adsabs/FOCAL huggingface dataset:
A262 (RA = 01:52:46.299, Dec. = +36:09:11.80) is a bright, nearby poor cluster at z = 0.0162 (Struble & Rood 1999) with mean ICM temperature ≈2 keV( see e.g. Vikhlinin et al. 2005, 2006; Sato, Matsushita & Gastaldello 2009; Sanders et al. 2010). Due to its low mass and temperature, it may be considered as an intermediate between clusters and groups. A262 was observed for 110.7 ks in ACIS-S and a blank-sky observation of 450 ks was used for the background fitting. We used dmcopy tool in CIAO: Chandra's data analysis system (Fruscione et al. 2006) to restrict the energy range to 0.7−7 keV for both imaging and spectral analysis in all of the four data product files: event file, blank-sky observation, RMF and ARF. This reduced the number of PI channels to 433. Also, since the output of CIAO is in fits format, we used ftools-fv6 to export the event and background files as ASCII files. We then performed a 2 arcsec binning to both the event and the background files to generate the 3D data cubes for the spectral analysis without having to re-group the energy and PI columns. We used our in-house binning software tool for binning the data; this software is also available in the online package. This reduced the size of the data to a manageable level without adversely affecting the subsequent inference of the cluster. The output is a photon counts in a grid of 256 × 256 × 433 to be read in by bayes-x. Our Bayesian framework also allows us to analyse the data from one CCD. The X-ray images (Fig. 2) were then generated by summing up the counts at each pixel. To illustrate the large-scale features in the image we also binned the events with a cell size of 16 arcsec (right-hand panel of Fig. 2). It should be noted that the images are for illustration purposes only. We applied the rmfimg tool in CIAO to convert and expand RMF and ARF files into 2D images (matrices) for the spectral analysis. Similarly, we used ftools-fv to export the 2D RMF and ARF as ASCII files to be read in by bayes-x.
Citation: Fruscione et al. 2006
Citation function: Uses; Uses
We performed a preliminary, zeroth-order analysis of the images by constructing a syndyne–synchrone map for each observing date. From those maps, we inferred that the activation time of the asteroid should be close in time to the discovery date, owing to the absence of dust features that could have shown up at the corresponding locations of synchrones approximately two months before discovery or earlier. In particular, no neck-line or trail features appear in the image from 2016 January 7 (PlAng ∼ 0°), which could have indicated past activity. In addition, there are no dust condensations along the direction of isolated synchrones, which could have indicated short bursts of activity (e.g., the case of P/2012 F5 (Gibbs), Moreno et al. 2012a), or several separated short bursts, as in the case of P/2013 P5 (Jewitt et al. 2013; Moreno et al. 2014). According to this, it is reasonable to start the search for a minimum in the function χ defined above by placing the activation time (t0) between a few days before the discovery date (102.5 days before perihelion) and about 60 days before. Regarding the duration of the activity, the smooth variation in absolute magnitudes (from Hv = 17.88 to Hv = 18.16, see Equation (1)) over the ∼40 days period of observation and the aforementioned lack of single-synchrone dust features would suggest a long-lasting process and not an impulsive, short-duration event, such as a collision with another body. In any case, we considered both long- and short-duration events by varying HWHM in a wide range between a few days and several months in the starting simplex of the search of the five-dimensional parameter space. For the peak dust mass loss, we imposed a wide range between a minimum of 0.1 kg s−1 and 100 kg s−1, while for the velocities we set broad limits for the parameters v0, v1, and v2, so that the velocities ranged from 0 to 5 × 103 m s−1 (the mean velocity in the asteroid belt), and the parameter γ from 0.5 to 0, i.e., from typical gas drag to a nearly flat distribution of velocities.
Citation: Jewitt et al. 2013
Citation function: Compare/Contrast; Compare/Contrast
Elsässer variables (Elsässer 1950) are usually employed in solar wind studies to separate the outward-propagating waves (denoted by z+) and the reflected or inward propagating waves (denoted by z−). The separation is exact for even fully nonlinear, unidirectionally propagating waves in homogeneous and incompressible plasma, i.e., Alfvén waves, and it even holds for radially inhomogeneous (along a purely radial magnetic field) but otherwise homogeneous plasma without nonlinear interactions (Hollweg & Isenberg 2007; Magyar et al. 2019b). However, beyond pure Alfvén wave dynamics, it is often overlooked that transverse inhomogeneities, compressibility, and the nonlinear interaction of waves renders the separation of fluctuations into inward and outward-propagating waves inexact. For example, inhomogeneity and compressibility allows for waves (e.g., fast, slow MHD waves, surface Alfvén waves, kink waves, etc.) that are mostly described by both Elsässer variables as they propagate (Magyar et al. 2019a, 2019b). In fact, waves other than pure Alfvén waves generally perturb both Elsässer variables as they propagate. While kink waves, both propagating and standing, are routinely observed in the corona (e.g., Nakariakov et al. 1999; Tomczyk et al. 2007; Anfinogentov et al. 2015; Wang 2016; Nechaeva et al. 2019), evidence of surface Alfvén waves in the solar wind is as of yet inconclusive (e.g., Horbury et al. 2001; Vasquez et al. 2001; Paschmann et al. 2013). Besides waves that are not pure Alfvén waves, structures (inhomogeneities) advected by the solar wind also perturb both Elsässer variables (Tu & Marsch 1990, 1995; Zank et al. 2012; Adhikari et al. 2015). The nonlinear interaction of Alfvén waves can generate purely magnetic fluctuations, 2D modes (k∥ = 0) or condensates which as well perturb both Elsässer variables (Boldyrev & Perez 2009; Howes & Nielson 2013). Indeed, the nature of the inward z− Elsässer variable is often not clear (Wang et al. 2018). Previous studies on Alfvén wave dynamics in radially inhomogeneous models often mention the existence of an “anomalous” z− component that is co-propagating with z+ (Velli et al. 1989; Verdini et al. 2009; Perez & Chandran 2013). The issue of anomalous waves is solved by Hollweg & Isenberg (2007), who showed that, while the continuously generated, reflected z− components might show up as co-propagating in a harmonic analysis, their impulse response analysis shows that these reflected Alfvén waves still follow sunward characteristics, i.e., that there are no truly co-propagating Elsässer variables in these studies. Nevertheless, the coherence of the Elsässer variables resulting from this linear coupling of Alfvén waves seems to influence their spectrum (Verdini et al. 2009).
Citation: Wang 2016
Citation function: Background
Here we outline our sample of strongly lensed Planck-selected, dusty star-forming galaxies, hereafter “LPs” (Table 1). Our sample of 24 LPs began with a Planck and Herschel cross-match identification of eight objects (8/24) with continuum detections at 857 GHz (Harrington et al. 2016) greater than 100 mJy. The remaining 16/24 LPs were selected based on continuum detections by Planck, at 857, 545, and/or 353 GHz in the maps of all the available, clean extragalactic sky. These bright Planck point sources were then analyzed through a filtering process using a WISE color selection for the four WISE bands (3.4, 4.6, 12, 22 μm; Yun et al. 2008; D. Berman et al. 2021, in preparation). Other methods to identify strong gravitational lenses using (sub)millimeter data were independently verified by other teams using Planck and Herschel color criteria (Cañameras et al. 2015). The 24 LPs presented in these analyses include eight systems identified by Cañameras et al. (2015). The use of Planck and WISE data resulted in the discovery of the brightest known, dusty starburst galaxy at z > 1, the “Cosmic Eyebrow” (Díaz-Sánchez et al. 2017; Dannerbauer et al. 2019), which has also been independently recovered as one of the LPs presented in this survey work. Note that LPsJ1329 corresponds to the location on the sky associated with the Cosmic Eyebrow-A lens component (Dannerbauer et al. 2019). Table 1 shows the size of the lensed emission for each of the LPs, in which there are 21/24 with lens sizes ≤10″. Half of the LPs are galaxy–galaxy lenses, while the other half are a mix of cluster or group lensing. The foreground lens galaxies have a negligible contribution to the observed FIR emission of the lensed galaxy (Harrington et al. 2016). The LPs have CO-based spectroscopic redshifts ranging from zCO ∼ 1.1 to 3.6 (Harrington et al. 2016, 2018; Cañameras et al. 2018b; this work). They are comparable or brighter in CO and FIR luminosity than other strongly lensed SPT- (Weiß et al 2013; Strandet et al. 2016, 2017) or Herschel-selected; dusty star-forming galaxies (Harris et al. 2012; Bussmann et al. 2013, 2015; Yang et al. 2017). The Planck and Herschel wavelength selections preferentially target z ∼ 2–3 galaxies, versus the millimeter-selected SPT sources with a median closer to z ∼ 4, although with a wide range between z ∼ 2 and 7 (Weiß et al 2013; Spilker et al. 2016; Strandet et al. 2016; Reuter et al. 2020).
Citation: Díaz-Sánchez et al. 2017
Citation function: Background; Similarities
Classical bulges (hereafter ClBs) are the central building blocks in many early-type spiral galaxies. Classical bulges might have formed as a result of major mergers during the early phase of cosmic evolution (Kauffmann et al. 1993; Baugh et al. 1996; Hopkins et al. 2009; Naab et al. 2014), or through a number of other mechanisms such as the monolithic collapse of primordial gas clouds (Eggen et al. 1962), the coalescence of giant clumps in gas-rich primordial galaxies (Noguchi 1999; Immeli et al. 2004; Elmegreen et al. 2008), violent disk instability at high-redshift (Ceverino et al. 2015), multiple minor mergers (Bournaud et al. 2007; Hopkins et al. 2010), and accretion of small companions or satellites (Aguerri et al. 2001). Although most of these studies do not provide quantitative predictions for the bulge kinematics, it is generally believed that ClBs formed through these processes have low rotation compared to the random motion. For example, Naab et al. (2014) showed that spheroids produced by minor and major mergers (which include ClBs) in full cosmological hydrodynamical simulations have a wide range of rotational properties; the massive ones have V/σ less than 0.5. Elmegreen et al. (2008) reported dispersion dominated clump-origin ClBs with upper limit on V/σ ~ 0.4−0.5, where V is the rotation velocity and σ is the central velocity dispersion. A similar study by Inoue & Saitoh (2012) suggests that clump-origin bulges have exponential surface density profiles and rotate rapidly with V/σ ~ 0.9, resembling pseudobulges (Kormendy & Kennicutt 2004). However, using cosmological hydrodynamical simulations with continuous gas accretion, Ceverino et al. (2015) showed that massive classical bulges with non-zero angular momenta are produced at high redshift, but provided no estimate on the bulge V/σ. Overall, there is no clear quantitative picture of the rotational motion induced during the formation of classical bulges in numerical simulations.
Citation: Hopkins et al. 2010
Citation function: Background; Background
Until recently, only β Pic and 49 Cet were known as CO-bearing debris disks (Vidal-Madjar et al. 1994; Zuckerman et al. 1995; Roberge et al. 2000). Later, more such objects were discovered, and the first statistical studies could be done (Greaves et al. 2016; Lieman-Sifry et al. 2016; Péricaud et al. 2017). Our present list partly incorporates these earlier samples and extends them to a full list of 17 dust-rich debris disks in the solar neighborhood (Section 2). In Figure 3(a), we display the 12CO 2–1 (or 12CO 3–2 for HR 4796) line luminosities (or upper limits), as a function of the fractional luminosity for our sample. Line luminosities of detected CO-bearing disks span almost two orders of magnitude, in which the brightest disks have luminosities that are comparable to those of fainter Herbig Ae and T Tauri disks (Ansdell et al. 2016; Péricaud et al. 2017). Since the sensitivity of the HR 4796 observation is nearly two orders of magnitude worse than that of the other measurements, we discard this object from the following analysis, reducing the size of our statistical sample to 16. For the other objects, by adopting the highest upper limit (source No. 7) in Figure 3(a), we derived a detectability threshold of ∼1.4 ×104 Jy km s−1 pc2 for the 12CO 2–1 line luminosity. With our 3 new discoveries, we found 11 disks in this sample that harbor CO gas, resulting in a very high detection rate of 68.8







−
13.1


+
8.9


%


. Because of the small sample, we computed the uncertainties (corresponding to 68% confidence interval) using the binomial distribution approach proposed by Burgasser et al. (2003). Our result indicates that the presence of CO gas in dust-rich debris disks around young A-type stars is more likely the rule than the exception. The obtained incidence rate of 11/16 is valid above our detectability threshold for the 12CO J = 2–1 line luminosities. Nevertheless, we cannot rule out that all of our targets harbor CO gas at some level. Remarkably, as Figure 3(a) shows, apart from HR 4796, all of the disks with 






L


disk



/



L


bol


>
2
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10


−
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 contain detectable levels of CO gas.
Citation: Ansdell et al. 2016
Citation function: Background
For the clustering measurements, we use the sample of galaxies compiled in Data Release 11 (DR11) of the SDSS-III project. The SDSS-III is a spectroscopic investigation of galaxies and quasars selected from the imaging data obtained by the SDSS (York et al. 2000) I/II covering about 11,000 deg2 (Abazajian et al. 2009) using the dedicated 2.5 m SDSS Telescope (Gunn et al. 2006). The imaging employed a drift-scan mosaic CCD camera (Gunn et al. 1998) with five photometric bands (





 and z; Fukugita et al. 1996; Smith et al. 2002; Doi et al. 2010). The SDSS-III (Eisenstein et al. 2011) BOSS project (Ahn et al. 2012; Dawson et al. 2013) obtained additional imaging data of about 3000 deg2 (Aihara et al. 2011). The imaging data was processed by a series of pipelines (Lupton et al. 2001; Pier et al. 2003; Padmanabhan et al. 2008) and corrected for Galactic extinction (Schlegel et al. 1998) to obtain a reliable photometric catalog. This catalog was used as an input to select targets for spectroscopy (Dawson et al. 2013) for conducting the BOSS survey (Ahn et al. 2012) with the SDSS spectrographs (Smee et al. 2013). Targets are assigned to tiles of diameter 3° using an adaptive tiling algorithm designed to maximize the number of targets that can be successfully observed (Blanton et al. 2003). The resulting data were processed by an automated pipeline which performs spectral classification, redshift determination, and various parameter measurements, e.g., the stellar-mass measurements from a number of different stellar population synthesis codes which utilize the photometry and redshifts of the individual galaxies (Bolton et al. 2012). In addition to the galaxies targeted by the BOSS project, we also use galaxies that pass the target selection but have already been observed as part of the SDSS-I/II project (legacy galaxies). These legacy galaxies are subsampled in each sector so that they obey the same completeness as that of the CMASS sample (Anderson et al. 2014).
Citation: Ahn et al. 2012
Citation function: Background; Background; Background
In this scenario, the present γ-ray emission from the Fermi bubbles arises predominantly through inverse Compton scattering of an energetic non-thermal cosmic ray (CR) electron population in the remnant structures with ambient radiation supplied by the interstellar radiation field (ISRF) and the cosmological microwave background (CMB). A subdominant component due to non-thermal bremsstrahlung may also be present, emitted primarily from regions of high gas and non-thermal electron density within the bubble (OY22). Models of this nature are broadly referred to as ‘leptonic’ models (see also Su et al. 2010; Zubovas, King & Nayakshin 2011; Su & Finkbeiner 2012; Fujita, Ohira & Yamazaki 2013 for similar configurations). Other similar approaches invoking Sgr A* activity, but where the CR composition is not specifically required to be leptonic, invoke a pair of jet-driven outflowing bubbles assuming constant AGN activity or continuous energy injection over Myr time-scales (e.g. Zhang & Guo 2020). Notably, these models have been able to account for the bi-conical X-ray structures observed near the GC as part of the same phenomenon as the Fermi bubbles. Alternative proposals have also been discussed, where the bubbles arise from the confluence of a number of processes operating more gradually within the inner part of the Milky Way (Thoudam 2013). These could include tidal disruption events (TDEs) occurring at regular intervals of 10s to 100s kyr (Cheng et al. 2011; Ko et al. 2020), or the action of a bipolar galactic outflow driven by the ongoing intense GC star-formation activity and/or the processes associated with Sgr A* (Lacki 2014), with the resulting γ-ray glow instead arising from a hadronic CR population interacting with an advected supply of entrained gas in the wind (the ‘hadronic’ models – see  Crocker & Aharonian 2011; Cheng et al. 2014, 2015; Crocker et al. 2014, 2015; Mou et al. 2014, 2015; Razzaque & Yang 2018).
Citation: Fujita, Ohira & Yamazaki 2013
Citation function: Background
The diffuse hot gas X-ray luminosities in the 0.3–8 keV band are taken from the work of KF15. They have carefully removed the contribution from discrete sources such as low-mass X-ray binaries (Fabbiano 2006) to the total X-ray luminosity, leaving the diffuse gas contribution LX, Gas. A correction to bolometric would increase the X-ray luminosities by 0.08 dex on average. Most of the X-ray data come from Chandra observations. However, for some high-mass galaxies, the X-ray emission is particularly extended (e.g. NGC 4374, 4486, 4649, 5846) and in those cases ROSAT data from O‘Sullivan et al. (2001), corrected to the Chandra energy band, are used. Although the contribution from discrete sources in the ROSAT data cannot be subtracted as accurately as it can for Chandra data, their contribution is only about 1 per cent of the diffuse gas luminosity for these high-mass galaxies (see O'Sullivan et al. 2001). For further details, see KF15. Here we make a very small correction to the KF15 LX,Gas luminosities for the distances used in the SLUGGS survey (Brodie et al. 2014). The KF15 compilation did not include several galaxies that appear in the Alabi et al. (in preparation) study. Here we also include the X-ray luminosities for NGC 720, NGC 1316 and NGC 3115 from Boroson et al. (2011), and for NGC 5128 from KF13. Su et al. (2014) conducted a detailed XMM and Chandra study of NGC 1400. As well as the X-ray emission centred on NGC 1400, they detected an enhanced region of X-rays to the NE of the galaxy that they associated with stripped gas. Here we use the X-ray luminosity centred on NGC 1400 with a small adjustment to our X-ray band and distance, and assume an uncertainty of 20 per cent. We note that the X-ray luminosity would double if the enhanced region were also included. Two galaxies in Alabi et al. (in preparation) but not included here are NGC 2974 (not observed by Chandra) and NGC 4474 (the Chandra observation was only 5 ks).
Citation: O'Sullivan et al. 2001
Citation function: Uses
Supernovae (SNe) play a dual role in the evolution of interstellar dust. On one hand, they are the most important source of dust production in galaxies, but on the other had had also the most important source of grain destruction. Theoretical models show that most of the heavy elements produced can precipitate out of the gas and form refractory grains (Sarangi & Cherchneff 2013, 2015; Sluder, Milosavljević & Montgomery 2018; Sarangi, Matsuura & Micelotta 2019). Infrared and submilimetre observations of Cassiopeia A (Barlow et al. 2010; Arendt et al. 2014; De Looze et al. 2017), SN 1987A (Matsuura et al. 2011; Indebetouw et al. 2014; Matsuura et al. 2015), Crab Nebula (Gomez et al. 2012), and young Galactic (up to ∼2000 yr old) SN remnants (Chawner et al. 2019) confirm the presence of ∼0.1–1.0 M⊙ of dust, indicating that a substantial fraction of refractory elements in their ejecta went to dust grains. If the majority of dust in SNe can survive the shock interactions, SNe could be an important source of dust production in the ISM (Dwek & Cherchneff 2011). The fate of this newly-formed dust is still a subject of active studies. The reverse shock traveling through the ejecta can destroy newly-formed dust (Dwek, Foster & Vancura 1996; Schneider, Ferrara & Salvaterra 2004; Nozawa et al. 2007; Biscaro & Cherchneff 2014, 2016; Silvia, Smith & Shull 2010; Micelotta, Dwek & Slavin 2016; Kirchschlager et al. 2019). Any grains surviving the reverse shock may also be destroyed during the injection phase into the interstellar medium (ISM; Slavin et al. 2020) Thereafter, ISM dust will be subject to destruction as it encounters the SN remnant shocks. The grain destruction efficiency and ISM dust lifetimes are highly uncertain since they depend on a long list of parameters. Macroscopic parameters include the energy of the SN explosion, the morphology of the medium surrounding the SN (Slavin et al. 2020), and that of the general ISM. Microscopic parameters include the composition and size distribution of the SN condensates, and the detailed interaction of the dust with the shocked gas and other grains (Dwek & Arendt 1992; Jones, Tielens & Hollenbach 1996; Slavin, Dwek & Jones 2015; Kirchschlager, Mattsson & Gent 2021; Priestley, Chawner & of 2021). Because the evolution of dust in the ISM is a fine balance between dust production and destruction, intense investigations are currently underway into dust production and destruction by SNe. In this paper, we examine the latter point of view, and investigate how dust grains are impacted by SN shocks over time.
Citation: Indebetouw et al. 2014
Citation function: Background; Background; Background