adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...850..154S (fulltext)
( 2015 ). 2.1. Data Data for these studies were collected by reviewing published papers and searching both exoplanet.eu (Schneider et al. 2011 ) and exoplanets.org (Han et al. 2014 ). We start with the six planets in Table 2 of Schwartz Cowan ( 2015 ) that have thermal eclipses and phase amplitudes (at wavelengths longward of 0.8 μ m): HD 149026b, HD 189733b, HD 209458b, WASP-12b, WASP-18b, and WASP-43b. Then we add WASP-14b (Wong et al. 2015 ), HAT-P-7b, and WASP-19b (Wong et al. 2016 ) to our sample. We also incorporate new data from Zhou et al. ( 2015 ), Evans et al. ( 2015 ), and Line et al. ( 2016 ). We collect first-order phase offsets (Sections 2.2 and 2.2.1 ) from Knutson et al. ( 2009a , 2009b ) and Wong et al. ( 2015 , 2016 ). Knutson et al. ( 2009a ) concluded the offset they found was not statistically significant, so we use their largest uncertainty (72° ± 61°). Phase offsets through second order come from Cowan et al. ( 2012 ), Knutson et al. ( 2012 ), Maxted et al. ( 2013 ), Zellem et al. ( 2014 ), and Stevenson et al. ( 2014c , 2017 ). However, the second-order components in Zellem et al. ( 2014 ) were found to be unnecessary, and those at 4.5 micron in Cowan et al. ( 2012 ) are disputed, so we use neither in our fits. Also, Cowan et al. ( 2012 ) reported two sets of fit parameters for WASP-12b based on different models for detector systematics 7 7 Stevenson et al. ( 2014a ) fit eclipse depths for WASP-12b but not phase parameters, so we do not use their values in our analysis. —we use parameters from their preferred polynomial model but test the other scenario in Section 3.1 . 8 8 WASP-12 has binary companion stars that affect photometry of the system (Bechter et al. 2014 ). We use dilution factors from Stevenson et al. ( 2014b ) to correct observations of WASP-12b when appropriate. All nine planets in our study have non-zero offsets in at least one waveband. 2.2. Model We take an energy balance approach to interpreting thermal phase variations (e.g., Cowan Agol 2011b ): we compare the radiation going into and coming out from a planet to infer bulk energetics of that planet’s atmosphere. For our study, we use the energy balance model described in Section 3.1 of Schwartz Cowan ( 2015 ), which accounts for uncertainties in system parameters, as well as reflected light contamination, meridional heat transport, and other sources of uncertainty. Those authors treated stars as blackbodies; we estimate better stellar brightness temperatures for each observation by using BT-NextGen spectra (Allard et al. 2012 ). Then we use our compiled data to calculate the relative intensity of planets and their host stars at each observed wavelength. In Cowan Agol ( 2011b ) and Schwartz Cowan ( 2015 ), this nightside intensity ratio, ψ n ( λ ), is defined aswhere δ ecl is the eclipse depth, δ var is the peak-to-trough phase amplitude of the full light curve, and δ tr is the transit depth. This is exact only when an observation has no phase offset
Paper: 2019ApJ...881..163M (fulltext)
ALMA data (Decarli et al. 2017 ; Champagne et al. 2018 ). In Figure 9 we show the postage stamps of our follow-up observations. Figure 9. Source adjacent to the quasar J2211, detected solely in the dust-continuum emission, i.e., with no secure redshift measure. Top: postage stamps (20′′ × 20′′) of our follow-up observations; labels are as in Figure 3 . Bottom: spectral energy distribution of J2211c. We assume that the source is located at the same redshift of the quasar. We report our photometric measurements/limits and, for comparison, various templates of local galaxies and the best SED fit from MAGPHYS -high- z . The labels and templates are as in Figure 6 . J2211c SED results to be intermediate between the low- z ULIRG Arp220 and the starbursting galaxy M82 (see Section 3.1 ). On the basis of our follow-up observations, and considering the predicted density of millimeter-sources, we are not able to exclude that this source is a fore/background (see the text for details, and Champagne et al. 2018 ). In analogy with the companions discussed in the main body of the paper, we compare the SED of J2211c with those of local galaxies, and we fit our photometric data with MAGPHYS -high- z (see Figure 9 ). From the latter, we find that the SED of J2211c is better reproduced by a galaxy model in between Arp 220 and M82 (i.e., a powerful local ULIRG and a starburst), with M * ∼ 3 × 10 10 M ⊙ and SFR ∼ 130 M ⊙ yr −1 . We further measure the obscured/unobscured SFR ratio of J2211c, following the procedure used for PJ167c (see Section 3.2 ). The star formation rate is dominated by the obscured contribution (SFR UV ∼ 2 M ⊙ yr −1 and f obscured ∼ 0.99). We report all these estimates in Table 6 . The lack of a secure redshift confirmation prevents us from drawing further conclusions on the nature of this source, or from placing it in the context of previous observations. Appendix B Quasar Photometry In the framework of our study of companion galaxies, we also perform forced photometry at the position of the quasars in the Spitzer /IRAC and HST /WFC3 images (see Section 2 for methodology). In Table 7 we report the derived quasars’ photometry. In Figure 10 , we show the quasars SEDs. The fluxes measured in our follow-up data are consistent with those expected from the observed optical/NIR spectra, when available, and/or from a lower- z quasar template (Selsing et al. 2016 ) rescaled to match the observed J band magnitude. Figure 10. Spectral energy distribution of the quasars in our sample. The observed photometric measurements (filled points) are obtained from our new follow-up data and from the literature (see Table 7 ; the filter responses are reported in the lower right panel). We also show the available optical/NIR spectra (light gray; see also Mazzucchelli et al. 2017b ), and a lower-redshift composite template shifted at the redshift of the quasar (black solid line; Selsing et al. 2016 ). The location of the Ly α line is highlighted with a light blue
Paper: 2017A&A...598A..47A (fulltext)
for % of the total instantaneous flux, and % of the total maximum flux, in good agreement with the results of Lamb et al. (2013) . Here and in the next paragraph again, we assume that, the term “total instantaneous (maximum) flux” corresponds to the sum of instantaneous (maximum) fluxes in those events considered in the context. 8. Cancellation in Lamb et al. (2013) accounts for % of the total flux. If we leave out the fluxes contained in surviving features in semi and partial cancellation (as done in Lamb et al. 2013 ), we obtain % total instantaneous flux and % total maximum flux that corresponds to cancellation event, which is comparable to the results obtained by Lamb et al. (2013) . 4.5. Lifetimes of the features In Fig. 11 we plot histograms of lifetimes of the features, where the lifetime is determined as described in Sect. 3.3 . A correction factor was proposed by Danilovic et al. (2010) to avoid underestimating the lifetimes for the same observations, as the duration of the time-series is itself comparable to the lifetimes of longer-lived features. The suggested weight to be multiplied to each bin of the lifetime distribution is , where is the number of frames a feature lives, and is the total number of frames. We show lifetime histograms before (yellow-shaded) and after (gray-shaded) applying the correction factor. The plotted histograms are for the two extreme ratios of the areas of the children or parents, 10:1 and 2:1 respectively. Nearly % and % of the features for the 10:1 and 2:1 cases respectively, in the lifetime histograms live for just one time step. The more restrictive the area-ratio criterion restricting the death of features, the larger the number of long lived features, since a higher number of splitting and merging events are not counted as contributing to the birth or death of the larger feature (although for the smaller features they are involved in these events). Fig. 12 Lifetimes of features for an area ratio criterion 5:1 vs. maximum fluxes (panel a) ) and maximum, average magnetic field strength values from COG technique (panel b) ). Here maximum refers to the maximum value of the magnetic flux or the field strength, gained during the entire lifetime of the feature. In panel a) the range of plotted maximum flux of feature is limited to Mx to enhance the visibility of flux-lifetime relationship. The over-plotted red cross symbols represent the average value of the maximum flux of features at each lifetime (see main text for details). The maximum averaged over each lifetime bin are displayed in the figure and are represented by over-plotted red cross symbols. The mean and median lifetimes are and min ( and s) respectively for the 10:1 case and are and min ( and s) respectively for the 2:1 case. Similar lifetimes were also reported by Zhou et al. (2010) . These small mean and median lifetimes are clearly dominated by the large number of features that live for just one time step. Thus, the mean lifetimes we
Paper: 2019MNRAS.488L..80M (fulltext)
in Section 3 and the recipe given in González Delgado et al. ( 2016 ). In a nutshell, for any given galaxy we compute the spaxel-to-spaxel mass of stars younger than 32 Myr. Then, by co-adding these masses over a given aperture we can estimate the average rate of star formation during this period. An extensive discussion about the optimal limiting age of the stellar population used to define the recent SFR is given in González Delgado et al. ( 2016 ). We tested the robustness of this assumption by repeating Fig. 1 using a limit of both 100 and 200 Myr. We find that the results are qualitatively in agreement. Our stellar population approach to compute the SFR is different from the usual H α luminosity proxy used in the literature (Kennicutt 1998 ; Catalán-Torrecilla et al. 2015 ). Nevertheless, it is most powerful for ETGs since it does not depend on the detection of emission lines. Fig. 1 also shows the SFR versus M ⋆ relation for our comparison sample of ellipticals (see Section 2 ) and for our ETGs without considering the bulge and disc components separately. It is worth noticing that, due to the optical coverage of the spectroscopic data, our SFR might be considered as upper limits for very low values of the SFR (see discussion in López Fernández et al. 2018 ; Bitsakis et al. 2019 ). This issue is more acute for our bulge SFR estimations, while it is almost negligible for the star-forming discs. However, this does not affect the main conclusions of this paper. Figure 1. Star formation rate (SFR) versus stellar mass ( M ⋆ ) distribution for our sample of ETGs. The measurements for bulges and discs are shown with red circles and blue stars, respectively. Black squares represent measurements of the same ETGs, but for the galaxy as a whole. Yellow triangles display the position of the sample of elliptical galaxies described in the text. The best fit to the SFMS from Elbaz et al. (Grey; 2007 ), Renzini Peng (Orange; 2015 ), González Delgado et al. (Navy; 2016 ), and Sánchez et al. (Green; 2019 ) are also shown for comparison. Downward arrows mark where the measured SFR is an upper limit (see text for details). It is clear from Fig. 1 that, whenever there is star formation in our ETGs, this is happening in the disc component and not in the bulge. We find that on average the specific SFR (sSFR) is smaller in bulges than in discs. The mean difference in logarithm accounts to at least 1.3 dex when integrated across the whole FoV of CALIFA. In addition, a qualitative comparison with literature values of the best fit to the SFMS at z ∼ 0 (Elbaz et al. 2007 ; Renzini Peng 2015 ; González Delgado et al. 2016 ; Sánchez et al. 2019 ) shows that several discs of our ETG sample are compatible (or over) with the SFMS, whereas bulges occupy the region of the diagram defined by retired galaxies (Crocker et al. 2011 ; Cano-Díaz et al. 2016 ; Sánchez et al. 2018 ), or even lower SFR values for a given stellar mass. In particular, we find that 30 discs and only
Paper: 2021A&A...650A..14L (fulltext)
and E05, PSP arrived at the perihelion of 27.8 R ⊙ (~0.13 AU) on January 29, 2020 and June 7, 2020, respectively. In Sect. 3.1 , we give an overview of the PSP measurements of solar wind density, speed, and energy flux for all available encounters including E01, E02, E04, and E05. We note that E03 observationsare not considered due to the lack of SPC observations near the perihelion. For each encounter, 12-day observations around the perihelion were used for calculations. The heliocentric distance for both E01 and E02 ranges from 35.66 to about 55 R ⊙ , and it ranges from 27.8 to about 57 R ⊙ for both E04 and E05. In Sect. 3.2 , we combine the observations from E01, E02, E04, and E05 to show the histogram distributions and the evolution of the energy flux as a function of heliocentric distance. 3.1 Overview of E01, E02, E04, and E05 Figure 1 shows an overview of the PSP measurements of solar wind density, speed, and energy flux during E01 (from October 31, 2018 00:00:00 to November 12, 2018 00:00:00 UTC). The top panel presents the electron number density ( n e ) obtained by the QTN method. In the second panel, the proton bulk speed is shown. The third and fourth panels present the solar wind energy flux ( W , from Eq. ( 2 )) and its value scaled to one solar radius (, from Eq. ( 6 )), respectively. In Fig. 1 , n α and n p were computed from n e based on n α ∕ n e = 2.5% for calculating W and . Most of the time, V p varies around 300 km s −1 , and varies around 70 W m −2 . The average values of W and are 0.045 and 77.3 W m −2 , respectively. The average value of of E01 is consistent with the long-term observations from Le Chat et al. (2012) (around 79 W m −2 ). We note that does not vary much with V p when V p increases abruptly (i.e., from November 8 to 10, 2018). Figure 2 , which follows the same format as Fig. 1 , summarizes the PSP measurements of solar wind density, speed, and energy flux during E02 (from March 30, 2019 00:00:00 to April 11, 2019 00:00:00 UTC). We deduced n p and n α with the same method used for E01to calculate both W and . We note that n e shows two successive low plateaus near the perihelion of E02 (from April 3 to 8, 2019 UT), as shown in the first panel of Fig. 2 , whereas V p shows two high peaks. This is in agreement with the well-known anticorrelation between the solar wind speed and density (e.g., Richardson et al. 1996 ; Le Chat et al. 2012 ). Both and W also show two low plateaus near the perihelion of E02 (from April 3 to 8, 2019 UT), similar to the solar wind density. Elsewhere, V p remains around 300 km s −1 and varies around 70 W m −2 . The mean values of W and during E02 are 0.032 and 59.4 W m −2 , respectively. Similarly, Fig. 3 illustrates the PSP observations during E04 (from January 23, 2020 00:00:00 to February 4, 2020 00:00:00 UTC). We used n p and n α , which were deduced with the same method used for both E01 and E02, when calculating both W and . The second panel of Fig. 3 shows that V p varies
Paper: 2018MNRAS.476..804D (fulltext)
paper. A direct comparison with our kinematic estimate is also difficult because they do not specify at which radius the rotation velocity is measured. Our data however seem to rule out the possibility of a value of 150 km s −1 for the line-of-sight velocity: In such cases, the combined effect of rotation velocity, velocity dispersion, and instrumental smearing would have made the galaxy to spread its emission in channels with velocities well beyond those detected in our data cube (for example, in the first channel of Fig. 2 ). Livermore et al. ( 2015 ) included the data set from Yuan et al. ( 2011 ) in a broader study on the kinematics of a sample of lensed galaxies. Similarly to Yuan et al., they fitted the delensed 2D kinematic velocity map with an arctangent model of the rotation curve (Courteau 1997 ) without any correction for beam smearing and found a rotation velocity V 2.2 = 59 ± 3 km s −1 at R 2.2 = 1.32 kpc for an inclination i = 45°, where R 2.2 is the radius at 2.2 times the scalelength of an exponential disc. Although MUSE data have lower spatial resolution than OSIRIS data, our rotation curve seems to exclude such a low rotation velocity at 1.32 kpc (Figs 4 and 5 ), even after correcting for the different inclination ( V 2.2 ≃ 73 km s −1 for i = 35°). Since Livermore et al. do not report their asymptotic velocity nor turnover radius of the best-fitting arctangent function, we cannot compare the values of the flat part of the rotation curve. To constrain the velocity dispersion, Livermore et al. fitted an exponential model to the velocity dispersion map, which they derived applying a correction for beam smearing and spectral broadening. They quote a luminosity-weighted average of σ = 50 ± 10 km s −1 . This value is however strongly affected by the two inner points (see their Fig. 2 ), which appear to be dominated by residual beam smearing. Finally, we stress that the Livermore et al.'s kinematic model does not satisfactorily reproduce their data (see the residual velocity field in their fig. 1). This is likely due to a combination of the low S/N AO data, some beam smearing, and the uncertainties related to the lensing modelling. Mason et al. ( 2017 ) made use of the 3D fitting code galpak 3D (Bouché et al. 2015 ) on KMOS –λ5007 Å data. Like B arolo , this code fits 3D models to data cubes and properly accounts for beam smearing. The main difference between this code and B arolo is that, in galpak 3D , the rotation curve follows a pre-selected functional form and the velocity dispersion is always assumed to be constant across the disc. Mason et al. have observations of both M1 and M3 images. They could not fully model the M3 image because it is unresolved. M1 image is poorly detected with S/N ∼ 2 in their data, and the KMOS FOV is not sufficiently large to include the entire galaxy disc. They modelled the M1 image without any reconstruction on the source plane and found an asymptotic rotation velocity of 227 ± 32 km s −1 , an
Paper: 2020MNRAS.495.4496M (fulltext)
half of the mass that has become unbound during the preceding orbit. The simulations used by Fujii et al. ( 2006 ) followed the orbital evolution of a self-consistent satellite galaxy (without dark matter). As they point out in their paper, since dark matter subhaloes are more extended than satellite galaxies, they typically experience more mass loss and thus should be susceptible to even stronger self-friction. In this paper we use idealized numerical experiments to study the nature of self-friction acting on dark matter substructure. Our main goal is to characterize the relative contribution of self-friction to the total drag force experienced by subhaloes as a function of orbital properties and the mass ratio between the sub- and host halo. The rest of the paper is organized as follows: in Section 2 , we discuss our numerical methods and simulations. Our results are presented in Section 3 and then discussed and summarized in Section 4 . Throughout, we adopt a Hubble parameter , which corresponds to a Hubble time of 13.97 Gyr for a flat universe with Ω Λ = 0.73. 2 METHODS To investigate the effects of self-friction, we perform numerical simulations of an N -body subhalo orbiting within an analytical host halo potential. We provide a brief description of the methods here and refer the reader to van den Bosch Ogiya ( 2018 , hereafter BO18 ) for full details on the numerical simulations and analysis methods. The host- and subhaloes are both assumed to initially be spherical NFW profiles (Navarro, Frenk White 1996 ) with concentrations c host and c sub , respectively. The host halo is represented by a static analytical potential. Throughout this paper, R and r are used to indicate radii with respect to the centre of the host halo and subhalo, respectively. The virial masses and radii of the two systems 2 are indicated by M host and R host , in the case of the host halo, and M sub and r sub in the case of the subhalo. Note that for the latter, these relate to the initial, unperturbed subhalo, prior to being introduced to the tidal field of the host halo. Positions and velocities of particles in the subhalo are initialized assuming an isotropic distribution function, f ( E ), using the method of Widrow ( 2000 ). In order to roughly account for the tidal mass loss that the subhalo would have experienced when approaching its initial position from an infinite distance, the initial distribution of particles is truncated at the instantaneous tidal radius of the subhalo (King 1962 ), given by Here, R is the (initial) distance from the centre of the subhalo to the centre of the host halo, m ( r tidal ) is the mass of the subhalo within r tidal , M ( R ) is the mass of the host halo within R , and Ω is the (initial) angular speed of the subhalo (Taylor Babul 2001 ; Zentner Bullock 2003 ; Peñarrubia Benson 2005 ; van den Bosch 2017 ). Note that we do not account for this truncation in the distribution function, which instead is computed under the
Paper: 2020ApJ...905...93H (fulltext)
criterion, and a maximum of five features, as this set of hyperparameters performed best for V19 (see also Appendix D ). After training the random forest on the oversampled training set, we apply it to the 10 sets of features (from the 10 random posterior draws) for each transient in the test set to get 10 sets of classification probabilities. We then average the 10 sets of classification probabilities for each transient and adopt the classification with the highest probability. 4. Results and Validation Applying our classification pipeline to the test set described in Section 2 yields 1257 photometrically classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. 26 26 To exactly reproduce our results, a seed of 0 must be used in the pseudorandom number generators during feature extraction, oversampling, and classification. Table 3 gives the full list of classifications, and Figure 5 shows a sample of photometrically classified light curves. These are among the largest samples of each of these classes of SNe in the literature from a single survey. In the remainder of this section, we assess the performance of our algorithm in general and discuss how to use these classifications in practice. Figure 5. Sample observed (circles) and model (lines) light curves of photometrically classified SNe. Downward-pointing arrows indicate 3 σ upper limits from nondetections. The left column shows the highest confidence classification in each class, and the right column shows the median-confidence classification. Notice that in three of the lower confidence classifications, the light curve is poorly sampled on either the rise or the decline because it was discovered close to the boundaries of the observing season. Table 3 Classification Results (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. 4.1. Cross-validation We first validate our classifier using leave-one-out cross-validation with the SNe in our training set. For each iteration of the cross-validation, we retrain the classifier on all but one of the SNe in our training set (still using the median parameters) and then use it to classify 10 sets of features derived from the posterior parameter distributions for that SN. We then average the 10 sets of probabilities to determine the cross-validation classifications listed in Table 4 . Figure 6 shows the resulting confusion matrices. Figure 6. Confusion matrices of our validation results. Each cell lists and is colored by the fraction of each class, with the raw number in parentheses. The top matrix aggregates by true label, so its diagonal shows completeness. The middle matrix aggregates by predicted label, so its diagonal shows purity. The bottom matrices are the same results but with all four non-SN Ia classes grouped together as CCSNe. N is the total size of the training set, A is the