for Astro-Engineering UC, funded by Basal PFB-06, QUIMAL 130008 and Fondequip AIC-57.


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Acknowledgments The project was supported partially by Polish National Science Center grants DEC-2011/01/N/ST9/06000 and DEC-2012/07/B/ST9/04412. We thank Nathan Leigh for very insightful comments and suggestions which helped us to significantly improve the text of the paper.


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

the turbulence-regulated star formation model of Krumholz McKee ( 2005 ) to allow time-variable ff in the prescription of Feldmann Gnedin ( 2011 ), we find that ff ∝ t 2 is most consistent with our data. We conclude that star formation is dynamic on the GMC-scale. 10 10 The time-varying ff may arise from the evolution of star-forming clumps embedded in each GMC. Our study concerns the properties of star formation in clouds that have already formed or in the process of forming stars. By construction, we have limited our analysis to 191 SFC-GMC complexes out of 5469. We found 1014 clouds unmatched to SFCs that have the potential to form star clusters (see Section 6.5 ). We surmise that most (5469-1014-191 = 4264; 78% by number) of the GMCs are gravitationally unbound and will never form massive star clusters. It is possible that some locally dense clumps within an unbound GMC form stars (see e.g., Dobbs et al. 2011 ). These clumps likely give birth to lower mass star clusters that our star formation tracers are not sensitive to. Throughout our analysis, we used free–free emission as a proxy for stellar mass. A more direct measurement would be to count YSOs. For known massive clusters, we find a generally good agreement in the measured stellar mass between free–free emission and YSO counts (see Figure 2 ). A more complete comparison may be possible using all-sky YSO catalogs (e.g., Marton et al. 2016 ). This is an important and natural avenue for future improvement. 8.1. Comments on Stellar Feedback If the rate of star formation in the most massive (10 6 M ⊙ and higher) GMCs does accelerate, the process of star formation must halt before ≳ 0.1, roughly the largest value we see in such clouds. Stellar feedback—in the form of stellar winds, radiation pressure, protostellar jets, and supernovae—from massive star clusters can disrupt the natal cloud and inhibit future star formation. The idea that stellar feedback destroys GMCs was proposed long ago by Larson ( 1981 ). Galactic-scale numerical simulations (e.g., Hopkins et al. 2011 and Faucher-Giguère et al. 2013 ) find that the effects of stellar feedback are necessary for regulating star formation rates to the Kennicutt-Schmidt value. Turbulence in the ISM alone cannot slow the rate of star formation. The existence of large expanding bubbles—a few to ∼100 pc wide—associated with regions bright in free–free emission is evidence for the effects of stellar feedback from young clusters (referred to as SFCs by Rahman Murray 2010 and Lee et al. 2012 ). These massive young clusters, which power most of the free–free emission in the Milky Way, are identified by clear cavities in the 8 μ m emission. The clusters are found in massive GMCs, but the star clusters are not enshrouded in molecular gas—they are instead in regions of ionized gas, which are enshrouded in molecular gas. In some cases, the massive stars may still be accreting gas from their natal protostellar disks, but the disks are not accreting gas


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation

from one epoch to the other, the polarised intensity distribution does not change significantly. The departure from axisymmetry is stronger in January 2015 but the location of the maxima and minima of the polarised flux does not change much between the two epochs. We divided the image in octants to facilitate our analysis and to minimise errors introduced by asymmetries of the dust envelope. Octant 1 is limited by the north and north-east directions and the numeration follows clock-wise. The value of the polarised intensity changes considerably for the different octants. In Table 2 we list the fraction of the polarised light measured per octant per filter. The ratio between the intensity from the octants with maximum and minimum polarised intensity is roughly 2.3 for and cntH in the first epoch and 3.7 for cntH in the second epoch. Table 2 Fraction of the polarised flux arising from the different octants for the image in and in cntH. 3. Analysis and modelling of the polarised light We now focus on our modelling efforts to derive the density radial profile of the grains that produce the scattered light. 3.1. Modelling approach We calculated spherically symmetric models using the continuum radiative-transfer code MCMax ( Min et al. 2009 ). The code calculates the direction-dependent scattering of radiation by dust grains and outputs images of the Stokes parameters. We convolved the Q and U images produced by the models with the PSF images. This typically caused a decrease by a factor of 1.5 of the output integrated polarised flux. This is because in the Q and U images the negative and positive lobes from a symmetrical envelope can overlap when the resolution is lowered. This leads to polarised signal in the images to cancel out. The poorer the angular resolution, the larger is the effect. The convolved Q and U images were then combined to obtain the polarised intensity for each model. We calculated the radial profile of the polarised intensity from the observations for each octant and compared these profiles independently to the models. We considered two different envelope structures: with dust grains distributed in a thin halo with constant density (model with no outflow), and with a dust density gradient given by , being the inner radius of the dust envelope and . We varied between 1.2 and 2.0 for both envelope structures and the halo thicknesses between 0.25 and 1.5 for the thin-halo scenario, where mas ( Norris et al. 2012 ). We use amorphous MgSiO grains with the optical constants obtained by Jäger et al. (2003) , as this dust species is one of the main candidates for driving the winds of oxygen-rich AGB stars (see, e.g., Bladh Höfner 2012 ). We have also experimented with optical constants for MgSiO and AlO, as discussed in Sect. 4.1.2 . To calculate the absorption opacities and the direction-dependent scattering properties, we approximate the actual size distribution of particle sizes by a distribution of hollow spheres ( Min et al.


O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation B-CelestialObject I-CelestialObject O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O B-Instrument I-Instrument O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument I-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O B-Wavelength I-Wavelength I-Wavelength O O O O O O O O O O O O O O O B-Telescope B-Telescope O B-Telescope B-Instrument O O O O O O B-Telescope O B-Wavelength O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O B-Telescope O O O B-Grant I-Grant I-Grant I-Grant I-Grant O O O O B-Grant O O O O O B-Grant O O O O O O O O O O O B-Wavelength B-Wavelength B-Wavelength B-Wavelength O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O B-Instrument O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software I-Software O O B-URL O O O O O O O O O O O O O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength

in the standard manner for mid-infrared ground-based imaging observations, including dark subtractions and flat fielding. The flux calibration of V1280 Sco was achieved using measurements of the two standard stars listed in Cohen et al. ( 1999 ): λ Sgr for N8.8, N11.7, and Q18.8, and μ Cep for Q24.5. The aperture size for the photometry was set to be 2′′ in radius. For the NL spectroscopy of V1280 Sco on Day 150, a slit width of 033 was used. As in the case of the imaging observations, only the secondary mirror chopping was performed at a frequency of ∼0.5 Hz with a 10′′ throw in the north–south direction. The position angle (PA) of the slit was set to 0° so that images at both chopping positions would fall on the slit. Spectra collected at both chopping positions were co-added, resulting in the total on-source integration time of 20 s for NL spectroscopy (see Table 2 ). The data reduction was performed in the standard manner for mid-infrared ground-based spectroscopic observations, including dark subtraction, flat fielding using domeflat, wavelength calibration using atmospheric lines, and distortion correction. Finally, a one-dimensional spectrum of V1280 Sco was extracted. Atmospheric absorption in the extracted spectrum was corrected by division by the spectrum of a standard star, λ Sgr, whose absolute spectrum is provided in Cohen et al. ( 1999 ). The absolute flux calibration including the correction for slit efficiency was performed by simply scaling the flux level of the obtained spectrum to match the photometric data in the 8.8 and 11.7 μ m bands. Table 2 Summary of Spectroscopic Observations Note. a The epochs of the observations with Subaru/COMICS, Gemini-S/T-ReCS, and AKARI /IRC in days after the discovery. 2.2. Gemini-S/T-ReCS data Mid-infrared imaging and spectroscopic observations of V1280 Sco on Days 1272, 1616, and 1947 were carried out at the Gemini-South observatory under the Subaru/Gemini Time Exchange Programmes GS-2010B-C-7 on 2010 August 1, GS-2011B-C-4 on 2011 July 10, and GS-2012A-C-5 on 2012 June 6. For the imaging portion, five medium-band filters, Si-1, Si-3, Si-5, Qa, and Qb, were used. The center wavelength, λ c , and bandwidth, Δ λ , are summarized in Table 1 . The pixel scale of the T-ReCS detector was 009 and the FOV was 288 × 216. Both chopping and nodding were performed to cancel out the high background radiation and the chopping residual pattern. The chopping was performed at a frequency of ∼3 Hz with a 15′′ throw in the north–south direction. The on-source integration time is summarized in Table 1 . The mireduce task included in the Gemini IRAF package ( https://www.gemini.edu/node/10795 ) was used to produce final co-added, chop-subtracted, and nod-subtracted data from the raw data files. The flux calibration was accomplished by observing the Cohen standard star HD 151680 (Cohen et al. 1999 ). The aperture size for the photometry was set to be 2′′ in radius. A slit width of 035 was used for the N-band


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We thank the anonymous referee for their thorough comments which helped to improve the method and the manuscript. We gratefully acknowledge the support of the UK's Science Technology Facilities Council (STFC Grant ST/H00260X/1 and ST/J500914/1). The calculations for this paper were performed on Cray XT4 at Center for Computational Astrophysics, CfCA, of the National Astronomical Observatory of Japan and the DiRAC facilities (through the COSMOS consortium) including the COSMOS Shared Memory system at DAMTP, University of Cambridge operated on behalf of the STFC DiRAC HPC Facility. This equipment is funded by BIS National E-infrastructure capital grant ST/J005673/1 and STFC grants ST/H008586/1, ST/K00333X/1 ST/J001341/1. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton. We would also like to thank PRACE for the use of the Cartesius facility. This work was carried out, in part, through the Gaia Research for European Astronomy Training (GREAT-ITN) network. The research leading to these results has received funding from the European Union Seventh Framework Programme ([FP7/2007-2013] under grant agreement number 264895). DK acknowledges the generous support and hospitality of the Kavli Institute for Theoretical Physics (KITP), University of California Santa Barbara during the ‘Galactic Archaeology and Precision Stellar Astrophysics’ programme, where some of this research was performed. This research was supported in part by the National Science Foundation under Grant No. NSF PHY11-25915. We would also like to thank Mercè Romero-Gómez and Francesca Figueras for providing the subroutine to calculate the Gaia performance errors, including the update to post-launch estimates, Sanjib Sharma for providing the galaxia extinction maps and isochrones and Sami Niemi for the suggestion of using KDE's to visualize the velocity distributions.


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Joung Mac Low ( 2006 ), we assign 20% of the explosions to SN Ia and 80% to SN II with scale heights of and , respectively (Tammann et al. 1994 ). However, estimates of the displacement of runaway OB stars suggests that the effective scale height of SNe II might be larger (Li et al. 2015 ). We separately vary the thermal energy injection per SN, , and the CR energy injection, , with . We discuss three different simulations: purely thermal SNe (, TH1-CR0 ), purely CR SNe (, TH0-CR1 ), and the physically most realistic setup with both energy injections (, TH1-CR1 ). 3. MORPHOLOGICAL EVOLUTION In all simulations, the SNe induce the formation of filaments, clumps, and voids. The presence of CRs leads to a smoother morphological appearance. An overview of the disk structure after is depicted in Figure 1 with cuts through the center of the box for the density edge-on (top) and face-on (middle) and the CR energy density (bottom). From left to right, we present TH1-CR0 , TH0-CR1, and TH1-CR1 . Both runs with CRs quickly push gas away from the midplane and form an extended atmosphere, whereas in the simulation with only thermal SN feedback, the disk remains compact without a noticeable extended layer of gas above the disk. The CR energy density shows relatively small spatial variations of about one order of magnitude. High CR energy peaks correspond to recent SNe and disappear quickly due to diffusion. Figure 1. Structure of the disk after of evolution. The left panel shows the run without CRs, the middle panel the run with purely CR feedback, and the right panel the simulation with both feedback energies. In the top and middle row, we plot the density edge-on and face-on in cuts through the center of the box. The bottom row depicts the CR energy density in the midplane. The disk in the run including CRs is significantly thicker compared to the purely thermal run. The thermal energy input of the SNe is primarily important in the denser part of the disk. CRs show their main impact in generating an extended atmosphere. The CR energy density is smooth in the midplane. Local variations are due to recent SNe and the resulting CR injection. Those variations disappear after less than a Myr due to diffusion. In Figure 2 , we show the vertical profile of the density for all simulations at . The arrows indicate the heights of 90% enclosed mass. The inclusion of CRs increases from to with similar values for both runs including CRs. The additional thermal energy injection from SNe in run TH1-CR1 changes the profile up to a height of . At larger altitudes, the profiles for TH0-CR1 and TH1-CR1 are basically indistinguishable. For comparison, we include the vertical density profile of the MW (Dickey Lockman 1990 ) showing reasonable agreement with both CR runs. Figure 2. Vertical profiles of the total gas density for all simulations. The arrows indicate the height of 90% enclosed mass. A fit to the observed density profile of the solar neighborhood (Dickey Lockman


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

ACKNOWLEDGEMENTS The contribution of JK has been funded by ERDF postdoctoral grant No. 1.1.1.2/VIAA/I/16/194 ‘Chemical effects of cosmic ray induced heating of interstellar dust grains’. The contribution of JRK has been supported by ERDF project ‘Physical and chemical processes in the interstellar medium’, No 1.1.1.1/16/A/213. Both projects are being implemented in Ventspils University College. We thank Ventspils City Council for the support.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset I-Dataset O O O O O O O O B-Model I-Model O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset O O O B-Citation I-Citation I-Citation I-Citation O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O B-Model I-Model B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-URL I-URL I-URL I-URL I-URL I-URL O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model O O O O O O B-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula O O O O O O O O O O O O O O O O O O O O O O O B-Model I-Model

modelling of the full-shape anisotropic correlation function of: The systematic errors between the three-multipole and three-wedge methods are similar and the errors reported are always the largest of the two possibilities. For the analysis of the final eBOSS quasar sample, further work on improving the fidelity of the OuterRim-based mocks and understanding the difference in the bias models is needed. In particular, Vlah, Castorina White ( 2016 ) extended the CLPT-GS formalism to take into account contributions from effective field theory and additional bias terms. They showed that the effects of the biasing scheme are as important as higher order corrections to the theoretical predictions. Therefore, it would be interesting to see how this model performs for the analysis of the final eBOSS sample. Improvements in the model to account for the shape of the redshift error distribution would also be valuable. 5 ANALYSIS This section reviews the weighting scheme applied to the data to treat the potential systematic effects. We denote the total weighting scheme by , where the subscript specifies the different methods to compute the total weight. With this notation, the total weighting scheme used for the DR14 quasar BAO analysis (Ata et al. 2017 ) is: The first term, FKP = 1/(1 + n ( z ) P 0 ), is the FKP weight (Feldman, Kaiser Peacock 1994 ) that corrects for the variations of the observed quasar density n ( z ) across the redshift range and also depends on the amplitude of the power spectrum at the scale at which the FKP weights optimize the measurements (here we choose k = 0.14 h Mpc which is the typical scale at which the BAO signal is optimally detected which gives P 0 = 6 × 10 3 h −3 Mpc 3 ). The second term, photo , is a photometry weight which corrects for the variation of the depth across the survey; cp , is a weight that accounts for the quasar targets that could not be measured due to fibre collision; and noz is a weight that addresses for the number of confirmed quasars for which a secure redshift could not be determined. We will show that the use of the redshift efficiency, ε( x, y ), across the focal plane as a weight, focal = 1/ε( x, y ) is more appropriate than noz . We adopt the following definition of the total weight : Each quasar in the DR14 catalogue is thus weighted by to correct for any spurious variation of the quasar densities and to provide a more isotropic selection. For the random catalogue, we apply the FKP weight alone as it corresponds to a Poisson sampling which should not be affected by inhomogeneities in the selection. In tests defined throughout the following subsections, using the ability to test against unbiased samples, we will demonstrate that this new weight reduces systematic effects on the quadrupole by a factor of three. Furthermore, the region close to μ = 1 is responsible for the remaining systematic shift, and we propose a method to take this into account. The fits are performed with the CLPT-GS model.


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Acknowledgments This paper is written in memory of our friend James R. Cohen (19482006) with whom this project was started. WAB has been supported as a Visiting Professor of the Chinese Academy of Sciences (KJZD-EW-T01). WAB thanks the Shanghai Astronomical Observatory staff for their hospitality during the tenure. TA has been supported by the NSTC 973 Program (2013CB837900) and Shanghai Rising Star programme. The e-MERLIN is a National Facility operated by the University of Manchester at Jodrell Bank Observatory on behalf of the UK Science and Technology Facilities Council (STFC). This research has made use of the NASA/IPAC Extragalactic Database (NED) that is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.


B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

Pillet et al. (1993) , Baumann Solanki (2005) , Lagrange et al. (2010) , and Meunier et al. (2010a) . Faculae properties. The faculae properties were similar to those used in Borgniet et al. (2015) , which included the ratio distribution between plage and spot sizes, and decay rates (here we considered the plages that were produced each time we generated a spot). Network properties. Network properties were similar to those used in Borgniet et al. (2015) , which include the diffusion coeficient from Schrijver (2001) , the fraction of plage flux that was used to build the network, and the decay rate. The diffusion coefficient was scaled with the amplitude of the convective blueshift, as discussed in Sect. 2.7 . 3 From structures to observables 3.1 Filling factor and photometric and radial velocity time series Because of the huge number of simulations, it is not possible to compute the observables as done in our previous work ( Lagrange et al. 2010 ; Meunier et al. 2010a ; Borgniet et al. 2015 ), in which we computed maps, then spectra, and finally RVs (and other observables) in the same way as for stellar observations. We therefore simplified the computations as follows: we directly summed the contribution from each structure to the RV, photometry, and astrometry. The filling factors were also computed. This implies that we assumed that the structures are point-like, which means that we did not need to check for superimpositions. This is different to what was done in Borgniet et al. (2015) . This also means that we neglected any geometrical effect that would be due to a large area covered by a structure, which is a good assumption because we modeled relatively quiet stars with moderate structure sizes. The assumption is very good for spots because the maximum size of a spot would correspond to a radius of about 3° (which would be a very rare case; typical spots are much smaller, see Table E.1 ). Plages may cover a larger area, which is expected to add a second-order distortion to an ideal time series: in most cases, the extreme sections of a structure (east and west) are expected to produce a signal that is very similar to the central part of the structures on average; in addition, we considered at each time step an irregular decay of the structures that has a random amplitude, so that the distortion produced by the assumption would not be identifiable given this other source of irregularity. The same structures were used for all stellar inclinations. The formulae are provided in Appendix C . We recall that we chose to study different laws for several of our parameters (as summarized in Fig. 1 ), so that several time series of spots, plages, and network features were produced for each of the 141 points of the 2D grid in ( B – V , Log ), each corresponding to a different parameter set ( Table E.1 ). This leads to 11 421 time series, or 22 842 when the two levels for Tspot are considered for each inclination and observable, hence a total of 228


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey I-Survey I-Survey I-Survey O B-Survey O O O O O O O B-Survey I-Survey I-Survey I-Survey B-Citation I-Citation I-Citation I-Citation O B-Citation O O O O O O O O O O O O O O O O O O O B-Survey I-Survey I-Survey O O O B-Survey B-Survey I-Survey I-Survey O B-Survey B-Survey I-Survey I-Survey O O B-Location O O B-Survey B-Survey I-Survey I-Survey O B-Location O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O B-Survey O O B-Wavelength O O O O O B-Survey O O B-Wavelength O O O O O B-Survey O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Software O B-Citation I-Citation I-Citation O O O O B-Wavelength I-Wavelength O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O B-Survey O O B-Survey O O O O O B-Survey O O O O O O O O O O B-Survey O B-Citation I-Citation I-Citation I-Citation O O O O B-Survey I-Survey I-Survey O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

to estimate the duty cycle and constrain the outburst recurrence time for a large sample of CVs. These properties will help constrain the mass-transfer rate and AML rate in the overall population. Better-constrained AML rates will improve evolutionary models and our understanding of the post-bounce population, and help to reconcile the observed and predicted position of the period minimum. Our catalogue was constructed with a goal towards answering these, and related, questions that require large samples of CV properties to answer. Additionally, it is meant as a reference for individual systems and a means of selecting CVs for targeted campaigns, based on their outburst properties. In Section 2 , we discuss the CRTS survey in detail. Section 3 describes the classification script, and Section 4 outlines the contents of our catalogue. The catalogue is then characterized in Section 5 . 2 CATALINA REAL-TIME TRANSIENT SURVEY The CRTS identifies transients in the data from the Catalina Sky Survey 1 (Larson et al. 1998 , 2003 ) – a photometric survey that searches for Potentially Hazardous Asteroids and Near Earth Objects. Three subsurveys constitute the Catalina Sky Survey, namely the original CSS (Catalina Schmidt Survey), the MLS (Mt Lemmon Survey) based in Arizona, and the SSS (Siding Spring Survey) in Australia, which ended on 2014 July 5. Each has a dedicated telescope with a 4k back-illuminated, unfiltered CCD camera (Djorgovski et al. 2010 ). The field of view and typical limiting magnitude for each survey (at ∼30 s integrations) are 82 and V ∼ 19.5 mag for the CSS, 11 and V ∼ 21.5 mag for the MLS, and 4and V ∼ 19 mag for the SSS. Together these surveys cover 30 000 deg 2 (, see Drake et al. 2014 for more details). The Galactic plane () is avoided due to overcrowding, as are the Magellanic Clouds. Each observation consists of four images (frames) that are separated by ≈10 min. The observing cadence is typically one to four times per lunation (depending on the subsurvey and field). Aperture photometry is performed using SExtractor software (Bertin Arnouts 1996 ) and converted to V -band magnitudes using standard stars as described in Drake et al. ( 2013 ). The CRTS began processing CSS data on 2007 November 8, MLS data on 2009 November 6 and 2010 May 5 for SSS data (Drake et al. 2014 ). Although there is Catalina Sky Survey data preceding these dates, the CRTS only use these data in the transient classification process – any object that was only variable during this time will not have been identified as a CRTS transient. To identify variability, the CRTS makes catalogues of the objects in each image and compare these to previously recorded magnitudes; they do not use image subtraction. An object needs to pass a number of tests to be classified as a transient. In each set of four frames (one epoch), it needs to be positionally coincident in at least three of the frames. This eliminates high proper motion objects (movement of > 0.1 arcmin min


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

to fit the blue wing of the bulk water absorption. The original dOH band and the shifted one are each fit using a Gaussian function. This fitting scheme is applied to each spectrum collected in the whole experiment to trace the diffusion of N 2 in the ASW. The band area of the shifted dOH band at 3672 cm −1 and the original dOH band at 3694 cm −1 during the isothermal experiment at each target temperature are shown in Figures 3 and 4 , respectively. It is evident that the shifting of dOH strongly depends on the target temperature. Figure 2. RAIR Spectra of: (1) 200 ML pure water ice deposited at 10 K; (2) after annealing at 70 K for 30 minutes; (3) after cooling down to 10 K and deposition of 16 ML N 2 on top; (4) after the isothermal experiment. The dashed lines are the fit while the solid circles are the experimental data. Spectra are offset for clarity. Figure 3. The band area of the 3672 cm −1 peak during isothermal experiments of N 2 on ASW at different target temperatures. The lines are fits using the model described in the Section 4 . Figure 4. The band area of the 3694 cm −1 peak during the isothermal experiment of N 2 on ASW at different target temperatures. 4. Modeling Fick’s law of diffusion was used to obtain the diffusion energy barrier as well as the pre-exponential factor D 0 , similar to other studies (Mispelaer et al. 2013 ; Karssemeijer et al. 2014 ; Lauck et al. 2015 ). The one-dimensional Fick’s law of diffusion is described by the following equation:where C ( z , t ) is the coverage (unitless) at depth z into the ice. If the amount of N 2 at depth z is just enough to cover the whole pore surface at that depth, then C ( z , t ) = 1. D ( T ) is the temperature dependent diffusion coefficient. We assume that 1 ML of water corresponds to a thickness of 0.3 nm. Therefore the total water ice thickness is h = 60 nm. Right after N 2 deposition at 10 K, N 2 should only cover the top few layers. We denote this initial penetration depth as d . After sufficient diffusion, N 2 penetrates into the ASW and evenly covers the pore surfaces. We have the following equations: The values of d , C 0 , and C inf can be obtained from Figures 3 and 4 . We denote the area of the original dOH band at 3694 cm −1 before and after isothermal experiment (such that N 2 covers the pore surface evenly) to be O i and O f , respectively. Similarly, we denote the area of the shifted dOH band at 3672 cm −1 before and after sufficient diffusion to be S i and S f , respectively. Figure 5 illustrates how the two dOH bands change with the fraction of pore surface covered by N 2 . Simple calculations show that In the model, we do not consider desorption, because in all of the isothermal experiment, the ice temperature is always below the temperature at which desorption occurs. Solving Equation ( 2 ) yieldsFigure 6 shows the solution at t = 0, 200, and seconds. At t = 0 s, N 2 molecules penetrate only to depth d . The shifted dOH band area should be proportional


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

current. We have also modified the weighting of the grid points in the velocity domain to check whether models that put less emphasis on small-scale dynamics in the corona could still resolve the bubbles. In all cases, the bubbles created were identical. However, changing the starting atmosphere will change whether a bubble is identified. This is due to the change in the transition region location with respect to the hydrogen column density. The beam will therefore not penetrate at the same location into a different starting atmosphere, which will not trap a portion of the chromosphere. It may very well be that the bubbles can be created in other starting model atmospheres but with a different set of beam parameters. In the example shown in Figure 2 , the initial bubble accelerates upward into the corona initially due to the pressure gradient caused by the beam heating in the chromosphere. However, a shock forms at T = 10 s, which highly accelerates the upper atmosphere, including the bubble. By T = 15 s into the simulation, this velocity is already over 300 km s −1 . The value of temperature, density, and electron density are all stationary within the bubble over time, which is essentially the mass motion of the material. Interestingly, due to the gas pressure increasing at the edges of the bubble, the overall width of the bubble decreases over time. For the bubble in Figure 2 , 45% of the mass is lost between 10 and 15 s due to redistribution of the bubble mass into the overall wave front. This compression can be most easily seen from T = 5 s to T = 10 s in Figure 2 . Individual well-defined bubbles were identified by investigating the temperature of each atmosphere. The location of the transition region was defined to be the lowest point in the atmosphere where the temperature gradient exceeds 5000 K between grid cells, while having a temperature value of at least 50,000 K. Any point above the transition region that has a temperature below 40,000 K was deemed to be a part of the bubble and logged. This resulted in 21/72 atmospheres with bubbles. Of these 21, all had beam fluxes above 1.0e11 erg cm −2 , and low-energy cutoff values above 15 keV. The most common value of low-energy cutoff being E c = 15 keV. This value provides the optimal penetration depth to efficiently trap a portion of the chromosphere while accelerating it upward (see Figure 1 ). The E c = 10 keV models mainly heated the upper chromosphere and some of the transition region, and did not lead to the creation of bubbles. The E c = 25 keV models only resulted in bubbles for the highest spectral indices ( δ > 5) with the highest levels of beam flux (3.0e11 erg cm −2 ). This is due to the extremely high energy density that needs to be applied to the lower chromosphere in order to heat it sufficiently and create a bubble. Harder beams tend to have a larger spread of energy into the denser portions of the photosphere and lower chromosphere. The bubble lifetimes can be as short


O O O O O B-ObservationalTechniques I-ObservationalTechniques O O O O O O O O O O O O O O O O B-ObservationalTechniques I-ObservationalTechniques O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O

pointings (what we will call translational dithering), and with multiple angles due to offsets of the camera rotational angles (what we will call rotational dithering). These aspects of the observing strategy can be used in addition to traditional methods to mitigate WL systematics. Related studies that explore possible translational dithers to determine how the LSST observing strategy can be used to reduce systematic errors in measurements of the LSS have already been conducted (Carroll et al. 2014 ; Awan et al. 2016 ; LSST Science Collaboration et al. 2017 ). In this paper, we study how different aspects of the observing strategy help mitigate the additive shear bias, and rank a set of simulated strategies based on their performance for WL systematics (while the statistical trade-offs are not addressed here). In Section 2 , we explain WL and its systematics in more detail; in Section 3 , we present a representative set of LSST survey strategies and separately the three translational dithering strategies that are studied in this paper, although our method can be easily applied to new observing strategies in the future as they are released. In Section 4 , we present our methodology, using both a direct effect on the cosmic shear bias, and simpler statistical tests of uniformity; and in Section 5 , we analyse the results, and discuss them in the context of the 2018 call 2 for proposals on optimizing the LSST observing strategy for different science cases. 2 BACKGROUND This section includes background information on WL, the observable quantities that are measured and used to constrain cosmological parameters, and the observational systematics that can be affected by different choices in observing strategy. 2.1 WL measurements WL is a ubiquitous statistical effect that modifies the light profiles of galaxies as the light rays from those galaxies pass by other mass along the line of sight before they are observed. WL by the LSS of the universe distorts the shapes and sizes of galaxies according to the distortion matrix: where κ is the convergence, a measure of the magnification due to WL, and γ 1 , γ 2 are spin-2 shear, a measure of the shape distortion due to WL (Bartelmann Schneider 2001 ). The distortion matrix can be used to transform lensed coordinates into unlensed coordinates, such that for unlensed and lensed positions on the sky and , (Mandelbaum et al. 2015 ). The cosmic shear signal can be measured using shear–shear correlation functions between pairs of galaxies, as follows: where t and × are the tangential and cross components of the shear, θ is the angular separation on the sky, φ is the polar angle (Kilbinger 2015 ), refers to the expected value, and R () refers to the real component. We only consider ξ + in our analysis because the biases on ξ − are much closer to 0, and so not much work is needed to mitigate them (Jarvis et al. 2016 ). It is clear from the definition of these correlation functions that any bias in the measured


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Acknowledgements This work was supported by WOW from INAF through the Progetti Premiali funding scheme of the Italian Ministry of Education, University, and Research. This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. M.P. and I.R. acknowledge support from the Spanish Ministry of Economy and Competitiveness (MINECO) and the Fondo Europeo de Desarrollo Regional (FEDER) through grant ESP2016-80435-C2-1-R, as well as the support of the Generalitat de Catalunya/CERCA programme. J.I.G.H., R.R., and B.T.P. acknowledge financial support from the Spanish Ministry project MINECO AYA2017-86389-P. B.T.P. acknowledges Fundación La Caixa for the financial support received in the form of a Ph.D. contract. J.I.G.H. acknowledges financial support from the Spanish MINECO under the 2013 Ramón y Cajal program MINECO RYC-2013-14875. A.S.M. acknowledges financial support from the Swiss National Science Foundation (SNSF). G.S. acknowledges financial support from “Accordo ASI-INAF” No. 2013-016-R.0 July 9, 2013, and July 9, 2015. A.S.M. acknowledges financial support from the Swiss National Science Foundation (SNSF). M.Pi. gratefully acknowledges the support from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement no. 313014 (ETAEARTH).


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Acknowledgements This work has made use of the VALD data base, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna. This work is based on observations obtained at the Canada–France–Hawaii Telescope (CFHT) that is operated by the National Research Council of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France, and the University of Hawaii. All authors acknowledge the advice and assistance provided on this and related projects by the members of the BinaMIcS and MiMeS collaborations. MS acknowledges the financial support provided by the European Southern Observatory studentship program in Santiago, Chile. MS and GAW acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC). EA acknowledges financial support from the Programme National de Physique Stellaire (PNPS) of INSU/CNRS. We acknowledge the Canadian Astronomy Data Centre (CADC). This project used the facilities of SIMBAD.


O O O O O O O O O O O B-Observatory B-Observatory I-Observatory O B-Location O O O O O O O O O O O O O O O O B-Observatory I-Observatory O O B-Observatory O B-Proposal O O O O O B-Database O B-Database O O O O B-Organization O B-Location I-Location O O O O O O O O O O O B-Organization I-Organization I-Organization B-Organization O B-Telescope O B-URL O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization B-URL O O O O B-Organization O O O O O O O O O O O O O B-Collaboration I-Collaboration I-Collaboration O B-Survey I-Survey I-Survey I-Survey I-Survey O O O O O O O B-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization O B-Survey I-Survey I-Survey I-Survey I-Survey B-Survey O O O O O B-Organization I-Organization I-Organization I-Organization O O O O B-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization O B-Organization I-Organization O B-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization O B-Telescope I-Telescope I-Telescope O O O O B-Organization I-Organization I-Organization I-Organization O B-Observatory I-Observatory O B-Telescope I-Telescope I-Telescope O O O O B-Observatory I-Observatory I-Observatory O O O O B-Location B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Location B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O B-Observatory I-Observatory O B-Wavelength O O O O B-Dataset I-Dataset I-Dataset I-Dataset I-Dataset I-Dataset O O O O B-Dataset O O O O B-Survey I-Survey B-Survey O O B-Survey I-Survey I-Survey I-Survey B-Survey O O O O O B-Telescope I-Telescope O O O O O O O B-Organization I-Organization O O O B-Observatory O B-Survey I-Survey I-Survey O O O O B-Organization I-Organization I-Organization I-Organization O B-Organization I-Organization O B-Grant I-Grant O B-Survey O O O O O O O O O B-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization O B-Organization I-Organization O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization B-Location O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Location O B-Organization I-Organization I-Organization B-Organization I-Organization O B-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization I-Organization O B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory O B-Organization I-Organization I-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O B-Grant O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O B-Organization I-Organization I-Organization O O B-Grant O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization B-Organization O O B-Organization I-Organization I-Organization I-Organization O B-Survey I-Survey O O O O B-Organization I-Organization I-Organization I-Organization B-Organization O O O O O B-Archive I-Archive I-Archive I-Archive I-Archive I-Archive I-Archive O O O O B-Archive I-Archive I-Archive I-Archive O O O O B-Organization I-Organization I-Organization I-Organization I-Organization


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We greatly acknowledge the anonymous referee for comments. We thank Dr. Shuhong Yang for many discussions, suggestions, and comments, and Dr. Xiaoshuai Zhu for helping to handle the HMI full-disk vector magnetic field. This research is supported by the National Natural Science Foundation of China (41474150, 41627806, 41474149, 41404135, and 41274178). Z.H. thanks the China Postdoctoral Science Foundation and the Young Scholar Program of Shandong University, Weihai (2017WHWLJH07). IRIS is a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research center and major contributions to downlink communications funded by the Norwegian Space Center through an ESA PRODEX contract. Courtesy of NASA/SDO, the AIA and HMI science teams, and JSOC.


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Acknowledgements We thank the referee for providing very useful comments which helped to improve the paper. We thank the staff of the GMRT that made these observations possible. GMRT is run by the National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research.


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simulation results revealed a variation in the plasma conditions in the vicinity of the planetary magnetosphere with changing wind conditions. Since many of the runs produced similar results, we present here a limited selection of four cases representative of the different stellar wind regimes experienced by TRAPPIST-1e throughout its orbit: two sub-Alfvénic regions, a super-Alfvénic region, and the transition between the sub-Alfvénic and super-Alfvénic regimes (Figure 3 ). The stellar wind conditions corresponding to these simulations are listed in Table 3 . Figure 3. Magnetospheric structure of a planet situated at the orbit of TRAPPIST-1e. The planet is represented by a magenta isosurface, and it was modeled to have a magnetic field strength of 0.3 G and an evaporative outflow with a temperature of 10 4 K, escape velocity 10 km s −1 , and density of 4 × 10 8 amu cm −3 . The stellar wind flows from the positive x -axis. The solutions are for the sub-Alfvénic (top panels), transition (bottom left), and super-Alfvénic (bottom right) stellar wind cases. The stellar wind conditions were extracted from a 3D MHD simulation of TRAPPIST-1a by Garraffo et al. ( 2017 ). The color scale shows an equatorial projection (plane z = 0) of the particle number density. Planetary magnetic field lines connected to the planet are shown in white, while stellar field lines are shown in cyan. The planetary outflow is denoted by velocity streamlines (black with arrows). Note that the lines transition in color as they pass through the equatorial plane. The motion of the planet is toward the positive y -axis. All of the stellar wind conditions experienced by TRAPPIST-1e are much more extreme than the solar wind conditions experienced by Earth. In the region surrounding the planets, the stellar wind speeds reach close to 1400 km s −1 and plasma densities reach 10 3 –10 5 times the solar value at 1 au (Garraffo et al. 2017 ). We consider the effect of these strong stellar wind conditions on a planetary outflow in Figure 3 . In each case, the planet is represented by a magenta isosurface at the center of the GM domain. The z = 0 plane of number density shows the planetary outflow, which is strongly dependent on the stellar wind conditions and therefore varies substantially between the different cases. Under all four stellar wind regimes, the planetary magnetic field lines are strongly advected by the wind, causing changes in the magnetospheric configuration, which results in an asymmetric outflow in three dimensions. The shape of the planet’s outflow is strongly influenced by the direction and relative magnitudes of the components of both the magnetic field and velocity of the incoming stellar wind. By comparing cases 1 and 2, where the planet is within the sub-Alfvénic region, the stellar wind components (shown in Table 3 ) are broadly similar apart from the y and z magnetic field components. As can be seen in Figure 3 , this produces wholly different magnetospheric


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per degree of freedom lies in the range 0.4–3.0 increases from 1098 to 1123 when fitting this simpler model. We therefore proceed in the knowledge that the excess rest-frame 1–3 μ m emission in our sample of quasars can be adequately described using a fixed temperature blackbody in our SED model, consistent with emission from dust at its sublimation temperature (e.g. Barvainis 1987 ). However, we ascribe no further significance to the exact value of this temperature, noting instead that it is merely the temperature describing the blackbody which best describes the infrared colours of the quasars, and not necessarily the exact physical temperature of the emitting dust. In particular, we note that this temperature is degenerate with the slope of the power-law continuum (−1.84) which we assume in our model, and a different continuum slope would lead to a slightly different hot dust temperature. Our subsequent results would be qualitatively unchanged. 3.4 Primary sample Fixing the blackbody temperature allows us to fit our model to higher redshift objects, where we no longer have two independent data points probing the hottest dust emission. For what we hereafter refer to as our ‘primary’ sample, we take all quasars in the redshift range 1.2 z 2.0 (see Appendix D for a discussion of the limitations of higher redshift objects). For our primary sample, the W2 band probes rest-frame wavelengths ≈2 μ m where the hot dust blackbody is expected to be the dominant contributor to the total flux. We require detections in all of the JKW12 bands. To avoid biases against hot-dust-faint objects which might drop out of one or more of these bands we restrict our primary sample to Y 19.3, where the sample is better than 95 per cent complete in all UKIDSS bands, giving a total of 12 294 objects. We have verified that restricting our sample to a brighter flux limit does not change any of the subsequent results. When constructing the sample, only 243 objects are lost due to missing unWISE data, meaning our primary sample is more than 98 per cent complete in W1 and W2 . Some of these objects will be missed due to crowded fields as the WISE point spread function is significantly broader than that of SDSS. However, we cannot rule out the possibility that a small fraction of objects are lost due to intrinsically faint fluxes in the rest-frame near-infrared. We fit our primary sample with two free parameters in the model, fixing T blackbody = 1280 K and allowing just the E ( B − V ) and normalization of the hot dust component to vary. The median χ 2 per degree of freedom when fitting our model to this sample is 1.30. We show in Appendix E that our subsequent results remain unchanged when instead using a hotter or cooler blackbody in our model quasar SED. 5022 objects from our primary sample with z > 1.56 also have C iv line information from Rankine et al. ( 2020 ). As the sample is restricted to z 2.0, Mg ii emission is present in the SDSS spectra to enable black hole


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Acknowledgements We thank Andrei Mesinger for making 21cmfast publicly available and an anonymous referee for helpful comments and suggestions that improved this paper. This research was supported by National Science Foundation (NSF) grants AST-1333360 and AST-1614333, NASA grant NNX17AG23G, and Hubble theory grants HST-AR-13895 and HST-AR-14326.


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ellipticals, reported in Fig. 3 , also occurs within the uncertainties considering five different redshift intervals for the ellipticals, from z = 0.1 to z = 0.35, each containing ∼200 sources. This is apparent in Fig. 3 , where the cases of ellipticals at 0.1 z 0.15 and at 0.1 z 0.33 are reported. Figure 3. Average excess surface density of galaxies (detected in i band, with magnitude threshold M * + 2) as a function of the projected distance from the BL Lac objects (green filled circles), inactive ellipticals matched in redshift with the BLL sample (orange filled triangles), FR Is (violet open stars) and inactive ellipticals (grey open triangles) matched in redshift with the FR I sample. The errors are the standard errors of the mean. The sample of FR I radio galaxies is limited to 0.1 z 0.15 (see Section 2 ), therefore, we can only do a direct comparison with the sample of inactive ellipticals in the same redshift range. We found that within ∼0.5 Mpc the excess density of the FR I radio galaxies is a factor of 2 larger than that of ellipticals (see Fig. 3 ). Since we have shown that the galaxy environment of ellipticals does not change in the redshift range 0.1 z 0.35, we are confident that also the comparison of environment between BLL and FR I radio galaxies is reliable. The result is illustrated in Fig. 3 . In particular, within 0.5 Mpc the excess density is ∼13 Mpc −2 for the FR Is, while for the BLLs it is ∼6 Mpc −2 . 5 CONCLUSIONS We find that the galaxy environment of low-redshift BL Lac objects is indistinguishable from that of low-redshift inactive ellipticals of comparable galaxy luminosity. This result is similar to that found by Karhunen et al. ( 2014 ) and Sandrinelli et al. ( 2018 ) who considered large samples of low-redshift quasi-stellar objects (QSOs), finding an overdensity within 0.5 Mpc of ∼1.1. So it seems that in the local Universe BLLs, quasars and inactive ellipticals are similar in terms of surrounding galaxies. We also find that the clustering of galaxies around radio galaxies of FR I type is on average a factor of ∼2 richer than that of BL Lac objects. In spite of the difference in the redshift distribution this difference appears robust since no variation of galaxy environment is found for inactive elliptical galaxies of same luminosity up to z ∼ 0.3 (see details in Section 4 ). This result is further illustrated in Fig. 4 , where we report the cumulative distributions of the excess density for the two classes of objects within the projected distance 350 kpc. The medians are 5.7 and 13.6 Mpc −2 for BLLs and FR Is, respectively, and the Kolmogorov–Smirnov (KS) test yields a p -value 4 × 10 −4 . The population of BLLs is positioned at lower excess densities with respect FR Is. Figure 4. The comparison of the cumulative distributions of excess galaxy density (within 350 kpc) of BL Lac objects (0.1 z 0.33) and FR I radio galaxies (0.1 z 0.15). The two distribution are significantly different (see text). The


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The authors thank Hideyuki Saio for his careful reading of the manuscript, as well as Luc Dessart and Ben Davies for interesting suggestions. G.M. acknowledges support from the Swiss National Science Foundation (project number 200020-146401) and thanks for the hospitality of the Lowell Observatory, where part of this work was done. C.G. acknowledges support from the European Research Council under the European Unions Seventh Framework Programme (FP/20072013)/ERC Grant Agreement No. 306901. P.M.’s involvement was supported by the National Science Foundation through AST-1008020.