adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.478.2999K (fulltext)
] sample, we notice that although the measurements are consistent with each other at a given stellar mass for all redshifts, the slope of the trend decreases such that by z ≳ 2.2, the trend is flat. Interestingly, these measurements occur in the same mass range (9.2 log 10 M stellar /M ⊙ 11.0) where for all redshifts, the trend is flat and then increases for higher stellar masses. A similar flat, redshift-independent trend is seen for [O ii ] emitters within the stellar mass range of10 9.75–11.0 M ⊙ . Figure 6. The minimum halo mass dependency with stellar mass. We find a strong relationship at all redshift slices for our H β + [O iii ] samples and for the z = 1.47 [O ii ] sample. The z = 2.25 [O ii ] sample also shows an increasing trend, but is limited only to two stellar mass bins. The other [O ii ] samples are limited due to sample size and could not be separated in stellar mass bins. We find no redshift evolution in the relationship. Interestingly, at stellar masses >10 9.75 M ⊙ , the halo mass is found to be constant at ∼10 12.3 M ⊙ for H β + [O iii ] emitters and ∼10 12 M ⊙ for [O ii ] emitters. Fig. 7 shows the SHM ratio as a function of stellar mass, where we find it to be redshift-independent for all stellar masses. We find the SHM ratio for z = 0.84 and 1.42 H β + [O iii ] emitters to be constant between 8.5 log 10 M stellar /M ⊙ 9.75 and increasing for all redshift slices at M stellar > 10 9.75 M ⊙ . The [O ii ] emitters show a continuous increase in the SHM ratio from the lowest masses to the highest masses probed. Figure 7. The SHM ratio as a function of stellar mass. We find our [O ii ] measurements show a continuous, redshift-independent increase in the SHM ratio for the full stellar mass range. The H β + [O iii ] measurements show a constant ratio up to ∼10 9.75 M ⊙ followed by a continuous, redshift-independent increase in the ratio. We compare our measurements with the abundance matching measurements of Behroozi, Wechsler Conroy ( 2013b ) overlaid in grey. We find that our [O ii ] and H β + [O iii ] measurements are in agreement within 1σ except for our H β + [O iii ] 10 9.75–10.00 M ⊙ measurements. Overlaid in Fig. 7 are the measurements of Behroozi et al. ( 2013b ), which used abundance matching along with constraints from observational measurements of global stellar mass functions to calculate the SHM ratio up to z ∼ 8. Behroozi et al. ( 2013b ) found that the ratio is redshift-independent and we therefore only highlight in Fig. 7 the 1σ confidence region of their measurements that correspond to the redshifts of our sample. We find that all four redshift slices for the [O ii ] samples are in agreement with the Behroozi et al. ( 2013b ) measurements. Our H β + [O iii ] measurements are also in agreement for M stellar 10 9.5 M ⊙ and >10 10 M ⊙ . Note that the Behroozi et al. ( 2013b ) measurements are based on ‘global’ (passive+active galaxy) stellar mass functions, while our samples are comprised of ‘active’ galaxies
Paper: 2016A&A...587A..27K (fulltext)
tests Fig. 11 Reduced distribution for 30 GHz, 44 GHz, 70 GHz, for white noise ( left ), ideal, and realistic simulation ( right ), for 100 realizations. Also shown is the distribution corresponding to an ideal result. The numbers within the plot give the mean value of the distribution. Fig. 12 Reduced distribution for 70 GHz subsets. From top to bottom : full (4 yr) 70 GHz data, years 14 (full frequency), horn pairs 18/23, 19/22, 20/21 (4 yr). From left to right : white noise only, independent destriping ideal/realistic, full destriping ideal/realistic. In the case of full mission, independent and full destriping options are identical. Fig. 13 Effect of various approximations on the result for the worst LFI18/23 case. We add to the ideal simulation one non-ideality at a time: destriping mask, horn-uniform weighting, finite baseline length, and destriping resolution. The result for the ideal simulation case is shown in the background. We validate the noise covariance matrices through tests. We generated 100 realizations of harmonic vectors , as described above. For each vector and corresponding matrix we compute the value Here is the covariance matrix, and is the number of degrees of freedom. Multipole contributes degrees of freedom, except for the monopole and dipole, which are identically zero for and . This yields in total = 3(+1) degrees of freedom, which for = 50 gives = 7795. For a matrix that perfectly describes the noise properties, the results obey the Chi distribution, For large values of the distribution becomes Gaussian, with expectation value 1 and standard deviation of , which for = 7795 is 0.016. We compared the white noise covariance matrix of Eq. ( 11 ) against white noise simulations, and the full noise matrix of Eq. ( 29 ) against ideal and realistic simulations. Results from tests at the three Planck LFI frequencies, 30, 44, and 70 GHz are shown in Fig. 11 . We show a histogram of the values for 100 realizations, and quote their mean. The white noise matrix models the white noise residual nearly perfectly, mean ranging from 0.9992 to 1.0004, Comparison between the full matrix and ideal simulation gives slightly larger values, from 1.0002 at 70 GHz to 1.0037 at 44 GHz, while the expected one-sigma deviation for 100 realizations is 0.0016. Deviations from one are larger in the case of realistic simulation, as expected, given the approximations involved. At 30 GHz we find a mean of 1.0196, at 70 GHz 1.0037. Results for 70 GHz subsets of are shown in Fig. 12 . In the first column we compare the white noise matrix against a pure white noise simulation. Second and third column show results for the full matrix. Results are shown for two destriping options: full 70 GHz destriping, and independent destriping. In all cases, we find very good agreement with the white noise matrix and corresponding simulations. The same can be said about the full matrix and the ideal simulations. The mean
Paper: 2017MNRAS.467L..66D (fulltext)
a year. The second scenario pointed out above implies the presence of an unseen massive perturber, i.e. a trans-Plutonian planet. Close encounters between minor bodies and planets can induce fragmentation directly via tidal forces (e.g. Sharma, Jenkins Burns 2006 ) or indirectly by exciting rapid rotation (e.g. Scheeres etal. 2000 ; Ortiz etal. 2012 ). Alternatively, wide binary asteroids can be easily disrupted during close encounters with planets. The existence of wide binaries among the populations of minor bodies orbiting beyond Neptune is well documented (e.g. Parker etal. 2011 ). Wide binary asteroids have very low binding energies and can be easily dissociated during close encounters with planets (e.g. Agnor Hamilton 2006 ; Parker Kavelaars 2010 ). Binary asteroid dissociation may be able to explain the properties of this pair of ETNOs, but only if there is a massive perturber well beyond Pluto. In order to test the viability of this hypothesis, we have performed thousands of numerical experiments following the prescriptions discussed by de la Fuente Marcos etal. ( 2016 ) and aimed at finding the most probable orbital properties of a putative perturber able to tilt the orbital plane of the pair 4746402013 RF 98 from an initial angular separation close to zero at dissociation to the current value of nearly 4. These simulations involve N -body integrations backwards in time under the influence of an unseen perturber with varying orbital and physical parameters (per numerical experiment). Our preliminary results indicate that a planet with mass, m , in the range 1020M moving in an eccentric (0.10.4) and inclined (2050) orbit with semimajor axis of 300600au, may be able to induce the observed tilt on a time-scale of 510Myr. Perturbers with m 10M or a 600au are unable to produce the desired effect. Fig. 4 illustrates the typical outcome of these calculations. A detailed account of these numerical experiments will be presented in an accompanying paper (de la Fuente Marcos, de la Fuente Marcos Aarseth, in preparation). The orbital parameters of this putative planet are somewhat consistent with those of the object discussed by Holman Payne ( 2016 ). Super-Earths may form at 125750au from the Sun (Kenyon Bromley 2015 , 2016 ). Our analysis favours a scenario in which 4746402013 RF 98 were once a binary asteroid that became unbound after a relatively recent gravitational encounter with a trans-Plutonian planet at hundreds of astronomical units from the Sun. An alternative explanation involving an asteroid break-up near aphelion, also after a close encounter with a planet, is possible but less probable because it requires an approach at closer range, i.e. 20 planetary radii versus 0.8au, for binary dissociation. Figure 4. Evolution of the angular separation between the orbital poles of the pair (474640) 2004 VN 112 2013 RF 98 for three representative test calculations with different perturbers. Red: a = 549au, e = 0.21, i = 47 and m = 16M . Blue: a
Paper: 2018A&A...615A.137S (fulltext)
with metallicity as [O/Fe], or other [ α /Fe] ratios do. Relevant discussion in previous comparison studies should therefore be revisited. In particular, Cooke et al. (2015) showed [O/Fe] as a function of [Fe/H] for [Fe/H] ≲−2, and [S/Zn] as a function of [Zn/H] for [Fe/H] ≳−2, assuming that both abundance ratios were tracing [ α /Fe] versus [Fe/H] in DLAs. By doing this, they observed a ‘knee’ in these abundance ratios where they started to decrease at [Fe/H] ≈−2, analogous to that observed in [ α /Fe] as a function of [Fe/H] in dwarf galaxies. However, it is clear from Fig. 8 that this assumption does not hold (see also Sect. 4 ). Therefore, although a ‘knee’ in [ α /Fe] abundance ratios in DLAs cannot be excluded at this point, further confirmation is needed, preferably using the same probe throughout the entire metallicity range. Fig. 8 Schematic view of the mean trends of [O/Fe] (magenta solid lines) and [S/Zn] (green dashed lines) in the Sculptor dSph as a function of [Fe/H] ( top ) and [Zn/H] ( bottom ). References: [O/Fe]: Hill et al. in prep. [S/Zn]: Skúladóttir et al. (2015a , 2017 ). 6 Conclusions For the first time we have made a detailed chemical comparison between DLAs and stars in dwarf galaxies by using the volatile elements S and Zn without the help of any other element as proxy. This was made possible by recent measurements of S and Zn abundances in around 80 RGB stars in the Sculptor dwarf spheroidal galaxy ( Skúladóttir et al. 2015a , 2017 ). Most of the stars in this galaxy formed >10 Gyr ago, which correspond to the redshifts at which the majority of DLAs are observed (i.e., z ≳ 1.8). Our analysis of the Sculptor data shows that S abundances behave as expected of an α -element, primarily created by core-collapse SN. However, there is also a non-negligible contribution to S from the yields of type Ia SN. This makes [S/H] a reasonabletracer of the metallicity evolution in galaxies. In low-metallicity stars, S measurements are challenging, so when not available, other α -elements can be used as proxy, preferably: Ca, Ti or the average of the two (see Appendix A ). The lighter α -elements, O and Mg, should not be used as proxies for S since, unlike S, they have negligible contribution from type Ia SN. In light of the available measurements in the Milky Way and its satellite dwarf galaxies, it is clear that [Zn/Fe] is neither the same at a given metallicity in all galaxies probed here, nor constant in a given galaxy throughout its entire evolutionaryhistory. The nucleosynthetic channels that lead to the formation of Zn remain unclear, but the following statement isin agreement with both theory and observations: Type Ia SN are not significant producers of Zn. The formation channels of Zn are therefore not the same as Fe, and the [Zn/Fe] ratio is highly dependent on the star formation history of each system. Thus, using [Zn/H] as a proxy for [Fe/H] is fraught with potential pitfalls. Furthermore, [S/Zn] should not be used as a
Paper: 2017ApJ...847....8L (fulltext)
2013 ), ∼50 days in OJ 287 (Pihajoki et al. 2013 ); on an LTV timescale, QPOs of ∼12 yr, and ∼400 days in OJ 287 (Sillanpää et al. 1988 ; Valtonen et al. 2008 ; Bhatta et al. 2016 ), ∼3 yr in S5 0716+714 (Raiteri et al. 2003 ), ∼8 yr in BL Lacertae (Villata et al. 2004 ), ∼585 days in PKS B0048–097 (Kadler et al. 2006 ), ∼0.92 yr in PKS 1510–089 (Xie et al. 2008 ), 5–6 yr in AO 0235+164 (Raiteri et al. 2008 ), ∼420 days in 1ES 2321+419 (Rani et al. 2009 ), ∼6.3 yr in GB6 J1058+5628 (Nesci 2010 ), ∼10 and ∼6 yr in NRAO 530 (An et al. 2013 ), ∼315 days in PKS 2155–304 (Sandrinelli et al. 2014 ), 2.18 ± 0.08 yr in PG 1553+113 (Ackermann et al. 2015 ), and 5.2 yr in PG 1302–102 (Graham et al. 2015 ). However, the physical mechanism causing these possible periodicities is still unclear and requires further research . The physical mechanism of the quasi-periodic behavior in blazars is still in general controversial and not understood well. Several models have been proposed to interpret this behavior, although a few evidences for QPOs were detected. However, many in the scientific community are deeply skeptical of QPO claims where very few cycles are observed and/or one fails to account for random red noise variability (e.g., Tagliaferri et al. 1996 ; Benlloch et al. 2001 ; Vaughan 2005 ; Vaughan Uttley 2006 ; Espaillat et al. 2008 ; Gaur et al. 2010 ; Press 2013 ). Recently, Vaughan et al. ( 2016 ) reexamined the detection of periodic modulations of 5.2 yr in PG 1302−102 reported by Graham et al. ( 2015 ) and found that a simple stochastic process is preferred over a sinusoidal variation. They also emphasized that such a red noise stochastic process can occasionally mimic the sinusoid-like periodicity in a few-cycle light curve while the periodic candidate can be distinguished from a stochastic process by observing it for ∼5 cycles. Furthermore, we also note that many of the QPO claims mentioned above suffer from incomplete statistical analysis, e.g., periodicity claims in Sandrinelli et al. ( 2014 ), Ackermann et al. ( 2015 ), and Bhatta et al. ( 2016 ) only persist for a small number of cycles ; the baseline PSD in Ackermann et al. ( 2015 ) was difficult to measure and their light curve can be explained by a pure red noise process; Valtonen et al. ( 2008 ) and Wang Su ( 2016 ) failed to properly account for the random red noise variations; and Sandrinelli et al. ( 2014 ) seem to discuss only single-trial confidence limits instead of global significance limits. In our QPO analysis, the temporary sinusoid-like trends visually appearing for a few periods in 2012–2014 might cause a certain test of LSP and PDM analyses to spuriously indicate a QPO detection, and the stochastic red noise variability might dominate the total variance of the light curve and lead to the emergence of spurious peaks/dips appearing in the DACF. Although the significance of the candidate QPO is relatively low when accounting for the red noise processes, some discussions on
Paper: 2021ApJ...909...81R (fulltext)
so that we could compare the value of the dimensionless growth rate directly to the predicted value. However, there does not appear to be an accurate way to measure this directly from the simulation data. Future simulations that insert a growing mode with a single, well-defined value for k would be desirable. In the dynamic simulations we present here, it is likely that there is significant power at a range of wavenumbers. In this case, we can expect growth to be slower initially, until sufficient power is in the fastest-growing modes. For the mode to fit in the disk (recall that we cannot bend the disk on scales ≲ H ), we must have Hk ≲ 1 (Ogilvie 2000 ), so we can expect the fastest-growing modes to appear with Hk less than, but not much less than, unity. However, to be consistent, we report the values of that can be measured from the simulation data. For simulation A with α = 0.03, H / R = 0.02, and θ = 30°, we find that the warp amplitude in the inner disk regions rapidly grows to greater than the critical value for R ≲ 15 R g (which for this viscosity is ψ c ≈ 0.5). This inner region breaks into multiple rings, starting from the inner parts and ending at ≈ 30 R g . Outside of this region, ψ remains below the critical value at all times, and no breaks are observed in this region. For this simulation, we analyze two clear breaks. The first break occurs between R ≈ 12–18 R g , with a peak in the warp amplitude at R p ≈ 15 R g while the warp amplitude is growing from ψ 0 = 2.5 to ψ 1 = 4. The second break forms an initial peak in the warp amplitude at R ≈ 20 R g , and by the time the peak warp amplitude is ≈ 2, the peak has settled at a radius of R p ≈ 27 R g . For the first break, we measure Δ t ≈ 1500 (in units of GM / c 3 ), and with the peak at R p ≈ 15 R g we have Ω ≈ 0.02. This yields χ ≈ 0.015. For the second peak, we have Δ t ≈ 4000, R p ≈ 27 R g , Ω ≈ 8 × 10 −3 , and therefore χ ≈ 0.015. This means that, when scaled to the local dynamical time, the growth rate was approximately the same in both cases, as expected. In this simulation, we expect the dimensionless growth rate to be in the range of 0.2–0.5 (see Figures 2 and 3 ). If , this would imply k −1 ≈ (3–5) H , which seems reasonable, but we also note that it is likely that the total level of viscosity in the simulation is slightly higher than α = 0.03 (see discussion in Section 2 ) and thus we would expect the growth rate to be reduced somewhat (i.e., if α ≈ 0.05, then the predicted dimensionless growth rate implies k −1 ≈ (2–3) H ). For simulation B, with α = 0.03, H / R = 0.02, and θ = 60°, we analyze two breaks in the disk, with the first occurring at R p ≈ 24 R g and the second beginning at R ≈ 30 R g and settling into a strong peak at R p ≈ 40 R g . For the first break, we measure Δ t ≈ 1650, and with the peak at R p ≈ 24 R g we have Ω ≈ 0.01. This yields χ ≈ 0.03. For the second peak, we have Δ t ≈ 5400, R p ≈ 40 R g , Ω ≈ 4 × 10 −3 , and therefore χ ≈ 0.02. As before, when
Paper: 2017A&A...598A..61B (fulltext)
resolved temperature profiles up to . The fifth cluster, PLCK G266.627.3, had already been observed to similar depth with Chandra ( ks, proposal , PI P. Mazzotta). In addition to our deep XMM-Newton data, the four SPT clusters had previously been observed as part of the Chandra X-Ray Visionary Project proposal (P.I. B. Benson). Exposure times for these observations (typically 80 ks), were tuned so as to obtain counts per cluster to allow for global properties to be measured ( McDonald et al. 2014 ). The original ks snapshot observation of PLCK G266.627.3, obtained as part of the Planck - XMM-Newton validation programme, is described in Planck Collaboration XXVI (2011) . Observation details are given in Table 1 . All the XMM-Newton observations were taken using the European Photon Imaging Camera (EPIC), combining the data taken with the MOS, MOS, and PN cameras. Chandra observations were taken using the Advanced CCD imaging spectrometer (ACIS, Garmire et al. 2003 ). XMM-Newton and Chandra images of all five objects are shown in Appendix A . Table 2 Global properties. Notes. (a) Spectroscopic temperature measured in the region. (b) is the product of and ( Kravtsov et al. 2006 ). (d) The gas mass within , derived using the combined density profiles. (c,e) and are determined iteratively using the relation of Arnaud et al. (2010) , assuming self-similar evolution. 2.2. Data preparation We processed Chandra observations using the Chandra Interactive Analysis of Observations (CIAO, Fruscione et al. 2006 ) ver. 4.7 and the calibration database 3 ver. 4.6.5. The latest version of calibration files were applied following the prescriptions detailed in cxc.harvard.edu/ciao/ guides/acis_data.html? using the chandra_repro tool. To process the XMM-Newton datasets, we used the Science Analysis System 4 pipeline ver. and calibration files as available in December . New event files with the latest calibration applied were produced using the emchain and epchain tools. 2.3. Data filtering We reduced the contamination from high-energy particles using the Very Faint Mode status bit 5 and the keyword PATTERN for the Chandra and XMM-Newton datasets, respectively. In particular, we removed from the analysis all the events for which the keyword PATTERN is and for MOS, and PN cameras. To remove periods of anomalous count rate, i.e. flares, we followed the prescriptions described in Markevitch’s COOKBOOK 6 and Pratt et al. (2007) for Chandra and XMM-Newton , respectively. For both datasets, we extracted a light curve for each observation and removed from the analysis the time intervals where the count rate exceeds times the mean value. If there were multiple observations of the same objects, these were merged after the processing and cleaning procedures. We list in Table 1 the effective exposure times, after cleaning, for each instrument. Point sources were identified using the CIAO wavdetect tool ( Freeman et al. 2002 ) on and exposure-corrected images. For
Paper: 2018ApJ...860....4O (fulltext)
reasons for these two regimes goes far beyond the scope of this paper. Compared to ETGs, there is a higher dispersion and a less inclined slope for the LTG relation, which is more noticeable in the massive galaxy range, as was already pointed out by Georgiev et al. ( 2016 ). This indicates that the nuclei in LTGs are on average less massive at a fixed host galaxy mass than nuclei in ETGs. The weighted linear and polynomial least-squares fits are shown in Table 3 . Based on considerations that the mechanisms that are responsible for the build-up of the central massive objects are similar for nuclei and massive BHs, previous studies have discussed their possible evolutionary connection (e.g., Ferrarese et al. 2006 ; Neumayer Walcher 2012 ). For the purpose of comparison, we illustrate the black hole–galaxy mass relation from McConnell Ma ( 2013 ) with its 1 σ and 3 σ uncertainty limits. This relation was obtained from fitting the compilation of 35 ETGs with dynamical measurements of the bulge stellar mass, with mass range of 10 9 to 10 12 M ⊙ . Their sample is well populated for bulges more massive than 2 × 10 10 M ⊙ (see their Figure 3). Assuming that we can extrapolate to lower bulge masses, we see in Figure 10 that the scaling relation for BHs and their ETG hosts is similar to the relation between nuclei and their host galaxy mass down to 10 9 M ⊙ . For galaxies with lower masses, the nucleus-to-galaxy mass relation becomes flatter. Table 3 Scaling Relations Note. For the nucleus versus galaxy mass relation (), we set and . For the mass ratio ( η n ) relation as a function of galaxy mass (), we define y ≡ log( η n ) and . The mass ratio between nucleus and its host galaxy () as a function of galaxy mass is shown at the bottom panel of Figure 10 . We find a clear anticorrelation of η n versus over the entire galaxy mass range; that is, the lower the galaxy mass, the more significant becomes the nucleus. In the massive galaxy regime (), we note a large scatter in the relation, which is the main reason why previous studies assumed a constant equivalent luminosity ratio for their samples, like for instance the ACS Virgo nucleated galaxies with (Côté et al. 2006 ), for the ACS Fornax nucleated galaxies (Turner et al. 2012 ), and for LTGs (Georgiev et al. 2016 ). However, in the faint dwarf galaxy regime (), a clear and strong trend appears, reaching to η n ≃ 10% for a dwarf galaxy with a stellar mass of 10 7 M ⊙ . The four low-mass ETGs studied by Nguyen et al. ( 2017 , 2018 ) have η n values up to 1.7%, which are in agreement with the general trend. The extension of the η n versus anticorrelation toward the faint dwarf galaxy population appears to be similar for ETGs and LTGs. This, in turn, suggests that nuclei at the smallest masses are subject to localized processes that work on parsec scales within the galaxy core regions independent of galaxy type. 5.3. Comparison with Theoretical Predictions The two proposed formation scenarios for