adsabs/FOCAL huggingface dataset:
Next we chose an optimum cut-off radius for the detector FOV. The PSPC has a circular FOV with a radius 57′. The PSPC entrance window has a rib support structure with an inner ring at a radius corresponding to 20′ (Pfeffermann et al. 1987; Hasinger & Zamorani 2000). Both the ROSAT telescope angular resolution and its vignetting function are roughly constant within the inner 20′ ring, but degrade significantly towards larger off-axis angles. The combined detector and telescope PSFs are described in detail in Boese (2000). To the first order, the PSF at each off-axis angle can be approximated by a Gaussian function with a half power radius (HPR) of 13, 22, 52, 93, 130, and 180″, at off-axis angles of 0, 12, 24, 36, 48, and 57′, respectively (at 1 keV). The vignetting function at 1 keV drops almost linearly to about 50% at an off-axis angle of 50′. Taking into account all these effects, the HPR of the overall RASS PSF is 84″ (Boese 2000). This means that the classical confusion limit (40 beams per source) is reached at a source density of about 15 sources deg−2, which is exceeded in the high-exposure areas of our survey. In addition, we need to optimally discriminate between extended and point-like X-ray sources, calling for an angular resolution that is as high as possible. We therefore have to reduce the detector FOV. The sharpest imaging is achieved within the inner 20′ of the PSPC FOV, corresponding to the inner ring-like rib of the PSPC support structure (see Fig. 1). However, there is a trade-off between image sharpness and the number of photons required for detection and image characterization. In particular in the outer areas of our survey, where the RASS exposure times drop significantly, a 20′ FOV radius does not provide sufficient exposure time. Taking into account the various competing factors in this trade-off, we made a few tests varying the FOV cut-off radius, and finally decided on an optimum FOV radius of 30′. The PSPC detector coordinates have a pixel size of 0.934″. We thus removed all X-ray events from the dataset, which are further than 1925 pixels from the PSPC centre pixel coordinate [4119,3929]. A similar cut had to be applied to the modified PSPC instrument map (MOIMP), which is used later for the construction of the survey exposure map.
Citation: Boese 2000
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Citation: Boese (2000)
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The H i mass fraction of every gas particle in the baryonic runs is calculated based on the particle’s temperature and density and the cosmic UV background radiation flux while including a prescription for self-shielding of H2 and dust shielding in both H i and H2 (Christensen et al. 2012). This allows for the straightforward calculation of the total H i mass of each simulated galaxy. We create mock H i data cubes only for the 42 halos that contain 





. Specifically, we create mock data cubes that mimic ALFALFA observations (Haynes et al. 2011). After specifying a viewing angle (see below), our code considers the line-of-sight velocity of each gas particle. The velocity of each particle is tracked in the simulation by solving Newton’s equations of motion, but any turbulent velocity of the gas is not taken into account. Velocity dispersions in dwarf galaxies can be on the order of the rotational velocity, ∼10–15 km s−1 (e.g., Stanimirović et al. 2004; Tamburro et al. 2009; Oh et al. 2015). Dispersions are thought to be driven at least partially by thermal velocities or supernovae (Tamburro et al. 2009; Stilp et al. 2013a, 2013b). In our simulations, supernovae inject thermal energy, and the thermal state of the H i gas needs to be considered in the mock H i linewidth for a realistic comparison to observations. To account for the thermal velocity, the H i mass of each gas particle is assumed to be distributed along the line-of-sight in a Gaussian distribution with a standard deviation given by the thermal velocity dispersion, 





, where T is the temperature of the gas particle. After this thermal broadening is calculated, a mock H i data cube can be generated by specifying the spatial and velocity resolution. For all of our galaxies, we adopt a spatial resolution of 54 pixels across 2Rvir. In practice, this corresponds to a range of ∼1 kpc resolution in our lowest-mass galaxies up to ∼9 kpc resolution in our most massive galaxies. However, the spatial resolution plays no role in our study, since measurements of the VF are based on spatially unresolved H i data. For the velocity resolution, we match the ALFALFA specification of 11.2 km s−1 (two-channel boxcar-smoothed).
Citation: Tamburro et al. 2009
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Citation: Tamburro et al. 2009
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Citation: Haynes et al. 2011
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Of the almost 4000 exoplanets known today, more than 3700 were discovered via the radial-velocity (RV) or transit methods1. According to the database, only three of them (V830 Tau b, Donati et al. 2016; K2-33 b, David et al. 2016, and TAP 26 b, Yu et al. 2017) are younger than 100 Myr. The main reason for this is the strong stellar activity of young stars, which makes it hard to find the subtle planetary signal in the large stellar variations. This is unfortunate for two reasons: first, planet formation takes place in young systems and at least gas giants need to form before the disk has dissipated after less than a few tens of millions of years (e.g., Ercolano & Pascucci 2017). Second, planets at large orbital distances (≳50 AU) are almost exclusively detected via direct imaging (DI), which is best applicable to young systems where the planets are still hot from their formation. Thus, in order to discover all planets in a system, one either needs to image old stars – which seems currently impossible given the already low detection rate around young stars probed by large DI surveys (e.g., Desidera et al. 2015; Lannier et al. 2016; Tamura 2016; Stone et al. 2018) – or try to minimize the impact of the stellar activity of young stars. A lot has been done to understand and characterize stellar activity (e.g., Dumusque 2018). Lindegren & Dravins (2003) further estimate the effects of stellar activity such as oscillation, granulation, meridional flow, long-term magnetic cycle, surface magnetic activity and rotation, gravitational redshift and many more on the RV measurement. Meunier & Lagrange (2019) then try to model the effect of this kind of activity signal on RV data of mature stars. With our data probing activity timescales of days to years, we are mainly probing the combined effect of stellar rotation, reconfiguration of active regions, and long-term magnetic cycles. Still, large uncertainties remain in the prediction and interpretation of any RV signal, in particular for young pre-main sequence stars. But since this is what we measure, knowledge about the typical RV variability is important, for example for developing and testing RV activity models or planning RV surveys. In this paper we therefore derive a model-free analytic relation between stellar jitter, stellar age, and lag, where lag denotes the timescale on which the jitter is measured.
Citation: Desidera et al. 2015
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Recently, polarized (sub)millimetre emission has been detected in an increasing number of discs by Atacama Large Millimeter/submillimeter Array (ALMA) with its high sensitivity and angular resolution. However, the origin of disc polarization remains uncertain, since grains do not have to be aligned with just the magnetic field (Kataoka et al. 2017; Yang et al. 2019). They may also be aligned in the direction of the radiative anisotropy (Lazarian & Hoang 2007a; Tazaki, Lazarian & Nomura 2017) or the drift velocity of the grains relative to the ambient gas (Gold 1952; Lazarian 1995; Lazarian & Hoang 2007b). Furthermore, even spherical grains can produce polarized emission by self-scattering of large grains in an anisotropic radiation field (Kataoka et al. 2015; Yang et al. 2016, 2017; Stephens et al. 2019). The scattering interpretation of the disc polarization is favoured in several targets (e.g. Stephens et al. 2014, 2017; Kataoka et al. 2016; Bacciotti et al. 2018; Dent et al. 2019; Girart et al. 2018; Harris et al. 2018; Hull et al. 2018; Lee et al. 2018). One way to gauge the effects of scattering and identify polarization from aligned grains would be to observe at multiple wavelengths since the efficiency for scattering for grains of given sizes decreases rapidly with the wavelength in the optically thin and small-particle (or Rayleigh scattering) limit. Indeed, in the disc of Class I protostar BHB 07-11, Alves et al. (2018) detected polarization with ALMA at three wavebands (Bands 3, 6, and 7 or ∼ 3, 1.3, and 0.87 mm, respectively) with consistent polarization orientations across three bands and increasing polarization fraction with wavelength, which is generally not expected for scattering-induced polarization. The rather high mean polarization fractions (∼7.9, 5.3, and 3.5 ${{\ \rm per\ cent}}$ for Bands 3, 6, and 7 respectively) are also higher than those typically produced in models of scattering-induced disc polarization ($\sim \!1 {{\ \rm per\ cent}}$). At least for this well-studied source, scattering is unlikely the main mechanism for producing the observed multiwavelength disc polarization and aligned grains are favoured.
Citation: Lazarian & Hoang 2007b
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Citation: Stephens et al. 2019
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Blazars are a subclass of active galactic nuclei (AGNs) with relativistic jets of high-energy particles pointing near our line of sight (e.g., Urry & Padovani 1995). Their nonthermal emission is generally detected across the entire electromagnetic spectrum from radio to γ-ray bands. Blazars are subclassified into flat-spectrum radio quasars (FSRQs) and BL Lac objects (BL Lacs), according to the equivalent width of the emission lines in their optical spectrum (Stickel et al. 1991; Stocke et al. 1991; Marcha et al. 1996). These two subclasses of blazars are thought to be intrinsically different, perhaps based on their accretion mode (Dermer & Giebels 2016). FSRQs have high luminosity and a thin and radiatively efficient black hole accretion disk (Malkan & Moore 1986), while BL Lacs are powered by an advection-dominated, low radiative efficiency accretion flow (Dermer & Giebels 2016; Blandford et al. 2019). The jet emission is relativistically beamed (Ghisellini 2019), with a Doppler boosting factor corresponding to a bulk Lorentz factor of several to greater than 10 (Pushkarev et al. 2009). In both cases, the broadband spectra consist of two broad humps, one peaking in the IR-to-X-ray regime and the other peaking in the γ-ray regime. The low-energy peak is believed to be due to synchrotron emission, while the high-energy peak is likely due to inverse Compton scattering of low-energy photons of either the same synchrotron photons (for BL Lacs) or external photons from the disk/BLR (for FSRQs) (e.g., Dutka et al. 2017). However, some blazars might not necessarily be detected in γ-rays (e.g., Paliya et al. 2017). Indeed, a recent study showed that blazars undetected in γ-rays are likely to have relatively smaller Doppler factors and more disk dominance (Paliya et al. 2017). In the case of strong Compton scattering, the beaming of γ-rays could be larger than, e.g., that seen in the radio (Dermer 1995), leading to the possible nondetection (or reduced detection efficiency) of γ-rays from sources not seen exactly pole-on.
Citation: Blandford et al. 2019
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Citation: Malkan & Moore 1986
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The pioneer of spectroscopic surveys, the Radial Velocity Experiment (RAVE), produced its first data release (DR) 15 years ago (Steinmetz et al. 2006) and its final one, DR6, last year (Steinmetz et al. 2020a,b). In the meantime, other surveys have been operated, and survey designers have developed new methodologies, learning progressively from their own and one another’s experience on how to reduce biases and uncertainties in the automated determination of APs and abundances from massive datasets of stellar spectra with various resolutions and spectral coverages. Besides RAVE, other surveys have published successive DRs that are available for public use. At the time of writing, there is open access to the DR16 of the Apache Point Observatory Galactic Evolution Experiment (APOGEE; Jönsson et al. 2020), the DR3 of the Galactic ArchaeoLogy with HERMES project (GALAH; Buder et al. 2021), the DR6 of RAVE (Steinmetz et al. 2020a,b), the DR5 of the Large sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST; Luo et al. 2015, 2019), Sloan Extension for Galactic Understanding and Exploration (SEGUE; Yanny et al. 2009), and the Gaia-ESO Survey (DR3; Gilmore et al. 2012; Randich & Gilmore 2013). Additional versions of APs, based on different methods, are also provided for RAVE DR6 (Guiglion et al. 2020) and for LAMOST DR5 (Xiang et al. 2019). The next generation of optical and near-infrared spectrographs, wide-field and massively multiplexed, is in preparation and will soon provide even larger catalogues of APs and abundances, such as the WHT Enhanced Area Velocity Explorer (WEAVE; Dalton et al. 2012), the Multi-Object Optical and Near-infrared Spectrograph (MOONS) on ESO’s Very Large Telescope (Taylor et al. 2018), the 4-metre Multi-Object Spectroscopic Telescope (4MOST; de Jong et al. 2019), the Prime Focus Spectrograph (PFS; Takada et al. 2014), and the Maunakea Spectroscopic Explorer (MSE; McConnachie et al. 2016). In terms of numbers of stars, the most revolutionary survey will certainly be that of the Gaia space mission (Gaia Collaboration 2016), which will deliver in its DR3 in 20221 estimates of the physical properties, including metallicities, for millions of stars obtained with various methods through an astrophysical parameter inference system (Bailer-Jones et al. 2013).
Citation: Jönsson et al. 2020
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where 




D
ϵ


Dt



$ \frac{D \epsilon}{Dt} $


 is the advective derivative of the energy density, 




κ

=

κ
0


T

5
/
2




b

̂



$ {\boldsymbol{\kappa}} = \kappa_0 T^{5/2} \hat{{\boldsymbol{b}}} $


 is the Spitzer conductivity along magnetic field lines, Qheat and Qcool are heating and radiative cooling rates, and ρ, v, T, P and g are the plasma density, velocity, temperature, pressure, and gravitational acceleration respectively. Such user-defined heating term usually has a form of an exponentially decreasing function along the vertical coordinate y; 




Q
heat

=

c
0

exp

(
−

y
λ

)



$ Q_{\mathrm{heat}} = c_0 \exp (-\frac{\mathit{y}}{\lambda}) $


, where c0 is the peak heating rate and λ is the heating scale height. The user defined heating is therefore highly stratified, spatially smooth and steady (e.g. Müller et al. 2003; Fang et al. 2013, 2015; Xia et al. 2017). The need for the user-defined heating in the previous coronal rain simulations arises from the fact that they typically do not include any self-consistent dissipation mechanisms. They also do not cover complete lower solar atmosphere including chromosphere, photosphere, and convection zone, therefore omitting key physical processes in the lower atmosphere, such as magneto-convection, associated magnetic buffeting, braiding, and flows. Another drawback of several coronal rain simulations is the commonly used approximation that all of the plasma cooling (i.e. the process essential for modelling the thermal instability and catastrophic cooling) occurs via optically thin radiative losses. This approximation is perfectly valid in the corona but ceases to apply for cool plasma (below temperatures of a few 100 000 K). Such assumption means that regardless whether the radiative loss function is calculated from CHIANTI (Dere et al. 2019) or using scaling law approximations (e.g. Rosner et al. 1978), there is a cut-off temperature for the radiative cooling of the plasma condensations. Once electron recombination starts during the cooling process, the energy gained (which depends on the ionisation potential and thus also on the ionisation degree of the plasma) is expected to slow down the cooling rate. The thermal evolution of the plasma condensations is therefore not modelled correctly at low temperatures in the previous coronal rain simulations.
Citation: Fang et al.; 2015
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In the case of systems detected via transits, passage through an inclination resonance is likely to prevent a planet from being observed in transit, assuming the natal planetary system is edge-on in the first place. Such excitations may explain inner holes in otherwise densely packed systems observed by Kepler. An example is the Kepler-11 system (Lissauer et al. 2011b), which shows six planets, five of which are between 0.09 and 0.3 au, but none interior to 0.09 au. If we examine the secular architecture of this system assuming the presence of a seventh, 1 M⊕ planet located in the interior hole,1 we find avoided crossings in both eccentricity and inclination near semi-major axes ∼0.065 au, and other secular inclination resonances at 0.0233 and 0.0763 au. Thus, it is possible that Kepler-11 contains a low-mass planet in the observed inner ‘hole’, but which is inclined relative to the outer planets. The inclination could be substantial if the current period is 8 d. The Kepler planet sample contains a large number of multiple planet systems with potential for secular resonances. KOI-94 (Weiss et al. 2013) is interesting because of the presence of a Saturn mass planet in a similar orbit to 55 Cancri. The innermost planet lies just outside a pair of secular resonances at 3.0 d (eccentricity) and 3.4 d (inclination) and so is consistent with a lack of inclination excitation. If we allow for the existence of a 1 M⊕ planet interior to this, eccentricity resonances are found at 0.9 and 2.1 d (and a mean motion resonance at 1.9 d) and inclination resonances at 1.2 and 2.1 d. Thus, a planet that has tidally migrated to orbital periods 2 d in this system could have had its inclination pumped up and have avoided transit. Another compact multiplanet system containing a massive planet is Kepler-30 (Fabrycky et al. 2012). However, the only resonances encountered by a tidally migrating 1 M⊕ inner planet lie at 14.8 d (both inclination and eccentricity) and at 13.9 d (a mean motion resonance). These may be too far out to be strongly affected by tides. Kepler-9 (Holman et al. 2010) shows two Saturn-class planets and an interior low-mass planet, that we will assume to be 1 M⊕ again. Since this inner planet does indeed transit, we must assume little inclination excitation has taken place in this system. Indeed, the sole inclination resonance for this configuration lies at 7.5 d, so that the planet would not experience any excitation if it started the migration interior to this point. Eccentricity resonances are found at 1.0 and 2.5 d, bracketing the observed period. Kepler-51 (Steffen et al. 2013) is an example of a compact Kepler planetary system with a large inner hole but no Jovian-class planets. In this case, an inner 1 M⊕ planet experiences no secular resonances. There is a frequency commensurability at 2.9 d, but no avoided crossing because the modes are almost completely decoupled (one is driven primarily by the relativistic precession).
Citation: Weiss et al. 2013
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Pulsar timing experiments (Sazhin 1978; Detweiler 1979) allow us to explore the low-frequency (∼1–100 nHz) part of the gravitational-wave (GW) spectrum. By measuring deviations from the expected arrival times of radio pulses from an array of millisecond pulsars, we can search for a variety of GW signals and their sources. The most promising sources in the nanohertz part of the GW spectrum are supermassive binary black holes (SMBHBs) that form via the mergers of massive galaxies. Orbiting SMBHBs produce a stochastic GW background (GWB; Lommen & Backer 2001; Jaffe & Backer 2003; Volonteri et al. 2003; Wyithe & Loeb 2003; Enoki et al. 2004; Sesana et al. 2008; McWilliams et al. 2012; Sesana 2013; Ravi et al. 2015; Rosado et al. 2015; Kelley et al. 2016; Sesana et al. 2016; Dvorkin & Barausse 2017; Kelley et al. 2017; Bonetti et al. 2018; Ryu et al. 2018), individual periodic signals or continuous waves (CWs; Sesana et al. 2009; Sesana & Vecchio 2010; Mingarelli et al. 2012; Roedig & Sesana 2012; Ravi et al. 2012, 2015; Rosado et al. 2015; Schutz & Ma 2016; Mingarelli et al. 2017; Kelley et al. 2018), and transient GW bursts (van Haasteren & Levin 2010; Cordes & Jenet 2012; Ravi et al. 2015; Madison et al. 2017; Islo et al. 2019; Bécsy & Cornish 2021). We expect to detect the GWB first, followed by detection of individual SMBHBs (Siemens et al. 2013; Rosado et al. 2015; Taylor et al. 2016; Mingarelli et al. 2017) that stand out above the GWB. Detection of GWs from SMBHBs will yield insights into galaxy mergers and evolution not possible through any other means. Other potential sources in the nanohertz band include cosmic strings (Damour & Vilenkin 2000, 2001; Berezinsky et al. 2004; Damour & Vilenkin 2005; Siemens et al. 2006, 2007; Ölmez et al. 2010; Sanidas et al. 2013; Blanco-Pillado et al. 2018; Chang & Cui 2021; Ghayour et al. 2021; Gorghetto et al. 2021; Wu et al. 2021a; Blanco-Pillado et al. 2021; Lin 2021; Chiang & Lu 2021; Lazarides et al. 2021; Chakrabortty et al. 2021; Ellis & Lewicki 2021), phase transitions in the early universe (Witten 1984; Caprini et al. 2010; Addazi et al. 2021; Arzoumanian et al. 2021; Di Bari et al.2021; Borah et al. 2021; Nakai et al. 2021; Brandenburg et al.2021; Neronov et al. 2021), and relic GWs from inflation (Starobinskiǐ 1979; Allen 1988; Lazarides et al. 2021; Ashoorioon et al. 2021; Yi & Zhu 2021; Li et al. 2021; Poletti 2021; Vagnozzi 2021; Sharma 2021), all of which would provide unique insights into high-energy and early-universe physics.
Citation: Cordes & Jenet 2012
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Citation: Dvorkin & Barausse 2017
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Citation: Rosado et al. 2015
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Citation: Rosado et al. 2015
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