adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...833..185J (fulltext)
assumed to be single Gaussians, using photographic density , and solving for , and c from these lines. Then, I was solved at neighboring wavelengths for the Fe x transitions. This procedure slightly reduced the lower intensity values at the base of the lines, without changing the results significantly. We do not know widths of Fe x lines that are unblended, nor do we know the precise wavelength of Fe x lines on the wavelength scale above. Therefore, the unresolved FS can only be derived as an upper limit by comparing the observed data with the two lines, estimating for the FS splitting itself (or by a least-squares optimization outlined below). In making these comparisons, we adopted the optically thin ratio of 2.09 for the two lines from CHIANTI (Young et al. 2016 ). Some typical profiles are illustrated in Figure 3 . Figure 3. Observed profiles and fits are shown for a specified line width of 0.23 Å, of the 1603.2 Å region of the S082B data obtained above the solar limb. The solid line shows the S082B data, the dotted lines show the two components of the Fe x transitions in their optically thin ratio, and the dotted–dashed lines show the total modeled emission for the given FS splitting. The panel labeled “4 cm −1 ” shows an acceptable by-eye fit, yielding an FS splitting close to 4 cm −1 . The other panels show the fits, with residuals at least 10 times higher, for splitting values of 1, 7, and 10 cm −1 . For values of FS splitting near and below the line width ( cm −1 ) there is some redundancy between FS splitting and line width. The figure shows observed profiles over-plotted with models with a given (1/ e ) width of 0.23 Å (taken from the optimization calculation below) for four values of the FS splitting. The figure suggests an FS splitting value of , certainly cm −1 . The variances, sum of model minus data squared evaluated over the fit to a 1 Å wide band centered at the line, are 2.5, 3, and 9 times the optimal value for 1, 7, and 10 cm −1 respectively. A trade-off exists between line width and FS splitting: the larger the width, the lower the splitting needed to fit the same data. Therefore, we can place only an upper limit on the FS splitting from this analysis of cm −1 . However, we can go a step further. While the data are photographic and subject to nonlinearities in photographic density versus intensity, we nevertheless performed a formal analysis of variances by fitting the two lines and varying the strong line's wavelength, line widths and the FS splitting. Both lines were given the same width and the ratio of the intensities was fixed at 2.09. The pikaia genetic algorithm was used (Charbonneau 1995 ). Figure 4 shows the surface as a function of width and FS splitting. We adopted a constant value for the observational error across the line profiles for the calculation, because the photographic densities lie on top of a large pedestal (equivalent to a dark current). We multiplied the errors by Gaussian functions centered near
Paper: 2018A&A...617A..57D (fulltext)
were mainly from the NEOWISE database ( Mainzer et al. 2011 ) with some values taken also from Akari ( Usui et al. 2011 ) and IRAS ( Tedesco et al. 2004 ) catalogs. 4.1. Pole latitude versus size The distribution of the pole latitude β for main-belt asteroids as a function of asteroid size is shown in Fig. 4 . The distribution is strongly bimodal for asteroids smaller than 20–30 km, which can be satisfactorily explained as an effect of a YORP-driven evolution ( Hanuš et al. 2011 , 2013b ). Due to the YORP effect, small asteroids evolve towards the extreme values of obliquity. In the lower panel of Fig. 4 , we show the fraction N p / N total of the number of prograde ( β > 0) rotators in a running box over N total = 100 asteroids as a function of size. For asteroids larger than 100 km, the number of prograde rotators is statistically higher ( N p = 68, N total = 108, probability of the null hypothesis that N p = N total /2 is p ≃ 0.7% assuming binomial distribution) in accordance with the model of Johansen Lacerda (2010) who suggested that the preferentially prograde rotation of large asteroids is a result of accretion of pebbles on planetesimals in a gaseous environment. On the other hand, for asteroids in the size range 1–10 km, there is an excess of retrograde rotators ( N p = 520, N total = 1276, p ≃ 2 × 10 −11 ). For asteroids between 10 and 100 km, the number of prograde and retrograde rotators is statistically the same ( N p = 441, N total = 919, p ≃ 22%). Because most asteroids smaller than about 30 km have large absolute values of β , the prograde/retrograde analysis is not sensitive to asteroids with | β | 30°. The ratio is almost the same even if we restrict ourselves to | β | > 30° where the distinction between prograde and retrograde rotation is unambiguous even for nonzero inclination. This is not true for asteroids larger than 100 km, where for | β | > 30° we have N p = 34, N total = 55, and p ≃ 8%. Fig. 4. The distribution of pole latitudes β for all full and partial models of main-belt asteroids. The bottom panel shows the running-box (over 100 asteroids) fraction of prograde ( β > 0 deg) models (red curve) and also the fraction of models with β > 30 deg (green curve). The dashed and dotted lines show the 5% and 1% significance levels at which the null hypothesis that the prograde and retrograde asteroids are equally frequent can be rejected. The excess of small retrograde rotators in Fig. 4 is statistically significant, however, it is not clear if the reconstructed distribution of β is the same as the real one. Although we are not aware of any bias in the observations or the method that could cause the asymmetry in ± β , there could still be some nontrivial systematic effect that we have not taken into account. 4.2. Pole latitude distribution across the main belt The distribution of pole latitude β is not only dependent on the size, but also on the proper semimajor axis, namely on the proximity to resonances. For asteroids in
Paper: 2017ApJ...834..180S (fulltext)
This is due not to continual acceleration, but instead to previously higher velocities and higher mass loss rates earlier in the model’s evolution. In Figure 4 , we show the linear (left-hand plot) and logarithmic (right-hand plot) temporal evolution of our main fiducial model as well as fiducial models with different WD and envelope masses. Black, red, and green lines represent core + envelope masses of , , and , respectively. In each plot, the top panel shows the evolution of the total luminosity, which is the sum of the luminosity at an optical depth of and the power from positron kinetic energy released by decays above the photosphere. We assume the positrons are able to thermalize and deposit their energy locally while the gamma-rays emitted during the and decays escape the wind and the surrounding SN ejecta. In practice, however, the positron kinetic energy in the optically thin region is smaller than the luminosity at the photosphere, so this contribution is not significant. Note that the luminosity from the SN Ia ejecta is not included. For comparison, the dotted line in the top panels represents the total power from radioactive decays of of initially pure under “lab” conditions. Figure 4. Evolution of winds launched from post-SN Ia surviving WD donors with envelope masses determined by the maximum bound ejecta mass. Three different models are shown, with core + envelope masses of (black), (red), and (green). The left-hand and right-hand plots use linear and logarithmic time axes, respectively. The top panel in each plot shows the sum of the luminosity at the photosphere and the luminosity from thermalization of positron kinetic energy from decays above the photosphere. The dotted line represents the total power from radioactive decays of of initially pure under “lab” conditions. Note that the luminosity from the SN Ia ejecta is not included. The middle panels track the effective temperature at the location. The bottom panels show the velocity at the photosphere (solid) and the mass-averaged velocity of material above the photosphere (dashed). The effective temperature, derived from the luminosity and radius at the surface, is shown in the middle panels. The velocity at the location is shown as a solid line in the bottom panels, and the mass-averaged velocity in the optically thin region is shown as a dashed line. The observed quasi-bolometric late-time light curve of the best observed SN Ia to date, SN 2011fe, is shown as bullets (Nugent et al. 2011 ; Tsvetkov et al. 2013 ; Kerzendorf et al. 2014 ; Shappee et al. 2016 ). The late-time flattening of SN 2011fe and other SNe Ia above simple predictions for decay within the SN ejecta has been attributed to light echoes and the decay of long-lived radioactive isotopes such as 57 Co and 55 Fe (Seitenzahl et al. 2009 ; Röpke et al. 2012 ; Kerzendorf et al. 2014 ; Graur et al. 2016 ; Shappee et al. 2016 ). Our models represent an additional contribution to the total luminosity, and
Paper: 2018ApJ...860L..30H (fulltext)
et al. 2018 ). In the X-ray, events up to 10 32 erg (10 32.7 erg bolometric) have been detected (Güdel et al. 2004 ). The Microvariability and Oscillation of Stars ( MOST ) satellite (Walker et al. 2003 ) performed the most comprehensive previous measurement of the Proxima flare rate. MOST observed Proxima for 37.6 days, observing 66 white-light flare events, the largest of which was 10 31.5 erg in the MOST bandpass (∼4500–7500 Å). No superflares were observed; extrapolating the cumulative flare frequency distribution (FFD) obtained by Davenport et al. ( 2016 ) from the MOST flare sample out by 1.5 dex predicts that ∼8 × 10 33+ erg events in the MOST bandpass occur per year. To search for superflares and other short-timescale phenomena, the Evryscope (Law et al. 2015 ) is performing long-term high-cadence monitoring of Proxima, along with every other bright star in the Southern sky. In 2016 March the Evryscope detected the first-known Proxima superflare. Although no M-dwarfs are usually visible to the naked eye (e.g., Shields et al. 2016 ), Proxima briefly became at least a magnitude-6.8 star during this superflare, at the limit of visibility to dark-site naked-eye observers. 2. Evryscope Flare Discovery and Observations We discovered the Proxima superflare as part of the Evryscope survey of all bright southern stars. The Evryscope is an array of small telescopes simultaneously imaging 8000 square degrees of the sky every two minutes (Law et al. 2015 ). The Evryscope observes essentially the entire Southern sky above an airmass of two, at two-minute cadence in g ′ and at a resolution of 13′′ pixel −1 . The system has a typical dark-sky limiting magnitude of g ′ = 16 and tracks the sky for 2 hr at a time before ratcheting back and continuing observations, for an average of ∼6 hr of continuous monitoring each night on each part of the sky. The Evryscope image archive contains 2.5 million raw images, which is ∼350TB of data. A custom pipeline analyzes the Evryscope data set at realtime rates (Law et al. 2016 ). Each image, consisting of a 30 MPix FITS file from one camera, is calibrated using a custom wide-field solver. After careful background modeling and subtraction, forced-aperture photometry is extracted based on known source positions in a reference catalog. Light curves are then generated for approximately 15 million sources across the Southern sky by differential photometry in small sky regions using carefully selected reference stars and a range of apertures; residual systematics are removed using two iterations of the SysRem detrending algorithm (Tamuz et al. 2005 ). In extremely crowded fields, such as for Proxima (−2° galactic latitude), we re-run the pipeline for particular targets, optimizing the aperture sizes to avoid nearby stars. As a very large event, the Proxima Superflare was discovered in routine by-eye checks of interesting targets in the Evryscope data set. Smaller flares are discovered and characterized with an automated
Paper: 2016ApJ...822...91W (fulltext)
mixes with nearby gas, producing a sharp peak in the PDF at the high metallicity end. More efficient diffusion tends to push this peak to lower metallicities. This trend is produced by enriched gas losing its metals more rapidly. However, this also depends on the recent star formation history, and is not a monotonic trend with diffusion strength. This peak is not present in Nodiff. Instead, there is a high-metallicity tail, consisting of the small number of particles that have been enriched by very many feedback events. The difference between the metal distributions has no clear impact on the distribution of cooling times, presumably because the number of particles in the high-metallicity tail is small. In general, the gas properties of Shear do not seem to stand out as particularly distinct among the other simulations. The differences between the simulations thus appears to depend only on the strength of diffusion, even when the diffusion coefficient is calculated in different ways. This suggests that diffusion can be accurately calibrated by adjusting the pre-factor, and that it is not necessary to introduce a more complex method for calculating the diffusion coefficient. 3.3. Spatial Distribution of Metals The spatial distribution of metals at t = 750 Myr is plotted in the second column of Figures 3 and 4 . This distribution is strongly affected by the choice of diffusion algorithm. Without any diffusion, the metal distribution is very clumpy as there is no mechanism to smooth out the metals. Highly metal-enriched particles can therefore move over large distances without losing their metals. Metal-rich gas in the Nodiff model at clearly has a large vertical extent, while Disp is more localized (Figure 4 ). We have plotted the mean metallicity, and [O/Fe], in vertical and radial bins in Figure 7 at intervals of 250 Myr to examine more clearly the difference between the diffusion algorithms. The radial metallicity bins are annuli that extend 2 kpc above and below the disk plane, and the vertical metallicity bins are pillboxes of radius 5 kpc. While many observed dwarf galaxies appear to have flat or near-flat metallicity gradients (Crnojević et al. 2010 ; Koleva et al. 2011 ), all of our simulations show significant slopes. This is likely a result of our feedback being weak, resulting in gas (and hence star formation) that is centrally concentrated. Stronger feedback at higher resolution can produce more distributed star formation ( K14 ). This dependence on feedback means that the simulated spatial distributions of metals can not be directly compared with observations, but only compared with other models with similar feedback strength. Figure 7. Metallicity distributions at t = 250, 500, 750, and 1000 Myr (from top to bottom). First column: metallicity as a function of height above the disk plane. Second column: Metallicity as a function of radius. Third column: [O/Fe] as a function of height above the disk plane. Fourth column: [O/Fe]
Paper: 2017ApJ...844..156R (fulltext)
errors for are 3%–7%. Because there is no physical motivation to assume that the prior distribution of the pressures is uniform, we also tested the inversion with priors that are uniform in the logarithm of pressure. The results of this test, with identical data as above and the Gaussian regularizer included, are shown in the bottom panel of Figure 2 . The top panel of Figure 2 shows that assuming a uniform prior introduces a preference toward higher pressures. On the other hand, assuming that the prior is uniform in the logarithm of pressures biases the results toward lower pressures. The inversion is particularly sensitive to this bias in the low-mass/low-density region of the EoS, where we lack data. Our Gaussian regularizer helps reduce this bias, as shown in the middle and bottom panels of Figure 2 , in which the results are similar for the two types of priors when the regularizer is also included. In the absence of a regularizer, the freedom introduced by using five polytropes in the parameterization combined with this sensitivity to prior distributions could lead to significantly skewed results, or even the false inference of a phase transition with a perceived high statistical confidence. It is therefore important to use a Gaussian regularizer on the second derivative, with a characteristic scale , to avoid this sensitivity to over-fitting. In Figure 4 , we explore potential correlations between the inferred parameters for this test. Specifically, we show the correlations for the inversion using uniform priors and the Gaussian regularizer (i.e., the middle panel of Figure 2 ). There are slight anti-correlations between adjacent inferred pressures, due to fact that, even with the addition of the regularizer, we are still over-parameterizing our model while trying to fit the effectively single-polytrope EoS SLy. Overall, however, the pressures that are not adjacent are uncorrelated with one another. Özel Psaltis ( 2009 ) showed that, for a three-polytrope parameterization, parameterizing with the pressures as free parameters instead of the polytropic indices reduced the correlations between the inferred values. Figure 4 shows that the low levels of correlations are maintained here. Figure 4. Correlation plots for the inferred pressures at our five fiducial densities. The inversion was performed using uniform priors and a Gaussian regularizer (). The red diamonds mark the pressures of the true EoS (SLy) that we are trying to infer. There are slight anti-correlations between adjacent pressures (e.g., P 1 and P 2 ), but the non-adjacent pressures are relatively uncorrelated. The triangular shape of the P 4 vs. P 5 correlation is due to the causality requirement, which is shown as the red line. In order to ensure that the Bayesian inference works well for other underlying EoS and that the regularizer does not adversely limit our ability to detect potential phase transitions, we tested the method on a number of different EoS as well. As an
Paper: 2020MNRAS.496.4906B (fulltext)
(top), central detectors of sparse observations (middle), and from mapping observations (bottom) are shown in blue, orange, and violet, respectively. Lines found by the FF employ round markers while those found by the low SNR search routine through line identification use triangular markers (see Section 3.1 ). The |SNR| cutoff in each case is shown by the dotted black line. The axes in each case have been set to the same scale to allow comparison. 5 CONCLUSIONS The extensions of the FF to include spectral observations from off-axis detectors in sparsely sampled single-pointing observations is detailed. An additional 30 720 spectral features with a |SNR| greater than 6.5 are aggregated into the SAFECAT by the inclusion of the analysis and extraction of features from the off-axis detector spectra. We developed a set of postcards to highlight and summarize results from these off-axis detectors in each observation that are of a similar format to the mapping postcards described by Paper I and are available from the ESA Herschel legacy area (see footnote 6). We have also extended the automated SPIRE FF routine ( Paper I ) to identify the atomic or molecular transition that corresponds to the features extracted from HR observations of the SPIRE FTS. This identification of atomic/molecular transitions is done by matching the spectral features to a template of 307 atomic fine structure and molecular features that are commonly found in SPIRE FTS observations. Line identification is reliant on the radial velocity estimates included in the catalogue by Paper II and is able to successfully match 77.3 per cent, 93.1 per cent, and 95.2 per cent of spectral features found by the FF from central detectors of sparse single-pointing observations, off-axis detectors, and mapping observations, respectively, to their atomic/molecular transitions. We find this line identification to be particularly effective at identifying the CO rotational ladder from J = 4–3 to J = 13–12 and the [N ii ] 3 P 1 – 3 P 0 transition in SPIRE FTS observations. The identification of all 15 atomic/molecular species in SPIRE FTS spectra from mapping, off-axis detectors in sparse observations, and central detectors in sparse observations is shown in Fig. 14 . Note that isotopologues and ionization states have all been grouped together for each species. Figure 14. The line identification of all 15 atomic/molecular species contained in the template for mapping and sparse SPIRE observations. The information provided by the line identification is also used to uncover low SNR features that fall below the |SNR| ≥ 5 cutoff that exists nominally in the FF ( Paper I ). This low SNR search routine adds an additional 1516 for the central detectors of sparsely sampled single-pointing observations and 1234 for the off-axis detectors. An additional 370 lines are added by this routine from mapping observations of the SPIRE FTS. APPENDIX: LINE MATCHING TEMPLATE A note on the quantum numbers
Paper: 2021MNRAS.503..726L (fulltext)
size equal to the sum of the number of HDs and CRs. This results in 118 HDs, 47 CRs, and 165 NDs (see Table 1 ). We show the distribution of the three samples in the plane in the right-hand panel of Fig. 1 , which is a zoom-in figure with galaxies with and . As can be seen, all three sub-populations have roughly similar sSFR, with CRs being slightly lower than the other two. In addition, we have also checked and confirmed that the total halo masses of the three galaxy samples are roughly within the same range. Table 1. Number of galaxies in different samples. Columns (1): All z = 0 central galaxies from the IllustrisTNG simulation; also the parent sample of Columns (2)−(5). Column (2): late-type galaxies; Column (3): selected hot disc galaxies (HDs); Column (4): selected counter-rotating disc galaxies (CRs); and Col.(5): control-sample normal disc galaxies (NDs). See Section 2.2 for definition and selection criteria. In Fig. 2 , we show the distributions of the three orbital fractions ( f cold , f hot , and f ccd ) for general late-type galaxies and the three selected samples (HDs, CRs, and NDs). As can be seen, the three samples exhibit distinctive differences in cold, hot, and counter-rotating cold orbital fractions: NDs, which host the highest fractions in f cold , also possess the lowest fractions in f hot and f ccd , while HDs indeed have lower fractions in f cold and f ccd , and CRs have the lowest fractions in f cold and moderate fractions in f hot . This also demonstrates that the three types of galaxies are intrinsically different from each other. Figure 2. Distributions of f cold , f hot , and f ccd (from left to right, see Section 2.1 for definitions) for late-type galaxies (black), NDs (blue), HDs (red), and CRs (green). Black dashed lines represent f cold = 0.4, f hot = 0.31, and f ccd = 0.2, which are used to classify NDs, HDs, and CRs, respectively. 3 WHAT DO THEY LOOK LIKE? 3.1 Morphology of stars and gas To discuss the visually apparent differences in morphologies between HDs, CRs, and NDs, we first present in Fig. 3 the stellar and H i mass density maps from both edge-on and face-on views for typical HDs (the first 2 columns), CRs (the middle 2 columns), and NDs (the latter 2 columns). As can be seen, the two NDs have the flattest stellar and gaseous disc morphologies with their spin axes well-aligned. In comparison, however, both CRs have less flat stellar and gaseous discs, the spins of which exhibit very large misalignment (∼180°); the two HDs possess much thicker stellar discs and display the least established gaseous discs among all three types. Interestingly, very often the optical images of NDs and CRs show clear extended spiral patterns which are absent in the HD samples. Figure 3. Examples of two HDs (ID: 582800, 512569), two CRs (ID: 486917, 532146), and two NDs (ID: 462986, 606707) from left to right. For each example, we show from the top to the bottom rows: (1) the face-on view of the stellar mass density map,