How to convert quasar flux time series from observed frame to rest frame?

How to convert quasar flux time series from observed frame to rest frame?

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I have flux time series of quasars in the observed frame and want to convert it in the rest or quasar frame. Any one who can help? Thanks

This is not an answer, but it is too long to put in the comment.

I am not familiar with quasars. In general, converting fluxes from an observed frame to a rest frame requires following consideration: extinction correction, cosmological expansion, k-correction. I think the simplest form is $f_{rest} = (1 + z)^n f_{obs}$ where $z$ is redshift and $n$ is some integer depending on how to express the unit of fluxes. This considers only the cosmological expansion. But, I am not sure if this transformation is valid with your quasars because they might be too far that $(1+z)^n$ is no longer a good approximation.

A possible close supermassive black-hole binary in a quasar with optical periodicity

Quasars have long been known to be variable sources at all wavelengths. Their optical variability is stochastic and can be due to a variety of physical mechanisms it is also well-described statistically in terms of a damped random walk model 1 . The recent availability of large collections of astronomical time series of flux measurements (light curves 2,3,4,5 ) offers new data sets for a systematic exploration of quasar variability. Here we report the detection of a strong, smooth periodic signal in the optical variability of the quasar PG 1302−102 with a mean observed period of 1,884 ± 88 days. It was identified in a search for periodic variability in a data set of light curves for 247,000 known, spectroscopically confirmed quasars with a temporal baseline of about 9 years. Although the interpretation of this phenomenon is still uncertain, the most plausible mechanisms involve a binary system of two supermassive black holes with a subparsec separation. Such systems are an expected consequence of galaxy mergers and can provide important constraints on models of galaxy formation and evolution.


Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER)

The team provides UV-optical-NIR photometry for 22 million stars in the central

0.1 deg^2 of M33 for the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region ('PHATTER') survey. They use the filters F275W and F336W on the WFC3/UVIS camera, F475W and F814W on ACS/WFC, and the F110W and F160W on WFC3/IR. UVIS data reach a magnitude limit of

25 in F275W and F336W. ACS data reach maximum depths of

28 magnitudes in F475W and

27 magnitudes in F814W in the uncrowded outer disk. In these same regions, WFC3/IR data reach maximum depths of

25.5 in F110W and F160W, respectively. However, the depths are crowding limited in the optical and NIR, and thus is a strong function of radius. As a result, photometry in the inner bulge fields is far shallower. The source catalogs and image mosaics from which the sources are extracted are provided by the team. The team also provides the WCS solutions for each subsection's reference image.



Object Type

Product Type

Measuring Young Stars in Space and Time (MYSST)

The 'Measuring Young Stars in Space and Time' (MYSST) project is a large, high spatial resolution, deep Hubble Space Telescope survey of the star forming complex N44 located in the Large Magellanic Cloud (LMC). Observing objects with masses as low as 0.09 M_sun (unreddened 1 Myr pre-main-sequence star), the project aims to draw a comprehensive picture of star formation on the scales of giant molecular clouds by quantifying the star formation history of N44 across space and in time. Observations were taken with the Advanced Camera for Surveys (Wide Field Channel) and the Wide Field Camera 3 (UVIS channel) in the broad band filters F555W and F814W, covering a field of view of approximately 12.2 x 14.7 arcmin^2 or 180 x 215 pc^2 at the distance of the LMC. This archive comprises the primary science output of the survey, i.e. the MYSST photometric catalog and the mosaic images.



Object Type

Product Type

Mapping the Escape Fraction of Ionizing Photons Using Resolved Stars (UVESCAPE)

The UVESCAPE team has demonstrated a new method for measuring the escape fraction of ionizing photons using HST imaging of resolved stars in NGC 4214, a local analog of high-z starburst galaxies that are thought to be responsible for cosmic reionization. Specifically, they forward model the UV through near-IR spectral energy distributions of

83,000 resolved stars to infer their individual ionizing flux outputs using the Bayesian Extinction And Stellar Tool (BEAST Gordon et al. 2016). They constrain the local escape fraction by comparing the number of ionizing photons produced by stars to the number that are either absorbed by dust or consumed by ionizing the surrounding neutral hydrogen in individual star-forming regions. They find substantial spatial variation in the escape fraction (0-40%). Integrating over the entire galaxy yields a global escape fraction of 25% (+16% / -15%). This value is much higher than previous escape fractions of zero reported for this galaxy. They discuss sources of this apparent tension, and demonstrate that the viewing angle and the 3D ISM geometric effects are the cause. If one assumes that NGC 4214 has no internal dust, like many high-z galaxies, they find an escape fraction of 59% (an upper limit for NGC 4214). This is the first non-zero escape fraction measurement for UV-faint (M_FUV) = -15.9 galaxies at any redshift, and supports the idea that starburst UV-faint dwarf galaxies can provide a sufficient amount of ionizing photons to the intergalactic medium. The team has made their catalog of stellar ionizing fluxes available as a High Level Science Product.

Fall Quarter 2020

Mapping Outflowing Gas in the Fermi Bubbles a UV Absorption Survey

The Fermi Bubbles are an example of extreme feedback in our own Milky Way. These two giant bubbles extend

10 kpc above and below the center of the Galaxy. They are thought to have formed via an outburst from our central supermassive black hole or nuclear star formation. Understanding the origins of the Fermi Bubbles requires careful measurements of their kinematics and chemical abundances. We have obtained FUV spectra from Hubble/COS to characterize the previously unexplored low latitude region of the southern Fermi Bubble, close to where the bubbles are launched. With these data we measured the kinematics and composition of the southern bubble and how they vary with both Galactic latitude and longitude. We combine these data with previous UV and atomic hydrogen datasets to characterize the Fermi Bubbles at all latitudes. Overall, these observations form a valuable set of empirical data on the properties of cool gas in nuclear winds from star-forming galaxies.

Exotic Transients and How to Find Them

Unbiased all-sky surveys such as the Zwicky Transient Facility (ZTF) or the Pan-STARRS Survey for Transients (PSST) have opened up the door for the discovery of new and exciting types of transients. The current discovery rate of optical transients makes it such that only a small fraction of them can get spectroscopically classified, and by the time the Legacy Survey of Space and Time (LSST) commences, the number of discovered transients is expected to increase by about two orders of magnitude. We have been running a program to follow up alerts from these streams in search of superluminous supernovae (SLSNe), since only a handful of these are known, and many questions remain open regarding their power source, progenitors, and diversity of features in their light curves. In the process of searching for SLSNe we have also encountered other exotic transients, such as tidal disruption events (TDEs) and a pair-instability supernova candidate. In order to decide which transients are most worthy of follow-up, we have developed a custom machine-learning pipeline to estimate how likely any new transient is to be a SLSNe, and in this way make the most efficient use of our telescope resources.

Utilizing Kepler and K2 to Advance Exoplanet Demographics

Over the course of several years the Kepler mission, which continuously collected photometric data from a single patch of the sky, provided a uniform set of transiting exoplanet detections. This catalog remains the gold standard for transiting exoplanet occurrence rate studies. However, 18 additional fields of data, sampling a variety of Galactic latitudes, were collected following the malfunction that led to the end of the Kepler prime mission. Better known as the K2 mission, these fields provide a unique opportunity to understand how exoplanet occurrence is affected by Galactic latitude, stellar metallicity, and stellar age. With a fully automated pipeline now able to detect and vet transit signals in K2 data, we can measure the sample completeness and reliability. Correspondingly, I will present the first uniform analysis of small transiting exoplanet occurrence outside of the Kepler field. Additionally, with the full K2 sample now processed, I will discuss how we can incorporate this new catalog of planets into our current demographics analysis to expand our understanding of system architecture and planet formation mechanisms.

Kinematics of the Circumgalactic Gas: How Do Galaxies Get Their Gas

Galactic disks grow by accreting cooling gas from the circumgalactic medium (CGM). Although decades of observations have demonstrated that galaxies need a continuous gas supply to explain the star formation history, direct observations of gas accretion onto galaxies remain sparse. We will present results from our survey of using background quasar sightlines to measure the kinematics of the cool (

10^4 K) CGM of low-redshift, star-forming galaxies. In particular, we will show that although the inner CGM corotates with the galactic disk, the centrifugal force only partially supports the circumgalactic gas, implying that the angular momentum of the CGM delays accretion onto the disk. We will also present our analysis with the EAGLE cosmological simulation and focus on the circumgalactic gas kinematics. Our study with EAGLE will provide insight into interpreting our circumgalactic kinematic observations and understanding how gas feeds the galactic disks.

Title: Measuring quasar variability with Pan-STARRS1 and SDSS

We measure quasar variability using the Panoramic Survey Telescope and Rapid Response System 1 Survey (Pan-STARRS1 or PS1) and the Sloan Digital Sky Survey (SDSS) and establish a method of selecting quasars via their variability in 10 deg surveys. We use 10 spectroscopically confirmed quasars that have been well measured in both PS1 and SDSS and take advantage of the decadal timescales that separate SDSS measurements and PS1 measurements. A power law model fits the data well over the entire time range tested, 0.01-10 yr. Variability in the current PS1-SDSS data set can efficiently distinguish between quasars and nonvarying objects. It improves the purity of a griz quasar color cut from 4.1% to 48% while maintaining 67% completeness. Variability will be very effective at finding quasars in data sets with no u band and in redshift ranges where exclusively photometric selection is not efficient. We show that quasars' rest-frame ensemble variability, measured as a root mean squared in Δ magnitudes, is consistent with V(z, L, t) = A (1 + z)(L/L )(t/1 yr), where L = 10 erg s and A = 0.190, 0.162, 0.147, ormore » 0.141 in the g , r , i , or z filter, respectively. We also fit across all four filters and obtain median variability as a function of z, L, and λ as V(z, L, λ, t) = 0.079(1 + z)(L/L )(λ/1000 nm)(t/1 yr). « less


The SDSS (York et al. 2000) provides homogeneous and deep (r < 22.5) photometry in five passbands (ugriz Fukugita et al. 1996 Gunn et al. 1998 Smith et al. 2002) accurate to 0.02 mag, of almost 12,000 deg 2 in the Northern galactic cap (NGC), and a smaller, but deeper, survey of 290 deg 2 in the Southern galactic hemisphere. For this 290 deg 2 area known as S82, there are on average more than 60 available epochs of observations. These data were obtained in yearly "seasons" about 2–3 months long over the last decade and the cadence effectively samples timescales from days to years. The light curve lengths are effectively shorter than the actual period of the survey because the better-sampled supernova observations begin about 5 yr into the survey. Because some observations were obtained in non-photometric conditions, improved calibration techniques have been applied to SDSS S82 data by Ivezić et al. (2007) and Sesar et al. (2007), and we use their results. For these data, photometric zero-point errors are 0.01–0.02 mag.

We have compiled a sample of 9275 spectroscopically confirmed quasars in S82 with re-calibrated ugriz light curves (see also Bhatti et al. 2010). Most (8974) of these are in the SDSS Data Release 5 (DR5) Quasar Catalog (Schneider et al. 2007) and the remaining are newly confirmed DR7 (Abazajian et al. 2009) quasars. Summed over all bands and epochs, the data set includes 2.7 million photometric measurements. For 41% of the sample, the random photometric errors are smaller than 0.03 mag. Only 1% have errors 𢙐.1 mag in g, r, and i, and 2.4% have errors exceeding 0.25 mag in u and z filters. In C. L. MacLeod et al. (2010, in preparation), these light curves, as well as a much larger sample of quasars with two SDSS epochs selected from 12,000 deg 2 of the sky, will be made publicly available. We adopt the K-corrected i-band absolute magnitudes from Schneider et al. (2007), and virial black hole masses and bolometric luminosities where available from Shen et al. (2008). The Shen et al. masses were estimated from emission line widths (Hβ for z < 0.7, Mg ii for 0.7 < z < 1.9, and C iv for z>1.9). However, we note that at low spectroscopic signal-to-noise, black hole masses tend to be overestimated (Denney et al. 2009).

3.1. Initial Light Curve Selection

The DRW model was fit to all available ugriz light curves for 9275 S82 quasars. Summed over five bands, there are 46, 375 best-fit values of the characteristic (damping) timescale τ and long-term SF. For further analysis, we select light curves that satisfy the following criteria.

Figure 2. Initial light curve selection. Top left: the distribution of the number of observations per light curve for the r (solid) and u (dashed) bands. Top right: distribution of ΔLnoise. We define light curves with ΔLnoise ≤ 2 to be more consistent with uncorrelated noise rather than our model. Bottom left: distribution of ΔL light curves with ΔL ≤ 0.05 likely have run-away timescales. In this panel and the previous panel, the x-axes are truncated at 0.5 and 5, respectively, but the histograms continue to greater values. Bottom right: distribution of χ 2 per degrees of freedom (Ndof) for the DRW model (solid line). The expected Gaussian distribution based on Ndof is also shown (dashed). The hashed region in each panel shows the values rejected from our final sample.

The resulting light curves are well fit by the stochastic model, as can be seen from the distribution of χ 2 /Ndof shown in the bottom right panel in Figure 2, where Ndof is the number of degrees of freedom. The expected Gaussian distribution with rms is also shown in the panel, where we have averaged over the Ndof distribution of the light curves. The observed distribution is centered at χ 2 /Ndof = 1.1. This difference is some combination of errors in the estimated errors, outliers in the light curves, and any poorly modeled physics. Koz10 noted a similar difference in their analysis of OGLE light curves. Only 5% of the light curves have χ 2 /Ndof>1.5, confirming that most quasars are variable (at the ΔLnoise>2 level), and that a DRW is a good description of quasar variability.

1. Introduction

[2] There are many unsolved problems related to the energy budget of the Earth's magnetosphere. Bright auroral forms, which perhaps are the most spectacular phenomena that regularly can be observed on the dark night sky, is an apparent proof of the existence of a set of complicated processes involving the energy conversion and transfer in the magnetosphere, from the solar wind and to the auroral ionosphere. Intriguing problems regarding the magnetospheric energy budget concern issues such as the energy input into the magnetosphere [e.g., Koskinen and Tanskanen, 2002 ], the nature of tail reconnection [e.g., Sharma et al., 2008 ], the location of the auroral generator [e.g., Rostoker, 1999 ], the role of the magnetosphere-ionosphere (M-I) coupling [e.g., Mauk et al., 2002 ], as well as the high-speed flows and the energy transport in the M-I system [e.g., Sergeev, 2004 ]. Large amounts of energy are released during substorms, and auroral arcs in the ionosphere are connected via magnetic field-aligned currents to the nightside magnetosphere in the auroral current circuit. Even though auroral processes have been investigated for a long time, our understanding of the detailed mechanisms behind the generation and evolution of auroras is still rather fragmented and uncertain. For example, where are the auroral generators explicitly located and what are their properties?

[3] Within the magnetosphere, energy is mediated between different forms. In load regions, magnetic pressure and tension accelerate the plasma, and electromagnetic energy is converted into kinetic energy (plasma bulk and thermal). The process is reversed in generator regions. The plasma sheet is known to play a central role for the energy budget of the Earth's magnetosphere [e.g., Lyons, 2000 Koskinen and Tanskanen, 2002 Pulkkinen et al., 2003 ]. During substorms, the amount of energy dissipated in the plasma sheet (in the form of plasmoid ejection and ion heating) is comparable to the ring current dissipation, auroral Joule heating and charged particle precipitation into the ionosphere [ Ieda et al., 1998 Slavin et al., 1993 ]. Since the plasma sheet maps to the nightside auroral ionosphere, various regions in the plasma sheet have been suggested to host auroral generators, for example, the low-latitude boundary layer and the plasma sheet boundary layer (PSBL). However, even though the plasma sheet on the average behaves as a load due to the dawn to dusk electric field and cross-tail current, it is a complicated plasma regime comprising both generators and loads [e.g., Birn and Hesse, 2005 Marghitu et al., 2010 Hamrin et al., 2009a ].

[4] The processes in the plasma sheet of course only constitute a few links in the long chain of processes controlling the energy conversion and transfer in the Earth's magnetosphere. The primary energy source for the magnetospheric energy budget is solar wind kinetic energy, which can be transferred into the magnetosphere by means of magnetopause reconnection. Indeed, reconnection is one key process at several stages of the magnetospheric energy budget [e.g., Paschmann, 2008 ]. Not only does it regulate the solar wind energy and momentum input at the magnetopause [ Paschmann et al., 1979 ], but it also controls the substorm magnetic energy release in the Earth's magnetotail [e.g., Fujimoto et al., 2001 ].

[5] The primary magnetospheric convection is believed to be controlled by the Dungey cycle with dayside magnetopause reconnection in combination with a reconnection X line in the distant magnetotail [ Dungey, 1961 ]. The corresponding cross-tail electric field and current systems are the cause for the average load behavior of the plasma sheet. During substorm expansion, another reconnection site is expected to form as a near-Earth neutral line (NENL) 20–30RE downtail in the plasma sheet [e.g., Nagai et al., 2001 ]. According to recent investigations, the plasma sheet energy conversion between magnetic energy on the one hand, and bulk kinetic and thermal energy on the other, may be associated with multiple, small-scale, and intermittent reconnection processes [e.g., Treumann et al., 2009 ] in a turbulent plasma environment.

[6] The solar wind kinetic energy powers generators located at the magnetopause. Generated electromagnetic energy is partially stored in the tail magnetic field, particularly the lobes. Tail reconnection and perhaps also other processes, e.g., resistivity, then convert the electromagnetic energy into kinetic and thermal energy. A popular notion is hence that the aurora is powered by the solar wind. Indeed, an open magnetopause at a few RE tailward of the dawn-dusk meridian has been observed to act like a generator with a significant component of the magnetic tension directed against the solar wind flow, thus producing Poynting flux pointing into the magnetotail [ Rosenqvist et al., 2006 ]. According to Rosenqvist et al. [2006] , estimates of the global Joule heating in the Earth's upper atmosphere and ionosphere during an intense storm amounted to roughly 35% of the power extracted from the solar wind at the magnetopause. However, under normal circumstances we can expect that a considerable fraction of the electromagnetic power dissipated in the ionosphere is not directly generated at the magnetopause, since nightside auroral field lines on the equatorward edge of the oval, and at least up into the central part, map from the ionosphere rather into the tail region (specifically the plasma sheet).

[7] The energy stored in the tail magnetic field is converted into kinetic energy at rather localized acceleration sites, and plasma is transported toward the Earth, or tailward into the interplanetary plasma. These high-speed flows can be manifested as bursty bulk flows (BBFs) and other sporadic and intermittent phenomena [e.g., Scholer et al., 1984 Angelopoulos et al., 1992 Chen and Wolf, 1993 Angelopoulos et al., 2002 ]. For example, the large substorm current wedge is believed to be caused by the braking and diversion of earthward directed flows closer to the inner boundary of the plasma sheet, resulting in the generation of electromagnetic power, which eventually can power the aurora [ Wygant et al., 2000 ] as well as cause ionospheric Joule heating.

[8] Alongside the process of reconnection, high-speed flows in the plasma sheet is hence another important key issue involved in the magnetospheric energy budget. Such flows are observed in various regions of the magnetotail, both in the central plasma sheet (CPS) and in the PSBL. However, the characteristics of the high-speed flows differ generally between the regions. In the CPS, the high-speed flows are generally bulk flows which are (quasi-) perpendicular to the ambient magnetic field, with GSM Vx being the dominant velocity component, and Vy occasionally substantial [ Angelopoulos et al., 1994 ]. In the PSBL, on the other hand, the high-speed flows can usually be characterized as field-aligned beams [ Nakamura et al., 1992 Petrukovich et al., 2001 ].

[9] The concept of BBFs was first introduced by Angelopoulos et al. [1992] who investigated the occurrence of bursty bulk flows in the inner CPS, as characterized by a large plasma β > 0.5. Typically, BBFs correspond to bursty high-speed flow events observed on a 10 min time scale, and composed of individual high-speed flow burst (≳400 km/s) on shorter time scales, of the order of tens of seconds [ Angelopoulos et al., 1992 , 1994 ]. Subsequently, field aligned burst have been observed also in lower β plasmas outside the PSBL, i.e., where β < 0.5 [ Raj et al., 2002 ]. Therefore, it is practical to include also the field aligned beams into the definition of BBFs [ Snekvik et al., 2007 ]. Observational investigations have shown that BBFs often are associated with ion heating and local magnetic field dipolarization (magnetic pileup) at the front or stopping region, corresponding to a locally enhanced northward Bz [e.g., Fairfield et al., 1999 Nakamura et al., 2005a Sergeev et al., 1996b ]. The BBFs are likely to show a reduction in the plasma pressure initially, but evolving toward values comparable to, or sometimes even greater, than the surrounding medium [ Chen and Wolf, 1999 ].

[10] A possible theoretical explanation for BBFs comes from the theory of plasma bubbles. As compared to the surrounding plasma, bubbles are depleted flux tubes with decreased entropy, and increased earthward propagation velocity (possibly propelled by a magnetic buoyancy force, related to the interchange instability) [ Pontius and Wolf, 1990 Chen and Wolf, 1993 , 1999 ]. Details of the propagation of a plasma bubble have been investigated in a 3-D MHD simulation by Birn et al. [2004] . Bubbles are expected to be created by reconnection processes and/or other processes in the plasma sheet [e.g., Sergeev, 2004 ], and simulations indicate that the tailward ejection of plasmoids from a reconnection site also can be explained by bubble theory [ Sitnov et al., 2005 ].

[11] The plasma bubble cannot support as much diamagnetic, curvature and drift current as the surrounding plasma. Current continuity is instead maintained by field-aligned currents at the sides of the flux tube. As shown by Birn et al. [2004] , flow vortices appear at the flanks of the bubble, twisting the magnetic field, and causing a downward (upward) field-aligned current at the dawnside (duskside) flank, and forming a local wedge like current system [e.g., Chen and Wolf, 1993 , 1999 Sergeev et al., 1996a Birn and Hesse, 1996 Birn et al., 1999 , 2004 Snekvik et al., 2007 Zhang et al., 2009 ]. A return plasma flow may occur at the flanks of the bubble, and the corresponding shear against the plasma flow in the bubble main channel may also be involved in the generation of the field-aligned currents [e.g., Chen and Wolf, 1999 Kauristie et al., 2000 Birn et al., 2004 Keiling et al., 2009 Ohtani et al., 2009 Walsh et al., 2009 Panov et al., 2010a , 2010b Birn et al., 2011 Ge et al., 2011 Pitkänen et al., 2011 ].

[12] High-speed flows in the plasma sheet, such as BBFs and bubbles, are central ingredients in magnetospheric energy budget. (Note that we will use the denomination BBF for high-speed plasma flows throughout the rest of the article, independently of the detailed character of the flows.) BBFs are believed to play a major role for magnetic flux, mass, and energy transport in the plasma sheet [e.g., Angelopoulos et al., 1992 , 1994 , 1999 Sergeev et al. 1996b Schödel et al., 2001 ], and it has been showed that BBFs have the largest capability of transporting energy during the substorm expansion phase, as compared to the growth and recovery phases [ YuDuan et al., 2010 ]. In the literature there are many reports of the relation between BBFs and auroral phenomena at the ionospheric end of the M-I coupling system, e.g., auroral expansions, localized brightenings, and auroral streamers [e.g., Fairfield et al., 1999 Lyons et al., 1999 Ieda et al., 2001 Sergeev et al., 2001 Nakamura et al., 2001a , 2001b , 2005b Miyashita et al., 2003 Forsyth et al., 2008 ].

[13] The multispacecraft Cluster mission is favorable for observational investigations of the energy conversion in the plasma sheet. The reason is that a minimum of four simultaneous measurements of the magnetic field is needed for estimating the full current density. Load and generator regions can be identified by analyzing observations of the power density E · J, where E is the electric field and J the current density. Local conversion from kinetic to electromagnetic energy occurs in generator regions where E · J < 0, and the process is reversed in load regions where E · J > 0. Using plasma sheet Cluster data we present in this article a review of recent observations of localized energy conversion regions (ECRs), and we discuss the high level of fine structure in the plasma sheet energy conversion. To our knowledge, the first experimental evidences for plasma sheet ECRs in the form of generator regions were obtained from Cluster data by Marghitu et al. [2006] and Hamrin et al. [2006] .

[14] There are reasons to expect that there is a relationship between ECRs and the energy transfer in the form of BBFs in the plasma sheet. In a statistical investigation, Morioka et al. [2010] showed that the generation of field-aligned currents and accelerated auroral electrons in the auroral current circuit is tightly coupled to flow burst in the plasma sheet. Indeed, 65% of the flow bursts observed from Geotail data appeared to correspond to the generation of auroral kilometric radiation (AKR) in the auroral acceleration region within the M-I coupling region [ Morioka et al., 2010 ]. Moreover, recent investigations by Marghitu et al. [2010] and O. Marghitu et al. (manuscript in preparation, 2011) suggest that ECRs are often associated with BBFs, even though there are occasions where ECRs are observed without any strong and distinct signatures in the ion velocity data. This could for example indicate that other processes dominate, or that the Cluster spacecraft miss the main plasma flow. Detailed investigations of the relation between BBFs and ECRs are needed to resolve this issue.


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B.T. is a Zwicky Fellow. I.A. is an Einstein Fellow. E.K. is a Hubble Fellow. We thank N. Caplar, J. Guillochon, Z. Haiman, E. Lusso, and K. Schawinski for useful discussions. We thank C. Tadhunter for providing the spectrum of the F01004-2237 transient and his helpful comments. Part of this work was inspired by discussions within International Team #371, ‘Using Tidal Disruption Events to Study Super-Massive Black Holes’, hosted at the International Space Science Institute in Bern, Switzerland. We thank all the participants of the team meeting for their beneficial comments. Support for I.A. was provided by NASA through the Einstein Fellowship Program, grant PF6-170148. C.R. acknowledges support from the CONICYT + PAI Convocatoria Nacional subvencion a instalacion en la academia convocatoria a no 2017 PAI77170080. P.G.J. acknowledges support from European Research Council Consolidator Grant 647208. A. Horesh acknowledges support by the I-Core Program of the Planning and Budgeting Committee and the Israel Science Foundation. G.H., D.A.H. and C.M. acknowledge support from NSF grant AST-1313484. M.B. acknowledges support from the Black Hole Initiative at Harvard University, which is funded by a grant from the John Templeton Foundation. G.L. acknowledges support from a Herchel Smith Research Fellowship of the University of Cambridge. Ł.W., M.G. and A. Hamanowicz acknowledge Polish National Science Centre grant OPUS no 2015/17/B/ST9/03167 to Ł.W. Research by D.J.S. is supported by NSF grants AST-1412504 and AST-1517649. E.Y.H. acknowledges the support provided by the National Science Foundation under Grant No. AST-1613472 and by the Florida Space Grant Consortium. This work makes use of observations from the Las Cumbres Observatory network. This publication also makes use of data products from the Wide-field Infrared Survey Explorer. WISE and NEOWISE are funded by the National Aeronautics and Space Administration.

This work made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the NuSTAR operations, software and calibration teams for support with the execution and analysis of these observations. This research made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA).

We thank the Swift, NuSTAR and NICER teams for scheduling and performing the target-of-opportunity observations presented here on short notice. The LRIS spectrum presented herein was obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The observatory was made possible by the generous financial support of the W. M. Keck Foundation. We recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.

These results made use of the Discovery Channel Telescope (DCT) at Lowell Observatory. Lowell is a private, non-profit institution dedicated to astrophysical research and public appreciation of astronomy and operates the DCT in partnership with Boston University, the University of Maryland, the University of Toledo, Northern Arizona University and Yale University. The upgrade of the DeVeny optical spectrograph has been funded by a generous grant from John and Ginger Giovale.

The FLAMINGOS-2 spectrum was obtained at the Gemini Observatory under program GS-2017A-Q-33 (PI: Sand), which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the National Science Foundation on behalf of the Gemini partnership: the National Science Foundation (USA), the National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina) and Ministério da Ciência, Tecnologia e Inovação (Brazil).


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