adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...592A...5F (fulltext)
GI surveys mainly focus on the XMM-LSS area. The XXL-S field is fully covered by GALEX GR6/7 with AIS, Medium Imaging Survey (MIS), and/or GI programs. VISTA Three public European Southern Observatory (ESO) large programme surveys have observed the XXL field in the near infrared (, , , , filters: ) with the Visible and Infrared Survey Telescope for Astronomy 6 (VISTA) in various depths. XXL-N is covered by all three surveys: VISTA Hemisphere Survey (VHS, PI: R., McMahon), VISTA Kilo-degree Infrared Galaxy Survey(VIKING, PI: W. Sutherland), and VISTA Deep Extragalactic Observations Survey (VIDEO, PI: M. Jarvis), while the XXL-S is covered only by the VHS survey. IRAC Both XXL fields have been targeted with the Infrared Array Camera (IRAC) on board the Spitzer Space Telescope 7 providing imaging at and over the whole area (XXL-N; PI: M. Bremer, XXL-S; SPT- Spitzer Deep Field, SSDF, Ashby et al. 2014 ). WISE The Wide-field Infrared Survey Explorer 8 (WISE) is an all-sky mission observing the sky from to . In this work we use the ALLWISE data release 9 , which provides imaging for the whole XXL field in all four WISE filters. 3.2. XXL-N Observations covering exclusively the XXL-N area: CFHT The Canada-France-HawaiiTelescope 10 (CFHT) hasobserved the parts of the XXL-N area as part of the CHFT-LegacySurvey 11 (CFHTLS) in two configurations a) wide area ( W1 field) and b)deep field (D1 field) observed with MEGACam, providingimaging in the filters: , , , , (). In this work we use the T0007 data release downloaded from Terapix 12 . We complemented the coverage of CFHT with three additional fields to the north of W1 . The WA and WB fields were observed in the , , filters while the WC field was observed in the filter (PI: M. Pierre). Additionally, XXL-N has been observed with the WIRcam camera on CFHT in the band (, VIPERS-Multi-Lambda Survey). Details on the observations and data reduction are provided in Moutard et al. (2016a , b) . SDSS The Sloan Digital Sky Survey 13 (SDSS) provides observations in the whole XXL-N area in the filters: , , , , (). In this work we use the DR10 data release ( Ahn et al. 2014 ). UKIDSS The UKIRT Infrared Deep Sky Survey 14 (UKIDSS, Dye et al. 2006 ) has two fields overlapping with XXL-N: the Ultra Deep Survey (UDS), and the Deep Extragalactic Survey (DXS) targeting the XMM-LSS area in , , and , bands, respectively. Here we use the DR10 data release downloaded from the WFCAM Science Archive 15 . 3.3. XXL-S Observations covering exclusively the XXL-S area: BCS The Blanco Cosmology Survey 16 (BCS; Desaiet al. 2012 ) has targeted an area of which overlaps with the XXL-S field using the MOSAIC II imager at the Cerro Tololo Inter-American Observatory 17 (CTIO). The observations cover the , , , bands (). The images used in this work were analysed as described in Menanteau et al. (2009) . DECam The Dark Energy Camera 18 (DECam) is the successor of the MOSAIC II camera at CTIO. We observed the XXL-S field (PI: C. Lidman) in
Paper: 2020ApJ...904...90P (fulltext)
with other studies where an agreement with Shakura Sunyaev ( 1973 ) is reported in appropriate regimes. This is seen, for instance, in the limit of weak wind-driving in the models of Scepi et al. ( 2019 , see their Figure 2) or the outer regions of the semiglobal simulations of Hogg Reynolds ( 2018 ). We note that the level of alignment with the Shakura Sunyaev ( 1973 ) models may change with a more realistic treatment of the system. On one hand, radiative cooling (planned for our future work; see Section 5 ) will introduce nonlocal interactions that may break the locality assumption of α -models. On the other, accretion disks in astrophysical semidetached binaries are colder and geometrically thinner than those presented here (as discussed in Section 1.2 ), which alleviates some of the tension related to large H / R ratio, bringing true accretion disks closer to the Shakura Sunyaev ( 1973 ) solution. 4.3.1. Spiral Structure Drives Accretion at Very Low Mach Numbers As we discussed in Section 1.2 , numerical resources limit our global models to accretion disks that are hotter (of M in = 5, 10) than real astrophysical systems (e.g., CVs, where M in ≳ 50). While the Mach 5 model is unlikely to be realized in nature, it can provide a useful context for the Mach 10 data. With this in mind, we use Figures 7 and 9 to explain the mechanisms driving accretion in our hot-disk model Mach 5. As seen in Figure 9 , the body of the disk (solid brown line, Θ ≲ 2.5 H / R ) dominates the accretion rate for most of the grid. Its has a minimum at r ∼ 0.15, where its impact becomes equal to that of the “corona” (dashed brown line, Θ ∈ [2.5, 5] H / R ), the latter’s being maximal at r ∼ 0.15. This radius also corresponds to the transition from a two-peaked to a triangle-like vertical profile in Maxwell stress (Figure 7 ; see Section 4.2 ), a likely related observation. In Figure 9 we see that Reynolds stress dominates over Maxwell stress both within the main body and the “corona.” In the former, the difference is about a factor of 2–3, comparable with the MHD model of Ju et al. ( 2016 ). The radial velocity slice in Figure 6 associates this Reynolds-stress-driven accretion with the grand-design spiral structure, pointing to it as the main accretion driver in Mach 5. 4.4. Long-term Evolution of the Mach 10 Model Within our current computational limits on the Mach 10 simulation’s run time, the disk is not seen to reach a statistically steady state. However, as we are only able to study a single viscous timescale of the disk (see Section 4.1.1 ), it is entirely possible that such a state would be achieved if the model was allowed to continue. Still, the long timescale (∼10 P orb ) variability observed in our Mach 10 model may be of interest for understanding disk accretion. Not only is it potentially present during a change in disk accretion rate, but there is some indication that it may be recurring in nature (see Section 4.1.1 ). In this section, we attempt to
Paper: 2021MNRAS.503.4537F (fulltext)
ΔΩ j indicates an increase of the quantity with cosmic time. 3 EXPECTED DUST DENSITY The basic ingredient of the calculation is represented by the cosmic star formation history (CSFH), ψ( z ). Adopt the analytical fit to the available data provided by Madau Dickinson ( 2014 ) with ψ 0 = 0.015. We can then compute the stellar mass density at any given redshift, z , by integrating In the previous expression H ( z ) = H 0 [Ω m (1 + z ) 3 + Ω Λ ] 1/2 is the Hubble parameter; for consistency with data, we will adopt the following values of the cosmological parameters (Ω m , Ω Λ , h ) = (0.3, 0.7, 0.7). The return fraction of gas from stars appropriate for a Chabrier initial mass function and instantaneous recycling approximation is R = 0.41. The previous expression can be cast in a more handy form where the is the non-dimensional integral in equation ( 4 ); further define , and note that , and . For the adopted cosmology, the stellar density parameter is . The density of stars formed from z = 1 to the present is ΔΩ * = 1.43 × 10 −3 . The metal density associated with Ω * is we have assumed a metal yield y = 0.033 ± 0.01, with errors accounting for uncertainties in the nucleosynthetic yields (Peeples et al. 2014 ; Vincenzo et al. 2016 ). Finally, the dust mass can be computed by knowing the dust-to-metal ratio, i.e. the fraction of metals locked into dust, , where and Z are the dust-to-gas ratio and gas metallicity, respectively. In the Milky Way, (Zubko, Dwek Arendt 2004 ); assuming solar metallicity, Z = 0.0142 (Asplund et al. 2009 ), then f d = 0.43 (Draine 2011 ). At higher redshifts f d in measured from the gas depletion patterns in, e.g. damped Lyman α (DLA) systems (Péroux Howk 2020 ). In the redshift range 0 z 1, (see Fig. 1 and Table 1 ) in the cold, neutral medium out of which stars form. We note that combines the effects of dust production, growth, and destruction, while we aim here at isolating the first process. Hence, we write the dust density associated with the metal density as where, in general, 2 . To determine f d , we proceed as follows. The minimum value, , is obtained by neglecting growth after grains are injected in the ISM by sources [supernovae (SNe) and asymptotic giant branch (AGB) stars]. SNe with progenitor mass form on average 0.3 M ⊙ of dust, 80 per cent of which is typically destroyed by the reverse shock on site (Todini Ferrara 2001 ; Bianchi Schneider 2007 ; Leśniewska Michałowski 2019 ). The AGB stars (>2 M ⊙ ) contribution per unit stellar mass formed for Z > 0.2–0.3 Z ⊙ is ≈4 times higher than the SN one (Zhukovska, Gail Trieloff 2008 ; Dell’Agli et al. 2017 ; Valiante et al. 2017 ). Hence, the combined effective dust yield per SN is y d = 0.3 M ⊙ , which gives , where is the number of SNe produced per stellar mass formed, according to the adopted Chabrier ( 2003 , equation 17) IMF. Larger values of f d might arise as a result of grain growth, which depends on ambient conditions (Asano et al. 2013 ;
Paper: 2019A&A...622A.176C (fulltext)
filter wheels. The gray cylindrical object below the filter and shutter unit represents the cryogenic camera. Center panel : same view of the T80Cam model after cover removal, showing the two filter wheels and the shutter. Right panel : T80Cam integrated at the Cassegrain focus of the JAST/T80 telescope. A summary of the technical characteristics of T80Cam is presented in Table 2 . In addition, full technical and managerial details on T80Cam can be found in Marín-Franch et al. (2012a , 2015) . Table 2. Main technical performances of the T80Cam panoramic camera at the JAST/T80 telescope. 2.3. J-PLUS filter set The main goals of J-PLUS depend on the accurate determination of SEDs of MW stars and nearby galaxies. It is therefore clear that the J-PLUS filter set must be sensitive to both the optical continuum and the most prominent spectral features. In this context, it has been frequently demonstrated that it is possible to retrieve reliable stellar SEDs with a set of around 10–15 intermediate- to broad-band optical filters centered at key spectral regions (e.g., Bailer-Jones 2004 ; Jordi et al. 2006 ; Carrasco 2006 ). Following this strategy, the J-PLUS filter set consists of the 12 filters defined in Table 3 . Four of these are SDSS filters ( g , r , i , and z ; Fukugita et al. 1996 ) that provide the low-frequency continuum, and six are intermediate-band filters of 200–400 Å width, centered on key absorption features. These are the u filter, in common with J-PAS and located at the blue side of the 3700–4000 Å Balmer-break region, and the filters J 0395 for Ca H+K, J 0410 for H δ , J 0430 for the G band, J 0515 for the Mg b triplet, and J 0861 for the Ca triplet. The J-PLUS filter set is completed with two narrow-band filters, J 0378 and J 0660, also in common with the J-PAS filter set. These two filters are sensitive to the [O II ]/ λ 3727 and H α / λ 6563 emission lines, respectively. The three filters in common with J-PAS are envisioned as an added value for the overall calibration procedure, as they will allow second-order corrections of the ZPs to anchor the J-PAS calibration. Table 3. J-PLUS filter system. Notes. (a) In common with J-PAS; (b) SDSS. Rest-frame key spectral features matching the location of narrow- and intermediate-band filters are also indicated. The J-PLUS filters have been manufactured by Schott (Switzerland). A detailed technical description of the filter requirements and design can be found in Marín-Franch et al. (2012b) . Figure 3 shows the overall efficiency of the transmission curves of the filters, whose tabulated data can be found at the J-PLUS website 4 . Fig. 3. Overall transmission curves measured for the set of 12 J-PLUS filters, including the effect of the entire system (sky, mirrors, lenses, and CCD). The filter arrangement is tuned to provide scientifically valuable data for many fields of Astrophysics. Since the intermediate-band filters are sensitive to the strengths of key features of old stellar
Paper: 2017MNRAS.465.1259G (fulltext)
over the whole solar disc. The umbra area is the area of the umbra of the sunspot, and pores are excluded. The total umbra area is the area of the umbrae summed over the whole solar disc. Projected areas are measured in millionths of the solar disc ( md ) and corrected areas in millionths of the solar hemisphere ( mh ). The relationship between the areas of a solar feature in two catalogues (C 1 and C 2 ) is modelled by linear regression in the form A 2 aA 1 , where A 1 and A 2 are the areas in C 1 and in C 2 , respectively. We provide three types of plots to show details of the regression: the scatter plot of the regression, the moving root mean square residual (MRMR) and the moving relative root mean square residual (MRRMR). MRMR measures how the root mean square residual (RMR) relative to the regression line depends on the area. For a given area A , the MRMR is determined from the n -nearest areas to A . Similarly, MRRMR measures how the relative root mean square residual (RRMR) relative to the regression line depends on the area. For a given area A , the MRRMR is determined from the n -nearest areas to A . We choose the value 20 for n . To compile the various sunspot catalogues compared in this paper, we used the software package SAM (Sunspot Automatic Measurement). SAM is a set of cooperative computer programs that embraces every aspect of compiling a sunspot catalogue, from (1) setting up the necessary data base for the observational data of the solar images and the telescopes and (2) automatically detecting sunspots (umbra and penumbra) on solar images and determining their heliographic coordinates and area through (3) to making the catalogue ready for publishing (Gyri 1998 ). To identify sunspots on solar images, we used the method published by Gyri ( 2015 ). A solar image may contain not only features belonging to the Sun but also various artefacts too, such as emulsion deficiency, emulsion abrasion, scratches, dust grains, clouds, inscriptions, patches, orientation markers, and much more. The properties of some of these artefacts are like sunspots (especially when the image quality is not too good), so it is hard to filter them out automatically, but a trained human eye/brain can do this in many cases (not always). Even when the average seeing of the image is good, there are regions with poorer seeing and small sunspots in these regions cannot be surely perceived. However, if, in a close-in-time image, these spots are situated in regions with good seeing, then they will show up clearly and could provide conformation. Near the solar limb, especially when the image quality is not too good, the penumbra and the umbra borders are not too definitive. In this case, the human intervention could increase the accuracy of finding these borders. To cope with these situations and with others not mentioned here, the automated sunspot catalogue created by SAM can be humanly revised by using special software, SAMm (Sunspot Automatic Measurement
Paper: 2021MNRAS.503.5046L (fulltext)
either N prog 2 or N prog 5, which correspond to at most 10 or 40 per cent probability of being star forming during the mini-halo phase, respectively. We obtained results from this simple approach as an academic exercise, and in order to be able to compare results with previous work by Dijkstra et al. ( 2014 ). In the second approach, instead, which we employ as our fiducial case, we self-consistently account for SF in mini-haloes that can cool via H 2 , and exclude these from the list of candidate DH mini-haloes. For environmental pollution, we follow all haloes able to form stars in our cosmological volume, i.e. mini-haloes not DH and all ACHs, and compute the size of the metal bubble starting from the time of the first SN explosion. Hence, the metal bubbles start to expand in our model Δ t = t ff + Δ t SN after the halo is first declared star-forming. To model SF, we assume that the stellar mass formed in ACHs is f ⋆ = 0.05 of the baryonic mass, translating to M ⋆ = f ⋆ Ω b /Ω m M vir . For mini-haloes, instead, we consider a lower star-forming efficiency f ⋆ = 0.005. In both cases, we assume that SF starts with a burst as soon as one free-fall time (defined at the maximum density achievable via adiabatic contraction ρ gas = η gas ρ max ) has elapsed since (i) dynamical heating conditions have failed or (ii) the ACT has been exceeded (if no previous SF in the mini-halo had already occurred), and then proceeds smoothly following the increase in stellar mass with time in the halo. Following Weaver et al. ( 1977 ) and Madau, Ferrara Rees ( 2001 ), we define the bubble radius as where is the average SN luminosity associated to the stellar population in the halo, t the time since the burst of SF, and n ≡ Δ gas Ω b ρ crit (1 + z ) 3 / m H is the gas density (in cm −3 ) the bubble expands through. Now, we assume for simplicity that L SN ≈ E SN η SN M ⋆ ( t )/ t , where is the energy released per SN, M ⋆ ( t ) = f ⋆ (Ω b /Ω m ) M vir ( t ) is the stellar mass at time t , and is the number of SNe per stellar mass formed, and replace them in the bubble radius equation, we obtain, for t > Δ t SN , We notice that this equation reduces, for t ≫ Δ t SN , to the equation reported in Dijkstra et al. ( 2014 ), apart from the slightly lower normalization. In Dijkstra et al. ( 2014 ), the baryon overdensity through which the bubble expands is assumed to be Δ gas ≈ 60, typical of the overdensities found within haloes (60 ≲ Δ gas ≲ 1000), although the bubble spends most of the time in the intergalactic medium at Δ gas ∼ 1−10. This results in the bubble size being underestimated (Dijkstra et al. 2014 ; Habouzit, Volonteri Dubois 2017 ). In our model, instead, we also account how the overdensity changes outside the halo, by fitting the asymptotic case of an extremely rare halo with from Barkana ( 2004 ), and simply express Δ gas as where x ≡ log 10 ( R / R vir ), R is the bubble radius, and the factor 0.787 has been chosen to join the inner constant value and
Paper: 2016MNRAS.456.2877M (fulltext)
Mathematically, this is a two-point boundary problem and it will assure that we have a well-behaved solution in the whole r − θ space. We have tried to obtain such solutions but failed. Therefore, we only require the solution satisfying the boundary condition at θ = 90°. As we will explain in the next section, we think the solution obtained in this way should still be physically meaningful. 3 RESULTS We solve the above equations ( 34 )–( 40 ) numerically. We integrate these equations from the equatorial plane (θ = 90°) towards the rotational axis (θ = 0°). We adopt the values of β θ0 and β φ0 with reference to the numerical simulation data of Yuan et al. ( 2012b ), but our results are not sensitive to the exact values. As we have explained before, the toroidal component of the magnetic field, which is produced by shear, should be negative. We do find that the toroidal component is negative at the beginning of integration. But at a certain critical value of θ, denoted as θ B , we find that it begins to change its sign. At the region of θ θ B , the toroidal component of the magnetic field becomes positive thus the solution is no longer physical so we stop our integration at θ B . We think our solution in the region of θ B θ 90° is still physical. 4 This is because, on the one hand, the solution satisfies the equations and the boundary conditions at θ = 90°. On the other hand, the values of all physical quantities of the accretion flow at θ = θ B are physical. Therefore, we can reasonably treat these values as boundary conditions at θ B . In fact, as we will describe below, the main properties of the solutions we obtain in this way are in good agreement with those obtained in Yuan et al. ( 2015 ) from numerical simulations. From the bottom-right plot of Fig. 2 , it seems that there is a sonic point for v θ / c s and the integration stops when the sonic point is approached because of the singularity there. This is not true. This is because, the value of v θ must be equal to zero at the pole because of the axisymmetric boundary condition there. Therefore, v θ / c s should not pass through a sonic point. We note that Jiao Wu ( 2011 ) obtained a similar result with us, i.e. their solution also has to stop at a certain θ angle. An interesting explanation for such a ‘truncation’ is given in their work. Figure 2. Angular profile of a variety of variables. The up-left plot shows that the radial velocity changes its sign at θ ∼ 47°. 3.1 Overall inflow-wind structure of the solutions Fig. 1 shows the poloidal magnetic field configuration. We emphasize that while the even symmetry of the field is our assumption, the strength of the field is obtained by solving equations ( 34 )–( 40 ). Above the equatorial plane, the radial component of the magnetic field is positive (pointing to large radii). So the toroidal component of the magnetic field, which is produced by shear, is negative. We find that such kind of magnetic field configuration is similar to the
Paper: 2017ApJ...850...87M (fulltext)
for a standing shock just downstream of the radio core that compresses and aligns the magnetic field (Cawthorne Cobb 1990 ). Figure 5. Variations of the emission parameters of 3C 273 transverse to the jet axis (dashed red line) for slice 6 (shown in Figure 3 ). From top to bottom: total intensity, polarized intensity, fractional polarization, and EVPA. The fractional polarization increasing toward the edges of the jet is a predicted polarimetric signature of a jet sheath (as discussed in Section 1 ), with the EVPA transverse to the axis. The black line in the lower panel delineates EVPAs that are exactly ⊥ to the jet spine. The beam width along this transverse slice is shown in the upper panel to the left. 4.2. 4C 71.07 Figure 6 presents a stacked radio map of the blazar 4C 71.07 (0836+710). A very prominent and continuous polarimetric signature of a jet sheath is evident down the edges of the jet in this stacked radio image. Given the continuous nature of the jet sheath on the image, we can estimate the bolometric luminosity of the sheath. This is accomplished by first using the length of the polarized intensity profile along each of the slices to trace out the shaded gray regions shown in Figure 9 (see also Figure 6 ). The size of the sheath on each side of the slice is arbitrarily set by the location where the polarized flux falls below 0.85 times the peak value. An estimate of the bolometric luminosity () of the sheath is determined by summing up all of the deconvolved “sheath” flux (see Figures 8 and 9 ) contained within the gray shaded regions of our stacked map. We convert this extrapolated sheath flux at 43 GHz into a spectral luminosity and assume a power law (), which we then integrate over a range of frequencies that encompass the sheath’s radiation to obtain where we have adopted a spectral index of and limits of integration of and . Our estimate indicates that the jet sheath can potentially provide a substantial source of seed photons even at parsec scales along the jet. It should be pointed out, however, that the beam width is ∼0.2 mas along our transverse slices and that the sheath region we detect is within ∼0.05–0.15 mas of the jet spine. Convolution with the beam, therefore, adds a significant contribution of flux from the spine to our nominal sheath region. As illustrated in Figures 8 and 9 (solid red lines) we attempt to remove this contaminating spine flux from our estimate. The bolometric luminosity given in Equation ( 1 ), however, should only be regarded as a rough estimate of the actual sheath luminosity. Figure 6. Stacked map of 43 GHz images of 4C 71.07 spanning 20 epochs of observation from 2008 to 2016. As in Figure 3 , the black contours correspond to total intensity (contour levels are: 0.9, 1.9, 3.9, 7.9, 15.8, 31.7, 63.4, 126.9, 253.9, 507.8 mJy beam −1 ), whereas the underlying color scheme corresponds to polarized intensity (see the color bar to the right for the flux levels), with the EVPAs denoted by the