adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...872..134Z (fulltext)
Since there are many possible choices for stellar libraries, IMFs, and isochrones, many SPMs are available. The systematic differences in AGN identification due to the choice of SPM are explored in detail by Chen et al. ( 2018 ). SPMs based on theoretical stellar libraries are subject to model dependence on line profiles and opacity uncertainties. Empirical stellar libraries are not model dependent but need a large number of observed stars that cover the parameter space as completely as possible. The MILES SPM (Vazdekis et al. 2010 ) is constructed from the MILES stellar library (Falcón-Barroso et al. 2011 ), currently the largest and most well-calibrated empirical stellar library. The MILES stellar library consists of 985 stars with a wavelength range of 3525–7500 Å and a spectral resolution of 2.5 Å. In this work, we use the MILES SPM, with a unimodal IMF of slope 1.3, the Salpeter IMF (Salpeter 1955 ), and the Padova isochrone (Girardi et al. 2000 ). It contains SSPs with ages from 63 Myr to 17.78 Gyr and metallicities ranging between [M/H] = −2.32 and +0.22. For each spectrum in our galaxy sample, wavelength ranges with emission lines and telluric contamination bands are masked. Since each galaxy from 6dF was observed in two bands, V (4000–5500 Å) and R (5500–7500 Å), we combine them into a single spectrum by matching in the overlap region and smooth the combined spectrum to the lower ( R -band) resolution, before fitting. The SSPs are broadened to match the spectral resolution of the observed spectra. Each data spectrum is fit as a linear combination of the MILES SSPs using the full-spectrum-fitting program pPXF (Cappellari Emsellem 2004 ). A large fraction of the spectra are fit by a linear combination of one or two SSPs, and nearly all have fewer than five SSPs in the linear combination. For the SDSS spectra only an overall normalization is added to the templates. For 6dF, CTIO, and FAST spectra that do not have absolute flux calibration, the fitting algorithm adds a polynomial, of up to eighth order, to account for the instrumental effects on the spectral shape. The fitting algorithm further broadens the templates with a Gaussian kernel to mimic the stellar velocity dispersion in the integrated spectrum of the host galaxy. The fits are required to yield a physical stellar velocity dispersion, σ fit 1000 km s −1 , in order to be considered acceptable. The fitting routine uses the error spectrum, provided with the data, to calculate a reduced χ 2 value for each acceptable fit. The acceptable fit with the lowest reduced χ 2 is taken to be the best-fit spectrum for the galaxy. Figure 4 shows a typical spectrum from the SDSS sample (black), the error (blue), the best fit (red), and the residual spectra after subtraction in the regions around the emission lines used for AGN identification. Figure 4. Full-spectrum fitting of an illustrative SDSS DR8 spectrum. (a) The DR8 data spectrum is shown as a black line, the error spectrum is shown as
Paper: 2015ApJ...813...12H (acknowledgments)
Funding for this work was provided by the NASA Planetary Atmospheres Program.
Paper: 2019MNRAS.490.5770C (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank the anonymous referee for their thorough feedback that has improved the clarity of our paper. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/ (Eisenstein et al. 2011 ). SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. We acknowledge studentship fundng by the Science and Technology Facilities Council (STFC), travel support provided by STFC for UK participation in LSST: All Hands Meeting through grant ST/N002512/1 and NASA’s SkyView facility located at NASA Goddard Space Flight Center. We also would like to thank Min-Su Shu at the University of Michigan, James Schombert at the University of Oregon, Edward L. Wright at the University of California, Los Angeles (Wright 2006 ) and Dat Tran ( https://github.com/datitran ) for allowing free distribution of their code.
Paper: 2020MNRAS.495.1531G (fulltext)
the observed light curves, RVs, and SED of Mon-735. Jointly modelling the SED allows us to propagate our optical spectroscopic light ratio constraints into the IR Spitzer light-curve bands and use them to help constrain the fundamental parameters of both stars. This is particularly important for Mon-735, as the system displays near-equal eclipse depths in the Spitzer light curves, which can lead to a degeneracy between the radius ratio, surface brightness ratio, and inclination when modelling the data. Details of the modelling are given in Sections 3.1 – 3.3 below. 3.1 Light curves The Spitzer 3.6 and 4.5 μ m light curves are shown in Fig. 2 . In addition to eclipses on both the primary and secondary stars, both light curves display reasonably rough aperiodic out-of-eclipse (OOE) variations, which are similar to Spitzer light curves of other young EBs (e.g. Morales-Calderón et al. 2012 ; Gillen et al. 2017a ). Given the rough OOE variations, we opted to use a Matérn kernel with smoothness parameter ν = 3/2 (as approximated within the celerite package), which is appropriate for such rough variations (Rasmussen Williams 2006 ). The Matérn 3/2 covariance kernel k is given by where τ = | t i − t j | is the time interval between times t i and t j , and A and l are the characteristic amplitude and time-scale of the variations, respectively. Figure 2. Spitzer /IRAC 3.6 and 4.5 μ m relative flux light curves of Mon-735 (black points, top and bottom, respectively) with the gp-ebop model in red and residuals below. In both light curves, the red line and pink shaded regions show the mean and 1σ and 2σ confidence intervals of the predictive posterior distribution. The system displays near-equal eclipses on both stars, which are ∼36–37 per cent deep, as well as relatively rough short time-scale out-of-eclipse variability. We applied theoretical constraints on the quadratic limb darkening parameters using the predictions of Claret, Hauschildt Witte ( 2012 ). We used the Claret tables rather than ldtk because the specific intensities of the PHOENIX model spectra (Husser et al. 2013 ), which ldtk is based on, only extend up to a wavelength of 2.6 μ m, so the IRAC 3.6 and 4.5 μ m bandpasses are not covered. We assumed T eff = 3200 K and log g = 4.0 for both the primary and secondary stars (setting Z = 0.0, which is the closest value available to the cluster metallicity; King et al. 2000 ). 2 The T eff and log g values were set based on preliminary fits to the SED data (using PHOENIX models) and the light curves and RVs, respectively, and then rounded to the nearest value in the Claret tables. Uncertainties on the limb darkening coefficients (LDCs) were set based on the spread of coefficient values within a representative range in T eff and log g , which were determined from initial modelling of the light curves and RVs, and further inflated by comparing the Claret LDC values using both the least-square and flux conservation methods. In practice, given the
Paper: 2018ApJ...857...21L (fulltext)
to study the variation of LOS plasma velocity during the formation of the penumbra, we also analyzed the SHARPs Dopplergrams, which are taken in the Fe i 617.3 nm with a resolution of 1′′. The velocities inferred by the Dopplergrams were calibrated by assuming that the plasma flow in the sunspot umbrae is at rest. We set the umbra region at rest in order to avoid the effect of plasma convection blueshift in the quiet Sun region (Dravins et al. 1981 ). We also used the images of the He ii 30.4 nm line taken by the SDO /Atmospheric Imaging Assembly (AIA) to investigate the upper atmospheric layers above the developing sunspot. The He ii 30.4 nm line is one of the strongest lines in the AIA observed in the solar spectrum, providing information on the upper chromosphere or lower transition region (Andretta et al. 2012 ; Lemen et al. 2012 ). The NST/TiO and SDO /HMI observations were coaligned by using the first image of TiO taken by the NST at 16:38 UT on 2016 August 11 as a reference and the continuum intensity images at 16:36 UT taken by SDO /HMI. We used the IDL Solarsoft mapping routines to solve the problem of the differential rotation. In addition, we traced the photospheric horizontal motions of the plasma flow by using the Fourier local correlation tracking (FLCT) method in the TiO images (Welsch et al. 2004 ; Fisher Welsch 2008 ). 3. Results Figure 1 shows the surroundings of the interesting sunspot in the continuum intensity images and LOS magnetograms taken by SDO /HMI. The black boxes in Figure 1 outline the region of the interesting sunspot. The interesting sunspot was a developing sunspot. The developing sunspot had two umbrae with some preexisting penumbral filaments. It was formed as a result of the coalescence of sunspots “S1” and “S2” (see Figure 1 (a1)). The newly formed penumbra sector was located at the northwest side of the developing sunspot, as shown by the red boxes in Figures 1 (a2) and (b2). The developing sunspot changed its initial shape in the process of new penumbra sector formation, as shown in Figures 1 (a1)–(a3). With the growth of the developing sunspot, sunspots S1 and S2 gradually combined. The developing sunspot was characterized by negative polarity. Some thin filaments around the developing sunspot stretched to the region that was characterized by positive polarity (see Figures 1 (a1), (b1), (a2), and (b2)). Figure 1. Evolution of active region NOAA 12574. (a1–a2): continuum intensity images observed by SDO /HMI. (b1–b2): corresponding LOS magnetograms. The white contours indicate the boundary of the umbra as seen in the continuum intensity images. The developing sunspot is marked by the black boxes. The two sunspots in the black box of Figure 1 (a1) are marked “S1” and “S2”. The black boxes also indicate the field of view of Figures 2 – 5 , 7 , and 8 . Each red box in Figures 1 (a2) and (b2) indicates the position of the newly formed penumbra sector. Figure 2 shows the evolution of the developing
Paper: 2016MNRAS.455.2216C (acknowledgments)
We are grateful to the following people who took part in the infrared photometry programme at Sutherland, Brian Carter, Dave Laney and Enrico Olivier. MWF and PAW gratefully acknowledge a research grant from the SA National Research Foundation. We thank an anonymous referee for comments and specifically for raising the question of the helium abundance. We made use of the VizieR and SIMBAD databases of the CDS in preparing this paper. This paper uses observations made at the South African Astronomical Observatory (SAAO).
Paper: 2019MNRAS.487.5508A (fulltext)
spectra are known in detail. Most importantly, geometrical effects cause the spectra to differ from a Planck spectrum. In particular, in the case of the photosphere occurring in the coasting phase, i.e. above the saturation radius, r s , the spectrum is significantly different from a Planck function (Goodman 1986 ; Beloborodov 2011 ; Lundman, Pe’er Ryde 2013 ). Its spectral shape is much broader, while the asymptotic, low-energy spectral slope is still very hard, with α ∼ 0.4. The most common way to assess different emission models in GRBs is by studying the sub-peak spectral shape. This is characterized by the photon index, α , of the Band function or, similarly, of the cut-off power-law function (Band, Matteson et al. 1993 ). The values of α can vary significantly depending on the emission process that is taking place. In an optically thick, thermal scenario, the Rayleigh–Jeans limit has α = 1, the Wien limit has α = 2, and the non-dissipative photosphere in the coasting phase has the expected α of 0.4. Non-thermal emission is always in the regime α 0. The limiting value can be reached by synchrotron emission in many specific cases, such as from electrons with a small pitch angle distribution (Lloyd Petrosian 2000 ), jitter radiation (Medvedev 2000 ), with attenuation by scattering (Dermer Böttcher 2000 ), and thermal synchrotron emission (Petrosian 1981 ). Likewise the limiting values of α = 0 can be reached by synchrotron self-Compton emission from monoenergetic electrons scattering off a self-absorbed seed photons field (Stern Poutanen 2004 ). More generally, instantaneous or fast synchrotron cooling can explain α −3/2 and slow synchrotron cooling is expected to occur at α −2/3 (e.g. Tavani 1996 ). However, before comparing predictions of physical emission models to the observed characteristics of bursts, such as the distribution of α values, limitations of the detector and the analysis methods must also be taken into account. Such limitations could cause spuriously deviation from the expected α (e.g. Preece et al. 1998 ; Lloyd Petrosian 2000 ). One such limitation is due to the limited band-width of the detector which prevents the full spectrum to be detected. Another limitation is due to the typically empirical models that are used to fit the data; the Band function and the cut-off power-law function (see e.g. Yu et al. 2016 ). If these empirical models do not match the true spectral shape, such as its curvature, then the fitted parameters might not be readily interpreted. There are two possible routes to address these limitations. One way is to fit physical models directly to the data, which eliminates the need for fits to empirical functions (e.g. Lloyd Petrosian 2000 ; Zhang Yan 2011 ; Ahlgren et al. 2015 ; Giannios 2006 ; Vianello et al. 2018 ; Burgess et al. 2018 ; Ahlgren et al. 2019 ). However, such analysis is computationally very demanding and, moreover, with the present, limited understanding of GRBs it is not clear what
Paper: 2017ApJ...836...83S (fulltext)
by a characteristic timescale, t esc . The evolution of the particle number density, , in the CR can then be conveniently described by the kinetic equation (Kardashev 1962 )where is the energy loss rate due to synchrotron and SSC processes. Assuming a power law dependence of escape timescale with energy , a semianalytical solution of Equation ( 8 ) can be attained when the loss processes are confined within the Thomson regime (Atoyan Aharonian 1999 ). However, detection of the source at VHE energies suggests that the Thomson scattering approximation of SSC process may not be valid and one needs to incorporate Klein–Nishina correction in the cross-section (Tavecchio et al. 1998 ). Hence, we numerically solve Equation ( 8 ) using a fully implicit finite difference scheme (Chang Cooper 1970 ; Chiaberge Ghisellini 1999 ), while incorporating the exact Klein–Nishina cross-section for IC scattering (Blumenthal Gould 1970 ). The case (no escape) gives rise to a BPL with the break occurring at the energy where the observation time is equal to the cooling timescale of the particle, while for the case (constant t esc ), a steady state BPL particle distribution is eventually attained where the break corresponds to the particle energy at which the escape timescale equates to its cooling timescale. However, in these cases, the spectral transition at the break energy is too sharp to reproduce the observed SED, and a gradual transition can be achieved by considering . We found that the smooth spectral curvature demanded by the observation can be attained by fixing . In Figure 7 , we show the resultant model SED corresponding to (blue line) and , along with the observed fluxes for the state s3. The underlying particle distribution corresponding to these values of ξ is shown in Figure 8 . Figure 7. Model curves obtained by changing the energy dependence of the escape timescale for the state s3. The blue line is obtained for a energy independent escape (), and fails to reproduce the observed spectrum. The best match between the data and the model is obtained for and is represented by the solid green line. The magnetic field B is assumed to be 0.4 G and the Doppler factor . The injected particle spectrum is a power of index 2.1. Figure 8. Underlying particle spectra for the model curves shown in Figure 7 . The dashed blue line is obtained for an energy independent escape ( which fails to reproduce the observed SED), and the solid black line corresponds to the particle spectrum that best models the observed SED (). Alternate to this interpretation, a smooth curvature in the particle distribution can also be a result of time averaging of an evolving particle distribution. For instance, an episodic injection of a power-law particle distribution into CR can cause the high energy cutoff to shift toward lower energy with time, which will be reflected as a smooth curvature at high energy in the time averaged spectrum. Also, an episodic injection with an
Paper: 2020A&A...639A...2P (fulltext)
gas: we see the bubble expanding in two directions. Since the [C II ] emission from NGC 1977 is fainter than that of M 42, we have opted to determine the expansion velocity from spectra towards this region ( Fig. 18 ), resulting in v exp ≃ 1.3 km s −1 . This is consistent with the pv diagram shown in Fig. 17 , from which we obtain the bubble radius r ≃ 500′′ ≃ 1.0 pc with the expansion velocity of v exp ≃ 1.5 km s −1 . As for the Veil shell and M 43, the systematic uncertainty of the mass estimate are of the order of 50%, systematic uncertainties of the estimates of the extent of the shell and of the expansion velocity are about 30%. Table 4 Gaussian fit parameters of spectrum in Fig. 18 . 4 Discussion OMC1, including the Trapezium cluster, is the nearest site of active intermediate- and high-mass star formation (e.g., Hillenbrand 1997 ; Megeath et al. 2012 , 2016 ; Da Rio et al. 2014 ; Großschedl et al. 2019 for discussions of the stellar content). Most of the stars formed and forming are low-mass stars, but also some high-mass stars, that have profound impact on the characteristics and evolution of their environment. Outside the Huygens Region, a significant massive (triple) star is the O9.5IV star θ 2 Ori A, located just to the southeast of the Orion Bar. The radiation of this star dominates the ionization structure of the gas towards the south-east of the HuygensRegion ( O’Dell et al. 2017 ). It also possesses strong winds. The most dominant star in the Orion Nebula is the O7V star θ 1 Ori C, the most massive Trapezium star. It is itself a binary, with possibly a third companion ( Lehmann et al. 2010 ). While its exact peculiar velocity is controversial, the θ 1 Ori C is plowing away from the molecular cloud, the site of its birth, and will have travelled some 25 pc before it explodes as a supernova ( O’Dell et al. 2009 ; Kraus et al. 2009 ; Pabst et al. 2019 ). While the small, 0.5 pc sized Huygens Region has been extensively studied, studies on the much fainter EON are less numerous. O’Dell Harris (2010) determine the temperature of the ionized gas within the large EON H II region to be T ~8.3 × 10 3 K, while electron densities decrease from n e ~ 3000 cm −3 at the Trapezium stars to about n e ~ 30 cm −3 20′ away, roughly as d −2 , with d the projected distance. Güdel et al. (2008) report that the EON exhibits significant X-ray emission, emanating from hot ( T ~ 2 × 10 6 K), dilute ( n e ~ 0.1−0.5 cm −3 ) gas. While, in principle, ionization followed by thermal expansion of the H II region can create a bubble of ~ 2 pc size, the hot gas is the tell-tale signature that the stellar wind of massive stars, rather than stellar radiation, is driving the expansion and forming the EON cavity. As Güdel et al. (2008) note, the emission characteristics in conjunction with its structure and young age render it unlikely that the bubble is a supernova remnant. While the observed morphology is in qualitative agreement with simple models for stellar
Paper: 2016A&A...586A.133P (fulltext)
extrapolation to 150 GHz. The Planck 2013 best-fit CDM CMB model based on temperature anisotropies, with a tensor amplitude fixed at , is overplotted as a black line. 6.2. Statistical estimate of the dust B-mode level in the BICEP2 field We have seen in Sect. 5.2 that the dust spectra amplitudes statistically follow an empirical scaling law of the mean dust intensity for the patch on which they are computed. We can use this scaling law to assess the most probable dust value for . For this field, which has a mean dust intensity of = 0.060 , the expected value at 353 GHz is K at , taking into account the uncertainty on the fitted amplitude from Sect. 4.3 . In order to extrapolate this value to the BICEP2 observing frequency, 150 GHz, we proceed as in Sect. 5.3 , using the same typical dust SED from Planck Collaboration Int. XXII (2015) . For patches the size of the expected dispersion of (Sect. 2.2.1 ) is 0.11, which introduces an uncertainty in the extrapolation. The extrapolation, unlike in that section, is made to 150 GHz taking into account the BICEP2 bandpass 16 . The conversion factor from the Planck 353 GHz band to the 150 GHz BICEP2 band, taking into account both Planck and BICEP2 colour corrections, as well as the statistical error on , is thus in the SED, and therefore this value squared in . The resulting expected dust amplitude at 150 GHz is K at . The uncertainty quoted here applies to the most probable value for patches with a similar , but it ignores the intrinsic dispersion around this value (see Sect. 5.2 ). Therefore, a direct measurement of the dust spectrum in is required to complete the assessment of its polarized dust level. In Sect. 5.3 , we have localized the amplitudes from the analysis of patches on a map. The pixels falling inside the approximate BICEP2 deep-field region shown in Fig. 8 give a mean value of , i.e., an expected dust power of K at 150 GHz and ; we note that in this region the Planck estimate of the dust contamination is significantly higher, by a factor of about 2, than it is in some more optimal patches. Nevertheless, a more accurate estimate of the dust polarization amplitude in the BICEP2 observed field requires the direct computation of the angular power spectrum on a similar region. 6.3. BB angular power spectrum of dust in the BICEP2 field We now compute the spectrum of the dust at 150 GHz on presented in Sect. 6.1 , using the Planck 353 GHz data. For this purpose, we compute the 353 GHz spectrum by cross-correlating the two DetSets that have independent noise at this frequency in the three -bins defined by the intervals between multipoles 40, 120, 250, and 400. The -bin sizes were increased with respect to the previous sections in order to increase the S/N, especially around the range of the CMB recombination bump. Appendix D.1 confirms that the result does not depend on the method of computing the power spectrum. This power spectrum is extrapolated to 150 GHz as in Sect. 6.2 , with an
Paper: 2019MNRAS.483.5301G (acknowledgments)
ACKNOWLEDGEMENTS We thank Emanuele Giallongo, Andrea Grazian, Frederick Davies, and the anonymous referee for useful comments. This work is carried out within the Collaborative Research Centre 956 ‘The Conditions and Impact of Star Formation’, sub-project C4, funded by the Deutsche Forschungsgemeinschaft (DFG). We are thankful to the community developing and maintaining software packages extensively used in our work, namely: matplotlib (Hunter 2007 ), numpy (Walt, Colbert Varoquaux 2011 ), scipy (Jones et al. 2001 ), yt (Turk et al. 2011 ).