adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJS..247...63J (fulltext)
order of 100,000 white dwarfs with stellar mass companions, meaning that such systems are far from extraordinary. Furthermore, many WDMS systems will be post-common-envelope systems (perhaps still even being surrounded by the remnant common envelope in the form of a planetary nebula; Jones Boffin 2017 ; Boffin Jones 2019 ), which may lead to increased local extinction as a result of dust formation in the ejected envelope (Lü et al. 2013 ). As such, they are rather appropriate systems to constrain the importance of extinction in deriving their stellar parameters. An important caveat that must be noted is that many of these WDMS systems could also exhibit significant levels of irradiation—a problematic effect when it comes to the treatment of extinction as irradiation alters the emergent stellar spectrum, while here we only consider non-irradiated atmosphere models (see the discussion of Horvat et al. 2019 for more details). 10 10 As the discussion here is considered only illustrative, we fix the bolometric albedos and bolometric limb-darkening coefficients of both components to standard values. As a starting point, let us take one of the best constrained eclipsing post-common-envelope binaries known: SDSS J235524.29+044855.7 (hereinafter referred to as SDSS J2355; Rebassa-Mansergas et al. 2019 ). SDSS J2355 is a short-period post-common-envelope binary comprising a relatively cool white dwarf ( K) and a low-mass, metal-poor, subdwarf star (spectral type ∼sdK7). As before, calculating synthetic light curves for the system with no extinction and then with extinction consistent with the Galactic bulge, we now see significant deviations between the two models in u , g, and r bands (See Figure 4 ). The u band shows the strongest difference at more than 0.25 mag during primary eclipse, closely followed by the g band (at 0.13 mag) and the r band (at 0.02 mag, approximately 4 σ ). Even in the i band, the deviation is almost 0.01 mag—representing a more than 1 σ difference. The deviation during secondary eclipse would not be detected in any band given that the eclipse is not complete and the primary is far more luminous. Figure 4. Synthetic LSST light curves of the white-dwarf–subdwarf binary SDSS J2355 (Rebassa-Mansergas et al. 2019 ), calculated without (dashed line) and with extinction (solid line) corresponding approximately to the average extinction of the Galactic bulge. The residuals between extincted and unextincted cases are shown below each light curve, highlighting that the difference would be detectable at several σ by LSST in u , g , and r bands and marginally detected in the i band (in each residual plot, the shaded region represents ). 4. PHOENIX Atmospheres To increase the fidelity of computed observables on the low-temperature end, PHOEBE 2.2 now incorporates PHOENIX model atmospheres (Hauschildt et al. 1997 ) as computed by Husser et al. ( 2013 ). PHOENIX is one of the leading alternatives to Castelli Kurucz that incorporates 3D
Paper: 2016MNRAS.460.4098P (fulltext)
et al. 2011 ), was required to have an overall completeness (the fraction of spectroscopic targets that were observed) over 70 per cent and a redshift completeness (the fraction of observed galaxies with good spectra) over 80 per cent. We use these galaxies to construct an overdensity map , where i denotes the pixel on the sky. n i = ∑ j ∈ pixel i w j , where w j is the systematic weight (Anderson et al. 2014 ) of galaxy j . The map is given a healp ix pixelization with N side = 1024. Note that we do not weigh the pixels by their observed area because the healp ix pixels are much smaller than the observed sectors for which the completeness is computed, and we did not want to introduce extra power due to possible errors in the completeness on small scales. We also confirm (see Section 7 ) that including pixel weights have only a small effect on the final result. 4 ANGULAR POWER SPECTRA 4.1 Theory We model the theoretical galaxy-CMB lensing convergence angular cross-power spectrum and the galaxy clustering angular auto-power spectrum using standard methods. We assume ΛCDM with parameters consistent with Planck 2013 (Planck Collaboration XVI et al. 2014a ) and BOSS Data Release 11 (Anderson et al. 2014 ). We use these models to estimate statistical errors from mocks and systematic corrections to E G (see Section 5 ). However, our measurement of E G along with errors from jackknife resampling, which we use in our final result, does not use our power spectrum models and is independent of ΛCDM. In addition, the corrections we determine from these models are well within statistical error bars. Using the Limber approximation for small scales (l ≳ 10) and assuming the ΛCDM model, the galaxy-CMB lensing convergence angular cross-power spectrum can be written as where f g ( z ) is the galaxy redshift distribution, W ( z ) = χ(1 − χ( z )/χ CMB ) is the CMB lensing kernel, χ( z ) (χ CMB ) is the comoving distance out to redshift z (the CMB surface-of-last-scattering redshift z CMB = 1100), and P mg ( k, z ) is the matter–galaxy 3D cross-power spectrum as a function of z and wavenumber k (Hirata et al. 2004 ). The cosmological parameters present are the Hubble parameter today H 0 and the current matter density parameter Ω m, 0 . The galaxy redshift distribution for CMASS is shown in fig. 1 of Anderson et al. ( 2014 ). The galaxy clustering angular auto-power spectrum can be written as where H ( z ) is the Hubble parameter at redshift z and P gg ( k, z ) is the galaxy 3D auto-power spectrum. Figure 1. An equatorial map of the CMASS survey divided into 37 regions used for jackknife resampling. 4.2 Mock galaxy catalogues from <italic>N</italic>-body simulations We compute the power spectra P mg ( k, z ) and P gg ( k, z ) using N -body simulations in order to model both non-linearities and the occupation of haloes with galaxies. The N -body simulation runs using the TreePM method (Bagla 2002 ; White, Hernquist Springel 2002 ; Reid et al. 2014 ). We use 10
Paper: 2018A&A...615A.164V (fulltext)
of the bolometric luminosity are presented in Fig. 10 . The bulk of the NIR emission is produced close to the inner edge of the thin disk, that is, the sublimation radius, consistent with observations (continuum reverberation mapping: e.g., Suganuma et al. 2006 ; interferometry: e.g., Kishimoto et al. 2011 ). Without a wind component, the model NIR luminosities are about 50% higher than those of the model with a wind component for type 1 objects, because the wind provides a nonnegligible NIR extinction. As expected, the NIR luminosities decrease significantly once the inner thin gas disk is hidden by the thick gas disk. We observe a less significant drop of the NIR luminosities for high inclination angles ( i >65°) in the presence of a wind. We interpret the additional NIR emission as the contribution of the unobscured basis of the wind to the NIR emission. Whereas the ratio between the NIR and bolometric luminosity is about 1∕10 for the basic wind model, it increases by a factor of ~1.5 and ~2 when the puff-up is located at a and 2 times smaller distance from the central black hole ( Fig. 11 ). The increase of the NIR luminosity due to higher dust temperatures is stronger than the decrease due to the smaller area where hot dust can be found. Fig. 10 Model NIR luminosities as a function of the bolometric luminosity. Blue represents type 1, red represents type 2. The Circinus model is shown with crosses and pluses; the NGC 1068 model with squares and triangles. The inclination angles of type 2 objects are indicated: i = 50° (green line), i = 70° (orange line), i = 90° (yellow line). Left panel : wind with 1∕ z density profile. Right panel : no wind component. The dotted lines correspond to L NIR = ξ L bol with ξ = 1, 1∕3, and 1∕10. Fig. 11 Model NIR luminosities as a function of the bolometric luminosity for the 1∕ z winds for a ( left panel ) and 2 ( right panel ) times smaller inner radius of the thin gas disk. The dotted lines correspond to L NIR = ξ L bol with ξ = 1, 1∕3, and 1∕10. The NIR luminosities as a function of the MIR luminosities are presented in Fig. 12 . As for the previous correlations, the differences between the 1∕ z and 1∕ z 2 models are minor. Whereas type 1 objects show NIR/MIR ratios between 0.5 and 1, the NIR luminosities of type 2 models are more than five times smaller than the MIR luminosities. For luminosities smaller than 10 44 erg s −1 and inclination angles between 50° and 60° the MIR/NIR luminosity ratio is about ten. The model with an inner radius located at 2 × r sub (right panel of Fig. 13 ) reproduces the observations of Burtscher et al. (2015) best. Fig. 12 Model NIR luminosities as a function of the MIR luminosity. Blue represents type 1, red represents type 2. The Circinus model is shown with crosses and pluses; the NGC 1068 model with squares and triangles. The inclination angles of type 2 objects are indicated: i = 50° (green line), i = 70°(orange line), i = 90° (yellow line). Left panel : wind with 1∕ z
Paper: 2018A&A...615A...7L (fulltext)
however, the fraction of dust in the clumpy component ( F ) does evolve strongly with redshift, similar to the gas-mass fraction. Fig. 8 Evolution of massive galaxy properties from their local values out to z ~ 1. The evolution of the dust-mass fraction ( M d ∕ M * ) was determined from a power-law fit to data from Santini et al. (2014) , Béthermin et al. (2015) (for z ≤ 1), and Rémy-Ruyer et al. (2014) for z = 0. The upper bound to the M d ∕ M * evolution is given by assuming M d ~ 0.5 × Z × M g , and the evolution of gas and metallicity over this redshift and stellar mass range. The evolution of specific SFR is shaded purple between the relationship in Sargent et al. (2014) and . The evolution of the molecular gas fraction (blue) is from Tacconi et al. (2018) . We find that the fraction ( F ) of dust in clumps surrounding nascent star-forming regions has increased by a factor of ~5.5, and that the overall attenuation of UV (SFR MS /SFR UV ) has increased by a factor of ~2.5 (ranging from 3.4 to 1.9 for face-on to edge-on galaxies, respectively). da Cunha et al. (2010) , Smith et al. (2012) , Sandstrom et al. (2013) , and Rowlands et al. (2014) found that, in the local Universe, M d ∕ M * is proportional to sSFR. Studies such as Sargent et al. (2012) and Tasca et al. (2015) , that look at how sSFR changes with redshift, find that the sSFR varies as sSFR out to redshifts z 2. This would give a sSFR increase by a factor of ~3.6 between z = 0.07 and z = 0.7. We note, however, that this trend also is stellar mass dependent (e.g. Schreiber et al. 2015 ), and that lower-mass galaxies have a shallower evolution in sSFR than the most massive galaxies. Recent studies of molecular gas at high redshifts indicate that the cosmic density of molecular gas, similar to the cosmic density of star formation, peaks between redshifts 2 and 3 ( Decarli et al. 2016 ). Genzel et al. (2015) , Sargent et al. (2014) , and Tacconi et al. (2018) used compilations of studies that measure gas mass via CO line luminosities and found that M g ∕ M * varies like , which corresponds to an increase by a factor of ~3.5 between z = 0.07 and z = 0.7. On the other hand, Bauermeister et al. (2013) expect which would imply an increase in the gas mass fraction by a factor of ~4.4. In any case, the gas mass is expected to increase by a factor of ~4 between our two redshift slices. In Fig. 7 , we show with two solid lines the relations SFR UV /SFR and . The parameter that most strongly controls the dust-to-gas mass is the metallicity (e.g. Leroy et al. 2011 ; Accurso et al. 2017 ). According to the parametrization of Zahid et al. (2014) , a galaxy with log ( M * ∕ M ⊙ ) = 10.2 would have a metallicity of 0.069 dex less at z = 0.7 than at z = 0.07. A galaxy at M * ~ 10 10.5 M ⊙ , on the other hand, will have a change in metallicity of 0.035 dex between the same two redshifts. For our sample, the dust to gas ratio should not change significantly between z ~ 0 and z ~ 0.7, however, we
Paper: 2016ApJS..224...32H (fulltext)
that we also tested the Cartesian grid initial distribution, similar to Hopkins ( 2015 ). The main mechanism for the decay of the disk is the same. In this paper, therefore, we do not show the results for the Cartesian grid case. In order to carry out a quantitative comparison of the results, we introduce a rough estimate of the disk lifetime. Let us consider the root mean square of the radius of the particles initially located at the inner edge of the disk. When this value exceeds 10% of the radius of the inner edge, we regard the disk as disrupted. Figures 1 and 2 show the lifetime and snapshots of the Keplerian disk for each AV implementation. The differences among the lifetimes of the disks obtained with different implementations are very large. In the case of vNRL-B95-CD10-G+12, the disk survived for a time more than 10 times longer than with the standard method used today (M97-B95-R+00-SPH). Note that the longest case, vNRL-B95-CD10-G+12′, indicates the results with the vNRL-B95-CD10-G+12 scheme, but with and . These parameters in the AV switch would also play important role for the long-time evolution of the disk. Figure 1. Lifetime of the disk for each AV implementation in units of Kepler time at r = 0.5 (inner edge of the disk). Note that the disk lifetime is shown in the log scale. Figure 2. Snapshots from the Keplerian disk test at ∼28 orbits at r = 0.5 (inner edge of the disk) with each AV implementation. The color-coding of the density is given at the bottom. Since no disk lifetime with M97 AV exceeds ∼28 orbits at r = 0.5, we only show the results of four cases. To clarify the effects of each option on the disk lifetime, we show the dependences of the disk lifetime for each option in Figure 3 . Here, we show how the lifetime of the disk changes when we change the method in one type, while keeping the methods for the other three types unchanged. We can clearly see the following tendencies. (i) The disk lifetime obtained with vNRL AV is much longer than that with M97 AV. (ii) Both of the two shear switches extend the disk lifetime. The CD10 switch works better than the B95 switch for M97 AV, while the B95 switch works better than the CD10 switch for the vNRL AV. (iii) Both shock indicators extend the disk lifetime. The CD10 shock indicator works better than R+00 does. (iv) The G+12 derivative operator works better than the SSPH derivative operator. This might be due to the fact that the SPH derivative operator omits the surface term (for details see Section 3.1 in Price 2008 ). Figure 3. The difference of the lifetime of the disk when (a) the AV scheme, (b) the shear switch, (c) the shock indicator, and (d) the derivative estimator are changed. Points connected by lines indicate the runs performed with the same method, except for that shown in panel. Figures 4 – 6 show the evolution of the inner part of the Keplerian disks for three representative runs (M97-No-No-SPH, M97-B95-R+00-SPH, and vNRL-B95-R+00-G+12). In Figures 7 – 9 ,
Paper: 2016MNRAS.456.4266L (acknowledgments)
This research was enabled in part by support provided by WestGrid ( www.westgrid.ca ) and Compute Canada Calcul Canada ( www.computecanada.ca ), and by the National Science Foundation under Grant No. 1066293 to the Aspen Center for Physics. AB, FD and LL acknowledge support from NSERC (Canada) through the Discovery Grant program. RD acknowledges support from the South African Research Chairs Initiative and from NASA grant NNX12AH86G. NSK and MAF acknowledge support from NATA ATP grant NNX10AJ95G and NSF grant AST-1009652, respectively, to the University of Massachusetts. TQ was partially supported by NSF award AST-1311956. We would like to thank Shuiyao Huang and Jared Gabor for helpful discussions, and RD, AB and FD would like to acknowledge the simulating collaborative atmosphere of the Aspen Center for Physics, where this work was completed. Finally, we would like to thank the anonymous referee for valuable comments that improved the clarity of the paper, as well as M. L. Balogh, J. M. Gabor and A. H. Gonzalez for providing us with access to detailed observational and simulation results in advance of publication.
Paper: 2019ApJ...884...93C (fulltext)
in the vicinity. Details of the array configurations are given in Table 2 . Observations were reduced following standard procedures in Miriad (Sault et al. 1995 ), with the flux density scale set by observations of calibrators PKS 1934−638 and/or PKS 0823−500. Initially, observations were made covering the nominal position of the 3FGL source, and the positions of radio sources cataloged by Schinzel et al. ( 2017 ) and candidate X-ray sources. Observations differed in length, hour-angle coverage, angular resolution, and sensitivity to extended radio emission in the vicinity, resulting in a heterogeneous data set. Schinzel et al. ( 2017 ), who observed with the ATCA in the relatively compact H168 and H214 array configurations, detected four sources within the 3 σ error region around 4FGL J1405.1−6119, all of which were brighter than 15 mJy, and are resolved out on longer ATCA baselines. Schinzel et al. ( 2017 ) also list a further 60 objects in the field detected outside the 3 σ region, which appear to be associated with the H ii region cataloged by Caswell Haynes ( 1987 ) at ( l , b ) = (311.627, +0.27). Observations of the candidate X-ray sources in more extended ATCA array configurations revealed the fainter (∼2 mJy), variable radio counterpart. The final observations of the series were conducted as targeted observations, with the phase calibrator PKS 1420−679 used throughout. 3.4. Near-infrared Observations and Analysis We observed the candidate near-IR counterpart to 4FGL J1405.1−6119 (2MASS J14051441−6118282, allWISE J140514.40−611827.7; Landi et al. 2017 ) with FLAMINGOS-2 (Eikenberry et al. 2004 ) on Gemini South on 2018 June 7 through Program ID GS-2018A-Q-412. We used the R3K grism and a 4 pixel slit, giving a resolution of R ∼ 1800 across the K -band. The target was observed with 10 separate 120 s exposures in a standard ABBA nod, giving 1200 s of exposure time total. We also observed the nearby A0V star HD 119942 as a telluric standard immediately before our science observations. The data were reduced using standard near-IR procedures, with optimal extraction of the spectra from subtracted, flat-fielded nodded pairs. The final combined spectrum has a signal-to-noise of about 140 per resolution element in the continuum. After interpolating over the intrinsic Br γ feature in the telluric standard, we corrected the 4FGL J1405.1−6119 spectrum for telluric absorption by scaling the telluric spectrum to minimize the residuals in the regions of strong telluric lines. 4. Results 4.1. Gamma-Ray Results The power spectra of the light curves of all 3033 3FGL and 5523 FL8Y sources were examined for indications of periodic modulation that could arise from previously unknown binary systems. For candidate new binaries our usual threshold for further investigation has been for a source to have a peak power ≥18× mean power level (FAP 5 × 10 −4 ) and for the period not to coincide with a known artifact. We also exclude very long periods that are
Paper: 2019A&A...621A..32M (acknowledgments)
A.M. and G.L. warmly thank Matthieu Tristram for his invaluable help in the use of Xpol. We acknowledge financial support from the “Programme National de Cosmologie and Galaxies” (PNCG) funded by CNRS/INSU-IN2P3-INP, CEA and CNES, France, from the ANR under the contract ANR-15-CE31-0017 and from the OCEVU Labex (ANR-11-LABX-0060) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the “Investissements d’Avenir” French government programme managed by the ANR. The research leading to these results has also received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 617656 “Theories and Models of the Dark Sector: Dark Matter, Dark Energy and Gravity”.
Paper: 2017ApJ...834...31A (fulltext)
the inverse time axis and divide the result by 2 to obtain an overall weighted summation versus time, . The maximum value of the obtained overall weighted summation yields the mirror time delay estimation , . Figure 2 plots 5, 8, and 11 illustrate the results from the mirror time delay estimator for , respectively. Among the three mirror time delay estimations, we choose (Figure 2 ; plot 5) since it follows the form of the basic definition in statistics and also shows high performance results (Section 3.5 ). We use the rest of the results for reliability testing of our time delay estimation, as we will describe later in this section. 5. Weighted average correlation, : To check the reliability of the estimated time delay, we will need a normalized maximum weighted summation (between −1 to +1). This can ensure that the best estimated time delay corresponds to a reasonable correlation between the two light curves. Dividing the maximum weighted summation of correlations by the sum of the corresponding weights yields weighted average correlations in the form ofand similarlyand also for the mirror estimator we havewhere the above equations are valid for , and the weighted average correlations have values in the range of . It is worth noting that using the weighted summation of correlations instead of the weighted average correlation enables us to consider the number of segments of data that contribute in the estimation of the best time delay. This gives more weights to time delays that are derived using more segments of the data, in comparison to time delays that are derived by considering only one or two segments of data. Therefore, we obtain the best time delay using the maximum weighted summation, while the weighted average correlation shows the reliability of the estimation. We see in our analysis that this approach can improve our analysis by minimizing the possibility of having any outliers in our results. 6. Reliability test: To check the reliability of the time delay estimation, we consider several criteria. First we follow Aghamousa Shafieloo ( 2015 ) in assigning and and () as an acceptance limit for correlation values. Next we check the consistency of each pair of , time delays by checking their sign. We expect the consistent pairs have opposite signs. In addition, we calculate the mean and standard deviations of the absolute values of three , time delay pairs and expect the reliable results to show a low discrepancy with days and . If any of the above conditions is not satisfied, we consider our method disqualified to attain a reliable time delay estimation for the case of study. Figure 2. Plots 1 and 2 show the weighted correlation coefficients and for seven data segments and smooth fit segments for the lensed quasar SDSS J1001+5027 light curve data, respectively. Plots 3, 6, and 9 represent the weighted summations, that is, , which are calculated from the weighted correlation coefficients shown in plot 1 using three different