O O O O O O O O O O O O O O O O O O O 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 B-CelestialObjectRegion I-CelestialObjectRegion O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O B-CelestialObjectRegion I-CelestialObjectRegion O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset 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 O O O O O O O O O O O O B-CelestialObject I-CelestialObject 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 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 B-Wavelength O O O O O O O O B-Dataset O O O O O O B-CelestialObject I-CelestialObject 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-CelestialObject I-CelestialObject 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 B-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 B-Citation I-Citation I-Citation 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

those integrated orbits. Both these quantities correlate with age, with OCs younger than 300 Myr being all contained within 180 pc of the plane, while half the older clusters have orbits that extend beyond this limit. The oldest OC in our sample is NGC 2682, one of the oldest known open clusters, and its orbit strays more than 400 pc from the Galactic plane. We also computed the eccentricity of each orbit ( where r p and r a are the perigalacticon and apogalacticon of the orbit), and found no apparent correlation of eccentricity with age (bottom panel of Fig. 12 ). The orbital parameters of the integrated orbits are listed in Table 3 . 6 Discussion The two-million-star TGAS sample allows us to identify members and derive mean parallaxes in clusters located further than 1000 pc from us, thus enabling studies of the Galactic disk on a larger scale than the works based on the results of the H IPPARCOS catalogue (e.g. Robichon et al. 1999 ; Bouy Alves 2015 ), which is severely incomplete beyond 100 pc and for which the relative parallax error reaches 50% at 350 pc. The distribution of OCs identified in this paper is shown in Fig. 13 , along with the schematic location of the spiral arms of the Milky Way in the model of Reid et al. (2014) . We remark that almost all OCs in this study are located in the inter-arm region. In order to trace the spiral structure of the thin disk, our sample would require a significant number of objects younger than 20 Myr (log t 7.3), as clusters drift away from their birthplace over this timescale (see e.g. Dias Lépine 2005 ). These young clusters and associations are often sparsely populated and/or embedded in their progenitor molecular cloud (thus requiring the use of infrared photometry). Due to the magnitude limits of the TGAS solution, even nearby clusters such as Chamaeleon I (2 Myr old, 160 pc from us, Whittet et al. 1997 ) are not present in the catalogue. Hints of objects such as IC 2395 (6 Myr, 800 pc Clariá et al. 2003 ) can be seen, but not with a sufficient number of stars for our method to establish secure membership. Another common tracer of the spiral structure of the disk are the so-called OB associations, which are even sparser groups of bright, short-lived blue stars, whose identification requires the use of multi-band photometry (and ideally spectroscopy). Such a search is well beyond the scope of this paper, aimed at identifying stellar clusters from astrometric data. Fig. 13 Position of the OCs studied in this paper in Galactic rectangular XY Z coordinates. Black dots: OCs from this study with relative parallax errors under 50% (considering an additional 0.3 mas calibration error for all OCs). Crosses: OCs from this study with relative parallax error over 50%. Open symbols: OCs from Gaia Collaboration (2017) . The spiral arms are traced according to the model of Reid et al. (2014) . Out of the 128 OCs for which we derived mean astrometric parameters, 67 have mean proper motions obtained from


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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

because of the strength of the field. We changed the initial conditions of the blobs as little as possible from one case to the other. The compromise was to use the same initial velocity, which the blobs may acquire when they flow inside very large arches, such as in huge prominences. The evolution that we find is strikingly different. In the fully aligned case, the blobs and the slow-mode shocks flow along the field lines and do not perturb the intense magnetic field. The blobs remain compact and move inside independent magnetic channels. In the misaligned case, the shuffling of the field lines driven by the blobs creates a feedback on the blobs themselves and mixes them. At the same time, the conglomeration is structured into thinner filaments. In this case, it is impossible to establish the native channel or the initial shape of the blobs, they lose their identity completely. Misaligned propagation is also an efficient way to excite fast Alfvén wave fronts, which travel ahead of the cloud, in addition to shocks. In summary, this work highlights the possible back-effect of the confining magnetic field on the propagation of fragmented flows inside it. When they are perfectly channelled, plasma fragments keep their identity as single blobs with no mixing, and the magnetic field is left unchanged as well. When there is some misalignment, the magnetic field can react with a shuffling of the field lines that mixes and merges the fragments, thus changing the plasma configuration completely. This represents a very effective mechanism of plasma mixing in the presence of a magnetic field, different from standard shear-like instabilities. The field lines can be effectively shuffled by irregular plasma motion and its feedback to the plasma is naturally chaotic. We may wonder which is the most common situation: aligned or misaligned fragment motion. We expect that if the plasma is confined since the beginning and the magnetic field does not change much along the track, the motion should be mostly aligned to the field and even more if blob velocity is lower, as in the coronal rain, in which similar blobs fall by gravity and reach loop footpoints with a velocity of about 60 km s ( Fang et al. 2013 , 2015 ; Moschou et al. 2015 ). All our evolution occurs on timescales about two orders of magnitudes shorter than in these other studies, so that our results might only apply to the very final stages of their modelling. On the other hand, downfalling from large distances through a significantly changing magnetic field might result in misaligned fragment motion. A situation like this may occur in the accretion onto young protostars from circumstellar disks, both at the flow origin (disk) and close to the flow impact, where the magnetic field of the star might become very complex. This process might therefore lead to further mixing of downflows and increase their fine substructure. For this exploratory work, the initial conditions of our simulations differ in


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 B-CelestialRegion I-CelestialRegion O O O B-Citation I-Citation I-Citation O O B-CelestialRegion I-CelestialRegion B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O B-CelestialObject 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 B-CelestialRegion I-CelestialRegion O O O O O O O O O O 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 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 B-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 B-CelestialRegion I-CelestialRegion O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O B-CelestialObject O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O B-CelestialObject O O O O O O B-CelestialRegion I-CelestialRegion B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O B-Citation I-Citation I-Citation I-Citation O O B-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 B-CelestialRegion I-CelestialRegion 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

as a function of metallicity and the data (see e.g. fig. 10 of Nomoto et al. 2013 ). Regardless of this fact, we can empirically compare the derived values of [V/Fe] for our stars with those from known populations. Fig. 3 shows the derived values of [V/Fe] as a function of metallicity for the extreme-velocity stars (black) compared to the Galactic disc (light green circles Battistini Bensby 2016 ), the Galactic halo (Yong et al. 2013 ; Roederer et al. 2014 ), and the LMC (Van der Swaelmen et al. 2013 ). The candidate extreme-velocity stars have a wide range in [V/Fe], again consistent with the large scatter observed for the Galactic halo. Cu : Copper (Cu) is an odd-Z element that is thought to be largely produced via SNIa and hypernovae but is also produced by secondary phenomena in massive stars and the weak s-process (e.g. Mishenina et al. 2002 ). Like many elements, the exact nucleosynthetic process that forms Cu is still under debate (e.g. Andrievsky et al. 2018 ). As shown in Fig. 4 , previous surveys found a large scatter in [Cu/Fe] at the metallicities of our stars, and similar low values in the LMC and stellar halo. Again, the stars in our sample fit within the scatter of the stellar halo, but Cu does not allow us to distinguish between stars born in the Galactic disc, halo or LMC. Figure 4. The [X/Fe] ratios of the program stars (black filled symbols) as a function of [Fe/H] for Cr, Mn, Ni, and Zn from top to bottom, respectively, on the left and Sc, Co, and Cu from top to bottom, respectively, on the right. These largely represent the Fe-peak elements. The symbols are the same as in Fig. 3 . Sc : Scandium (Sc) is an odd-Z element that, similarly to vanadium, has a poorly understood nucleosynthesis production mechanisms. This is evident by the large discrepancies between the theoretically computed and observed trends of [Sc/Fe] as a function of [Fe/H] (e.g. Kobayashi et al. 2006 ; Nomoto et al. 2013 ). As seen in Fig. 4 stars belonging to either the LMC or Galactic disc show [Sc/Fe] increasing with decreasing metallicity, with [Sc/Fe] lower in the LMC at fixed metallicity compared to the Galactic disc (Van der Swaelmen et al. 2013 ). On the other hand, stars in the Galactic halo have [Sc/Fe] values which are either slightly enhanced (Yong et al. 2013 ) or solar (Roederer et al. 2014 ) though with large intrinsic scatter. The candidate extreme-velocity stars are all enhanced in [Sc/Fe] at levels consistent with the Galactic halo but not with the LMC. 4.4 Fe-peak elements: Cr, Mn, Co, Ni, Zn Unlike the α or odd-Z elements, the Fe-peak elements, such as chromium (Cr), scandium (Sc), manganese (Mn), cobalt (Co), nickle (Ni), and zinc (Zn), are formed through a variety of paths (Iwamoto et al. 1999 ; Kobayashi et al. 2006 ) but are largely dispersed into the ISM in a similar way as iron. As such, these Fe-peak elements largely track the abundance of Fe. At the metallicity range of the candidate extreme-velocity stars (–2 [Fe/H] –1),


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B-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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 sheared arcades were likely to reconnect during the flare, and led to the formation of a flux-rope-like core structure that drove the outward eruption. The structure rose slowly with writhing motion and mass drainage, and finally stopped, and gradually became invisible afterward. The process fits into the scenario of a failed eruption of a helical kinked flux rope (e.g., Fan 2005 ; Guo et al. 2010 ; Török et al. 2010 ; Hassanin Kliem 2016 ). The core structure had a peak velocity of 178 km s −1 , and ceased at a height around 0.24 R Sun . For the successful eruption (eruption2), two sets of sheared arcades above the PIL were also identified before its onset. Their corresponding footpoints brightened during the flare, indicating a reconnection between them. The eruption rapidly evolved into a fast CME (with a peak velocity of 1041 km s −1 ) that propagated into the heliosphere. 2. Before both eruptions, continuous flux emergence existed in the source AR. For the source polarity pair, the positive one displayed a faster westward motion than the negative one, resulting in a net convergence toward the PIL. Due to continuous flux emergence and shear motion on the photosphere, magnetic helicity was injected, with a dominating shear term. A larger quantity of magnetic free energy and magnetic helicity was accumulated before the successful eruption. Before the onset of the eruptions, the core region, i.e., the source FPIL of the successful eruption, displayed a larger non-potentiality than the failed eruption. For example, before the successful eruption, more magnetic free energy resided above the FPIL, and larger values were derived for the mean current density, current helicity, and the shear angle that measures the core region’s non-potentiality. The decay index distributions showed no significant difference, although the critical height for the successful eruption was slightly lower (around 3 Mm) than that for the failed eruption. The ratio of magnetic fluxes at 42 Mm and 2 Mm (in the FPIL mask) was smaller before the successful eruption, indicating a relatively weaker confinement. 3. Before the failed eruption, two sets of sheared field lines that corresponded well with the observed sheared arcades, could be identified in the coronal magnetic fields extrapolated by the NLFFF method. After the eruption, the sheared field lines disappeared, while near-potential loops were identified above the PIL. Combined with the observed eruption process, we conjecture that a reconnection between the sheared arcades may have occurred. A similar topology change was found during the successful eruption. Significant enhancements of B h were found in the FPILs after both eruptions, while the value of the Lorentz force change during the successful eruption was an order of magnitude larger than that for the failed eruption. Decreases in magnetic free energy were also found after both eruptions, although the magnitude for the successful eruption was larger


B-Person O O B-Organization B-Grant I-Grant O O

RM thanks the NRF's Thuthuka Grant for funding.


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MP is supported by NASA through Einstein Postdoctoral Fellowship grant number PF3 140113 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060 . We thank Katya Giannios for suggestions on the title. The simulations were performed on XSEDE resources under contract No. TG-AST120010 , and on NASA High-End Computing (HEC) resources through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center. We also gratefully acknowledge access to the PICSciE-OIT High Performance Computing Center and Visualization Laboratory at Princeton University.


O O O O O O B-Grant I-Grant I-Grant O O O O B-Organization I-Organization I-Organization O O O B-URL O O O O O O O O O O O

ACKNOWLEDGEMENTS We acknowledge support from the ISF-ICORE grant 1829/12. The authors acknowledge the University of Maryland supercomputing resources ( http://hpcc.umd.edu ) made available for conducting the research reported in this paper.


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

2 ) are subtracted from the DM values to produce this plot. The DMs obtained with only using BAND5 data are not shown in the plot as their uncertainties are large. The four pulsars presented in this work have different frequency evolution of their parameters like flux density, profile shapes, scintle sizes and scatter broadening. To illustrate this, frequency-resolved profiles for all four pulsars are shown in Fig. 1 . As a result, we had to obtain ToAs with different frequency resolution for each of them as described in Sect. 4 . 4. Results and discussions The DM time series obtained using the methods described in Sect. 4 for the four pulsars are shown in Fig. 3 . The left panel shows the DM measured using METHOD1 and the right panel shows DM obtained using METHOD2 . The median DM values and their uncertainties for the four pulsars are listed in Table 2 . We have only reported the measurements for which a reduced χ 2 10 is obtained with TEMPO2 . Some epochs show reduced χ 2 values worse than 10, but looking at each of them individually showed that they were affected by heavy RFI. Although we put a higher limit on χ 2 for getting the good measurements, most of them have reduced χ 2 much less than 10 and close to 1. The median value of the DMs estimated using METHOD1 and METHOD2 differs slightly for PSRs J1713+0747 and J1909−3744, while there is a clear offset between the DMs for PSRs J1939+2134 and J2145−0750. The possible cause of this difference is the underlying difference in the profile alignment methods used. Table 2. Median values of ToA uncertainties and DM. Notes. The ToA uncertainty for each pulsar is shown using the frequency and time averaged profiles with the analytic template created in METHOD2 for both bands. The median DM and DM uncertainty obtained from the DM time series of each pulsar using METHOD1 and METHOD2 are also given. In the present work, we have used the data obtained by observing simultaneously at both BAND3 and BAND5 using a 100 MHz bandwidth. The fractional bandwidth at BAND5 is a factor of ∼4−5 smaller than those used by most of the other PTAs, which reduces the precision with which we can obtain DM at BAND5. But a better fractional bandwidth at BAND3 enables us to get a good handle on the DM. The DM precision we can obtain in general by using this dataset with the BAND3 data alone is ∼10 −3 and while combining the two bands it gets better by an order of magnitude to ∼10 −4 . In particular, we achieve an order of magnitude better precision of 10 −4 with BAND3 and 10 −5 with BAND3 and BAND5 for PSR J1939+2134. Combining the two widely separated bands for measuring DM can create a bias due to the slightly different IISM the rays of these two bands pass through ( Cordes et al. 2016 ). Comparing our results obtained using the two methods described in this work to that of the recently published ones by Alam et al. (2021) and Donner et al. (2020) show interesting trends. For two pulsars, J1713+0747 and J2145−0750, we


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This paper makes use of the following ALMA data: ADS/JAO.ALMA#2015.1.00582.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST 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. N.K. acknowledges support from the Department of Science and Technology via a Swarnajayanti Fellowship (DST/SJF/PSA-01/2012-13).


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

F.L. thanks G. Battaglia, F. Fraternali, G. Pezzulli, R. Sancisi, and M. Verheijen for many inspiring discussions over the years. Support for this work was provided by NASA through Hubble Fellowship grant # HST -HF2-51379.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. This publication was made possible through the support of a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation.


O O O O O B-Telescope I-Telescope B-Location I-Location O O B-Organization B-Observatory I-Observatory I-Observatory B-Telescope I-Telescope I-Telescope I-Telescope O O O O O O O O B-Location I-Location I-Location I-Location O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Location B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory O O B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Proposal B-Proposal O B-Proposal O B-Telescope I-Telescope O O O O B-Location I-Location I-Location I-Location O B-Organization I-Organization I-Organization I-Organization O O B-Location B-Observatory I-Observatory I-Observatory I-Observatory I-Observatory I-Observatory O O B-Organization I-Organization I-Organization I-Organization O O O O O B-Location B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O O O O B-Organization I-Organization I-Organization O O B-Location I-Location B-Grant I-Grant I-Grant B-Grant O O O B-Grant B-Grant O O O O O O O B-Organization O B-Fellowship I-Fellowship O O B-Fellowship I-Fellowship O O O B-Organization I-Organization I-Organization I-Organization O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O B-Organization O B-Grant O B-Telescope I-Telescope O B-Telescope I-Telescope O B-Telescope O

Based on observations collected at Copernico telescope (Asiago, Italy) of the INAF—Osservatorio Astronomico di Padova. The William Hershel Telescope and its service programme are operated on the island of La Palma by the Isaac Newton Group of Telescopes in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofsica de Canarias (prog. ID: W/2017A/25, W/2017A/30, and SW2017a12). The Liverpool Telescope is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto deAstrofisica de Canarias with financial support from the UK Science and Technology Facilities Council. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 320964 (WDTracer). Support for this work was provided by NASA through Hubble Fellowship grant # HST -HF2-51357.001-A, awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. Facilities: 1.82-m Copernico(AFOSC) , 4.2-m WHT(ISIS) , Liverpool:2m .


O O O 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 O O O B-Location B-Organization I-Organization I-Organization O B-Grant O O B-Organization I-Organization I-Organization O O O O O O O B-Location B-Organization I-Organization I-Organization I-Organization I-Organization O O B-Grant O B-Grant I-Grant O O O B-Organization I-Organization I-Organization B-Grant O O B-Grant O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Person O O O O B-Organization I-Organization I-Organization I-Organization O O O O O B-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility B-ComputingFacility O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization B-Location O B-URL O B-Person O O O O O B-Organization B-Grant B-Person O O B-Organization O B-Grant O B-Grant I-Grant

Acknowledgments We thank the referee for the useful discussion and comments that have greatly helped to improve this Letter. This Letter is based on data from the CALIFA Survey, funded by the Spanish Ministry of Science (grant ICTS-2009-10) and the Centro Astronómico Hispano-Alemán. This work has been supported by the Spanish Ministry of Science and Innovation under grants AYA2016-77237-C3-1-P, and Consolider-Ingenio CSD2010-00064; and by the Junta de Andalucía (FQM-108), and the AYA2014-53506-P grant, funded by the ‘Ministerio de Economía y Competitividad’ and by the ‘Fondo europeo de desarrollo regional FEDER’. RAM acknowledges support by the Swiss National Science Foundation. We acknowledge the contribution of Teide High-Performance Computing facilities. TeideHPC facilities are provided by the Instituto Tecnológico y de Energías Renovables (ITER, SA). URL: http://teidehpc.iter.es . IMV has been partially supported by MINECO AYA2014-583308-P. SFS thanks the Conacyt programmes 180125 and DGAPA IA100815.


O O O O O O O O O B-Organization I-Organization I-Organization B-Fellowship I-Fellowship I-Fellowship I-Fellowship O O O B-Fellowship I-Fellowship O O B-Organization I-Organization I-Organization O O O B-Grant O B-Observatory I-Observatory I-Observatory O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O B-Organization I-Organization I-Organization O O O O B-ComputingFacility I-ComputingFacility O O O O O B-Organization I-Organization I-Organization O O B-Grant O O B-Organization I-Organization I-Organization I-Organization O B-ComputingFacility I-ComputingFacility O O O O O O B-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 O O O B-Organization I-Organization I-Organization I-Organization O B-Location O O O B-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event I-Event O O O O O O O O B-Person I-Person O O O B-Model O O O B-Person I-Person O O O O O O O O O O O O O

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under grant No. DGE 1144083 and the National Science Foundation under grant No. 1616538. The National Solar Observatory is operated by the Association of Universities for Research in Astronomy under a cooperative agreement with the National Science Foundation. This work utilized the Janus supercomputer, which is supported by the National Science Foundation (award number CNS-0821794) and the University of Colorado Boulder. The Janus supercomputer is a joint effort of the University of Colorado Boulder, the University of Colorado Denver and the National Center for Atmospheric Research. The authors are grateful for the International Space Science Institute in Bern for hosting the International Team 335 “Toward New Models of Solar Spectral Irradiance Based on 3D MHD Simulations,” which resulted in fruitful discussions. The authors thank Matthias Rempel for providing the MHD snapshot, and both Han Uitenbroek and the referee for useful feedback to improve the quality of the work.


O O O B-Person I-Person B-Person I-Person O B-Person I-Person O O O B-Person O O O O B-Fellowship I-Fellowship O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Person O O O B-Organization O B-Organization B-Grant O O O O O O O O O O O O O B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization O O O O O O O O B-Telescope O B-URL O O B-Organization O O O O O O O O O B-Organization O O B-Organization O B-Location O O 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 O B-Location B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization O B-Survey O O O B-URL O B-Survey O O O O B-Collaboration I-Collaboration I-Collaboration O O O O O O B-Collaboration I-Collaboration O O B-Organization I-Organization I-Organization O B-Collaboration I-Collaboration I-Collaboration B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O B-Collaboration I-Collaboration I-Collaboration O B-Collaboration I-Collaboration I-Collaboration B-Organization I-Organization O B-Organization I-Organization I-Organization I-Organization I-Organization O B-Collaboration I-Collaboration I-Collaboration I-Collaboration I-Collaboration B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization I-Organization B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization B-Organization I-Organization O B-Collaboration I-Collaboration I-Collaboration B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization B-Organization I-Organization B-Organization I-Organization I-Organization B-Organization I-Organization I-Organization O B-Organization I-Organization

ACKNOWLEDGEMENTS We thank J. Aguirre, A. Lidz, and A. Myers for helpful comments. AP was supported by a McWilliams Fellowship of the Bruce and Astrid McWilliams Center for Cosmology. SH is supported by DOE and NSF AST1412966. Part of the research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This work is based on observations obtained with Planck ( http://www.esa.int/Planck ), an ESA science mission with instruments and contributions directly funded by ESA Member States, NASA, and Canada. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/ . SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University.


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Acknowledgments LX and GDL acknowledge financial support from the MERAC foundation. MH acknowledges financial support from the European Research Council via an Advanced Grant under grant agreement no. 321323 NEOGAL.


O O O O O O B-Organization I-Organization O O B-Organization O O O O O O B-Organization I-Organization O O O O O O O O O O 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-Person I-Person O O O O O O O O O O O O O O O O B-Database I-Database I-Database I-Database I-Database I-Database I-Database I-Database B-Database O O O O B-Organization B-Grant I-Grant I-Grant O B-Grant O O B-Organization O O B-Grant O O O O O O B-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility B-ComputingFacility O O O O O B-Organization I-Organization I-Organization O O B-Location B-Organization I-Organization I-Organization O O B-Grant I-Grant O O O O O O O B-ComputingFacility O B-ComputingFacility I-ComputingFacility O O B-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility I-ComputingFacility B-ComputingFacility O O O B-ComputingFacility B-ComputingFacility O O O O O O B-Organization I-Organization I-Organization O O B-Location B-Location

We thank the members of the ENIGMA group at the MPIA, as well as the members of office 217, for the fruitful discussions and helpful comments. We thank the anonymous referee for carefully reading our manuscript and for insightful comments and suggestions that significantly improved the quality of this work. Special thanks to Martin White for providing us with collisionless simulations. Some data presented in this work were obtained from the Keck Observatory Database of Ionized Absorbers toward QSOs (KODIAQ), which was funded through NASA ADAP grants NNX10AE84G and NNX16AF52G, along with NSF award number 1516777. This research used resources of the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the U.S. Department of Energy under contract No. DE-AC02-05CH11231. Calculations presented in this paper used the hydra and draco clusters of the Max Planck Computing and Data Facility (MPCDF, formerly known as RZG). MPCDF is a competence center of the Max Planck Society located in Garching (Germany).


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

called NLTE equations) for non-monotonic velocity law. To our knowledge, there is no such code that is able to treat this problem in its full complexity. Therefore, to make the problem tractable, we calculate a mean value of the mass-loss rate in the region close to the star, where the wind blanketing originates, where ρ and v are the wind density and radial velocity in particular time. Most of the flux in the optical domain is emitted from the region below the sonic point, which typically appears at radii 1.01 − 1.02 R * in the numerical simulations. Therefore, we selected r 1 = 1.01 R * and r 2 = 1.02 R * . However, the final light curve does not significantly depend on the choice of r 1 and r 2 . As a result of this choice, in Eq. ( 1 ) we average over 50 grid points of hydrodynamical simulation. A plot of is given in Fig. 1 . Fig. 1. Mass-loss rate from the numerical simulations averaged over r ∈ [1.01 R * , 1.02 R * ] (see Eq. ( 1 )). 2.2. Global wind models The variations of the emergent flux with wind mass-loss rate were derived from spherically symmetric stationary METUJE global wind models ( Krtička Kubát 2017 ). The code calculates the wind model in a global (unified) approach that integrates the description of the hydrostatic photosphere and supersonic wind. The model solves the radiative transfer equation in the co-moving frame (CMF) with opacities and emissivities calculated using occupation numbers derived from the kinetic equilibrium (NLTE) equations. For given stellar parameters, the model enables us to predict the radial dependence of the density, velocity, and temperature from hydrodynamical equations and to derive the wind mass-loss rate and the stellar emergent flux that accounts for the wind blanketing. For our modelling we assumed a typical parameters of O stars corresponding to HD 191612 , T eff = 36 000 K, R * = 14.1 R ⊙ , and M = 29.2 M ⊙ . To model the dependence of the emergent flux on the mass-loss rate with fixed stellar parameters, we artificially scaled the radiative force in the wind. This yields a series of wind models and emergent fluxes parameterized by the wind mass-loss rate. Because the amount of the flux redistributed by the wind depends on the effective temperature, we expect slightly different dependence for stars with different temperature. However, because we are concerned with typical light variations, we limited our study to just one star. On the other hand, such selection affects only the amplitude of the light curve. The preliminary results derived for 300-1 model of Krtička Kubát (2017 , the model has parameters close to that used in hydrodynamical simulations) indicate that the amplitude of the variability due to the wind blanketing does not significantly depend on stellar parameters. The light variability is derived from a relation between the H p magnitudes and mass-loss rate ( Krtička 2016 , Eq. ( 2 )) where Δ F = 1.6 × 10 7 erg s −1 cm −2 , F 0 = 9.0 × 10 8 erg s −1 cm −2 is the emergent


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This work is supported by the Youth Programme of the Applied Basic Research Projects of Yunnan Province, China (2013FD056) and the Science Research Project of Education Department of Yunnan Province, China (2016ZZX230). We thank the REM team and we acknowledge the use of public data from the Swift data archive.


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

a cooling break blueward of the X-rays. Finally, GF17 use modelling of the radio data ( Hancock et al. 2011 , 2012 ) to derive further lower limits on the circumburst density, again finding moderately high values contrasting our estimation given above under the assumption that the Ghirlanda relation is valid. The spectral shape of the radio detections is peculiar, which GF17 explain by interstellar scintillation. Alternatively, the anomalous spectrum may be created by the overlap of multiple components; for example, a reverse shock and a forward shock, as in the cases of GRB 130427A ( Laskar et al. 2013 ; Perley et al. 2014 ) and GRB 160625B ( Alexander et al. 2017 ; Troja et al. 2017 ). In such a case, scintillation would not be necessary to fully explain the deviation from the expected spectrum (though of course a lower amount of scintillation may still exist), the source size could be significantly larger and again, the limits on the circumburst medium density could be lower. As there are no further X-ray or radio observations, to our knowledge, however, neither of our alternative explanations can be pursued any further. Metzger et al. (2018) recently presented a study on the effects of fallback accretion on rapidly spinning proto-magnetars. They find that under certain conditions, the additional accretion can keep the magnetization within a certain regime which enables the launch of a relativistic jet for thousands of seconds, while at the same time also enabling additional energy injection into the late-time supernova, creating a viable solution to GRB 111209A/SN 2011kl. One caveat of this model is that it does not imply that the maximum extracted energy from the magnetar increases compared to the case without any additional accretion. The total rotational energy budget of a magnetar is where I ≈ 1.3 × 10 45 g cm 2 is the moment of inertia of the neutron star (assuming a typical mass M NS = 1.4 M ⊙), Ω 0 is the initial angular velocity and P 0 is the initial spin period. For a typical magnetar near break-up rotation speed ( P 0 ≈ 1 ms), it is E rot ≈ 3×10 52 erg, though Metzger et al. (2015) argue that neutron stars near the maximum possible mass (see Margalit Metzger 2017 ; Shibata et al. 2017 ; Ruiz et al. 2018 ; Rezzolla et al. 2018 , for recent research) and with larger radii and extreme spin periods could have up to an order of magnitude higher E tot . We note that GF17 argue that “the majority of this [energy] is carried away by neutrinos”. Such a mechanism would be expected in Type II core-collapse SNe, but in the case of a spindown-powered central engine, such as a magnetar, the energy is released as a Poynting flux which is converted into radiation and kinetic energy. While neutrinos may be produced in the dissipation region via p - γ interactions, the expected cross section and neutrino flux are very low ( Zhang Kumar 2013 ). Therefore a large fraction of the energy is available to drive the GRB, afterglow, and supernova. Without


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O 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 B-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O O B-Survey O B-Survey 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 O O O O O O O O O O O O O O O O O O O O B-Software O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O B-Software O O 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 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 B-Software O O O O O O O O O O O O O O O O O O O O O O

correct. A more detailed analysis of the disagreements between the classifiers (including the visual inspection) will be provided in a follow-up paper. However, we note that this superficial comparison between the classifiers should be enough to establish a classification strategy for new surveys. We recommend that all the available classifiers are used in a combined way. Because the different classifiers have completely different approaches to the classification problem, their agreement will reinforce the confidence of the classification. We currently recommend that all automated analysis are applied to a representative sample that is visually inspected in order to develop a decision tree. 7 SUMMARY AND CONCLUSIONS We have presented squeze , an algorithm that follows the human visual inspection to classify quasars and estimate their redshift. We have then tested the performance of this algorithm on visually inspected data from BOSS. We now summarize the key points we have discussed so far. squeze mimics the procedure of human visual inspection of the spectra to identify quasars and estimate their redshift. It performs a three step classification of quasars by identifying emission peaks, computing the redshift assuming an emission line, and selecting the correct quasar redshifts from the different possible values of z try . If we assume a confidence threshold of 0.32, we recover a purity of and a completeness of for the entire sample. This purity and completeness increases to 99.59 ± 0.06 and to , respectively, if we restrict to objects with z try ≥ 2.1. We can achieve virtually 100 per cent purity, if we assume a higher confidence threshold. For instance, for p min = 0.7, we recover a purity of and a completeness of for objects with z try ≥ 2.1. The analysis of a more realistic quasar redshift distribution produced by combining plates from BOSS and SEQUELS suggests that our results are slightly optimistic: for this analysis, and considering p min = 0.32, yields a purity and completeness of and , respectively. We find the standard deviation of our redshift errors to be 1500 km s −1 . We recommend the use of squeze redshift estimates as a prior to some additional redshift tuning. We discuss options to reduce these errors, but further study is required. Preliminary analysis suggests that the errors could be reduced to less than 1200 km s −1 . The performance of squeze presented here can be regarded as a conservative assessment since it is assumed that, where squeze disagrees with visual inspection, visual inspection is always correct. We will assess this in future papers in this series. We will also explore the versatility of squeze and its applicability to survey data that differs significantly from the BOSS spectra used here. APPENDIX A: OPTIMIZATION OF STEP 1 In Section 2.1 , we have explained squeze three-steps algorithm in detail. In particular, we have explained that the identification of emission peaks is done using a native peak finder.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B-Citation I-Citation I-Citation O O O B-Citation I-Citation I-Citation O O O O O B-Citation I-Citation I-Citation O O O B-Tag 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 B-Citation I-Citation I-Citation I-Citation O O O B-Citation O O O O O O O O O O O B-Citation O B-Software O B-Software O O O O 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 O O O O B-Identifier I-Identifier O O 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 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 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 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 B-Citation 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 B-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 B-Citation O O O B-Citation O O O O O B-Citation O O B-Citation I-Citation I-Citation O O O B-Citation I-Citation I-Citation O O O O B-Citation I-Citation I-Citation O O O B-Tag O O B-Tag O O O O O O O O O O O O

for the ground and metastable levels weighted by their experimental ion populations (76 per cent ground, 24 per cent metastable) is in good agreement with the experiment, particularly near the threshold and at higher energies. Interestingly, the area where the cross-section from this work lies outside the experimental uncertainty is at the energies just below the peak, where their experiment differs most with the adjusted Loch et al. results. Figure 5. Total collisional ionization cross-section for O v with 24 per cent metastable population; blue solid line: this work; blue dashed line: difference in cross-section owing to metastable population uncertainty; green dotted line: Dere (ground only); red dash–dotted line: Loch et al.; orange dashed line with circles: Fogle et al.; red circles: Loch et al. experiment; green triangles: Loch et al. experiment (adjusted); light blue squares: Fogle et al. experiment. 3.1.6 O <sc>vi</sc> The DI cross-section from this work is 5 per cent lower than Younger ( 1981a ) and 10 per cent lower than Dere ( 2007 ). The cross-section from Younger is obtained from the scaled cross-section for the isoelectronic sequence, while Dere uses FAC. Since FAC is used for the present work, also, and tests with different optimizations and configurations produces the same results, perhaps a possible explanation is that a different version of FAC may have been used here (v. 1.1.4) than used by Dere. Inner shell EA makes a notable contribution to the cross-section and has been clearly identified by experiment. The calculated EA cross-section is 25 per cent higher than Dere ( 2007 ), but this will not affect the rate coefficients substantially because the EA threshold is so much higher than that of DI. In comparison with experiment, the computed cross-section is favourable with the work of Rinn et al. ( 1987 ), lying close to it from threshold up to the peak, and thereafter the experiment is noticeably higher, as shown in Fig. 6 . The experiment of Defrance et al. ( 1990 ) has few data points at energies below the peak in cross-section, but this work does agree in that region. Above that, they lie higher than this work and their results are closer to Dere. Crandall et al. ( 1979 ) has a large uncertainty, which encompass all of the theoretical results in most cases. The rate coefficients are in very good agreement with Dere and Bell et al. ( 1983 ) for most of their range, and only differ at temperatures above 10 6 K, varying by 10–20 per cent. Figure 6. Total collisional ionization cross-section for O vi ground level; blue solid line: this work; blue dashed line: this work (DI only); green dashed line with circles: Dere; green dotted line: Dere (DI only); red dash–dotted line: Younger; green circles: Crandall et al. experiment; red triangles: Defrance et al. experiment; light blue squares: Rinn et al. experiment. 3.1.7 O <sc>vii</sc> and O <sc>viii</sc> The various DW calculations for He-like O vii are all in very


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ACKNOWLEDGEMENTS We wish to thank the anonymous reviewer for excellent suggestions and comments that have greatly improved this manuscript. UM and HBP acknowledge support from the Minerva center for life under extreme planetary conditions, the Israeli Science and Technology ministry Ilan Ramon grant and the ISF I-CORE grant 1829/12. HBP is supported by the Kingsley distinguished visitor program in Caltech.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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-Software O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

and the detailed shape of the break. In fact, this is what we show in Fig. 5 . Type-II galaxies with strong breaks (less influenced by radial redistribution, especially accretion) display steeper stellar age and metallicity profiles than type-II systems with weak breaks or even type-I galaxies. A consistent behaviour is also found in the case of the age profiles for type-III galaxies in which systems with weak breaks (less affected by accretion) display steeper profiles than galaxies with strong breaks; in which case, even positive gradients are displayed. However, in the case of the metallicity gradients, this trend is inverted. Type-III galaxies with weak breaks have shallower profiles than systems with strong breaks (which are more affected by accretion). This inverted trend can be explained by the characteristics of the accreted stars. In Ruiz-Lara et al. (2016a) we showed that accreted stars populating the RaDES galaxies are mainly old, metal-poor stars. As a consequence, the accumulation of accreted stars in the outer parts of type-III galaxies giving rise to these kind of systems causes the outer parts to be old (positive age gradients) and relatively metal-poor (producing steeper negative profiles). Based on Fig. 5 we can conclude that, not only does radial redistribution of material affect the shape of observed light distributions, but also accretion has to play a key role in shaping observed stellar age and metallicity profiles. All these results and the similarities between the observational results (TRL17) and the theoretical work presented in this paper seem to indicate that radial redistribution of material and accretion shape the SB profiles and generally flatten the age and the metallicity profiles with the exception of the metallicity profiles for type-III galaxies due to the accumulation of metal-poor, accreted stars in the outer disc. The greater the efficiency of this redistribution, the larger the flattening effect. As a consequence, we would expect steeper age and metallicity gradients at birth than what we observe presently due to the effect of the radial redistribution of material. To shed light on this aspect and the true role of this redistribution in shaping the age and metallicity profiles we have also analysed the inner gradients of these stellar parameters in the absence of radial motions, that is, considering birth locations (see Fig. 6 ). Figure 6 shows the same information as Fig. 4 but focusing on the effect of radial motion of stars on the stellar age and metallicity gradients. In this case we show mean values for type-I, II and III systems, not the values for all the individual galaxies. Grey circles represent the mean values of the stellar age and metallicity gradients (averaging among SB types) considering stars located at their current locations. Grey squares represent the same mean quantities but this time computed using stars located at their birth locations (thus avoiding the effect of radial


O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B-Citation I-Citation I-Citation I-Citation O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O B-Citation I-Citation I-Citation O B-Citation I-Citation 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-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation 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 B-Citation I-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 B-Model 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-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O B-Model I-Model I-Model O O O O O O O O O O O B-Model I-Model O O O O O B-Model I-Model I-Model B-Citation I-Citation I-Citation O B-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation O B-Citation O B-Citation I-Citation I-Citation I-Citation O B-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 B-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 B-Model I-Model I-Model B-Citation I-Citation O O O O O O B-Model I-Model I-Model I-Model O O O O O O O O O B-Model I-Model O O O B-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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

These levels of abstraction are learned in an unsupervised (i.e., autonomous) fashion from a large catalog of input spectra. Once these features are learned from the data, a second, supervised learning stage is used to assign the learned features to their correct labels (e.g., H 2 O, CH 4 , etc.). Neural networks are now commonly used in complex classification tasks such as image recognition (e.g., Krizhevsky et al. 2012 ; Liu et al. 2014 ; Wang et al. 2016 ; Shen et al. 2015 ), speech and music recognition (e.g., Hung et al. 2005 ; Jaitly Hinton 2011 ; Zhang Wu 2013 ; Pradeep Kumaraswamy 2014 ), biology (e.g., Head-Gordon Stillinger 1993 ; Plebe 2007 ; Wu McLarty 2012 ; Spencer et al. 2015 ), and are finding increased use in the classification of galaxies and cosmology (e.g., Collister Lahav 2004 ; Agarwal et al. 2012 , 2014 ; Reis et al. 2012 ; Karpenka et al. 2013 ; Dieleman et al. 2015 ; du Buisson et al. 2015 ; Ellison et al. 2015 ; Huertas-Company et al. 2015 ). Whereas an in-depth derivation of DBNs is beyond the scope of this paper, we will briefly outline its underlying architecture and implementation. We refer the interested reader to Bengio ( 2009 ), Hinton ( 2012 ), and Fischer Igel ( 2014 ) for detailed derivations. 2.1. Restricted Boltzmann Machines Figure 1 shows a schematic of the the deep-belief network. The multi-layer DBN can be constructed from several Restricted Boltzmann Machines (Freund Haussler 1992 ; Bishop 2006 ; Le Roux Bengio 2008 ; Bengio 2009 , 2012 ; Lee et al. 2011a ; Hinton 2012 ; Montavon et al. 2012 ; Fischer Igel 2014 ) with the addition of a logistic regression layer at the top of the network. The RBM is a two-layer neural network able to learn the underlying probability distribution over its set of input values. It represents a particular kind of Markov Random Field (Davison 2008 ) consisting of one layer of binary or Gaussian stochastic visible units (the input data) and one layer of binary stochastic hidden units. In RBMs, all of the hidden units are connected to all of the visible units but have no intra-layer dependence. Hence, all of the hidden units given the visible units are statistically independent and we can write the probability of all visible units given all hidden units and vice versa as the product of the individual probabilities:where and are the column vectors of the visible and hidden units, respectively, and i and j are their corresponding indices. We now want to find a configuration of the hidden layers, , that allows us to reconstruct the input, , with minimal error. Since and are factorial, we can write the activation functions of the individual visible and hidden binary units aswhere ς is the sigmoid functionAssuming that both the visible and hidden units are binary, RBMs assign an energy term for each configuration of and :where and are the bias vectors for the visible and hidden units, respectively, and W is a matrix of connection weights between and . The probability over


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the discrepancy is that the numerical values are obtained by fitting where V (1) and H (1) are considered independent. The second major difference is that the numerical data for sufficiently rapid rotation (Ω ⋆ ≳ 1) is incompatible with expressions of V and H which consider only terms proportional to sin 2 θ . Furthermore, in the foregoing analysis the anisotropy parameter a was kept as a free parameter and tuned such that in the slow rotation limit. It is also possible to compute a directly using Eq. (A14) in Appendix A of Kitchatinov Rüdiger (2005) by substituting and , and defining : where L is a length scale corresponding to large-scale inhomogeneity. Assuming L = L d and l corr = l gives l corr / L d = l/ L = k 1 / k f ≈ 0.1, allowing Eq. ( 49 ) to be solved with b as an input from simulations. The maximum anisotropy used in the bulk of the simulations is A V ≈ 0.52, which yields b ≈ 2.1 and a ≈ 2.7. This is somewhat greater than the values used in the fitting above. However, this discrepancy is mostly due to the freedom in choosing the value of ν t . 4.4. Dependence of Λ effect on Reynolds number The simulations in the preceding sections were made at low Reynolds numbers in comparison to the astrophysically relevant regime. Figure 6 shows the Λ coefficients for a representative case where the Coriolis number (Ω ⋆ ≈ 1.0) and turbulence anisotropy ( A V ≈ 0.3) were fixed in the range Re ≈ 0.6...99. The Coriolis number was chosen such that the higher-order coefficients V (1) , V (2) , H (2) , and M (2) did not appear according to the criteria in Sect. 3.3 . At low Reynolds numbers Re = 0.6...2 all of the coefficients are proportional to Re as expected from mean-field theory. At low Re the error bars for H (1) and M (0, 1) increase because the mean values of Q x y and Q x z are small. Furthermore, the coefficients level off beyond Re ≳ 10 where they are consistent with constants although with large error bars in particular for H (1) . These results suggest that the results obtained at Re ≈10 are representative of what can be expected in more turbulent cases. 4.5. Influence of large-scale magnetic fields Dynamically significant magnetic fields are ubiquitous in astrophysical objects where the Λ effect is thought to be important. In particular, stars with convection zones harbour dynamos that produce magnetic fields on various scales. The effects of large-scale magnetic fields on the Λ effect have been studied analytically by Kitchatinov et al. (1994b) and Kitchatinov (2016) . These studies indicate that large-scale magnetic fields tend to quench the Λ effect, but also that an additional H (0) effect arises in the presence of a horizontal mean magnetic field. Here the study of Kitchatinov et al. (1994b) is followed where the quenching formulae for V (0) and H (0) were calculated as functions of the magnetic field assuming the field to be horizontal. They found that where β = B 0 / B eq , and These equations correspond to the case where the


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it could be due to tidal stripping of the outer disk, or to “classical” accretion of dwarfs. The recent discovery of a substantial population of low-mass dwarfs associated with the Large Magellanic Cloud (Koposov et al. 2015 ; Drlica-Wagner et al. 2016 ) strengthens the latter scenario. The Clouds are not the only dwarfs with known satellites: dwarf galaxy companions have been found around various Magellanic analogs (Martínez-Delgado et al. 2012 ; MADCASH Survey: Carlin et al. 2016 ). A reasonable expectation is that a subset of these satellites will be tidally destroyed and accreted by their host halos, providing additional motivation to search for accreted stellar halos in dwarf galaxies. Stellar halos of galaxies are predicted and observed to be of very low surface brightness. This makes it challenging to study their presence quantitatively through diffuse light. Instead, the most tractable way to search for stellar halos is through deep, wide-field imaging now available on a number of large telescopes located at excellent sites. The isolated Local Group dwarf irregular IC 1613 is a good candidate to search for a stellar halo. It is a gas-rich, low-luminosity galaxy with an absolute magnitude of M V = −15.2 (McConnachie 2012 ), corresponding to a stellar mass of ∼10 8 M ⊙ . Its high Galactic latitude implies low foreground extinction, while it is near enough (725 ± 17 kpc; Hatt et al. 2017 ) that its bright stars are accessible in a reasonable exposure time from the ground. IC 1613 has been continuously forming stars throughout its lifetime, and hosts stars ranging from [Fe/H] ∼ −2 to −0.8 (Cole et al. 1999 ; Skillman et al. 2003 , 2014 ; Bernard et al. 2007 ; Weisz et al. 2014 ). A variety of studies sensitive to different stellar populations at different radii have found that the young stars are more centrally concentrated than the intermediate-age and old stars (Cole et al. 1999 ; Albert et al. 2000 ; Borissova et al. 2000 ; Skillman et al. 2003 ; Sibbons et al. 2015 ; McQuinn et al. 2017 ); though, there has been no definitive detection of a stellar halo as distinct from an extension of the primary body of IC 1613. The aim of this paper is to map IC 1613 with Subaru/Hyper Suprime-Cam (HSC) over a wider area and to fainter magnitudes than previous studies, generating the most sensitive map of its outer structure to date. The paper is organized as follows. The observations and information about how the data were reduced is presented in Section 2 . Section 3 describes the method of selecting different stellar tracers from the galaxy and goes on to explain the various analysis done with them. Section 4 compares our results with past works and places this project in a theoretical context. The conclusions are summarized in Section 5 . 2. Observations and Data Reduction IC 1613 was observed on 2015 October 16, with the HSC (Miyazaki et al. 2012 ) on the Subaru Telescope. The large aperture (8.2 m) of the telescope, along with the 15 field


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We would like to take the opportunity to thank the referee for his/her detailed reading of this paper and the useful comments provided.


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Acknowledgments The numerical simulations in this work were performed at the Centre for Intensive Computing Msocentre sigamm hosted by the Observatoire de la Cte dAzur. This work was supported by Brazil FAPESP Grant 2015/17962-5.


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ACKNOWLEDGEMENTS This work was supported by the Science and Technology facilities Council (STFC) ST/L00075X/1. AJG acknowledges an STFC studentship funded by grant ST/N50404X/1. CL is funded by an Australian Research Council Discovery Early Career Researcher Award (DE150100618) and by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. This work used the Distributed Research using Advanced Computing (DiRAC) Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC High Performace Computing Facility ( www.dirac.ac.uk ). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grants ST/H008519/1 and ST/K00087X/1, STFC DiRAC Operations grant ST/K003267/1, and Durham University. DiRAC is part of the National E-Infrastructure.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B-Model I-Model 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 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 B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula O O O O O O O O O O O O O B-Survey 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 B-Survey 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 B-Dataset I-Dataset I-Dataset I-Dataset O O O O O B-Formula I-Formula I-Formula I-Formula I-Formula I-Formula 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 O O O O O O O O O O O O O

assumptions on the mass profile. By introducing a mass-sheet degeneracy parameter, we add the maximal degree of freedom in H 0 while having minimal constraining power by lensing data on their own. This is the most conservative approach when adding a single degree of freedom in our analysis. While mathematically this result is clearly understood, it is worth discussing the physical interpretation of this choice. If we had perfect cosmological numerical simulations or perfect knowledge of the internal mass distribution within elliptical galaxies, we would not have to worry about the internal MST. The approach chosen by our collaboration ( Wong et al. 2020 ; Shajib et al. 2019 ; Millon et al. 2020 ) was to assume physically motivated mass profiles with degrees of freedom in their parameters. In particular, the collaboration used two different mass profiles, a power-law elliptical mass profile, and a composite mass profile separating the luminous component (with fixed mass-to-light ratio) and a dark component described as a NFW profile. The good fit to the data, the small pixellated corrections on the profiles from the first lens system ( Suyu et al. 2010 ), and the good agreement of H 0 inferred with the two mass profiles was a positive sanity check on the result ( Millon et al. 2020 ). In this paper we have taken a different viewpoint, and asked how much can the mass profiles depart from a power-law and still be consistent with the data. By phrasing the question in terms of the MST we can conveniently carry out the calculations, because the MST leaves the lensing observables unchanged and therefore it corresponds to minimal constraints and assumptions, and thus maximal uncertainties with one additional degree of freedom. However, after the inference, one has to examine the inferred MST transformed profile and evaluate it in comparison with existing and future data to make sure it is realistic. We know that the exact MST cannot be the actual answer because profiles have to go zero density at large radii, but the approximate MST discussed in Sect. 2 provides a convenient interpretation with the addition of a cored mass component. Figure 17 illustrates a cored mass component approximating the MST inferred from this work, λ int = 0.91 ± 0.04, in combination with a power-law model inferred from the population mean of the SLACS analysis by Shajib et al. (2020b) . The analysis presented here guarantees that the inferred mass profile is consistent with the properties of TDCOSMO and SLACS lenses. We discuss below how additional data may allow us to constrain the models even further and thus reduce the overall uncertainty while keeping the assumptions at a minimum. Fig. 17. Illustration of the inferred mass profile of the joint TDCOSMO+SLACS SDSS + IFU analysis. A pure power-law with γ pl = 2.10 ± 0.05 is shown in orange. In blue is the result of this work of λ int = 0.91 ± 0.045 when interpreted as a cored mass component with R c uniform in [3′′,


O O O O O O O O B-Person I-Person O O O B-Observatory O O O O O O O B-Person O O O O O O O O O O O O O O O B-Observatory O O 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-Observatory O O O O O O B-CelestialObject I-CelestialObject 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 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 B-Organization I-Organization O O O O O O O O O O O O O O O O O O O B-Archive I-Archive I-Archive O O O O O O O O O O O O O O O O O O B-Software I-Software I-Software I-Software B-Software O O O O O O B-Organization I-Organization I-Organization I-Organization B-Organization B-Location B-Person O O O O B-Organization O O B-Grant B-Grant O B-Grant B-Person O O O B-Organization O O B-Grant O O B-Person O B-Person O O O B-Organization O B-Grant B-Person O O O B-Organization O B-Grant B-Person O O O O O B-Organization O B-Grant I-Grant I-Grant B-Person O B-Person O O O O O O O O O B-Organization I-Organization I-Organization B-Organization O B-Location I-Location B-Person O B-Person O O O O B-Location B-Organization I-Organization B-Person O B-Person O O O O O O B-Organization O B-Grant O B-Organization I-Organization O O B-Organization I-Organization I-Organization I-Organization B-Person O O O O B-Organization O O B-Grant O B-Grant B-Person O O O O B-Organization O B-Grant O O O B-Organization I-Organization O O O O O B-Fellowship I-Fellowship I-Fellowship B-Person O O O O O B-Location B-Organization I-Organization I-Organization I-Organization O O O B-Grant I-Grant

We dedicate this paper to the memory of Neil Gehrels, the P.I. of Swift and a leading author of this paper. Neil was a great scientist who also brought out the best in others. He led the Swift team with enthusiasm and expertise, always happy and eager to take the satellite in new directions. For example, his strong and unwavering support is what allowed Swift to gather these unprecedented data on NGC 5548 and NGC 4151, providing a powerful method that should continue to inform our understanding of AGN physics for years to come. Specifically, without his courageous approval of increased UVOT filter changes well beyond the design lifetime of the filter wheel, the extraordinary 11-band light curve in Figure 3 , which forms the basis of this work, could not have been gathered. This is but one tiny piece of the rich legacy that he leaves us. We further thank the entire Swift team for their tireless dedication and flawless execution of these programs and acknowledge the use of public data from the Swift data archive. We also thank the anonymous referee for helpful and timely reports. This research has made use of the XRT Data Analysis Software (XRTDAS) developed under the responsibility of the ASI Science Data Center (ASDC), Italy. R.E. gratefully acknowledges support from NASA under awards NNX13AC26G, NNX13AC63G, and NNX13AE99G. J.M.G. acknowledges support from NASA under award NNH13CH61C. Research by A.J.B. and H.A.V. was supported by NSF grant AST-1412693. K.H. acknowledges support from STFC grant ST/M001296/1. M.B. gratefully acknowledges support from the NSF through CAREER grant AST-1253702. S.M.C. and E.R.C. gratefully acknowledge the receipt of research grants from the National Research Foundation (NRF) of South Africa. P.E. and K.P. acknowledge support from the UK Space Agency. A.V.F. and W.Z. are grateful for financial assistance from NSF grant AST-1211916, the TABASGO Foundation, and the Christopher R. Redlich Fund. D.C.L. gratefully acknowledges support from NSF under grants AST-1009571 and AST-1210311. T.T. has been supported by NSF grant AST-1412315 and by the Packard Foundation in the form of a Packard Research Fellowship. M.V. gratefully acknowledges support from the Danish Council for Independent Research via grant no. DFF 4002-00275.


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IK thanks Lodovico Coccato and Sergey Khoperskov for useful discussions. The work was supported by the Russian Science Foundation project 14-22-00041 ‘VOLGA – A View On the Life of GAlaxies’. The final interpretation of the data and paper writing was performed during visits to Chamonix workshop and The Research Institute in Astrophysics and Planetology in Toulouse which were supported by MD-7355.2015.2, RFBR 15-32-21062, and RFBR-CNRS 15-52-15050 grants. The project used computational resources funded by the M.V. Lomonosov Moscow State University programme of Development. The Russian 6-m telescope is exploited under the financial support by the Russian Federation Ministry of Education and Science (agreement No14.619.21.0004, project ID RFMEFI61914X0004). This research has made use of the NED which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration, and of the Lyon Extragalactic Database (LEDA). In this study, we used the SDSS DR12 data. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the US Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS web site is http://www.sdss.org/ .


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(Dubois et al. 2014 ; Codis et al. 2018 ; Wang et al. 2018 ; Ganeshaiah Veena et al. 2019 ; Kraljic, Davé Pichon 2020b ). The mass dependence of the spin alignment signal is however debated. While some works confirmed the existence of a galaxy spin transition from parallel to perpendicular with respect to the filament’s direction (Dubois et al. 2014 ; Codis et al. 2018 ; Kraljic et al. 2020b ), and analogously with respect to walls (Codis et al. 2018 ; Kraljic et al. 2020b ), others (Ganeshaiah Veena et al. 2019 ; Krolewski et al. 2019 ) found preferential perpendicular orientation with respect to filaments at all masses with no sign of a spin transition. A possible interpretation of this lack of detection of a clear transition is the nature of the filaments, with galaxies in thinner filaments having their spins more likely perpendicular to the filament’s axis, compared to galaxies of similar mass in thicker filaments (Ganeshaiah Veena et al. 2019 ). This can be in turn understood recalling the multiscale nature of the problem and the conditional TTT (Codis et al. 2015 ) predicting larger transition mass for denser, thus thicker, filaments. Further support for this interpretation was provided by the findings of stronger impact of large-scale tides on the galaxy spin orientation in denser filaments (Kraljic et al. 2020b , using filament density as a proxy for the thickness of filaments). In addition to the stellar mass, the spin-filament alignment was shown to depend on other internal properties of galaxies. Blue or rotation-supported galaxies were found to dominate the alignment signal at low stellar mass, while red or dispersion-dominated galaxies tend to show a preferential perpendicular alignment (Codis et al. 2018 ; Wang et al. 2018 ; Kraljic et al. 2020b ). The orientation of the galaxies’ spin with respect to their large-scale environment has been quite extensively studied also on the observational side. When focusing on disc galaxies, some groups find preferentially parallel orientation for spirals (Tempel Libeskind 2013 ; Tempel, Stoica Saar 2013 ), Scd types (Hirv et al. 2017 ), or both red and blue galaxies (Zhang et al. 2013 ). Others report a tendency for a perpendicular orientation for spirals (Lee Erdogdu 2007 ; Jones, van de Weygaert Aragón-Calvo 2010 ; Zhang et al. 2015 ) and Sab galaxies (Hirv et al. 2017 ), or no evidence for a clear signal (Pahwa et al. 2016 ; Krolewski et al. 2019 ). A much better agreement seems to exist for elliptical/S0 galaxies, for which a preferential orthogonal orientation of their spin (or minor axis) with respect to their host filaments is found (Tempel et al. 2013 ; Pahwa et al. 2016 ), in agreement with with results of shape measurements (e.g. Okumura Jing 2009 ; Joachimi et al. 2011 ; Singh, Mandelbaum More 2015 ; Chen et al. 2019 ; Johnston et al. 2019 ). More recently, Welker et al. ( 2020 ) studied the alignment of galactic spin in projection with respect to the filaments of the cosmic