adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.498.4721O (fulltext)
at PA = 259o as the distance to the source increases. Table 8. Parameters of the continuum and CH 3 CN line best-fitting model, which consists of an Ulrich envelope and a flared disc. Parameters in bold under the last column are the 10 free parameters that can be constrained by the observations. The inner radius depends on the stellar luminosity. a Defined at a height z = 10 4 au. b 2 models had the same best overall ranking value. The parameters of the best-fitting model during the visual inspection step are listed in Table 8 and the ranking and reduced χ 2 values for each observation are listed in Table 9 . The best-fitting model has a type A density distribution (Ulrich 1976 envelope and a flared disc). The envelope radius was well-fitted during this step and has a value roughly a factor of 1.4 smaller than the initial visual inspection model and has a similar size to the model with disc of Johnston et al. ( 2013 ). The disc scale height is smaller by roughly one order of magnitude than those in Johnston et al. ( 2013 ), but this parameter cannot be constrained by our observations, thus it was less explored. The shape of the cavity changed from the initial exponent of 1.5 to 2.0, and its reference density decreased an ∼15 per cent from its initial value and its density exponent did not change. Table 9. Overall ranking and reduced χ 2 values for the continuum best-fitting models of each type. a Average weighted ranking as described in Appendix A1.2 (online). Similarly, in Table 8 and 9 , we list the results for the overall best-fitting model after the grid-fitting step. The disc mass and radius increased with respect to the visual inspection best-fitting model. The former is roughly a third of the disc mass in the control model of Johnston et al. ( 2013 ), but the disc radii are similar. This difference is explained by the difference in the disc scale height. The cavity opening angle is equal to the one from the visual inspection best-fitting model, and the inclination angles differ by 15o. The luminosities are the same between the best-fitting models at each step, whilst the envelope infall rate is ∼1.8 times higher in the overall best-fitting model. The model SED agrees reasonably well with the observations as shown in Fig. 10 . The total mass of the model, M = 2.1 × 10 3 M ⊙ , is enough to fit the 10- μ m silicate feature relatively well at an inclination of 25o and closely fit the submm points. This is 900 M ⊙ more massive than the model with disc of Johnston et al. ( 2013 ), but 600 M ⊙ less massive than their envelope only model. Models with WD01 dust can also fit in the submm with lower masses, however, they underestimate the fluxes between 3 and 8 μ m by a factor of ∼10. The near-IR maps in Fig. 7(a)–(c) show that the near-IR band images are well reproduced. However, Fig. 8 shows that the small scales closer to the star position probed by the speckle observation are not well reproduced. In general, most models with a cavity
Paper: 2016ApJ...829L..20A (fulltext)
tens of solar masses (Abbott et al. 2016a ). Based solely on the gravitational-wave signals recorded by two LIGO detectors, the current hypothesis is that GW150914 was the result of a merger of two black holes with initial masses of and at a luminosity distance of Mpc. The Fermi Gamma-ray Burst Monitor ( Fermi -GBM) reported a possible weak gamma-ray transient source above 50 keV at 0.4 s after the GW150914 trigger (Connaughton et al. 2016 ). However, the upper limit provided by the INTEGRAL Advanced Camera for Survey instrument in a gamma-ray energy band similar to the Fermi -GBM energy band is not consistent with a possible gamma-ray counterpart of GW150914 suggested by the Fermi -GBM (Savchenko et al. 2016 ). No electromagnetic counterpart of GW150914 was found in radio, optical, near-infrared, X-ray, or high-energy gamma-rays (Abbott et al. 2016b ). GW151226 (LIGO-Virgo trigger ID: G211117) is the second gravitational-wave candidate identified by both LIGO Hanford Observatory and LIGO Livingston Observatory with a high significance (the false-alarm rate of less than one per 1000 years by the on-line search) at 3:38:53.647 UT on 2015 December 26 (Abbott et al. 2016c ). According to a Bayesian parameter estimation analysis, the event is very likely a binary black hole merger with initial black hole masses of and and a final black hole mass of (Abbott et al. 2016d ). The luminosity distance of the source is estimated as Mpc, which corresponds to a redshift of . As far as the electromagnetic counterpart search of GW151226 in the gamma-ray regime is concerned, Fermi -GBM (Racusin et al. 2016 ), Fermi Large Area Telescope (LAT; Racusin et al. 2016 ), High-Altitude Water Cherenkov Observatory (Wood the HAWC Collaboration 2016 ), and Astrosat -CZTI (Bhalerao et al. 2016 ) reported no detections around the GW trigger time. According to Racusin et al. ( 2016 ), the flux upper limit of Fermi -GBM is from 4.5 × 10 −7 to 9 × 10 −7 erg cm −2 s −1 in the 10–1000 keV band. The Fermi -LAT flux upper limit using the first orbit data after the LIGO trigger is from 2.6 × 10 −10 to 7.8 × 10 −9 erg cm −2 s −1 in the 0.1–1 GeV band. The CALorimetric Electron Telescope ( CALET ; Asaoka the CALET Collaboration 2015 ; Torii et al. 2015 ) mission, which was successfully launched and emplaced on the Japanese Experiment Module—Exposed Facility of the International Space Station (ISS) in 2015 August, was fully operational at the time of GW151226. CALET consists of two scientific instruments. The Calorimeter (CAL) is the main instrument, which is capable of observing high-energy electrons from ∼1 GeV to ∼20 TeV, protons, helium, and heavy nuclei from ∼10 GeV to 1000 TeV and gamma-rays from ∼1 GeV to ∼10 TeV. The field of view (FOV) of CAL is ∼45° from the zenith direction. Another instrument, CALET Gamma-ray Burst Monitor (CGBM; Yamaoka et al. 2013 ), is a gamma-ray burst (GRB) monitor using two different kind of scintillators (LaBr 3 (Ce) and BGO) to achieve a broad
Paper: 2019A&A...622L...3E (fulltext)
except when the wind speed at the orbital separation is so low compared to the orbital speed that the effective cross section given by the BHL formula is larger than the orbital separation and looses any meaning: relying on the Roche lobe instead makes much more sense in this regime. Fig. 3. Comparison of the mass-transfer rates we measure as a function of η (solid lines) to the BHL mass-accretion-rate formula (dashed lines) for a filling factor of 90% and mass ratios of 2 ( upper panel ) and 15 ( lower panel ). The BHL prescription leads to mass-accretion rates much lower than the mass-transfer rates we derive, sometimes by more than an order of magnitude. 3.2. Accretion luminosity Another insight revealed by this analysis is the identification of the configurations where ULXs cannot be the product of mass transfer via wind-RLOF and can be explained only if the star fills its Roche lobe. In Fig. 2 , we represented in black the regions where μ is inferior to 0.2%, the minimum fraction of stellar wind captured under which mass transfer without RLOF is not able to produce a stationary X-ray luminosity of 10 39 erg s −1 , even for a stellar mass-loss rate as high as 10 −4 M ⊙ yr −1 : Given the maximal μ we found at ∼20%, this analysis suggests that mass transfer via wind-RLOF in a ULX is only possible for stellar mass-loss rates above ∼10 −6 M ⊙ yr −1 . We note here that matching a mass-transfer rate Ṁ high enough to reach ULX levels is a necessary but not sufficient condition: the final X-ray luminosity is determined by the physics at stake within the Roche lobe of the accretor, where the flow geometry can be disk-like ( El Mellah et al. 2019 ) or spherical ( Shakura et al. 2012 ) and where, for an accreting NS, the magnetosphere eventually dominates the dynamics ( Bozzo et al. 2008 ). For accreting BHs, powerful disk outflows come into play in the vicinity of the compact object, lowering the final mass-accretion rate ( Pinto et al. 2016 ). Rather than covering the large scope of regimes possible, let us focus on three donor stars in one SgXBs and two ULXs. In the SgXBs Vela X-1, Gimenez-Garcia et al. (2016) reported a stellar mass-loss rate Ṁ ⋆ ∼ 6.3 × 10 −7 M ⊙ yr −1 for the B0.5 Ib supergiant donor star. In M101, Liu et al. (2013) inferred a mass-loss rate of 2 × 10 −5 M ⊙ yr −1 for the Wolf-Rayet donor. In P13, we think the stellar mass-loss rate of the B9 Ia supergiant donor star could have been underestimated: in Motch et al. (2014) , it is assumed that the donor star fills its Roche lobe on the basis that the wind mass-loss rate derived by Kudritzki et al. (1999) would be inferior to 10 −6 M ⊙ yr −1 . However, Kudritzki et al. (1999) did not include late-type B supergiants in their study because of the difficulty to measure their terminal wind speed ( Howarth et al. 1997 ). Vink et al. (2000 , 2001) provided fits for the mass-loss rate and the terminal wind speed which extend below the second bi-stability jump (i.e., for effective
Paper: 2019MNRAS.490.2112B (fulltext)
a comparison needs to be made statistically using a large sample of stars. Unfortunately measurements are available for very few A and B stars in Kepler and K2 . A comparison using 30 stars with T eff in the range 8300–12 000 K supports the identification of the photometric period with the rotation period (Balona 2017 ). A similar study using TESS observations of B stars comes to the same conclusion (Balona et al. 2019 ). Both these studies involve small numbers of stars. TESS observations have revealed the rotation frequencies of many more A and B stars for which measurements are available. Table 2 shows that there are 274 stars with both rotation frequencies and . In Fig. 1 a comparison between and the photometric v is made for 193 stars with T eff > 8000 K. The temperature limit was chosen to minimize contamination by stars in which surface convection may still be present. Since cannot exceed v , the points are expected to lie below the line in the figure. Figure 1. The projected rotation velocity, , as a function of the equatorial rotation velocity, v , estimated from the photometric period for stars with effective temperatures T eff > 8000 K. The straight line is . The 1σ error bars are shown on the top left. Table 2. The number of stars with known photometric periods, n , and the total number of stars, N , in the given effective temperature range, T eff , is given. The number of stars with both photometric periods and measurements is . The number of stars used in the frequency distribution is . The increase in numbers of stars above the line at low rotation rates is to be expected. Measurements of are mostly obtained by measuring the widths of selected spectral lines and applying a calibration to convert to . The calibration is obtained using high-dispersion spectra and modelling line profiles. The error in is approximately constant at all values of . Analysis of the rotational velocity catalogue of Glebocki Gnacinski ( 2005 ) gives a standard deviation of of about 30 km s −1 . This means that at low values of , the error is comparable to, or larger than, itself and many points will lie above the line. This is further compounded by the fact that is constrained to be greater than or equal to zero, although measurements of line width may lead to negative values of on applying the calibration. As the rotation rate tends to zero, measurements of ever increasing precision are required to ensure that stays below the line. This is clearly not possible to attain. Another consideration is that low photometric frequencies are more difficult to measure because a longer time span is required to resolve the frequency peak in the periodogram. Thus instrumental drift becomes important. This is further compounded by the fact that the periodogram noise increases sharply towards zero frequency. Thus the uncertainty in v increases as v approaches zero, contributing to moving the point above the line. The standard deviation for v is about 40 km s −1 as
Paper: 2018MNRAS.475.1410P (fulltext)
values of polynomial fits at each pixel and plot the resulting model over the original spectrum. In the lower panel of Fig. 3 , we show the residuals between the model and the input spectrum, with horizontal lines marking the median uncertainty about zero for this spectrum. With just eight principal components, we can model this particular spectrum to within the typical measurement uncertainty. In Fig. 4 , we show the distribution of model residuals for all spectra in our example slice of the red clump for models using zero, four, and eight principal components. As we expect, increasing the number of principal components used in the model causes the residual histogram to become more peaked towards a value of 0, with shallower wings. Given this continual improvement in modelling, it is necessary to define a cut-off to determine when improvements to the model explain variations due to noise, not intrinsic stellar differences. Figure 4. Histograms of the residual normalized flux after subtracting from each spectrum in the example slice a model using 0, 4, and 8 PCs. As more PCs are used, the histogram becomes more sharply peaked in the centre, with shrinking wings. 3.3.2 Assessing principal components We now define some useful statistics that can be computed from the results of EMPCA, which allow us to assess the results of the algorithm. We define V data mean as the mean-based variance of , We define a similar quantity V data MAD as the median absolute deviation (MAD) based variance of where is the p th row of , the operation ‘med’ is taking the median of the data, and the factor of 1.4826 scales the MAD to the standard deviation for normally distributed data, i.e. V data mean = V data MAD for data drawn from a Gaussian distribution. Equations ( 11 ) and ( 13 ) represent the total variance in the data. We wish to compare these to the variance after removing the principal component model produced by EMPCA. To do this, we define two additional quantities that compute the variance in the data after the contribution from the first n principal components has been subtracted out: Here, is the original data set after the model using the first n principal components has been subtracted (equation 10 ), and is the p th row of . Note that in the case where n = 0, V model mean (0) = V data mean and V model MAD (0) = V data MAD . Also note that when the number of components is maximized ( n = min ( N, P ) = n limit ), V model mean ( n limit ) = 0 and V model MAD ( n limit ) = 0, because in that case the model can perfectly represent all of the data. In what follows, we use V data and V model without superscripts when either the mean-based or MAD-based versions could be used. We compare these variances using R 2 ( n ), defined as The R 2 ( n ) curve should increase in height with the number of iterations on the model components. The maximal value for R 2 ( n ) is 1, which indicates the data can be perfectly reproduced by a linear combination of n components
Paper: 2018ApJ...857..136W (fulltext)
by the WIND spacecraft at 1 au on 2011 June 24, with n i = 2.4 cm −3 , V SW = 599.0 km s −1 , and . (a)–(c) Time variations of the magnetic field vector in geocentric solar ecliptic coordinates. (d) Time variation of field magnitude. (e) Power spectrum of the magnetic field obtained from the FFT method with 11-point centered smoothing. The spectrum with spacecraft frequency f 10 −3 Hz is not shown here, since there are not enough points at this domain when smoothing the spectrum. The two red solid lines show the linear fits to the spectrum in the low-frequency and high-frequency ranges, respectively. Red dots indicate the upper and lower frequency limits used to perform the fits. The obtained spectral indices for the low-frequency ( α L ) and high-frequency ( α H ) ranges and their uncertainties are shown, along with the values of f b , f di (blue arrow), f ρi (green arrow), f ri (black arrow), and β . The vertical dashed line denotes the frequency of the spectral break. Panel (e) of Figure 1 shows the power spectrum of the magnetic field fluctuations. The spectrum is computed by applying the Fast Fourier Transform (FFT) on the three components of B . The trace of the spectral matrix gives the total power spectrum (hereafter power spectrum for simplicity). The spectral break frequency is determined as follows. First, we estimate the location of the break point by eye, which is roughly at 0.3 Hz. The break point divides the power spectrum into two parts: a shallower low-frequency part and a steeper high-frequency part. We then perform linear least-squares fit to the spectrum on the two sides of the roughly estimated break point, respectively (see the two red solid lines in Figure 1 (e)). The upper and lower frequency limits (the red dots) used in the fitting procedure are determined by the frequency range that is best fitted by power law based on chi-square minimization. For the low-frequency part, the band used to perform the fit is between 0.008 and 0.1 Hz. The spectral slope as well as its 1 σ uncertainty is estimated as α L = − 1.694 ± 0.025, which is close to the typical Kolmogorov scaling f −5/3 in the inertial range of the solar wind turbulence (Matthaeus et al. 1982 ; Goldstein et al. 1995 ; Tu Marsch 1995 ; Podesta et al. 2006 ). The high-frequency part of the spectrum beyond the break point is also fitted by a red solid line in the same way, but at the frequency range between 0.4 and 1.2 Hz. The intersection of the two red solid lines associated with the power-law fits thus gives the exact frequency of the break point f b = 0.288 Hz (marked by the red vertical dashed line). This method about determining the break frequency has already been used in several previous studies (e.g., Leamon et al. 1998 ; Smith et al. 2006 ; Hamilton et al. 2008 ; Markovskii et al. 2008 ; Bruno Trenchi 2014 ; Wang et al. 2018 ). In addition, we notice that at the frequencies higher than 1.5 Hz, the power spectrum becomes shallower again, which
Paper: 2021ApJ...908..121E (fulltext)
resolution. In order to compare clumps in our galaxies with those in high-redshift galaxies, we identified clumps by visual inspection of the z = 2 images in the F435W band. Measurements were made on the original non-redshifted images since the simulated redshifted images were Gauss-blurred and re-pixelated with artificial noise added, so flux would have been difficult to determine accurately. Instead, contour plots were made on the local images and compared with the z = 2 images where the clumps were distinct. In practice, the contours at 10× sky σ on the local images matched the clumps in the high -z images, so that is where the boxes were defined for photometry. The IRAF task imstat was used to determine total pixels and mean counts per pixel within the boxes. Magnitudes were then determined for each clump, using the zero-points from the WFC3 and ACS handbooks. There was no background subtraction, because the photometric fitting described below included an underlying component plus the star-forming component, described further below. This is the same procedure as we used in measuring clumps in high-redshift galaxies (Elmegreen et al. 2009b ), so our results for clumps in our galaxies can be directly compared to the high-redshift results. For the galaxies in this paper, this means the clump boundaries sometimes contain more than one large clump, since some get blended at high z . The measured clumps are indicated as boxes in Figure 8 . For Haro 11, contour plots are also shown as an example of how clumps were identified. The right-hand contour plot is on the simulated z = 2 F435W image, where the three clumps are distinct. The middle contour plot on the original image has an arrow indicating the ∼10× sky σ contour where the boxes were then drawn. A total of 51 clumps were identified in the seven galaxies (the number in each galaxy is listed in Table 1 ). The average absolute B- band magnitude of the clumps for the seven galaxies in this sample is −15.4 ± 2.6, while the average ( B − V ) color is 0.4 ± 0.5. The clump diameters are approximated by the square root of the area of the boxes defined for photometry, and range from 170 pc to 2.9 kpc, with an average of 1.0 ± 0.7 kpc. Figure 8. The seven galaxies studied here are shown in the F555W filter. Boxes identify the clumps that were measured; see Figures 1 – 4 for physical scales. For Haro 11, contour plots are also shown to indicate how clumps were identified. Measurements were done on the non-redshifted images for the boundaries determined from the redshifted images. In practice, these boundaries were at a surface brightness ∼10× sky σ in the F435W image, as indicated in the middle image. The right-hand contour plot is for the simulated z = 2 F435W image, where the three clumps are distinct. 4.2. Clump Ages and Masses The photometric measurements of the clumps were used to determine ages, masses, and extinctions through spectral energy distribution (SED) fitting, as detailed in Elmegreen
Paper: 2021MNRAS.505.3562L (fulltext)
is undetected in one scenario, an upper limit on its abundance can suffice to disfavour biological production for certain molecules. Fig. 7 shows the posterior distributions for the mixing ratio of each molecule from our retrievals. A 10 Prism transit campaign suffices to constrain the CO 2 and CH 4 abundances of the modern Earth to within an order of magnitude (0.78 dex and 0.85 dex, respectively); for the prebiotic Earth only CO 2 is constrained (0.62 dex). An increase to 20 Prism transits offers two new results: (i) an order of magnitude constraint on the H 2 O abundance for the prebiotic Earth (0.70 dex); and (ii) a upper limit on the prebiotic Earth CH 4 abundance in tension with the confidence interval of the modern Earth CH 4 abundance. The latter is a diagnostic difference between the modern and prebiotic scenarios accessible with only 20 transits. A further increase to 100 Prism transits offers dramatic improvements: constraints on the CO 2 , H 2 O, and CH 4 abundances to ∼ a factor of 2 (0.3 dex), and an order of magnitude constraint on the modern Earth N 2 O abundance (0.78 dex). Constraints on the O 3 abundance of the modern Earth can be obtained, to within an order of magnitude, with either 100 Prism + 100 MIRI transits (0.63 dex), or 200 Prism transits (0.59 dex). However, 200 Prism transits result in significantly better constrained abundances for the other molecules. As above, we recommend additional Prism transits instead of dividing time between the Prism and MIRI. Figure 7. Retrieved atmospheric composition from simulated JWST observations of TRAPPIST-1e. The predicted mixing ratio constraints for CO 2 , H 2 O, CH 4 , N 2 O, and O 3 for the 1 bar modern Earth (green) and prebiotic Earth (orange) are shown by the marginalized posterior histograms. Each row corresponds to a different JWST observing campaign, ranging from 10 NIRSpec Prism transits (top) to 200 Prism transits (bottom). Various combinations of NIRSpec Prism + MIRI LRS transits are also included. Predicted detection significances for each molecule are annotated (green text for the modern Earth; orange text for the prebiotic Earth). Abundance constraints (error bars) are overlaid, with the interval annotated where constraints 1 dex are possible. For non-detections, abundance upper limits are shown (vertical lines with arrows). The prebiotic and Modern scenarios can first be distinguished with 20 Prism transits via the CH 4 abundance (the prebiotic upper limit precludes the modern abundance). Similarly, the N 2 O abundance can be differentiated with 200 Prism transits. Even 200 Prism transits (or 100 Prism + 100 MIRI transits) are insufficient to distinguish the O 3 abundances. O 2 is unconstrained in all cases. Adding MIRI data slightly increases the detection significances of O 3 , but adding the same quantity of Prism transits produces tighter constraints on all other atmospheric parameters. Our analysis demonstrates that the most effective way to