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

UFD1. There are 12 resolved stars brighter than I = 28.5 mag in the net CMDs (Figures 2 (c) and (d)). The number of field contaminants with I 28.5 mag in the area covered by the UFD field is estimated to be 4.8 ± 0.3, while there are 15 stars in the area of UFD. Thus, the net number of the member stars in the area of the UFD is estimated to be 10 ± 4, which is consistent with the number derived after CMD decontamination. Eight of the 12 stars have a narrow range of colors with , showing a vertical sequence, while three of them show much redder colors than the others. Therefore, these eight stars are considered to show the RGB of Fornax UFD1, while three redder stars are probably foreground or background sources. Because of the small sample of the RGB stars in the CMD, it is not easy to derive an accurate distance to Fornax UFD1. We estimate approximately the distance to this UFD, assuming that the mean magnitude of the two brightest blue RGB stars in the CMD corresponds to the magnitude of the TRGB. The mean magnitude and color of these two stars are I = 27.38 ±0.14 mag and . This magnitude value is 0.24 mag fainter than the value of Virgo UFD1, I = 27.14 ±0.04 mag (Jang Lee 2014 ; see the lower panels in Figure 2 ). Applying the TRGB calibration in Jang Lee ( 2017 ) to this TRGB magnitude, we derive a value for the distance to Fornax UFD1, d = 19.0 ± 1.3 Mpc (). This value is consistent with the distance to NGC 1316. This shows that Fornax UFD1 is likely to belong to Fornax. In Figure 2 , we overlaid 12 Gyr isochrones for[ α /Fe] = 0.2 (Fabrizio et al. 2015 ) and [Fe/H] = −2.4 to −0.4 in the Dartmouth model (Dotter et al. 2008 ), which were shifted according to the distance and foreground reddening for Fornax UFD1. Although the number of stars is small, the RGB of UFD1 is, on average, bluer and narrower compared with the RGB of NGC 1316 in the control field. This shows that the mean metallicity of this new UFD is much lower than that of the NGC 1316 halo. The CMD of these RGB stars is matched roughly by the RGB part of the isochrones for low metallicity. It is noted that the RGB of Fornax UFD1 is even bluer than the RGB of the lowest metallicity with [Fe/H] ≈ −2.4. We estimate roughly the mean metallicity of the RGB stars to be [Fe/H] ≈ −2.4 ± 0.4. 3.3. Basic Parameters of Fornax UFD1 We constructed radial profiles of surface brightness and cumulative integrated magnitude of Fornax UFD1 using IRAF/ELLIPSE, as plotted in Figures 3 (a) and (b). We masked out cosmic rays and background galaxies in the original image and ran the ELLIPSE task adopting a smoothing option of 3 × 3 pixel binning. The radial profiles of surface brightness in Figure 3 show a flat core in the inner region at r 1′′ and decline rapidly in the outer region, showing that this UFD is a dynamically relaxed system like typical old star clusters. The radial profiles of surface brightness for r 4′′ are fitted well by a Sérsic law (Sérsic 1963 ) with an index n = 0.54 ± 0.06 for


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The author thanks the anonymous referee for helpful comments that helped improve this work, A. P. Milone for providing the observed data of NGC 1866, and Daniel Kister for help in improving the English, and also acknowledges the support from the NSFC 11773005, U1631236, and 11273012.


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

but the authors note that the resulting parameters A and B are not particularly sensitive to this, provided that the function can adequately reproduce the variation of s ad . This function comprises six parameters that define the relationship of s ad across the – plane, and the least squares minimization algorithm of Markwardt ( 2009 ) was then used to determine their values (listed in Table 1 ). The process is repeated for different convective envelope compositions (see Figure 3 ), each of which require a unique projection of the – plane. This fitting process effectively reduces the dimensionality of the initial variation of s ad by projecting the – plane onto an axis that is aligned with the convective envelope adiabats. The process used in this work is only one possible method for reducing the dimensionality of the problem, and further study using other statistical tools, such as principle component analysis, may yield additional insights into the fundamental relationship between convection zone entropy and the stellar surface parameters. Figure 3. Adiabatic entropy from sets of 3D simulations with varied chemical compositions (similar to the lowermost panel in Figure 2 ) presented along projections in the – plane. Each metallicity requires different coefficients for A and B , which correspond to different adiabatic contours in the – plane. Table 1 Parameters for the Function Fit to s ad from Magic et al. Simulations in the Projected – Plane () Since the convection zone adiabatic entropy value in a stellar model is determined by the mixing length parameter, the curves in Figure 3 basically show how we need to change α as a function of and . Of course, the first step would be to determine which numerical value of α yields a particular s ad given the rest of the physics to set the mixing length scale, and thus determine how s ad changes with α . After setting the relationship between s ad and α , all that is required is to follow this relationship (i.e., the curve in Figure 3 ) as the star evolves. Since each time step in a stellar evolution calculation changes and T eff , we will need to keep changing α as we evolve a model. The two panels of Figure 4 outline, in principle, the steps that must be taken to translate the adiabatic specific entropy derived from a 3D simulation into the corresponding entropy calibrated value of α to be used in the 1D MLT stellar model. Figure 4 (left) shows, in the same entropy calibration plane as Figure 3 , the locus of a set of 3D simulations, all with the same chemical composition, but with different values of s ad in the deep part. Also in the left panel are three 1D MLT models, all with the same metallicity and surface conditions as the 3D simulations. Presented relative to the projected and coordinates in this way, the evolution tracks begin on the zero-age main sequence with a relatively low s ad , which increases as the model approaches the terminal age. For the purpose of demonstrating our calibration


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I-Dataset O B-Dataset I-Dataset O O B-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 B-Formula I-Formula I-Formula I-Formula O B-Formula I-Formula I-Formula I-Formula 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 O O O O O O O O B-Wavelength O O O O O B-Survey B-Wavelength O B-Dataset I-Dataset I-Dataset I-Dataset B-Dataset O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-Dataset B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O B-Wavelength 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 B-Formula I-Formula I-Formula I-Formula B-Formula I-Formula I-Formula I-Formula O B-Formula I-Formula I-Formula 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 B-Dataset O B-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 B-Dataset O B-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 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 B-Citation I-Citation I-Citation I-Citation O O O O O O O B-Wavelength O O O O O O O O B-Dataset O O O O O B-Wavelength O O O O O O O O O O O B-CelestialObject O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O

that by defining a cluster subsample using clusters appearing both dynamically relaxed and hosting a CC, the luminosity–temperature ( LT ) relation appears self-similar, compared to unrelaxed and NCC clusters. While this method may be the preferred choice for defining subsamples of clusters for mass derivations, this limits the cluster sample size used for mass calculations; thus, the derived scaling relation may not be representative of the whole cluster population. This paper aims to measure the masses for a complete sample of 34 clusters to measure the X-ray LM scaling relation, utilizing hydrostatic mass estimates and fully accounting for selection effects. The outline of the paper is as follows. In Section 2 , we discuss the sample selection and data analysis. Section 3 details the cluster analysis and determines the dynamical state of individual clusters. Notes on individual clusters are given in Section 4 . Our results are presented in Section 5 . The discussion and conclusions are presented in Sections 6 and 7 , respectively. Throughout this paper, we assume a Wilkinson Microwave Anisotropy Probe 9 ( WMAP 9) cosmology of H 0 = 69.7 km s −1 Mpc −1 , Ω M = 0.282, Ω Λ = 0.718 and σ 8 = 0.817 (Hinshaw et al. 2013 ). 2 SAMPLE AND DATA PREPARATION The sample of clusters used in our analysis was defined by the conditions given in Dahle ( 2006 ). The sample of clusters represents a complete sample of X-ray luminous clusters taken from the RASS-based, X-ray flux-limited ROSAT Brightest Cluster Sample (BCS) of Ebeling et al. ( 1998 ) and its low-flux extension (eBCS; Ebeling et al. 2000 ). Dahle ( 2006 ) imposed a lower cutoff in X-ray luminosity of L = 6 × 10 44 erg s −1 (the limit based upon a cosmology assuming Ω M = 0.3, Ω Λ = 0.7 and h = 0.7), corresponding to a sample of 36 clusters within the redshift range 0.15 ≤ z ≤ 0.30. Fig. 1 plots the luminosity–redshift distribution of the BCS and eBCS, with the yellow shaded region highlighting the region enclosed by the luminosity and redshift cuts defining our sample selection. Note that two of the clusters were dropped from the cluster sample, as detailed below, leading to a final sample of 34 clusters. Figure 1. Plot of the luminosity–redshift distribution of the BCS and eBCS clusters. The yellow shaded region highlights the region enclosed by the luminosity and redshift cuts imposed to define our cluster sample (see Section 2 ). The cluster A689 satisfies these selection criteria, but was noted in the original detection as having a large portion of its flux coming from embedded point sources, and was therefore excluded from the cluster sample. The source of the embedded point source was found to be a central BL-Lac object (Giles et al. 2012 ), with the re-analysis determining a cluster X-ray luminosity ∼10 times lower than quoted in the BCS, well below the sample cutoff X-ray luminosity. Furthermore, we found that the redshift given for the cluster Zw5768 in Ebeling et al. ( 1998 ) was


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APY gratefully acknowledges financial support from an EXPERTS III Erasmus Mundus doctorate fellowship.


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full parameter space are shown in Table 1 , describing both the prospects per field and averaged over all Galactic Plane fields tested. Figure 4. Colour maps displaying the period determination of LMXBs possible in LSST field 630 with observing strategies astro_lsst_01_1004 , Minion_1020 , baseline2018a , and Minion_1016 . The y -axis denotes the orbital period in days, and the x -axis denotes the reddened r magnitude before adding contributions from ellipsoidal modulation, flaring, and noise. The colour denotes the significance of the period detected. If the measured period differed from the actual period by more than 5 per cent, then the significance was set to zero. The graph shows a bimodality in the significances of period determination, as recovered periods that had low significance were often incorrect and manually set to zero. Table 1. The fraction of the simulated parameter space for which P orb was correctly recovered for each observing strategy, both for the individual LSST fields and the total, combined over all three Galactic Plane fields. The initials denote which cadence was used for that field; South Celestial Pole (SCP), Galactic Plane (GP), or Wide-Fast-Deep (WFD). The reddening is listed in magnitudes. The reddening and coordinates refer to the centre of the field. The P orb recovery is worst for the baseline2018a observing strategy, as it only correctly recovers 0.23 of the simulated parameter space. This is to be expected as although it has a similar number of observations per field as Minion_1016 , a 25 per cent segment of the observations corresponding to potential outburst durations was removed from the full survey lifetime. Therefore, it offered the fewest usable observations per Galactic Plane field of any strategy. This is then followed by Minion_1016 that correctly recovers 0.46 of the parameter space, the low fraction is again due to the relatively small number of Galactic Plane observations per field. The two strategies that performed best were Minion_1020 and astro_lsst_01_1004 that correctly recovered P orb for 0.70 and 0.69 of the simulated magnitude- P orb parameter space (respectively), averaged over the Galactic Plane fields. The vast majority of the incorrectly recovered periods for both strategies had no observations in LSST’s visible magnitude range, within that region of parameter space. In these regions, there is no potential for good recovery of P orb , regardless of the number of observations. In order to evaluate the relation between Galactic reddening and period determination, the P orb recovery and reddening were plotted against both magnitude and P orb . In order to construct the magnitude-reddening graph, the significance of the P orb recovery was first averaged over all 20 periods for each region of parameter space with a distinct magnitude, field, and strategy. This average P orb recovery significance per magnitude was then plotted against Galactic extinction. Each significance-extinction


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

exact derivative of P ) and (the derivative when just GWs are considered) because this difference will be important for subsequent discussions. In this case , so thenGiven that will be difficult to measure, I focus on the simpler scenario where η = 1 with . To find the second derivative including tides, it is helpful to first rewrite Equation ( 6 ) asTaking the derivative of both sides of this expression, Rewriting all the first derivatives in terms of , dividing by J orb , and then collecting similar terms, the final result isWhen combined with Equation ( 6 ) for (using for consistency), this expression provides an analytic relations for . Comparing Equations ( 9 ) and ( 12 ) shows that tidal interactions make the second derivative even more negative than the GW only case. 4. Braking Index Beyond the values of the first and second derivative, another way to think about tides is in terms of a braking index. The basic idea is analogous to pulsars (Shapiro Teukolsky 1983 ) where one wishes to measure n such thatThe main difference is that here Ω refers to the orbital frequency of the binary rather than the pulsar spin frequency (and perhaps it should be referred to as an “acceleration index” here), although also see discussions of the braking index by Nelemans et al. ( 2004 ) and Stroeer et al. ( 2005 ) in the context of AM CVn systems. It is straightforward to showUsing the relations from Sections 2 and 3 , for GWs only (Webbink Han 1998 ),while when tides are includedThus fitting for the power law in real systems could be another way to infer the presence of tides. Although the exact value of n depends on the specific parameters of the binary and the degree of tidal locking, simply showing that n > 11/3 would demonstrate tides are occurring without having to know these parameters. 5. The Rate of Change of Tidal Locking An additional factor in that is often not considered is , the rate of change of the tidal locking. This will depend in detail on the model for the tidal interaction. In Piro ( 2011 ), the tide is treated using a parameterization with a standard quality factor Q . When this tidal interaction is integrated forward in time, it is found that the binary reaches an equilibrium spin at any given point where the ratio of the tidal forcing frequency to the orbital frequency is roughly the ratio of the tidal synchronization time τ tide to the GW inspiral time , i.e.,Furthermore, this work finds that for constant Q , . This ratio thus gets smaller at shorter orbital period, resulting in a more tidally locked binary. In reality, this will depend on the details of how the tides act, and so I consider a general form of with β > 0. Assuming a model of this form and taking the derivative results inThis particular model has a couple of important consequences for . First, it makes even more negative. Second, it adds corrections to even when η ≈ 0 because it implies a rapidly changing η , while for η ≈ 1 this model gives . In the future, it would


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The authors thank the referee K. Heng for useful and constructive comments. This research is based on observations made with the Gran Telescopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos, operated on the island of La Palma by the Instituto de Astrofísica de Canarias. This work is partly financed by the Spanish Ministry of Economics and Competitiveness through projects ESP2013-48391-C4-2-R, ESP2014-57495-C2-1-R, and AYA2012-39346-C02-02. G.C. also acknowledges the support by the National Natural Science Foundation of China (Grant No. 11503088) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20151051). This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France ( Wenger et al. 2000 ).


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I am grateful to Matthias Bartelmann and Rüdiger Vaas for their strong support. I wish to thank Mauricio Carrasco, Agnese Fabris, Matteo Maturi, Sven Meyer, Björn-Malte Schäfer, Gregor Seidel, Nicolas Tessore, and the anonymous referee for helpful comments and discussions, and gratefully acknowledge the support by the Deutsche Forschungsgemeinschaft (DFG) WA3547/1-1.


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 O O O O O O B-Location I-Location B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant O O O O O O B-Organization O B-Grant I-Grant B-Organization O O O O O B-Organization O O O B-Location I-Location I-Location O O O O O O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Grant I-Grant O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization B-Location O O B-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization I-Organization B-Organization B-Location B-Person O O O O O B-Location I-Location B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant O B-Organization I-Organization I-Organization O B-Grant B-Person O O O O B-Fellowship I-Fellowship I-Fellowship I-Fellowship B-Fellowship O O O O O O O O B-Software O B-URL O B-URL O B-URL O B-URL O O O O O O O B-Database B-Database O O O O O B-Organization I-Organization I-Organization B-Organization O O O O 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 I-Organization I-Organization B-Organization O O O O O O B-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant I-Grant O O O B-Organization I-Organization B-Person O B-Person O O O O O O O O B-Person I-Person O O O O O O O O O O O O B-Location B-Person B-Person O B-Person O B-CelestialObject O O O O O O O

We thank the anonymous referee for his/her comments that helped to improve this work. We thank Carlton Baugh for his careful reading of the manuscript that has helped us to improve the text. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 734374. This work has been supported by MINECO grants AYA2015-71825 ESP2015-66861. IEEC is partially funded by the CERCA program of the Generalitat de Catalunya. This work was also partially supported by Agencia Nacional de Promoción Científica y Tecnoóogica (PICT 2015-3098), the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina) and the Secretaría de Ciencia y Tecnología de la Universidad Nacional de Córdoba (SeCyT-UNC, Argentina). MS has been supported by the European Union’s Horizon 2020 research and innovation programme under the Maria Skłodowska-Curie grant agreement No 754510 and National Science Centre (grant UMO-2016/23/N/ST9/02963). MM acknowledges support from the Beatriu de Pinos fellowship (2017-BP-00114). We made an extensive use of the following python libraries: http://www.numpy.org/ , http://www.scipy.org/ , https://www.astropy.org/ and http://www.matplotlib.org/ . This work has made use of CosmoHub. CosmoHub has been developed by the Port d’Informació Científica (PIC), maintained through a collaboration of the Institut de Física d’Altes Energies (IFAE) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), and was partially funded by the “Plan Estatal de Investigación Científica y T’ecnica y de Innovación” program of the Spanish government. EJG and FR would like to specially thank the contribution of Darío Graña who helped to run the algorithms. Gracias por las picadas. Go team Barcelona! EJG, FR and AO thank Orion for the laughter and the good luck.


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

of 5000 simulated white noise time series; d) : LS periodogram of the average of 5000 simulated red noise time series; e) : integrated WWZ power spectrum of 5000 simulated red noise time series; f) : WWZ power spectrum of the average of 5000 simulated red noise time series. Fig. 6 Statistical significance test of WWZ-detected periods in Figs. 2 c and 3 c. The x -axis represents the peak power of simulated periodograms at the periodicity under investigation. The y -axis gives the occurrence number within equally wide bins of 1. Top : histogram obtained from 5000 simulated white noise time series. Bottom : histogram obtained from 5000 simulated red noise time series. The vertical red dashed lines denote the peak powers of the periodic components identified in Figs. 2 and 3 . In the regions to the right of the line, the WWZ power of the simulated fake light curves is higher than the observed value. The ratio of the accumulated count of these light curves to the total number used in the MC test (i.e. 5000) determines the probability of the observed periodicity caused by the noise. As in to Sect. 2.1 , we also evaluated how the noise affected the WWZ power spectrum. The statistical confidence tests were performed using Monte Carlo (MC) simulations ( Linnell Nemec Nemec 1985 ). We first generated 5000 white noise time series, which had the same number of sampling points, temporal baselines, and sampling intervals as the observed light curve. We then calculated the WWZ periodograms for the 5000 white noise time series using the same parameters as for Figs. 2 c and 3 c. The averaged white noise power spectrum is shown in the top of Figs. 4 and 5 . The red noise power spectrum was also obtained in the same way (bottom of Figs. 4 and 5 ). No significant peaks were found at the positions of P1, P2, and P3, in either of the white noise or red noise spectra. For a quantitative evaluation of the statistical confidence, we counted the number of simulated light curves, which show higher power than the observed periodic components (P1, P2, and P3) at their peak positions. The ratio of the accumulated number to the total number of 5000 test light curves gives the probability of the false detection (i.e. the detected peak could be due to random noise). The percentage of the false detections in each case is below 0.1 (see Fig. 6 ). Therefore, the possibility that P1, P2, and P3 are generated by stochastic noise can easily be ruled out. 3. Discussion The periodicity analysis in Sect. 2 results in the detection of three possible periods. The properties of individual periodic components are discussed in the following: 1. P1 = 2.65 d . This is the most prominent pe-riodic component in both the LS periodogram and the WWZpower spectrum, which confirms the first detection by Griséet al. (2013) using the LS tech-nique. The component P1 is remarkable in the first two-thirdsof the interval covered by the light curve. It starts with a slightlylonger period of 2.67 d, then


B-Wavelength O B-Wavelength O B-Wavelength O B-Wavelength 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 B-Wavelength O O O O O O O O O O O O O B-CelestialObject O B-Wavelength O B-Wavelength I-Wavelength O O O O O O O B-Telescope I-Telescope I-Telescope I-Telescope 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 B-Telescope O O B-CelestialObject O O O O O O B-Telescope 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 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 B-Archive I-Archive I-Archive I-Archive B-Archive 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 B-Wavelength 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 B-Wavelength I-Wavelength 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 B-Wavelength O B-Wavelength O O O O O O O O B-Wavelength O B-Wavelength I-Wavelength 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 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 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 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 B-Wavelength 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 O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O B-Observatory B-Telescope O

uvm2 − u and u − v colors of ASASSN-15lh. The SN begins as quite blue but reddens with time until the rebrightening, during which the SN becomes bluer in the MUV–NUV. Bottom panel: the integrated luminosity (in units of erg s −1 ) of ASASSN-15lh between 1600 and 6000 Å. Overplotted is the integrated luminosity of the SLSN II SN 2008es, which is of comparable brightness to the rebrightening event, as well as a bright SN IIn and a normal SN Ia. Table 1 Swift /UVOT Photometry of ASASSN-15lh Note. Magnitudes are given in the UVOT/Vega system for easier comparison with other SNe published in that system (see Brown et al. 2014 for a review). The 3 σ upper limits and lower (saturation) limits come from the exposure and background parameters. Most of the photometry of ASASSN-15lh is not close to either of these limits, but they are given for completeness and easy comparison with other SN photometry tables from the Swift Optical/Ultraviolet Supernova Archive (SOUSA; Brown et al. 2014 ). This table gives a portion of the data. (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. One might be concerned about the effect of the optical tails of the uvw2 and uvw1 filters (sometimes referred to as the “red leaks”), which still have 1/1000 of the peak transmission all the way to 5000 Å (see Breeveld et al. 2011 and Brown et al. 2010 for graphical descriptions of the revised filter throughput curves). To estimate the contribution from the optical tails, we compare the count rates to those expected through synthetic filters similar to uvw2 and uvw1 but cutting off sharply to zero throughput at 2500 and 3300 Å (Brown et al. 2010 ). Using a blackbody spectrum corresponding to 16,000 K (the hottest estimated below), the counts coming from the redder portion correspond to 12% and 3 % of the total in the uvw2 and uvw1 filters, respectively. Using a blackbody spectrum corresponding to 10,770 K (the coolest estimated below), the extra counts coming from the redder portion correspond to 20% and 5 % of the total in the uvw2 and uvw1 filters, respectively. Thus the contribution from the red tails increases as the SN reddens, but never contributes a large fraction of the counts. The wavelengths beyond which are considered to be part of a red leak (as opposed to just the long wavelength edge of the filter) can be arbitrary and require a spectral model as well as the filter curves to quantify. Even if the red leak contributed a significant fraction of the observed count rate in the UV filters, the analysis below takes the red leaks into account. For example, blackbody fits are performed by comparing the observed count rates to those predicted from blackbody spectra through the whole filter. Brown et al. (2016, AJ accepted) give a detailed description of issues involved with the conversion of Swift /UVOT broadband


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2013 ) and radial velocities (Saar Donahue 1997 ; Korhonen et al. 2015 ). Stellar activity has even been the culprit behind false detections of exoplanets around M dwarfs in high-precision data (Robertson et al. 2014 ). The role of M dwarf magnetic activity on planetary habitability, particularly from X-ray and UV flux due to quiescent emission and flares, is an ongoing area of research (Scalo et al. 2007 ; Tarter et al. 2007 ; Seager Deming 2010 ). Planetary habitability is impacted by the properties and evolution of both the star and planet (e.g., Vidotto et al. 2013 ). While models from Segura et al. ( 2010 ) show that single large M dwarf flares do not pose a great risk to planetary habitability, young planets may be bombarded with much stronger and more frequent flares than previously predicted (Armstrong et al. 2016 ). M dwarf activity can strongly affect planetary atmospheres through photoevaporation on Gyr timescales (Cuntz Guinan 2016 ; Owen Mohanty 2016 ). Whether this has a net positive or negative impact on atmosphere loss and planetary habitability is debated (Luger et al. 2015 ; Owen Mohanty 2016 ). Accurate studies of M dwarf magnetic activity in X-rays, UV, and the blue optical are challenging due to the intrinsic faintness of these cool stars at short wavelengths. However, the nearest M dwarf to the Sun, Proxima Cen, has long been known as an active flare star (Thackeray 1950 ), with a moderately strong magnetic field for its spectral type of M5.5 (Reiners Basri 2008 ). At a distance of 1.3 pc, Proxima Cen enables unique characterization of activity in X-ray through optical (Güdel et al. 2004 ; Fuhrmeister et al. 2011 ), and even radio frequencies (Lim et al. 1996 ). High time resolution flare data from Proxima Cen has been used to better understand flare heating mechanisms (Reale et al. 2004 ; Kowalski et al. 2016 ). Decades-long spectroscopic monitoring has even revealed a possible activity cycle for Proxima Cen (Cincunegui et al. 2007 ). Proxima Cen has been the subject of multiple searches for exoplanets (e.g., Benedict et al. 1999 ; Endl Kürster 2008 ). Recently, Anglada-Escudé et al. ( 2016 ) discovered the existence of Proxima b, a 1.27 M ⊕ planet orbiting within the star’s habitable zone, using multiple years of high-precision radial velocity monitoring. Proxima Cen is therefore an important benchmark object for understanding planet formation around low-mass stars, the evolution of the magnetic dynamo, and the impact of stellar activity on planetary atmospheres. Using data from the Microvariability and Oscillations of STars microsatellite (hereafter MOST ; Walker et al. 2003 ), we have conducted a study of the white-light flares from Proxima Cen. Flares have previously been studied with MOST for the famous active M3.5 dwarf, AD Leo (Hunt-Walker et al. 2012 ). Despite its long history of study as an active M dwarf, the census of flares from Proxima has not been constrained by modern space-based photometric studies.


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at epoch t , the catalog positions at t 0 would be corrected by applying the Galactic aberration model correction. We have investigated what is the effect of the aberration on estimated source positions. Figures 3 – 6 show the Calc/Solve differences in source positions (RA, Dec) versus RA and Dec for an aberration constant A G of 5.8 μ as yr −1 , which is the value we recommended for the IAU ICR3 solution as discussed below in Sect. 6 . The variation (scatter) of the differences at RA or Dec in these plots is due to the fact that the difference in the mean epochs from the chosen reference epoch 2015.0 vary significantly over the set of sources. The source position differences range over ±30–40 μ as. Fig. 3. Change in right ascension due to aberration versus right ascension with an aberration constant of 5.8 μ as yr −1 and reference epoch of 2015.0. Fig. 4. Change in right ascension due to aberration versus declination with an aberration constant of 5.8 μ as yr −1 and reference epoch of 2015.0. Fig. 5. Change in declination due to aberration versus right ascension with an aberration constant of 5.8 μ as yr −1 and reference epoch of 2015.0. Fig. 6. Change in declination due to aberration versus declination with an aberration constant of 5.8 μ as yr −1 and a reference epoch of 2015.0. 4. Estimates derived from Galactic astronomy measurements Aberration can also be derived from recent (2009–2017) Galactic astronomy measurements (e.g., Reid et al. 2014 ; Rastorguev et al. 2017 ; Brunthaler et al. 2011 ). These measurements are trigonometric parallaxes and proper motions of masers in high-mass star-forming regions in the Milky Way galaxy. The measurements were made using the Very Long Baseline Array (VLBA), the European VLBI network (EVN), and the Japanese VLBI Exploration of Radio Astronomy Project (VERA). The most recent investigation noted here ( Rastorguev et al. 2017 ), used a maser sample of 136 sources. Using these parallax and proper motion measurements, different investigators have derived models of the galaxy. Among the parameters of these models are the radial distance R 0 (kpc) to the Galactic center and circular rotation speed Θ 0 (km s −1 ) of the solar system barycenter around the Galactic center, which is located at a position with right ascension and declination of α G = 266.4° and δ G = −29.0° ( Reid Brunthaler 2004 ). To determine an aberration constant, we just consider the effect of the Local Standard of Rest (LSR) circular revolution with speed Θ 0 around the Galactic center. We note that there could be a small pecular acceleration an order of magnitude less than galactocentric acceleration due to solar motion with respect to the LSR as discussed in the introduction. Based on the estimated parameters R 0 and Θ 0 and their uncertainties from each investigator, one can determine the acceleration vector amplitude of (see Kovalevsky 2003 ) and its uncertainty assuming a circular orbit. This can be converted to an aberration


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ZZ acknowledges support by NASA through Hubble Fellowship grant HST-HF-51333.01-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 NAS 5-26555. We thank Olivier Berné, Simon Casassus and Zsolt Regály for helpful discussions, and Eugene Chiang, the reviewer, for a prompt and constructive report.


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It is a pleasure to thank A. Grenzebach, Y. Mizuno, Z. Younsi and A. Zhidenko for useful discussions and comments. We are also grateful to the referee, D. Psaltis, for comments and suggestions that have improved the presentation. This research was partially supported by the Volkswagen Stiftung (Grant 86 866) and by the ERC Synergy Grant ‘BlackHoleCam – Imaging the Event Horizon of Black Holes’ (Grant 610058). AAA and BJA are also supported in part by the project F2-FA-F113 of the UzAS and by the ICTP through the projects OEA-NET-76, OEA-PRJ-29. AAA and BJA thank the Institut für Theoretische Physik Goethe Universität' for warm hospitality during their stay in Frankfurt.


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We would like to thank Tobias Wiengarten for assistance with the magnetic field data, as well as Malina Reytemeier, Paul Evenson and the entire IceCube collaboration, as well as members of the Ruhr Astroparticle Plasma Physics (RAPP) Center for helpful discussions on the subject. We are further grateful for the support from the BMBF (05A14PC1) and from MERCUR (St-2014-0040). This work utilizes data from the National Solar Observatory Integrated Synoptic Program, which is operated by the Association of Universities for Research in Astronomy, under a cooperative agreement with the National Science Foundation and with additional financial support from the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, and the United States Air Force. The GONG network of instruments is hosted by the Big Bear Solar Observatory, High Altitude Observatory, Learmonth Solar Observatory, Udaipur Solar Observatory, Instituto de Astrofísica de Canarias, and Cerro Tololo Interamerican Observatory.


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ACKNOWLEDGEMENTS We thank the referee for a thoughtful read of the paper and comments that helped strengthen our paper. We thank Alexander Stephan and Bao-Minh Hoang for useful discussion and suggestions. CL acknowledges the UCLA Van Tree Foundation, Undergraduate Research Scholars Program Scholarship as well as the Stone Foundation Summer Research Scholarship. CL thanks Izuki Matsuba for discussion and help. SN acknowledges the partial support of the Sloan fellowship and from the NSF through grant No. AST-1739160.


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

converged when the emcee walkers had reached a steady state with measured best-fit values changing minimally over a large number of steps. 3. Results Figure 1 shows the 1.3 mm dust continuum, the 12 CO (2–1) integrated intensity map (moment 0), and the intensity-weighted velocity map (moment 1) of the FW Tau C disk. Similar to Kraus et al. ( 2015 ) and Caceres et al. ( 2015 ), no signal is found from the close binary FW Tau AB, and the 3 σ upper limit for a unresolved source suggests a dust mass ≲0.07 M ⊕ . It is known that close binaries can shorten disk lifetimes (e.g., Cieza et al. 2009 ), so the disk around FW Tau AB may be depleted already. For the companion FW Tau C, its dust disk is compact and likely unresolved; in contrast, the gas disk is more extended and clearly shows a Keplerian rotation. Figure 2 is the position–velocity diagram constructed along the major axis of the gas disk. On top of the PV diagram we also plot the Keplerian rotation curves for 10 M Jup and 0.1 M objects. It is clear that the velocity profile of the FW Tau C disk is incompatible with the 10 M Jup rotation curve, but more consistent with that of a 0.1 M star. Our disk modeling (see Figure 3 and Table 2 ) also indicates that FW Tau C’s mass is ∼0.1 M , about 10 times higher than the 10 M Jup suggested by Kraus et al. ( 2014 ). Hence, FW Tau C is not a planetary-mass object, but is rather a low-mass star with an inclined disk, as suggested by Bowler et al. ( 2014 ). The high mass of FW Tau C is also in agreement with some features in its spectrum that closely resemble T Tauri stars (Bowler et al. 2014 ). Our observations therefore suggest that FW Tau is a young triple system in which each of the stars has a mass of ∼0.1 M . Figure 2. Position–velocity diagram constructed along the major axis of the FW Tau C disk. Keplerian rotation curves for a 0.01 M (∼10 M Jup ) planet and a 0.1 M star are also plotted. FW Tau C is more consistent with a 0.1 M star. Figure 3. We show our CO (2–1) channel maps (top) as well as the best-fit Keplerian disk model synthetic channel maps (center) and the residual channel maps (bottom). The model channel maps are made by sampling the model at the same baselines as the data and Fourier transforming to produce images. The residual channel maps are produced by subtracting the model visibilities from the visibility data and Fourier transforming to produce maps. The contours start at 3 σ = 13.5 mJy beam −1 and are at intervals of 3 σ . Table 2 FW Tau C Disk Properties Searches for accretion disks in millimeter around directly imaged planet-mass companions have not yielded positive results (e.g., Isella et al. 2014 ; Bowler et al. 2015 ; MacGregor et al. 2017 ; Ricci et al. 2017 ; Wolff et al. 2017 ; Wu et al. 2017 ), although the disk around the free-floating planet OTS 44 has recently been imaged (Bayo et al. 2017 ). Current flux upper limits imply that these wide companion disks might have 0.1 M ⊕ of dust, which in turn could imply a


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Acknowledgments The authors are grateful to Ray Sharples for the use of KMOS GTO time for some observations and for useful discussions regarding instrumentation; our thanks also to Renske Smit and Helen Johnson for obtaining additional observations of NGC1407 and NGC3379 for this project. We wish to thank Charlie Conroy for providing up-to-date versions of the CvD models and the anonymous referee for helpful comments that improved the paper. PA was supported by an STFC studentship (ST/K501979/1) and RJS by the STFC Durham Astronomy Consolidated Grant 20142017 (ST/L00075X/1). This work is based on observations obtained at the Very Large Telescope of the European Southern Observatory (programme IDs: 093.B-0693 and 094.B-0061). The data used here are available through the ESO science archive (programme IDs: 093.B-0693(A), 094.B-0061(C)).


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We gratefully acknowledge the support of the STScI team responsible for ensuring timely and accurate implementation of our M87 programs. Support for programs #10543 and #14618 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. PyRAF is a product of the Space Telescope Science Institute, which is operated by AURA for NASA.


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

In two cases, additional variable stars are found (rms significantly larger than for the remaining objects), they are excluded from the sample of comparison stars. A small number of epochs (not more than one per field) produces outliers in the master light curve probably due to changing in atmospheric conditions not recognized during the run; these images were excluded from the analysis. The master light curve was re-calculated using the remaining set of comparison stars and images and again subtracted from the raw light curves. Fig. 2 presents the final differential light curves and rms plots (rms versus average magnitude) for each of our seven objects. In these plots, our targets are labelled. The typical photometric error (defined as the floor of the rms of the comparison stars) is ∼ 1–3 per cent, significantly smaller than the amplitudes in the variable targets. Some of the target light curves exhibit a distinctive morphology – V2737 shows a continuous downward trend, V2739 a periodic behaviour, and V2227 multiple short-period eclipses. The remaining light curves appear at least partially irregular. Although the sampling frequency of the current data set is less dense than in previous studies, the overall light-curve morphology and variability time-scales are comparable to the previously published ones for all objects. Figure 2. Observed light curves and rms plots for all seven objects. Left column contains light curves, right column contains corresponding rms plot for each target's field. The light curves for each object span four nights and shows relative magnitudes from differential photometry. We observe a spread of periodic, quasi-periodic and irregular behaviours. rms plots indicate variable objects in each target's field. Figure 2 – continued Figure 2 – continued We calculate amplitudes for the light curves by taking the min–max difference for each star. To ensure no extreme outliers are included in this calculation, we check for any 3σ outliers but find none. Table 3 lists the derived amplitudes and compares them to previously published values ( SE04 ; SE05 ). The amplitudes in SE04 and SE05 refer to the peak-to-peak values in the binned light curves. Table 3. Light-curve amplitudes ( A ) derived in this work (see Section 6.1 ) and previously published values (see text for details). a SE04 ; table 4, I band. b SE05 ; table 1, I band. 4.2 Spectroscopy Standard spectroscopic data reduction was performed with iraf routines from the onedspec and twodspec packages. Background was fit and subtracted during spectrum extraction. Wavelength calibration is done using an HeAr arclamp. The A-type star HD292956 was used for relative flux calibration. For each epoch, three spectra (see Section 3 ) were co-added using a median filter. Fig. 3 presents the complete sequences of calibrated spectra for our stars over the four nights of observations. Every spectrum is normalized to the central wavelength of the I -band filter (7680 Å). In the final


O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O B-Telescope 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 O O O O O O O O O O O O O O O

can also be seen in more detail in Fig. 9 , where we show the running median spot properties and the typical uncertainties in spot coverage versus spot temperature plots. Phase 1: a general decline of amplitudes in V and R c , while remains almost constant. During the first half, the spot temperature declines from about 5500 to 5300 K at constant spot coverage (0.3), followed by a shrinking of the spot coverage to 0.2 at constant temperature. The initial decrease of temperature occurs at a rate of about −2.2 K/d. Overall, this is a phase where spot activity declines. Phase 2: there are only minor variations in the amplitudes, with a maximum in roughly in the middle of the phase. The spot temperatures are constant (5450 K) within the uncertainties, with a weak trend of slightly increasing values at a rate of about +0.2 K/d. The spot coverage (0.2) is also constant within the uncertainty. Phase 3: there is a clear trend of decreasing amplitudes in all filters, by at least a factor of 2. As for the previous phase the spot temperatures do remain constant within the uncertainties at about 5600 K. The spot coverage decreases from 0.35 to 0.2, with a small increase towards the end of this phase. This phase contains the high cadence data (indicated as grey in Fig. 8 ). Due to the large amount of data the amplitude uncertainties are much lower, and hence also the spot temperature and coverage uncertainties. The analysis of this data with a much smaller ±15 d window is shown in the bottom left panel of Fig. 6 . The results are consistent with the results obtained for the entire light curve. They show the spot temperature decreasing from 5650 to 5500 K. At the same time, the spot coverage drops from 0.35 to 0.2. Overall, the characteristics of phase 3 are similar to phase 1, but at slightly higher spot temperatures. Phase 4: the amplitudes remain constant for a large part, but start to slowly increase towards the end. Accordingly the spot temperature (5400 K) and coverage (0.15) remain constant throughout, and only increase towards the end. The temperature increases at a rate of +2.9 K/d towards the end. We note that the duration of all phases is significantly longer than our time window of ±60 d, and they are hence unlikely to be an artefact. One possibility to interpret these phases is as lifetime of large spots or groups of spots. If that is the correct interpretation, the spot lifetime in the four phases would be >180, 230, 360, and >230 d, overall, or between half a year and one year. These values are consistent with spot lifetimes determined in the literature for active stars. For example, Giles, Collier Cameron Haywood ( 2017 ) used Kepler data to estimate spot decay time-scales between 20 and 300 d for K-stars. A fast rotating single star like V1598 Cyg would be expected to have a shorter spot lifetime, but a potential close companion and tidal coupling may extend the lifetime. As described in Section 4.1 , we find a long-term change in the


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Based on observations carried out with the IRAM Plateau de Bure Interferometer. IRAM is supported by INSU/CNRS (France), MPG (Germany), and IGN (Spain). We acknowledge the IRAM Plateau de Bure team for the observations. We thank Sabine König for her support in the data reduction. We warmly thank Eric Rosolowsky, our referee, for his very helpful and constructive comments. We are grateful to Pauline Barmby for providing us with reduced Spitzer data in the nucleus region. ALM has benefited from the support of Action Fédératrice Structuration de l’Univers et Cosmologie from Paris Observatory. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research made use of Montage, funded by the National Aeronautics and Space Administration’s Earth Science Technology Office, Computational Technnologies Project, under Cooperative Agreement Number NCC5-626 between NASA and the California Institute of Technology. The code is maintained by the NASA/IPAC Infrared Science Archive. This research makes use of GALEX, SDSS, 2MASS, Spitzer , and WISE archive data of Andromeda.


O O O O O O O O O O O O O O O O O O B-Observatory O O O B-Wavelength I-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 B-Wavelength 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B-Wavelength I-Wavelength I-Wavelength I-Wavelength I-Wavelength 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-Wavelength I-Wavelength I-Wavelength I-Wavelength I-Wavelength O O O B-Wavelength I-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 B-Wavelength I-Wavelength I-Wavelength I-Wavelength O O O O O O O B-Wavelength I-Wavelength I-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 B-Wavelength I-Wavelength 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 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 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 O O O O O O B-Wavelength O O O O O O O O O O O O

triggering of the radio nuclear activity, and detect in-falling molecular clouds that may accrete onto the SMBH. The ALMA observations of the 12 CO (2–1) line reveal that multiple clouds are distributed in a complex warped disk. Between 7.5 kpc and 2.5 kpc from the central radio AGN, the disk is oriented as the dust lane of the galaxy in the north-south direction ( PA ~ 180°). At inner radii (2.5 kpc – 700 pc), the disk changes slightly in orientation ( PA ~ 150°) and molecular clouds are found in proximity of dust ( Fig. 2 ) and of warm H 2 gas (see the left panel of Fig. 9 ). In the very central regions ( r ~ 700 pc), the disk changes abruptly its orientation and the molecular clouds form an edge-on circumnuclear disk oriented approximately east-west (PA ~ 72°). The carbon monoxide is a tracer of the cold phase of the molecular hydrogen. Depending on the conversion factor assumed, the total mass of cold molecular hydrogen is M (H 2 ) 3.2 × 10 8 –1.9 × 10 9 M ⊙ (see Table 3 ), while the circumnuclear disk has total mass of cold molecular hydrogen between 5.3 × 10 7 M ⊙ and 3.1 × 10 8 M ⊙ . The circumnuclear disk of molecular gas of PKS B1718–649 may form the reservoir of fuel for the radio emission. Although the rotation of the gas follows what is expected from the mass of the central SMBH and the distribution of the stellar body, the velocity dispersion of the gas is higher in the centre of the disk ( r 150 pc, 0.45′′) than at larger radii ( Fig. 7 ). This suggests that close to the radio source the gas may have non-circular orbits. The circumnuclear disk is also traced by the H 2 1-0 S(1) line emission ( Maccagni et al. 2016 ). The ratio between the column density of the H 2 gas traced by the H 2 1-0 S(1) line and by the 12 CO (2–1) line increases by a factor ten in the innermost 75 pc of the circumnuclear disk with respect to its outer regions (~ 110 and ~ 16, respectively). This suggests that, in the centre, the physical conditions of the molecular gas are different. It is possible that the radiative field of the AGN is photo-dissociating the cold component of the molecular gas, traced by the 12 CO (2–1) line, before the warmer component, traced by the H 2 1-0 S(1) line. This would indicate that the newly born AGN is actively changing the physical conditions of the surrounding ISM. Against the 230 GHz emission of the radio AGN, we detect 12 CO (2–1) gas in absorption (see Sect. 3.3 ). The absorption line has a width ( FWHM = 54 km s −1 ) much larger than the typical velocity dispersion of molecular clouds. Hence, either the absorption is tracing multiple clouds, or a single cloud with non-circular orbits, or a combination of both. The absorption line is detected at red-shifted velocities with respect to the systemic velocity (Δ v = +365 ± 20 km s −1 ). Its kinematics deviate from the rotation of the circumnuclear disk and the gas may be falling towards the radio source. At the same red-shifted velocities we also detect in emission a


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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-Collaboration I-Collaboration 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B-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 B-Telescope O O O O O O O O O O O O B-Model I-Model I-Model I-Model B-Model B-Citation I-Citation I-Citation I-Citation 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

We present a quantitative analysis of the relative orientation in a set of nearby (pc) well-known MCs to quantify the role of the magnetic field in the formation of density structures on physical scales ranging from tens of parsecs to approximately one parsec in the nearest clouds. The present work is an extension of previous findings, as reported by the Planck collaboration, on their study of the polarized thermal emission from Galactic dust. Previous studies include an overview of this emission ( Planck Collaboration Int. XIX 2015 ), which reported dust polarization percentages up to 20% at low , decreasing systematically with increasing to a low plateau for regions with cm. Planck Collaboration Int. XX (2015) presented a comparison of the polarized thermal emission from Galactic dust with results from simulations of MHD turbulence, focusing on the statistics of the polarization fractions and angles. Synthetic observations were made of the simulations under the simple assumption of homogeneous dust grain alignment efficiency. Both studies reported that the largest polarization fractions are reached in the most diffuse regions. Additionally, there is an anti-correlation between the polarization percentage and the dispersion of the polarization angle. This anti-correlation is reproduced well by the synthetic observations, indicating that it is essentially caused by the turbulent structure of the magnetic field. Over most of the sky Planck Collaboration Int. XXXII (2016) analysed the relative orientation between density structures, which is characterized by the Hessian matrix, and polarization, revealing that most of the elongated structures (filaments or ridges) have counterparts in the Stokes and maps. This implies that in these structures, the magnetic field has a well-defined mean direction on the scales probed by Planck . Furthermore, the ridges are predominantly aligned with the magnetic field measured on the structures. This statistical trend becomes more striking for decreasing column density and, as expected from the potential effects of projection, for increasing polarization fraction. There is no alignment for the highest column density ridges in the cm sample. Planck Collaboration Int. XXXIII (2016) studied the polarization properties of three nearby filaments, showing by geometrical modelling that the magnetic field in those representative regions has a well-defined mean direction that is different from the field orientation in the surroundings. In the present work, we quantitatively evaluate the relative orientation of the magnetic field inferred from the Planck polarization observations with respect to the gas column density structures, using the histogram of relative orientations (HRO, Soler et al. 2013 ). The HRO is a novel statistical tool that quantifies the relative orientation of each polarization measurement with respect to the column density gradient, making use of the unprecedented statistics provided by the Planck


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We acknowledge the logistic support provided by Parkes operators. The Parkes radio telescope is part of the Australia Telescope National Facility, which is funded by the Commonwealth of Australia for operation as a National Facility managed by CSIRO. The American National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. We would like to thank A.D. Hincks, M. Hobson, A. Scaife for useful suggestions and comments. We thank the referee for providing constructive comments and help in improving the contents of this paper.


O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B-Survey O O O O O O O B-Formula I-Formula I-Formula B-Software O O O O O O O O O O O O O O B-Formula I-Formula I-Formula B-Survey 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 B-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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B-Formula I-Formula I-Formula O O O O O B-Model 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 B-Model 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 B-Model O B-Model I-Model I-Model B-Citation I-Citation I-Citation I-Citation O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation O O O O B-Citation I-Citation I-Citation I-Citation O O O O B-Citation I-Citation I-Citation I-Citation O O O O O O O B-Model O O O O O O O O O O O O O B-Model O B-Citation I-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation I-Citation 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 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 B-Model I-Model O O O O O O O O B-Model B-Citation I-Citation I-Citation O O O B-Model B-Citation I-Citation O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation O O O O B-Model O O B-Model O O O O O O O O

with mass loadings of . This leads to disks at high redshift with baryon fractions depleted relative to those at low redshift. This feedback effect is clear when a single galaxy, g 1536, is compared to the same galaxy simulated without SNe feedback. As Figure 3 shows, the redshift trend is nearly nonexistent without SNe feedback. Even at z = 2, the galaxy without feedback falls within the scatter of the SPARC observations, and within the scatter of the z = 0 MUGS2 relation. This tells us that we need not invoke feedback processes to explain the z = 0 SPARC RAR. Simple dissipational collapse of gas is sufficient to produce a similar relation. The evolution as a function of redshift is therefore dominated primarily by the stronger effect of feedback at higher redshift. Figure 2. The simulated relation is not constant with redshift. As this figure shows, at higher redshift the low g bar slope is much shallower than at z = 0. This shows that for high-redshift galaxies, their disks can be depleted of baryons compared with z = 0. We have focused on the low g bar end of the relation here, where the changes are most significant. Figure 3. The evolution seen in Figure 2 is primarily driven by feedback. This can be seen when looking at the same galaxy with and without feedback. Without feedback, the baryon fraction within the disk increases slightly from z = 0 to z = 2, but still roughly follows the RAR. At z = 2, strong outflows in the galaxy expel most of the baryons from the disk, flattening the acceleration relation. This effect is sensitive to the frequent merger-driven starbursts at high redshift, which can drive bursty outflows. 4. Discussion This Letter is the first work to show that the RAR’s acceleration scale and tight scatter, as reported by McGaugh et al. ( 2016 ), can arise from fully self-consistent hydrodynamical simulations. Efforts have been made in the past to explain related acceleration relations (the MDAR; the Baryonic Tully–Fisher, BTFR; McGaugh et al. 2000 , etc.) with analytic arguments (van den Bosch Dalcanton 2000 ; Kaplinghat Turner 2002 ), existing scaling relations (Di Cintio Lelli 2016 ), or hydrodynamical simulations (Santos-Santos et al. 2016 ). Analytic studies have found that the MOND-like scaling relations can arise as a consequence of exponential disks living within an NFW halo (van den Bosch Dalcanton 2000 ). van den Bosch Dalcanton ( 2000 ) showed that this can explain not only Tully–Fisher relation’s relatively tight scaling over a large range of masses and surface densities (McGaugh de Blok 1998 ), but even explain the appearance of a characteristic acceleration in disk galaxy rotation curves (McGaugh 1999 ). Subsequent studies have suggested that an even harder constraint for Λ CDM to match is the tight scatter in the MDAR (Wu Kroupa 2015 ) or the RAR (Milgrom 2016 ). A semi-empirical model recently published by Di Cintio Lelli ( 2016 ) showed that both the BTFR and the MDAR can arise as a result of galaxies that


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

uncertain by an order of magnitude. Zane et al. ( 2011 ) performed the first detailed analysis of its spectrum. Among numerous fits tried by the authors, only those with the interstellar hydrogen column density N H fixed to the Galactic value 2.7 × 10 20 cm −2 gave an acceptable reduced chi-square statistic and a reasonable distance (other fits gave distances down to 175 pc, too large N H and tiny emitting area). A fit with a two-temperature atmosphere model (2NSA) gave eV/ eV, with radius of the cold component fixed at the realistic value R = 12 km. This fit gave L ∞ ∼ 6 × 10 32 erg s −1 at d ≈ 1.55 kpc. Other acceptable fits indicated d in the range from ∼1 kpc to ≈2.25 kpc. The values of the unabsorbed flux for various statistically acceptable fits implied L ∞ ranging from 5.5 × 10 32 to 9.2 × 10 32 erg s −1 , assuming d = 2 kpc. Shibanov et al. ( 2016 ) fitted the XMM–Newton and Chandra data using nsmax models of magnetized hydrogen atmospheres covering the entire surface, assuming a centred dipole field and the ensuing temperature distribution. They found that an additional absorption feature at energy E abs = 740 ± 30 eV improved the fit. In this case, the best-fitting model yields T ∞ = 1.18 ± 0.05 MK. The resulting normalization (the effective emitting radius) can be reconciled with R = 12 km (assumed in the nsmax model) at d ∼ 4 kpc. The corresponding luminosity is L ∞ = (3.0 ± 0.5) × 10 33 erg s −1 . Other fits, performed for different data sets with or without involving the absorption, are only slightly worse statistically. They yield T ∞ from 0.79 ± 0.03 MK to MK, corresponding to L ∞ ∼ 5 × (10 32 − 10 33 ) erg s −1 . Bogdanov et al. ( 2019 ) have performed an analysis of the spectrum obtained with NICER . They fixed d = 2 kpc and obtained k B T ∞ = 205 ± 3 eV, km ( erg s −1 ) for a BB plus power-law (BB+PL) fit with an absorption at E abs = 780 ± 20 eV. An alternative fit including a Gaussian absorption at E abs = 760 ± 10 eV and two BB components (the G2BB fit) gives eV, km, totalling to L ∞ = (5.4 ± 0.6) × 10 32 erg s −1 . The ranges of L ∞ and T ∞ in Table 2 cover the results obtained by Bogdanov et al. ( 2019 ) and by Shibanov et al. ( 2016 ) for the different instruments. 7. CXOU J160103.1–513353 is a CCO in the SNR G330.2+1.0. Proper motion of the SNR fragments indicates an age of 0.8–1 kyr, if one neglects deceleration; because of deceleration the actual age should be smaller (Borkowski et al. 2018 ; Williams et al. 2018 ). McClure-Griffiths et al. ( 2001 ) derived the minimum distance to this SNR of d = 4.9 ± 0.3 kpc from the observed velocities in the absorption spectrum; a hypothetical maximum distance is d 9.9 kpc. The X-ray spectrum of the CCO is well described with either single-component carbon or two-component hydrogen atmosphere models (Doroshenko, Suleimanov Santangelo 2018 ). In the latter case, the observed spectrum is dominated by the emission from a hot component with a temperature ∼3.9 MK, corresponding to


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 O O O 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 B-Survey 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 B-Citation I-Citation I-Citation I-Citation I-Citation I-Citation 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 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 B-Software B-Citation I-Citation I-Citation O B-TextGarbage I-TextGarbage B-URL 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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

The model includes galactic and isotropic diffuse emission components and all 3FGL sources within 8° of the position of interest. We optimize the parameters of the model with unbinned likelihood analysis using the gtlike tool. Normalization of the galactic and isotropic diffuse emissions and spectral parameters of the target source were considered as free parameters, while the spectral parameters of all neighboring sources were fixed at the 3FGL values. The spectral form for each source is a power law, a log-parabola, or a power law with exponential cutoff based on the 3FGL data. Next, using the gtsrcprob tool each photon is assigned a weight—the probability that it originates from the given pulsar candidate. For computational efficiency we keep 40,000 events with the highest weights for each source. Finally, the photon arrival times are converted to the barycentric frame using the gtbary tool. 3. METHOD The search for pulsations is performed with the time-differencing technique first proposed by Atwood et al. ( 2006 ) and subsequently refined by Pletsch et al. ( 2012a ). We scan over the pulsar’s frequency f and spin-down rate using the array of barycentric photon arrival times t a and corresponding weights w a . First, the arrival times are corrected to compensate for frequency evolution:where and t 0 = 286,416,002 (MJD 55225) is a reference epoch. The spectral density P m is obtained with a Fourier transform of the time differenceswhere , , and is the rectangular function that takes the value 1 for and 0 otherwise. Technically we consider only positive time differences and bin the time interval into bins. Within the cycle over all event pairs one calculates the sum of the product of weights in each bin. This prepares an input for the fast Fourier transform performed with the open source library fftw (Frigo Johnson 2005 ). 3 3 http://www.fftw.org We use the same value of T as Pletsch et al. ( 2012a ). The Nyquist frequency is , which determines the choice of N . The relatively high value of the Nyquist frequency is required to minimize the artificial binning noise. keeping in mind that some pulsars may be found at the second harmonic of the frequency. We scan over the parameter γ from 0 to with a step equal to . The range corresponds to a characteristic age of the pulsar greater than 1 kyr. The step is selected taking into account the computation time available for the project. The value used introduces a moderate loss of power for pulsations with frequencies higher than 10 Hz; see Equation (3) of Ziegler et al. ( 2008 ). The values of f and corresponding to the highest P m are used as a starting point for the final coherent scan. The latter is performed with maximization of the weighed H -test statistic (Jager et al. 1989 ) defined as follows:where is a Fourier amplitude of the l th harmonic The maximization is performed by a scan over f and γ with steps of and . The steps are chosen to ensure coherence over the whole time interval of


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We thank the anonymous referee for useful suggestions that improved the paper. AB and MLB acknowledge support from the European Research Council under the EC FP7 grant number 280127. MLB also acknowledges support from an STFC Advanced/Halliday fellowship. We thank K. Grainge for providing the SKA specifications, and F. Abdalla and E. Chapman for useful comments and suggestions.


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We thank Kaylea Nelson for providing the simulation data used in this work. DN thanks Max-Planck-Institut für Astrophysik for hospitality when this work is finished. This work was supported in part by NSF grant AST-1412768, the Research Corporation, and by the facilities and staff of the Yale Center for Research Computing.