adsabs/FOCAL huggingface dataset:
When in a rotation-powered state, many spiders exhibit a nonthermal (synchrotron radiation; SR) orbitally modulated emission component in the X-ray band, attributed to particle acceleration in an intrabinary pulsar wind termination shock (IBS) and Doppler boosting of a bulk flow along the shock tangent (e.g., Wadiasingh et al. 2015, 2017; Romani & Sanchez 2016). This type of geometry/radation picture was identified over two decades ago for the BW system involving the MSP B1957+20 (Arons & Tavani 1993) and later for the gamma-ray-emitting PSR B1259-63/Be star binary system (Tavani & Arons 1997). The existence of a shock is also indicated by orbital-phase- and frequency-dependent radio eclipses of the MSP (since plasma structures at the shock attenuates the radio), where in RBs the eclipse fraction can be >50% (e.g., Archibald et al. 2009, 2013; Broderick et al. 2016; Miraval Zanon et al. 2018) of the orbital phase (while the MSP remains largely uneclipsed at inferior conjunction of the pulsar). The hard power laws inferred in X-rays imply emission due to an energetic electron population. The X-ray spectra may furthermore extend to at least ≳50 keV with no suggestion of spectral cutoffs (e.g., Tendulkar et al. 2014; Kong et al. 2017b; Al Noori et al. 2018), constraining the shock magnetic field to Bsh ≳ 1 G (Wadiasingh et al. 2017). For a given pulsar spin-down power 





 erg s−1, the magnetic field Bsh at the shock can be bounded using the Poynting flux 





 for a magnetic field Bw ∝ R−1 in the striped wind outside the light cylinder. The flux can be integrated over a spherical surface of area 





 at the shock radius Rsh to yield an isotropic electromagnetic luminosity 





. One therefore arrives at the constraint 





, which is detailed in Equation (1), being generally less than 100 G. This may be recast as a condition that the ratio of electromagnetic energy density to particle pressure, σ, is less than unity, i.e., that 





 is dominated by the plasma wind contribution. As will be apparent in due course (see Section 3), if the particle acceleration is as fast and efficient (attaining the synchrotron burn-off limit) as in pulsar wind nebulae, MeV synchrotron components may be observable by future medium-energy, gamma-ray Compton/pair telescopes, such as e-ASTROGAM (De Angelis et al. 2017) and AMEGO.8

8
AMEGO: https://asd.gsfc.nasa.gov/amego/index.html (McEnery et al. 2019).
Citation: Miraval Zanon et al. 2018
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In order to study the behaviour of the soft residuals and attempt to distinguish between possible origins, we require a source that evolves across a dynamic range in spectral hardness; as can be seen from fig. 8 and tables 2 and 3 of M15, the ideal source for such a study is NGC 1313 X-1. However, both the inferred (deabsorbed) hardness ratio and our ability to reliably characterize the residuals is sensitive to the modelling of the continuum. M15 show, via use of covariance spectra (see Wilkinson & Uttley 2009; Uttley et al. 2014 for a review of the technique), that the ULX continuum emission originates from at least two components that cross at ∼1 keV. Mismodelling of the continuum will therefore have an adverse effect on detecting atomic features imprinted between 0.7 and 2 keV, whilst an inability to separate out the components in the time-averaged spectrum can lead to misleading hardness ratios. To better address this problem we model each of the observations (using both PN and MOS data) using the continuum described above (and including a normalization offset to account for differing responses, typically within ∼10 per cent of unity), finding that in five out of the available 11 observations we cannot distinguish between a solution where the soft component is intrinsically strong or weak. Middleton et al. (2015) show, through use of the covariance spectrum, that the variability is entirely contained within the hard component but that a soft excess must still be present (see also Middleton et al. 2011) whilst the highest S/N data clearly show that the soft component dominates over the hard component below ∼1 keV. Whilst we can force the spectra for the five observations into what we believe to be a more appropriate deconvolution (namely by fixing the normalization of the soft component – see M15), accurate modelling of the residuals requires the normalizations to be free to vary and so we exclude these observations from our subsequent analysis.
Citation: Wilkinson & Uttley 2009
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Solar energetic particle (SEP) events as can be observed in the Earth’s orbit are determined by a combination of the underlying acceleration, injection and transport conditions, and therefore carry fundamental information on these processes. In the classical picture introduced by Reames (1999), SEP events are separated into two groups according to their characteristics: impulsive and gradual. Impulsive events, which are electron and 3He-rich, are believed to be flare-associated and therefore show a narrow angular spread in interplanetary space. These events have an impulsive increase and a shorter decay. Gradual events, on the other hand, show much broader angular SEP spreads because of their association to extended shock fronts. The gradual increase is caused by the continuous acceleration by the coronal mass ejection (CME)-driven shock front. Gradual events are usually observed in protons and heavy ions, which can be efficiently accelerated by shocks. Recent observations during the STEREO era, however, question this simple picture, especially since the nature of events showing wide longitudinal SEP spreads appears to be more complicated. It was found that 3He-rich events extend over more than 130° in longitude (Wiedenbeck et al. 2013), and that near-relativistic electron events can spread up to almost all around the Sun. Dresing et al. (2012) suggested that a strong perpendicular transport may cause the wide SEP distribution. On the other hand, it is also possible that the injection of SEPs at the Sun can be much wider than expected from a flare-like point source. The driver of such extended injection regions may be of a different nature. Diverging magnetic field lines below the source surface (e.g., Klein et al. 2008) as well as the presence of a coronal shock may play a role. The importance of EIT-waves intersecting the magnetic footpoints of far separated spacecraft are also under discussion (Rouillard et al. 2012; Park et al. 2013; Lario et al. 2014). Statistical analyses of multi-spacecraft SEP events (e.g., Lario et al. 2013) show that event characteristics, such as peak intensities, onset delays, rise times, and anisotropies, show strongly varying longitudinal dependencies from event to event. The lack of a clear correlation to a single mechanism, such as the CME speed or width as a representative of the shock strength or extent (Richardson et al. 2014), as well as varying event characteristics suggests that there is not a single process providing these wide SEP distributions (Dresing et al. 2014). Therefore, when describing an SEP event, a possible mixture of different processes as well as a varying strength of these mechanisms must be taken into account.
Citation: Wiedenbeck et al. 2013
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At present, direct detection of the CGM in emission poses an observational challenge due to its diffuse nature (with hydrogen densities of the order of nH 0.1 cm−3). Cosmological hydrodynamical simulations concur that the emission signal is faint by current observational standards (Augustin et al. 2019; Péroux et al. 2019; Corlies et al. 2020; Wijers, Schaye & Oppenheimer 2020; Byrohl et al. 2021; Nelson et al. 2021; Wijers & Schaye 2021). For these reasons, detections in emission at high-redshifts are currently limited to deep fields (Wisotzki et al. 2016; Leclercq et al. 2017; Wisotzki et al. 2018; Leclercq et al. 2020, 2022) or regions around bright quasars (Cantalupo et al. 2005; Arrigoni Battaia et al. 2015; Farina et al. 2019; Lusso et al. 2019; Mackenzie et al. 2021) while detections at z1 are now becoming available (Epinat et al. 2018; Johnson et al. 2018; Chen et al. 2019; Rupke et al. 2019; Burchett et al. 2021; Helton et al. 2021; Zabl et al. 2021). Given this limitation, absorption lines detected against bright background quasars at UV and optical wavelengths provide the most compelling way to study the distribution, kinematics and chemical properties of CGM atomic gas to date. In these quasar absorbers, the minimum column density (which is tightly correlated to the volumic gas density, see Rahmati et al. 2013) that can be detected is set by the apparent brightness of the background sources and thus the detection efficiency is independent of the redshift of the foreground absorber host galaxy. In addition, absorption line-based metallicity measurements are independent of excitation conditions (Kewley, Nicholls & Sutherland 2019; Maiolino & Mannucci 2019). In fact, unlike emission lines metallicity estimates, they are largely insensitive to density or temperature and high column density systems tracing neutral gas require no assumption on a local source of excitation (Vladilo et al. 2001; Dessauges-Zavadsky et al. 2003). Importantly, multiple state-of-the-art cosmological hydrodynamical simulations and early observational results indicate that the chemical properties of the CGM gas probed in absorption show an inhomogeneous metal distribution around galaxies with indication of a trend with galaxy orientation (Péroux & Howk 2020; Wendt et al. 2021).
Citation: Helton et al. 2021
Citation function: Motivation; Motivation
In order to understand the uniqueness of the recurrence of the 4U 1630–472 outbursts and to correlate this with the lack of high-energy emission, we investigated the ASM and BAT light curves of some of the brightest and active transient black hole X-ray binaries (BHXRBs) during the seven years (2005–2011) in which both instruments were operational. Many studies of the outburst evolution of these sources have been reported in the literature (e.g. Yu & Yan 2009; Capitanio et al. 2010; Dunn et al. 2010, and references therein); the various outbursts observed do not have any detectable recurrence period and, in addition, show a complex behaviour in both soft and hard energy ranges and very different luminosities. The only exception is H 1743–322; this BHC has shown only for some years outbursts equally spaced in time, as reported by Capitanio et al. (2010). In analogy with 4U 1630–472, the outburst of H 1743–322 that occurred in 2007 has a 2–12 keV behaviour mostly identical to some of the subsequent periodical outbursts but showing a fainter hard X-ray emission; the H 1743–322 ASM 2007 peak flux (1–12 keV) is ∼250 mCrab and the BAT 2007 peak flux (15–50 keV) is ∼60 mCrab. The subsequent H 1743–322 outburst (2008–2010) presents a similar 1–12 keV flux but with a 15–50 keV flux of ∼200 mCrab. As Fig. 10 shows, the difference in the ratio between the BAT HS–HSS transition luminosity and the ASM peak flux of H 1743–322 is not enough to bring the outburst totally out of the correlation reported by Yu & Yan (2009) as in the case of the 2006 and 2008 outbursts of 4U 1630–472. However, as reported by Corbel & Tzioumis (2008), the HS–HSS transition occurred before the peak of the hard X-ray emission and thus it is only an upper limit. For the 2007 and 2008–2010 outbursts of H 1743–322, the relation between the hard X-ray luminosity peak and the outburst waiting time is also not respected. In contrast, during the subsequent outbursts, a similar waiting time corresponds to a similar peak luminosity in the 15–50 keV energy range.
Citation: Dunn et al. 2010
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One major difficulty afflicts the study of SNRs to learn about their supernovae: separating ejected material from swept-up surrounding unmodified interstellar medium (ISM) or modified CSM. Abundance clues are powerful but have limitations. Ideally, one would observe the youngest possible remnant that is large enough for adequate spatial resolution. That remnant appears to be the youngest Galactic SNR, G1.9+0.3 (Reynolds et al. 2008; see Figure 1). This object is about 



100′′

 in diameter, the smallest angular size of any confirmed Galactic SNR. Unfortunately, it is very highly absorbed, with an X-ray column density of about 5 × 1022 cm−2 (Reynolds et al. 2009), implying A

V
 ∼ 23
m
, so radio and X-rays are the only useful observational channels. The angular expansion rate of 0.64 arcsec yr−1 obtained from comparing X-ray images from 2007 and 2009 (Carlton et al. 2011) gives an upper limit for the age of about 160 yr, less if (as is almost certainly the case) deceleration has occurred; spatial variations in expansion rate (Borkowski et al. 2014) are consistent with an age of about 100 yr, or a date of around 1900. The high extinction would have rendered it unobservable in optical telescopes of that era. Furthermore, its X-ray spectrum is almost entirely synchrotron emission, making it a member of the small class of X-ray synchrotron–dominated SNRs. However, long observations with Chandra have allowed the detection of thermal emission from small regions (Borkowski et al. 2011, 2013b), with spectroscopic widths of ∼14,000 km s−1 confirming the large expansion proper motion, refined with a second Chandra observation (Carlton et al. 2011). The distance is still uncertain; the high column—higher than the entire Galactic column along nearby sight lines—suggests an association with the Galactic center, and a provisional distance of order 8.5 kpc has been assumed. Nearer would be very unlikely in view of the high absorption, but too much farther would make the expansion proper motion unreasonably large. An H i observation with the Giant Metrewave Radio Telescope (Roy & Pal 2014) has been used to set a lower limit of 10 kpc, certainly consistent with the known properties of G1.9+0.3.
Citation: Carlton et al. 2011
Citation function: Uses; Uses
The ability to test the hypothesis that a set of data samples is drawn from some general n-dimensional probability distribution $f(\boldsymbol {x})$ has an interesting application in the validation of Bayesian inference analyses (indeed this application provided our original motivation for seeking to extend the K–S test to multiple dimensions). Bayesian methods are now pervasive across all branches of science and engineering, from cognitive neuroscience (Doya et al. 2007) and machine learning (Bishop 2006) to spam filtering (Sahami et al. 1998) and geographic profiling (Collins, Gao & Carin 1999; Le Comber & Stevenson 2012). In precision cosmology, Bayesian inference is the main tool for setting constraints on cosmological parameters (Hinshaw et al. 2013; Planck Collaboration XVI 2014), but very few attempts have been made to assess whether the derived posterior probability distributions are a truthful representation of the actual parameter constraints one can infer from the data in the context of a given physical model. This lack of validation has been highlighted by several authors, with the strong dependence of the inference results on the priors being of particular concern (Efstathiou 2008; Linder & Miquel 2008). There have been attempts to address this issue, ranging from the approach of Cook, Gelman & Rubin (2006), which was designed with software validation solely in mind, to a method based on the inverse probability integral transform (Smirnov transform) applied to posterior distributions that extends to spaces of higher dimensionality via marginalization (Dorn, Oppermann & Enßlin 2013). Also, validation of the Bayesian source-finding algorithm of Carvalho et al. (2012) was performed in Planck Collaboration XXIX (2014), but only point estimates deduced from the posterior distributions were actually verified. Our method for addressing this problem is based on our applying our multidimensional extension of the K–S test to sets of Monte Carlo simulations of the data and the posterior distributions derived therefrom. In particular, it can take advantage of the fact that one may typically generate simulations that are of greater sophistication and realism than may be modelled in the inference process, and thus allows for a more thorough validation of the inference than has been possible with the methods developed previously. In particular, our validation procedure enables us to test all the assumptions made (explicitly or implicitly) in the inference process, such as the statistical description of the data, model assumptions and approximations, as well as the software implementation of the analysis. Moreover, we consider the full posterior distribution, regardless of its dimensionality and form, without the need to resort to marginalization, and thereby keeping intact its n-dimensional character.
Citation: Hinshaw et al. 2013
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The origin of the GC VHE emission remains undetermined, due in part to source confusion and the limitations of current instruments. The source of VER J1745–290 may be Sgr A* (Atoyan & Dermer 2004; Aharonian & Neronov 2005; Ballantyne et al. 2011; Chernyakova et al. 2011; Fatuzzo & Melia 2012; Kusunose & Takahara 2012; Fujita et al. 2017; Rodríguez-Ramírez et al. 2019) or PWN G359.95-0.04 (Wang et al. 2006; Hinton & Aharonian 2007), with which it is spatially coincident (Acero et al. 2010). Other possible origins include the annihilation of dark matter particles (Bergström et al. 2005a, 2005b; Horns 2005; Profumo 2005; Aharonian et al. 2006c; Belikov et al. 2012; Cembranos et al. 2012, 2013; Gammaldi et al. 2016) or a population of millisecond pulsars (Bednarek & Sobczak 2013; Bartels et al. 2016; Guépin et al. 2018). The mechanism of gamma-ray emission may be predominantly due to hadronic processes, where relativistic protons interact with gas and subsequently produce gamma rays through neutral pion decay (Aharonian & Neronov 2005; Ballantyne et al. 2011; Chernyakova et al. 2011; Fatuzzo & Melia 2012; Linden et al. 2012; Guépin et al. 2018), leptonic processes where gamma rays are produced when electrons and positrons undergo inverse Compton scattering off a radiation field (Atoyan & Dermer 2004; Hinton & Aharonian 2007; Kusunose & Takahara 2012; Lacroix et al. 2016), or a combination of processes (hybrid scenario), where leptons produce high-energy, but not VHE, gamma rays (Guo et al. 2013). Both the correlation of VHE emission with the CMZ and the lack of a cutoff in the diffuse spectrum support a hadronic scenario, capable of explaining both VER J1745–290 and the diffuse emission (Aharonian et al. 2006b; Linden et al. 2012; Abramowski et al. 2016). Measurement of the diffuse spectrum by H.E.S.S. up to energies of tens of TeV with no evidence of a cutoff has also been interpreted as evidence for the existence of PeV protons within the central 10 parsecs of the GC, accelerated by Sgr A* (Abramowski et al. 2016). While cosmic rays are known to extend up to PeV energies (e.g., Hörandel 2003), few, if any, accelerators of PeV cosmic rays, or “PeVatrons,” have been clearly established (e.g., Abramowski et al. 2016; Abeysekara et al. 2020). Discovering the nature of PeVatrons in our Galaxy is thus a particularly important step in understanding the origins of cosmic rays.
Citation: Linden et al. 2012
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Citation: Linden et al. 2012
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Simulations are performed in Fig. 6 to compare the spectrum profiles of the S ii triplets for a synthetic absorption spectrum of a DLA with and without the magnetic alignment included. Fig. 6 was designed up to illustrate the effect in a realistic instrumental set-up, with a given typical pixel size and S/N for an optical spectrum taken by an 8m-class telescope. The graphical use of steps instead of curves, which is common in absorption spectroscopy, is intended to visualize the pixel-by-pixel noise variations in the data. The three transitions of singly ionized Sulfur (Sii; upper ionization potential 23.3 eV) at λλ1250.58, 1253.81, 1259.52Å represent important tracers for neutral and weakly ionized gas in the local interstellar and intergalactic medium and in distant galaxies (e.g. Richter et al. 2001; Welsh & Lallement 2012; Fox, Richter & Fechner 2014a; Kisielius et al. 2014). Being an α element, singly ionized Sulfur only has a weak depletion into dust grains (e.g. Savage & Sembach 1996). Thus the interstellar Sulfur abundance is often used as a proxy for the α-abundance in the gas. In addition, Sulfur has a relatively low-cosmic abundance (Asplund et al. 2009) and under typical interstellar conditions (in particular in low-metallicity environments) these lines are not saturated. The three lines are observed in the same wavelength region with identical S/N. The important parameters for simulations are presented in the caption, such as the assumption of S ii column density, etc. The synthetic spectra were generated using the fitlyman routine (Fontana & Ballester 1995) implemented in the eso-midas software package. Atomic data were taken from Morton (2003). To show the effect clearly, we zoom in the spectrum to the radial velocity range [ − 10km · s−1, 10km · s−1]. The enhancement and reduction of the spectral line profile due to the magnetic realignment change among the triplets. Given the fact that meanwhile optical QSO spectra reach up to a new standard of S/N of a few hundred (e.g. D’Odorico et al. 2016), the predicted effect is already VISIBLE, if the component structure of the DLA allows a detailed investigation.
Citation: Welsh & Lallement 2012
Citation function: Uses
The high-resolution imaging observations of TR from IRIS reveal the ubiquitous presence of network jets. We have used three different IRIS observations of the quiet sun, which are located near the disk center. On the basis of careful inspection, 51 network jets are identified from three QS observations and used for further analysis. These 51 network jets are very well resolved and are not affected by the dynamics of other jets. The study is focused on the rotating motion of network jets along with the estimation of their other properties (speed, height, and lifetime). The mean speed, as predicted by statistical distributions of the speed, is 140.16 km s−1 with a standard deviation of 39.41 km s−1. The mean speed of network jets is very similar, as reported in previous works (e.g., Tian et al. 2014; Narang et al. 2016). However, in case of their lifetimes, we found a value that is almost double (105.49 s) that of the previously reported mean lifetime of the network jets (49.6 s; Tian et al. 2014). As mentioned above, we took only those network jets that are very well resolved in space and in the time; these criteria exclude short lifetime network jets. Therefore, our statistical distribution of the lifetime predicts a higher mean lifetime. The mean length of the network jets is 3.16 Mm with a standard deviation of 1.18 Mm. In the case of CH network jets, Tian et al. (2014) have reported that most of the network jets have lengths from 4.0 to 10.0 Mm. However, the mean length for QS network jets is smaller (3.53 Mm; Narang et al. 2016). So, the mean length for QS network jets from the present work is in good agreement with Narang et al. (2016). In addition, the apparent speed and length of these network jets are positively correlated, which is very similar to what has already been reported in previous works (Narang et al. 2016). Finally, we can say that these networks jets are very dynamic features of the solar TR, as revealed by their estimated properties.
Citation: Narang et al. 2016
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Citation: Narang et al. 2016
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Citation: Narang et al. (2016)
Citation function: Similarities
Citation: Narang et al. 2016
Citation function: Similarities