adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...904..164N (fulltext)
so that the sightline passes through multiple separate structures with different ionization conditions and metallicities. In contrast, Veilleux et al. ( 2005 ) suggest that large variations in metallicity and ionization conditions between clouds across an absorption profile might be a signature of outflows in addition to broad complex kinematics spanning a few hundred kilometers per second, kinematics that are not consistent with rotation, α -rich abundances, and dual-trough absorption with weak absorption between. There have been few studies thus far with detailed metallicity and kinematic analyses in order to investigate this further. To improve the metallicity measurement and better determine the structures along the line of sight, a multiphase and multicomponent modeling approach would be best. Several works have undertaken such a study and found differing metallicities in each component and/or phase (e.g., Tripp et al. 2011 ; Crighton et al. 2015 , 2016 ; Muzahid et al. 2015 , 2016 ; Rosenwasser et al. 2018 ; Zahedy et al. 2019a ) and these insights could be used to determine if outflows, inflows, tidal material, etc., are present along the same sightline. In particular, a multiphase analysis would provide insights into whether outflows are primarily traced by hot, high-ionization gas with metallicities similar to the galaxy ISM metallicity and whether the lower ions along the same sightline trace cooler, more metal-poor gas entrained by the hot outflow, which is a possible solution to the total metallicity problem suggested by Kacprzak et al. ( 2019a ) that might fit the scenario we observe here. The difficulty with this method is that it requires partitioning each ion between VP components and phases, and modeling a consistent set of VP components across transitions with similar ionization phase. This is particularly challenging when absorption is saturated and the selection of lines is limited, i.e., the only H i line observed is saturated as in the system presented here. While much of our sample has coverage of multiple H i Lyman series lines and in some cases the full series to properly do a multiphase and multicomponent analysis, the J1430+014, z = 2.071 absorber is not a good candidate for this method. Without obtaining new spectra to better cover the H i Lyman series, we would need to make assumptions on how to partition the saturated H i between components and phases, which would result in over-interpreting the absorption. Furthermore, the vast majority of metallicities in the literature are total metallicities. To study the evolution of the CGM from Cosmic Noon to today, we have focused on total metallicities here. Even though metallicity is a useful diagnostic for ruling out some scenarios of gas flow origins, especially at higher redshift where CGM mixing appears to be less efficient, a variety of properties such as kinematics and galaxy morphologies must also be considered for a more accurate picture, which we have done here.
Paper: 2019MNRAS.486.3915A (fulltext)
major and minor axes for a particular case. Since the model is specifically constructed in such a way, the asymptotic behaviours of the density are indeed given by whereas the Taylor–Maclaurin series is where L 3 = 3ln [( a + b )/ r 0 ] − 1 and L 15/8 = 15ln [( a + b )/ r 0 ] − 8. This last series also implies that the density profile might be increasing near the origin if ( a + b )/ r 0 is chosen too small, but the examinations of and indicate that r 0 ≤ ( a + b )e −8/15 ≈ 0.5866( a + b ) is actually sufficient to guarantee the positive non-increasing density profile. Figure 4. Same as Fig. 1 except for the density profiles in equation ( 51 ) with r 0 = ( a + b )/e 8/15 . Figure 5. Density profiles along the major and minor axes for the models in equations ( 45 ) and ( 51 ). For both cases, a = 2.58 and b = 0.43 but r 0 = e −1/3 ( a + b ) ≈ 2.157 for equation ( 45 ) and r 0 = e −8/15 ( a + b ) ≈ 1.766 for equation ( 51 ). 6 THE PROLATE AND TRIAXIAL SUBSTITUTIONS One possible generalization of the Miyamoto–Nagai substitution is applying a similar transformation to the other coordinates. For instance, consider the transformation of a spherical function Φ s ( r ) to Φ gMN ( R, z ) = Φ s ( t ) where t 2 = ( c + R d ) 2 + ( a + z b ) 2 and . With Φ gMN ( R, z ) as the potential, the resulting gravity field is given by with The gravitational force points locally to a more vertically inclined direction than that to the centre if a / z b > c / R d and vice versa, whilst it points radially towards the centre everywhere (i.e. the spherical potential) if a = c = 0. The equipotential in the limit of R, z → 0 is oblate if a / b > c / d or prolate if c / d > a / b , whereas the equipotentials as R, z → ∞ become spherical. If d = 0 and c ≠ 0, we also find is non-zero finite and so the gravitational force becomes undifferentiable along any path that crosses the symmetry axis. This behaviour is understood from the fact that the corresponding density profiles are singular in this limit (like ∼ R −1 ) on the R = 0 axis. The Poisson equation indicates that the axisymmetric density profile generating the potential Φ s ( t ) can be expressed as generalizing equation ( 15 ), which corresponds to the c = d = 0 case. It is easy to observe that this density profile is also always non-negative if (actually ρ s ( r ) being non-negative and integrable at r = 0 is sufficient), and, from explicit calculation, we can furthermore demonstrate that and if (ρ s )′( r ) ≤ 0 (and ). Since , equation ( 56 ) is also regular everywhere with the Taylor-MacLaurin series unless bd = 0. 5 Here, If d = 0, on the other hand, equation ( 56 ) reduces to which exhibits an explicit R −1 -density singularity as R → 0 (i.e. the density along the symmetry axis diverges). This contrasts to the b = 0 case, for which a razor-thin disc develops. 6.1 The Miyamoto–Nagai spindle The simplest potential-density pair that results from this transformation is that due to the point mass potential Φ s ( r ) =
Paper: 2021MNRAS.501.4359Z (fulltext)
sample size of TNG50. For star-forming galaxies the Δ〈LLR〉 is the lowest for TNG50, followed by TNG100 and Illustris, indicating that TNG50 is the simulation that best models star-forming galaxies. Instead, all simulations struggle to accurately model quiescent galaxies, for which the Δ〈LLR〉 remains low in all cases. These trends are robust across the stellar mass bins considered. The left-hand top panel of Fig. 3 shows how the mean of the LLR distribution for simulated star-forming galaxies is the closest to SDSS for TNG50, followed by TNG100 and with Illustris being the furthest away from it. The higher LLR of TNG100 with respect to Illustris is suggestive that the improved physical model for galaxy formation adopted in the IllustrisTNG framework is overall an improvement compared to the original Illustris implementation (Pillepich et al. 2018 ). Furthermore, the unprecedented agreement with observations reached by TNG50 star-forming galaxies is also a sign that a higher resolution is key to effectively model star formation. We note, however, that all simulated data sets are still inconsistent at the 1σ level with SDSS. On the other hand, it can be seen that the improvement noted for star-forming galaxies does not seem to propagate to quiescent galaxies as well (the top right-hand panel of Fig. 3 ). We start by noting that in this case Illustris galaxies show a tail of high LLR that is consistent with IllustrisTNG at the 1σ level. However, the large variance suggests that this tail is very scarcely populated, whereas the very small variance found for the spike at low LLR is indicative that the bulk of the population of Illustris quiescent galaxies lies there, i.e. they are very far from reproducing SDSS. Yet, while there seems to be an overall improvement from the original Illustris framework to IllustrisTNG, the better resolution offered by TNG50 over TNG100 does not appear to significantly modify the overall LLR distribution for quiescent galaxies. Indeed, the distributions of the two IllustrisTNG volumes are practically consistent but at very high LLR, where the probability density of TNG50 is slightly higher. In short, what the top of Fig. 3 is telling us is that there has been a clear amelioration from the Illustris to the IllustrisTNG framework, in both the modelling of star formation regulation and of quenching, yet IllustrisTNG still produces small-scale stellar morphological details that differ from those in SDSS, and especially so for quiescent galaxies. Most importantly, while the higher resolution featured by TNG50 generates a sizeable improvement in the morphology of star-forming galaxies, this is not the case for quiescent galaxies. This is suggestive that the physics that couples small-scale stellar morphological details to star-formation quenching in the IllustrisTNG simulations warrants improvement, or that an even higher resolution is needed to accurately model the processes that lead to quiescence. 5.2 Mass dependence The
Paper: 2016MNRAS.456.1666A (fulltext)
Galactic latitude. The significance of this result can be assessed by a straightforward reduced χ 2 statistic: where ( x Bes , x MOA ) refer to the (model, observed) microlensing quantity. σ is the observational uncertainty within the field, and N fld is number of fields. The observational uncertainty within the field is calculated using formula from Han Gould ( 1995 ). The reduced χ 2 contribution of each MOA-II field are shown in Table 5 and Fig. 11 . The gb21 field is excluded due to the limit of the Besançon extinction maps. The model optical depth is in agreement with MOA-II data within 3σ MOA for most fields. The reduced χ 2 of resolved source and DIA sources optical depth are 2.4 and 2.0, respectively. Figure 11. The reduced χ 2 contribution of optical depth (a) and microlensing event rate per star (b) of resolved sources (left) and DIA sources (right). Table 5. Field-by-field contributions to the reduced χ 2 () between the Besançon model and the MOA-II data for the optical depth and microlensing event rate per star. The event rates show higher reduced chi-squared contribution than the optical depths. The Besançon resolved sources and DIA source results have values of 2.6 and 2.2, respectively. The low Galactic latitude area ( b 3°) of both parameters provide the bulk of the disagreement (see Section 5.1 ). In field gb1, there is a long time-scale event (gb1-3-1, t E = 157.6 d) which contributes more than half of optical depth in that field. This event provides a hotspot in MOA-II optical depth and average time-scale maps (Section 5.2 ). In order to check the reliability of reduced χ 2 test, the reduced χ 2 of field gb1 without gb1-3-1, gb1 Cut , is calculated. The result in Table 5 shows that the gb1 Cut field provide a better reduced than original gb1 field. Finally, we cut the events which have crossing time longer than 100 d which locate in 5 field; gb1, gb9, gb10, gb13 and gb 14. The new reduced chi-squared, , of optical depth (2.3 for resolved sources and 1.8 for DIA sources) and event rate per star (2.2 for resolved sources and 2.0 for DIA sources) is reduced. However, fields which locate around inner bulge except field gb6 still show high reduced chi-squared compare to high Galactic latitude field. Therefore, high reduced chi-squared region around inner bulge do not effect by long time-scale event, but show the mismatch of optical depth and event rate per star between the Besançon model and the MOA-II data at low Galactic latitude area. 6 MICROLENSING MODEL PARAMETRIZATION The MOA-II team parametrize the observed spatial microlensing distribution using a polynomial function. We can do likewise for our simulated maps. We model the structure of the optical depth, average time-scale and event rate maps shown in Fig. 5 using a 10-parameter cubic polynomial fit in l and b . The model function can be written as where x is the microlensing observable (rate, time-scale or optical depth). The best-fitting models are shown in Fig. 12 and
Paper: 2021MNRAS.503..292K (fulltext)
for the SR effect in H and K. To convert stellar fluxes to magnitudes, we set the zero-point magnitude to the instrumental zero-point (one ADU/s in H and K, are 25.2 and 24.8 magnitudes, respectively). To double check our calibration, we compared H and K magnitudes of 26 sources (located r ≥ 3 ′′ from R136a1) common between our catalogue and the VLT/MAD (Campbell et al. 2010 ) in H and K. 2 We cannot compare our zero-points with the MAD/H and K magnitudes in the central 2.8 ′′ region because of the completeness issue and the very low AO correction (SR ∼ 15–30 per cent) of their data. Fig. 2 shows the completeness and contrast problem of the previous data, leading to an inability to distinguish certain sources. By way of example R136a6 (H19) and H26 3 – are very close in terms of brightness (flux ratio of about 90 per cent) and location (separation of about 70 mas). These bright O-type stars are most easily detectable in our data compared with others (Figs 2 and A2 ). Cases like these close bright stars, bring confusion even in the HST spectroscopic analysis (Bestenlehner et al. 2020 ). The K magnitude of R136a1 and R136a2 (11.15 mag and 11.43 mag) increased about 0.08 and 0.11 mag, compared to their K mag in the first epoch (11.07 mag and 11.32 mag). The brightness of R136a3 and R136b in K (11.45 mag and 11.67 mag) is consistent with the first epoch K data (11.44 mag and 11.66 mag). R136c in the K (11.31 mag) is 0.17 mag brighter than in 2015 (11.48 mag), making this source the second brightest in our K catalogue. In previous studies, R136c shows indications of binarity based on its strong X-ray emission (Portegies Zwart, Pooley Lewin 2002 ; Townsley et al. 2006 ; Guerrero Chu 2008 ) and possible low-amplitude RV variations (Schnurr et al. 2009 ). Comparing the brightness of common detected sources within two epochs, the K magnitude of 56 per cent of the sources has changed more than twice their photometric errors. 4 To make sure that this is not due to Starfinder photometry analysis (which is based on the reference input PSF), we have used daophot algorithm for this analysis as well. The variation of the K magnitude of these sources still remains. 5 The K mag of about 67 per cent of the sources located outside of the core ( r > 3 ′′ ) has changed more than twice their photometric errors. Since the observations were made in different nights and the AO correction in the outer part of the FOV is not as good as its centre, this inconsistency is probably an observational effect; otherwise, it is originated from physical reasons like variables or multiple systems with periods less than 3 yr. 4 COMPLETENESS We produced 6.2 × 10 4 artificial stars (500 per magnitude) in H and K, in order to determine the completeness as a function of magnitude. These artificial stars are created using the same PSF as we used for source extraction process. They are added to the original image one by one (500 times per magnitude) and the same photometric tools and
Paper: 2018A&A...612A.101V (fulltext)
between sunspot numbers and flare occurrence as a function of the solar cycle has been extensively examined, revealing a surge of renewed solar activity occurs a few years after the cycle maximum ( Fritzová-Švestková Švestka 1973 ; Švestka 1995 ). In a correlation analysis of flare occurrence and sunspot number, Temmer et al. (2003) noticed that the flare occurrence lags behind the sunspot number. For solar cycle 23, Bai (2006) found high flare activity in the late declining phase of the cycle. Not only was an increased flare activity noted but many low-latitude coronal holes were repeatedly observed during the declining and minimum phases of solar cycle 23 ( Abramenko et al. 2010 ). In addition, Gopalswamy et al. (2003) determined that the occurrence rate of coronal mass ejections (CMEs) peaked two years after the cycle maximum, and in the analysis of Kilcik et al. (2011) , the maximum speeds of CMEs also peaked approximately at that time. Recently, Lee et al. (2016) reported that the occurrence rates of major flares and front-side halo CMEs are higher during the descending phase of solar cycle 23 as compared to other phases of the cycle. The recent rejuvenation in the Sun’s large-scale magnetic field during solar cycle 24 starting in the second half of 2014, was pointed out by Sheeley Wang (2015) . They concluded that this was the result of systematic flux emergence in active regions, whose longitudinal distribution greatly increased the Sun’s dipole moment. The role of horizontal shear flows in building up magnetic stress in flare-productive active regions was already noticed by Harvey Harvey (1976) . Horizontal shear flows are not only responsible for magnetic stress but are also drivers for magnetic reconnection ( Yurchyshyn et al. 2006 ). Yang et al. (2004) measured the horizontal flows in active region NOAA 10486 before an X10 flare and found shear flows with local correlation tracking (LCT; November Simon 1988 ) of up to 1.6 km s −1 along the polarity inversion line (PIL). These strong shear flows were correlated with white-light flare kernels in the optical range. Strong horizontal shear flows, which decrease with height in the atmosphere, were also observed by Deng et al. (2006) in the same active region. Based on photospheric flows and the flux around the PIL, Welsch et al. (2009) derived indicators for the occurrence of flares. Many case studies established crucial links between photospheric shear flows, build-up of free-energy in a stressed magnetic field topology, and major flares (e.g., Tan et al. 2009 ; Liu et al. 2010 ). More recently, Beauregard et al. (2012) and Wang et al. (2014) analyzed the horizontal flows in the active region NOAA 11158 in relation to an X2.2 flare. The photospheric signatures produced by this major flare were rather diminutive. On the other hand, during the impulsive phase shear flows around the PIL showed a sudden decrease and a change in the intrinsic rotation of the two main sunspots. Recently,
Paper: 2016A&A...587A..21C (fulltext)
of passive materials, e.g. the lens frame and satellite housing. The sensitivity was calculated as follows: where is the exposure time, set at 100 ks. The results for the sensitivities are shown in Fig. 9 b. The best sensitivities are achieved for cases 2 and 3. Finally, to compare the three focal spots, it was resorted to the S/N , as in Guidi et al. (2011) , where is the signal count within a specified encircled area with radius , is the background of the area, and is the useful volume of the detector. For a Laue lens, the size of the detector determines its field of view for off-axis sources. Here, an on-axis source was simulated and the noise of the detector was considered proportional to the focal spot size. In particular, was chosen to enclose 90% of the diffracted photons (see Table 3 ) in the focal plane and the signal within this area. Thus, the background was taken proportional to the volume of the detector, which faced the incoming X-rays with a surface as large as the encircled area. Because of different focalization properties, the obtained S/N for the three cases were significantly different. As a result of the simulation, the quotient between the S/Ns for the three cases turned out to be as follows: Thus, QM crystals are also the most efficient in terms of the signal-to-noise ratio. The comparison worked out in this paper is based on a photon beam with energy centred at 200 keV. This is mainly because of the high diffraction efficiency of Ge in this energy range. The obtained results also hold for different energies. For low energy, such as 100 keV, Si crystals can be used. For photons with energy greater than 600 keV, the diffraction efficiency of Si and Ge crystals decreases. With the aim of focusing such high energy photons at high efficiency, crystals with high , such as silver, gold, lead, etc. ( Barrière et al. 2009 ), are needed. These materials cannot be fabricated as perfect crystals, but only as mosaic crystals. Thus, CDP and QM crystals would be not available if high material are used, and a comparison between these types of crystal would be impossible. In this case, it would be important to take into account that metal crystals have particular mechanical and chemical properties, which make them very difficult to cut, align, and carry out the gluing and polishing process. In this paper, a comparison between different types of diffracting crystals has been worked out. Actually, the feasibility of a Laue lens depends on several criteria that have to be taken into account. With regard to the crystals, important parameters are the availability of crystalline materials, reproducibility of the crystal manufacturing, and alignment and mounting of the crystals on the lens frame. Moreover, in the context of a space-borne Laue lens, additional criteria must be considered, such as payload, mechanical robustness to shocks and vibrations, and long-time reliability with respect to chemical and temperature cycling. A simulation for
Paper: 2017MNRAS.466.3364C (fulltext)
sky positions of voids, if any, is at the sub-K level, which is negligible. We present results using the SEVEM map in practice. A common mask UT78 is applied to both the temperature and lensing maps (Planck Collaboration X 2016 ). The lensing situation offers less choice, as only a single convergence map is available: the 2015 lensing data are released directly in the form of the spherical harmonic transform of the (masked) field (Planck Collaboration XV 2016 ). 3.1 The optimal radius of the filter Corresponding to each void centre, the CMB signal is taken to be the average temperature T (or ) within a circular aperture r R filter minus the same quantities averaged over an annular aperture , where R filter is the size of the compensated top-hat filter. We will call the filtered temperature and lensing convergence T and , i.e. To maximize the ISW signal, Cai etal. ( 2014 ) showed that the optimal choice was R filter 0.6 r v , using mock void catalogues defined via haloes from N -body simulations. Using our HOD mocks, we re-investigate this scale factor for a possible dependence on void radius. We find that R filter 0.7 r v gives slightly higher amplitudes for the stacked filtered T signal as well as for the lensing signal for voids with 100 r v 150 h 1 Mpc. The corresponding outer radius of the filter is r v . For simplicity, we will use this size of the filter throughout out analysis, even though it may not be the optimal choice for all ranges of voids. 3.2 Stacking with all voids We now look at the results of stacking the CMB sky at the DR12 void locations. Because the predicted signal varies with void radius, as does the fidelity of the void catalogue, we divided the results into different bins of void radius. We sorted the voids in decreasing order of radius, and measured the average filtered T and imprints for several logarithmically spaced bins of r v . The results are shown in the top row of Fig. 2 . The filtered temperature T is negative at large void radii. The deepest temperature dip is approximately 6K between r v 100 and 150 h 1 Mpc, with a significance of 2.4. T crosses zero at r v 90 h 1 Mpc and remains slightly positive at smaller void radii. We can understand the presence of positive filtered temperature as an indication of voids-in-clouds, i.e. voids living in overdense environments. The gravitational potential at the scale of the void for a void-in-cloud is negative; i.e. it is a potential well rather than a potential hill as intuitively expected for a void. The dominant linear ISW effect thus yields a positive temperature perturbation (Cai etal. 2014 ). We also find that the simulated ISW signal crosses zero, though at a similar void radius of 30 h 1 Mpc. This indicates that the stacked signal for the CMB temperature qualitatively resembles an ISW signal in a CDM universe. For the largest voids, the observed T shows consistency with zero at r v 150 h 1 Mpc, which confirms our speculation from simulations that these objects