adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...629A..24P (fulltext)
RTE) applied to the spectra and polarized spectra emitted by the object. The RTE characterizes the interaction of the matter with the light that traverses through it. This interaction leaves its “fingerprints” in many different ways, for instance in the shape of the continuum of the spectra coming from the object, or in the presence or absence and the shape of spectral lines. Properly modeled, these features allow approximate characterization of the studied object. In recent decades, the advent of polarimetric observations has allowed the generalization of the RTE to polarized light ( Unno 1956 ; Rachkovsky 1967 ; Landi Degl’Innocenti Landi Degl’Innocenti 1972 ). This step was of great significance, as the polarization of the light encodes information on the magnetic field configuration as well as the geometry of the system, which allows a deeper insight into the system being studied. In solar physics, there are different approaches to solving the polarized RTE depending on the magnetic field regime adopted: atomic, Hanle, Zeeman, and/or Paschen (see e.g., Landi Degl’Innocenti Landolfi 2004 ). In this work, we focus on the solution of the polarimetric RTE under the Zeeman magnetic regime, which is usually the dominant one at this atmosphere layer. There are already several methods to address the solution of the RTE, each with its respective accuracy (see, e.g., del Toro Iniesta Ruiz Cobo 2016 ). There are several computational codes that allow one to infer the optical depth ( τ ) dependence of the physical properties in the solar atmosphere; for example, SIR ( Ruiz Cobo del Toro Iniesta 1992 ), SPINOR ( Frutiger et al. 2000 ), NICOLE ( Socas-Navarro et al. 2015 ), SNAPI ( Milić van Noort 2018 ), and STIC ( de la Cruz Rodríguez et al. 2019 ). These are of particular relevance here as they allow the furthest insight into the properties of the solar atmosphere in a three-dimensional volume. In these cases, the conversion between optical depth τ and geometrical height z is then performed by considering the relation d τ = − ρ κ c d z , where ρ is the density and κ c the continuum opacity, with the latter being a nonlinear function of the temperature T and gas pressure P g . Since the proper calculation of the gas pressure (and density) involves the whole three-dimensional volume, for which there is no account in the aforementioned codes, the gas pressure and density inferred by them are unrealistic, and hence also the conversion between τ and z . Deviations from hydrostatic equilibrium involve, among others, time-variation of the velocity, advection and viscous terms and, the most relevant at this atmospheric layer, the Lorentz force. The latter is responsible for the Wilson depression, a depression of the height at which photospheric spectral lines are formed with respect to the quiet-Sun because of the presence of magnetic field. Because the geometrical scale z is then inaccurate, so is the calculation of the spatial derivatives, such as those
Paper: 2021MNRAS.501.4064R (fulltext)
et al. 2009 ; Rosario et al. 2013 , 2016 , 2018 , and references therein) or mid-infrared neon ([Ne ii ] 12.81 μ m and [Ne iii ] 15.56 μ m) emission lines (Zhuang, Ho Shangguan 2019 ). The infrared observations used in these works are obtained using large apertures, including the whole galaxy emission. As discussed in Rosario et al. ( 2016 ), despite finding a good correlation between far-infrared emission and SFR (which can be attributed to leaking of the SF regions to the whole galaxy or that the SF is constant over several hundreds of Myr) a substantial component of the cold dust luminosity that is associated with a diffuse interstellar radiation field can come from evolved stars. Even if the contribution from an old age stellar population is not dominant, the far-infrared may not be a good tracer of SF since dust can be heated by stars that are older than a few hundred Myr (Kennicutt et al. 2009 ; Hao et al. 2011 ; Rosario et al. 2016 ). Other recent effort on the bluer part of the spectrum has been made using a re-calibration [O ii ] λ3727 emission line (Zhuang et al. 2019 ; Zhuang, Ho Shangguan 2020 ), which is metallicity and electron density dependent. From the above, it is clear that it is thus necessary to find an independent way to obtain SFRs in AGN hosts. A powerful technique to disentangle the components summing up to a galaxy spectral energy distribution (SED) is stellar populations synthesis (e.g. Cid Fernandes et al. 2004 , 2005 ; Riffel et al. 2009 , 2008 ; Walcher et al. 2011 ; Baldwin et al. 2018; Cid Fernandes 2018 ; Salim, Boquien Lee 2018 ; Peterken et al. 2020 , and references therein). The synthesis is based on the simultaneous fit of different proportions of composite or simple stellar populations (SSPs) templates from a base of such spectra to the observed spectrum. In the case of the starlight fitting code (Cid Fernandes et al. 2004 , 2005 ; Cid Fernandes 2018 ), it returns the values of the gas mass rate that has been converted into stars throughout the galaxy life as well as this rate for each single SSP included in the base. These values can therefore be used to compute the SFR via stellar population synthesis, SFR ⋆ . In fact, this was already applied in Asari et al. (2007) using single fiber Sloan Digital Sky Survey (SDSS) data and older stellar population models generations (they used Bruzual Charlot 2003 models). From the discussion above, it is clear that it is of utmost importance to characterize the SFR of AGN hosts – in particular in the narrow-line region (NLR) and extended narrow-line region (ENLR), which are photoionized by the AGN. And in order to verify the actual role of the AGN on the SFR, it is necessary to compare the SFR values obtained for the NLR and ENLR with those obtained for a matched control sample of non-active galaxies at similar distances from the nucleus, as well as with predictions from simulations. In Rembold et al. ( 2017 ), we described the method we have used to select
Paper: 2021ApJ...906...23F (fulltext)
polarized) radiation. Then, the radio spectral index S ν ∝ ν α can provide information as well, but it will often be ambiguous. Third, variability can be an indication, with rapid variability a sign of nonthermal emission (on timescales as short as a few minutes, e.g., Forbrich et al. 2017 ), but there is again some ambiguity on intermediate timescales. Fourth, radio brightness temperature is a rarely employed but powerful indicator, since this technique allows us to select sources with brightness temperatures that are above that of thermal emission. This can naturally be accomplished by using Very Long Baseline Interferometry (VLBI), with which it is thus possible to filter out any thermal emission, enabling a census of nonthermal radio emission that remains largely unaffected by thermal radio emission. Apparent brightness is inversely proportional to the synthesized beam size for an unresolved source, and for milli-arcsecond resolution VLBI observations in C -band (4–8 GHz), even faint detections have corresponding brightness temperatures of several 10 6 K, well above possible values of free–free emission ( ≈ 10 4 –10 5 K), even more so when considering that this is a lower limit. Even at this high resolution, the stellar radio emission remains unresolved, with a synthesized beam size corresponding to a linear scale of order 1 au in our case. Overall, the two most useful indicators thus are polarization and brightness temperature. In recent years, it has become possible to study populations of radio stars at unprecedented sensitivity, with a range of radio observatories. Our focus has been on observing YSOs in the Orion Nebula Cluster, the nearest young rich open cluster containing high-mass stars ( d ∼ 400 pc; Menten et al. 2007 ; Kounkel et al. 2017 ). Altogether, the ONC has 3500 stellar members that formed less that 2 Myr ago (Hillenbrand et al. 2013 ), many of which emit X-rays (e.g., Getman et al. 2005a ). Radio emission from YSOs in the ONC and the nearby Kleinmann–Low nebula was first observed when the NRAO Very Large Array became available (Churchwell et al. 1987 ; Garay et al. 1987 ), and prior to the VLA expansion, 77 radio sources had been observed in this cluster (Zapata et al. 2004 ). After the VLA expansion was completed, we obtained a deep pointing with what is now called the Karl G. Jansky Very Large Array, which allowed us to identify 556 radio sources in the ONC (Forbrich et al. 2016 ). Our analysis revealed extreme variability for many objects, strongly suggesting nonthermal emission (Forbrich et al. 2017 ). While this analysis included the derivation of in-band spectral indices for the brightest sources, it has been impossible so far to extract reliable wideband polarization information across the entire primary beam of this deep pointing. To obtain a more reliable census of the incidence of nonthermal emission, and to then use this information for a proper motion census of the inner ONC, we have utilized the
Paper: 2018ApJ...868..138B (fulltext)
T icb = 6.36 × 10 4 K; the density ρ icb = 1.47 g cm −3 . The composition is uniform with 100% silicates out to a temperature T = 2.1 × 10 4 K and radius 2.72 × 10 9 cm, decreasing to 50% at T = 5.42 × 10 3 K and radius 3.39 × 10 9 cm, and to 1% at T = 2960 K and radius 4.84 × 10 9 cm. During the isolated phase, the parameter in Equation ( 7 ) is set to 0.04. During the first 10 8 yr, an additional 0.25 M ⊕ is lost, giving M XY = 0.42 M ⊕ , R p = 7.43 R ⊕ , L = 2.0 × 10 −10 L ⊙ , and a cooling time of 8 × 10 8 yr. At this time, M ⊕ yr −1 . As in Run 0.128 (Rev), a radiative zone extends inward to T = 1500 K. Later, the luminosity declines to ≈10 −11 L ⊙ , the cooling time increases by an order of magnitude and the rate of mass loss declines significantly, by a factor of 400, to 2 × 10 −12 M ⊕ yr −1 , by the final time. Between t = 10 8 yr and t = 7 × 10 9 yr, a further 0.13 M ⊕ is lost from the H/He envelope. The radius of the final model planet agrees quite well with that of the actual planet, as does the total mass. The total heavy-element mass (including the inner and outer cores) is M Z = 7.52 M ⊕ , and the H/He mass M XY = 0.29 M ⊕ . The inner core of 1.87 M ⊕ has R icore = 1.075 R ⊕ and mean density 8.35 g cm −3 . The region of almost 100% heavy elements has radius 1.84 R ⊕ , and the mean density of the inner plus outer cores is 5.86 g cm −3 . The mass of this final model is very close to that of the in situ model 0.128 (Rev). The temperature at the boundary between the inner and outer cores ( T icb ) is similar (1.54 × 10 4 K versus 1.75 × 10 4 K), and the corresponding density is slightly lower (7.59 versus 8.0 g cm −3 ). These differences are presumably caused primarily by the different masses of the inner cores. Both final models have outer radiative zones, extending inward to T = 1850 K in the in situ case and to 1500 K in the present case; they include less than 1% of the envelope mass. The structure of the final model for Run 1.00 (Rev) is plotted in Figure 12 . Figure 12. Structure of the model in Run 1.00 (Rev) at time 7.03 × 10 9 yr. Symbols and curves: as in Figure 11 . The mean density of the inner core is 8.35 g cm −3 . 3.4. Migration Model: Run 1.00(Old) Run 1.00(Old) starts at 1 au with σ init = 190 g cm −2 , slightly lower than that in Run 1.00(Rev). Outer densities and temperatures are the same in the two runs. The masses and radius as a function of time are given in Figure 13 . At first, M core increases very rapidly until, at t ≈ 7 × 10 3 yr, it reaches 6.78 M ⊕ , close to the isolation mass of 6.81 M ⊕ . At this point, M env = 0.084 M ⊕ . The temperature T cb is 9.91 × 10 3 K, much lower than the value of T icb = 6.1 × 10 4 K reached at a comparable evolutionary phase in Run 1.00 (Rev). The lower mean molecular weight in the H/He envelope accounts for much of this difference. The luminosity during the solid accretion phase averages about 10 −4 L ⊙ , corresponding to a mass accretion rate onto the core of 1 to 1.5 ×
Paper: 2021MNRAS.505.2124L (fulltext)
8 . It can be seen from Fig. 8 that the age of red giants estimated by our model is usually younger than the age given by Pinsonneault et al. ( 2018 ). This is determined by the sample (Sanders Das 2018 ; Yu et al. 2018 ) we used to train the red giant age model. Pinsonneault et al. ( 2018 ) used the revised scaling relation when inferring stellar mass from seismic data, and they took the influence of the [ α /Fe] into account when estimating the age of red giants. Wu et al. ( 2018 ) also mentioned these two points when comparing the red giant ages of Sanders Das ( 2018 ). It should be mentioned that, in principle, using asteroseismology to estimate the age of red giants should be much more precise. In this work, we used the stellar age given by Sanders Das ( 2018 ) as a standard, established different stellar age models by using different stellar parameters, and compared the model results. By comparison, we infer which parameters are used to establish the stellar age model that is more accurate, that is, to filter out which stellar parameters are more relevant to the stellar age. Figure 8. The age of the red giant estimated using our model is compared with the age given by Pinsonneault et al. ( 2018 ). 4.2 EW of the magnetic activity lines and the age of red giants In Table 3 , by comparing the input parameter combinations ‘b’ and ‘j’, ‘d’ and ‘f’, ‘g’, ‘h’, ‘i’, we know that no matter adding all or part of the EW of the magnetic activity lines to the input parameters, the red giants age estimation cannot be improved. This suggest that in the red giants, the EW of the magnetic activity lines has no direct correlation with the age of the red giants. When studying the relationship between stellar chromospheric activity and age in the open clusters, Zhang et al. ( 2019 ) found that the chromospheric activity indices (log and log ) of low-mass stars with strong surface magnetic field are strongly correlated with the stellar age, while for those stars with relatively large mass and weak surface magnetic field, their stellar chromospheric activity indices are weakly correlated with the age. In other words, the relationships between stellar age and chromosphere activity indices are affected by the intensity of the stellar surface magnetic field. Red giants are formed when low- or intermediate-mass stars have finished burning the hydrogen in their cores. Stello et al. ( 2016 ) mentioned that in the red giants with asteroseismic signals, the outer layer dynamo has shut off at the end of core-hydrogen-burning, and most of these red giants have core-dynamo-generated fields. In Section 2 , by analysing the EW of the magnetic activity lines, we know that there is no strong magnetic activity in the chromosphere and photosphere of all red giants in this study sample. This is consistent with the conclusion of Stello et al. ( 2016 ). Moreover, the results of Chaplin et al. ( 2000 ) showed that the amplitude of the solar p mode decreases with the increase of
Paper: 2017MNRAS.469S.142V (fulltext)
here but contains also a 1/ r dependence. Recently, Vigren Eriksson ( 2017 ) developed a 1D model to test the ability of H 2 O molecules to interrupt ion acceleration along weak electric fields in the radial direction. From the model results, they raised concerns about the correctness in assuming u I, flow = u for r r IN as given by Equation ( 1 ) or ( 2 ). As an example, for perihelion conditions, r IN from equation (1 ) is at ∼1000 km, while the estimated effective ion flow speed at 200 km was calculated as several km s −1 , assuming a rather weak electric field strength of the order of ∼0.03–0.1 mV m -1 , which may commence due to the electron pressure gradient force. Their finding would support the observations of H 3 O + /H 2 O + number density ratios observed at times to be lower than predicted by models running with the assumption of collisionally coupled ions (see fig. 7 of Fuselier et al. 2016 ). The observed ratios were observed to be variable and at times also at the level predicted by collisionally coupled models (see in particular, Fig. 6 of Fuselier et al. 2016 ), particularly so when the RPC-Ion Electron Sensor (RPC-IES, see Mandt et al. 2016 ) did not see clear signs of ion acceleration. Vigren Eriksson ( 2017 ) referred also to preliminary values of effective ion speeds (not restricted to ion flow speeds) of several km s −1 as calculated from combined measurements by the Mutual Impedance Probe (MIP, Trotignon et al. 2007 ) and the Langmuir Probe (LAP, Eriksson et al. 2007 ), both being subsystems of the Rosetta Plasma Consortium (RPC, Carr et al. 2007 ). In this work, we will first (Section 2 ) discuss the MIP and LAP measurements and how they can serve to estimate the mean (or rather the effective) ion speed. The selected time interval for the study, 2015 August 2–4, near perihelion, is discussed in Sec-tion 3 . Results are presented and discussed in Section 4 . The derived effective ion speeds are markedly higher than the neutral outflow velocity and also shown to at least periodically be in good agreement with effective ion drift speeds derived from an independent method; a simple flux conservation model relying on MIP and COPS measurements. The work is briefly summarized in Section 5 . 2 METHOD Our method utilized to derive effective ion speeds is based on combining electron number densities measured by the MIP with voltage–current characteristics from the ion side (negative voltages) of Langmuir probe bias voltage sweeps (we used the LAP1 sensor, see Eriksson et al. 2007 for details). The MIP measurements consist of determining the mutual impedance between two electric antennas – a transmitting and a receiving one. The transmitting electrode injects an oscillating current in the plasma, at a given frequency, while the receiving one measures the potential difference induced in the plasma at the same frequency. The frequency is increased step by step, in a frequency range that ideally contains the plasma frequency. The
Paper: 2019ApJS..244...29M (fulltext)
magnification, which depends on the microlensing model and blending. Our method conserves the variability and noise in the original light curves, as well as information on the quality of individual measurements. Let be the flux of the source and the unmagnified flux from possible blended stars and/or the lens itself. The flux of the baseline object () is magnified during a microlensing event by a factorwhere A ( t ) is the model magnification and is the blending parameter. If there is no blending ( f s = 1), then A ′( t ) = A ( t ); if the blending is very strong (), the observed magnification . To inject a microlensing event into the database light curve, we needed to transform the observed flux and its uncertainty to . The naive transformation is incorrect because it preserves the original photon noise during the magnified part of the event. To illustrate this, let us consider a constant I = 19.5 star with a typical rms light-curve scatter of 0.1 mag (left panel of Figure 9 ). The transformation A ′( t ) = 100 would shift the mean magnitude to I ′ = 14.5, but it would preserve the original scatter, whereas the observed scatter of constant stars of that magnitude is much smaller (Figure 9 ). Figure 9. The rms light-curve scatter as a function of the mean magnitude of a star in the I band in the OGLE-IV field BLG521.12. Left panel: real data. Right panel: constant stars injected on top of real light curves (, where 0 ≤ Δ m ≤ 0.75 is drawn from a uniform distribution). The orange line is the best-fit model from Skowron et al. ( 2016 ). Assuming that every observed fluctuation in brightness of the initial light curve from its mean brightness ( F i − F 0 ) is due to the observational noise, the following transformation ensures that the photon noise is properly scaled:where F 0 and are the mean flux in the baseline and the magnified mean flux, respectively (the corresponding magnitudes are m 0 and ). The ratio can be calculated by assuming the photometric noise model of Skowron et al. ( 2016 ):In this simple model, the observed scatter is the sum of the Poisson noise contributions from the object and background (parameterized by m B ). To verify the proposed model, we injected “constant” stars on top of real light curves from the database (which correspond to the transformation ). The right panel of Figure 9 shows the rms scatter of simulated light curves, which is consistent with that of real data. Our simple model underpredicts the scatter of the brightest stars ( I ≲ 15), likely because the accuracy of their photometry is limited by the accuracy of modeling the PSF and thus the quality of image subtractions, not by the photon noise. This effect does not influence our detection efficiency simulations, because the vast majority of events are fainter than I = 15. Similarly, we used the noise model of Skowron et al. ( 2016 ) to transform the uncertainties:When the predicted uncertainties were equal to or below 0.003 mag, we assumed mag, as in the
Paper: 2019A&A...623A.140G (fulltext)
: pseudo-ADI image of the inner regions obtained by differentiating the Q Φ image (see Ligi et al. 2018 , for more details). The white cross marks the position of the star, and the cyan circle shows the size of the coronagraphic mask. The other labels refer to the blobs we discuss in this paper that are also visible in these images. Thecolor scale of the differential image is five times less extended to show the faint structures better. In both panels, N is up and E to the left; a segment represents 1 and 0.5 arcsec in the left and right panel, respectively. Fig. 2 Signal-to-noise ratio (S/N) maps for the individual epochs for IFS. The individual S/N maps are obtained from the ASDI PCA algorithm using 50 components and making a median over the wavelength (see Mesa et al. 2015 ). Upper row, left panel : JD = 57180.17, middle panel : JD = 57201.12, and right panel : JD = 57499.34. Lower row, left panel : JD = 57566.15, middle panel : JD = 57873.30, and right panel : JD = 58288.19. In all panels, the central 0.1 arcsec is masked, the solid white line at the bottom represents 1 arcsec, the white cross represents the position of the star. N is up and E to the left. The upper panels of Fig. 3 show images of HD 169142 obtained by combining the six individual images, assuming the distance given by Gaia DR2, a mass of 1.7 M ⊙ (see below), and circular orbits. 3.1 Coincidence images To improve our ability of discerning faint signals, we combined data from different epochs using a coincidence map (see the lower panel of Fig. 3 ). The principle of this coincidence map is to start with S/N maps for individual epochs. We used S/N maps after correcting for the small-number-statistics effect using the formula by Mawet et al. (2014) . The maps were then multiplied by each other. The S/N maps average to zero, with both positive and negative values for individual pixels. Of course, this may result in a false-positive signal for an even number of negative signals in the individual S/N maps. To avoid this problem, we arbitrarily set to negative the result for a given pixel when the signal for that pixel was negative for at least one epoch. Of course, this is not a realistic flux map: the aim is merely to identify consistent signals throughout all individual images. To consider the orbital motion around the star over the three years covered by our observations, we divided the field into 65 rings, each one 2 IFS pixels wide (15 mas, i.e., about 1.8 au at the distance of HD 169142). For each ring, we rotated the S/N maps obtained at different epochs with respect to the first reference image according to Kepler’s third law. When the distance to the star was fixed, the only free parameter remaining in this model is the (dynamical) stellar mass. If there is a companion orbiting the star, the signal is maximized for a value of the mass that, if the assumptions made (circular motion seen face on) are correct, is the dynamical mass of the star. If these hypotheses are not