adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...856L..27G (fulltext)
polarized intensity (grayscale), and polarization position angle segments (red bars) in the GGD27 MM1 disk. Bottom panel: similar as before, but the color image showing the polarization fraction (here, the polarization position angle segments appear as black bars). In both panels, the contour levels are 3, 10, 50, 120, 200, and 400 K. The wedge shows the scale of the polarized intensity and polarization fraction. The synthesized beam is shown in the bottom-right corner of the panels. Note that the 120 K contour appears at the transition radius, with an almost null polarization ring, between two distinctive polarization patterns. The region inside the transition radius, R turn , (hereafter the inner disk) has a very low polarization level region. Thus, within this radius the polarization degree has a mean value of 0.56% and a standard deviation of 0.33%. In the left panel of Figure 4 , the histogram of the polarization position angles shows two main directions. Most of the angles are partially aligned with the minor axis (226), with an average direction of ∼36°. The other region arises from the northeast quadrant of the inner disk, and shows an average position angle of −38°. Figure 4. Left panel: histogram of the distribution of the polarization position angles for the disk’s inner region ( R 010). The dashed line indicates the position angle of the disk minor axis. Right panel: histogram of the differences between the polarization position angles and the disk azimuthal angles for the disk’s outer region ( R > 010). The outer disk (i.e., the region outside R turn ) has significantly higher polarization levels than the inner disk, with an average value of 4.0% and a standard deviation of 2.3%. 17 17 This has been measured in a region enclosed between R turn and the disk radius of R disk . The polarization position angles have a nearly azimuthal pattern (i.e., they follow a direction tangent to the iso-intensity contours of the Stokes I emission). At the position of each polarization position angle segment shown in Figure 3 , we have computed the expected tangent angle for an ellipse centered in the disk center that crosses this point. The right panel of Figure 4 shows the histogram of the difference between this tangent angle and the observed polarization angle. The average angle difference, 11°, is relatively small. This confirms the azimuthal pattern in the outer disk. 4. Discussion 4.1. The Optically Thick Disk around the Massive Protostar Powering the HH 80–81 Jet The brightness temperature of the disk dust emission reaches high values, up to ∼400 K, but it drops well below 10 K in the outskirts of the disk. This suggests that the inner part of the disk is optically thick and that the dust optical depth decreases with radius, becoming optically thin. A multi-line analysis of SO 2 , at scales of a few hundred au, indicates that the average kinetic temperature of the traced molecular gas is ∼120 K (Fernández-López et al. 2011b ).
Paper: 2021ApJ...909...66H (fulltext)
of the code used here is essentially identical to the one employed by Hartlep et al. ( 2013 ). The code has been used over many years to study wave propagation in the solar interior in the presence of both sound-speed perturbations as well as flows, and was used to help improve and validate various helioseismic measurement and inversion techniques. This includes the far-side imaging technique for detecting solar active regions on the side facing away from Earth using observations from the Earth-facing side (Zhao 2007 ; Hartlep et al. 2008 ; Ilonidis et al. 2009 ); measuring the solar meridional flow in the deep interior (e.g., Hartlep et al. 2013 ; Roth et al. 2016 ); detecting sound-speed variation using time–distance helioseismology (Parchevsky et al. 2014 ) and the acoustic holography technique (Braun 2014 ; Díaz Alfaro et al. 2016 ); and studies related to magnetic flux emergence (e.g., Hartlep et al. 2011 ; Ilonidis et al. 2013 ). The code models solar acoustic oscillations in a spherical domain using the Euler equations linearized around a stationary background state, which is characterized by a background density, sound speed and acceleration due to gravity, all taken from a standard solar model. The code also takes into account the effect of mass flows on the propagation of waves. For the governing equations and more details on the code, we refer to Hartlep et al. ( 2013 ). A significant difference between how the code is used in the current study and previous works is the excitation of acoustic waves. In the Sun, turbulent flows near the photosphere act as sources for acoustic perturbations driving waves in a stochastic fashion. However, since this driving mechanism is a nonlinear process, it is lost by linearizing the governing equations. Therefore, for most previous studies, a stochastic term was added in the equations to mimic this excitation. This is not used here, however. Instead, each simulation excites and follows individual waves as described in the next section. 2.1.2. Simulation Setup In order to measure helioseismic travel times accurately, one usually needs many hours of data, for either actual solar observations or numerical simulations that are stochastically driven to mimic solar-like wave excitation. Such numerical simulations are feasible when only one or few sets of them are needed, as for instance in Hartlep et al. ( 2008 , 2013 ). However, for deriving sensitivity kernels, we need to probe many perturbations at many different depths and locations while keeping them spatially separated to avoid travel times to be shifted by multiple perturbations simultaneously. This requires many individual simulations, with only few perturbations in each, which quickly becomes infeasible. The alternative we have chosen here is to simulate not a stochastic Sun-like wavefield but excite individual waves and measure their travel times from their source location to other points on the surface. There is no stochastic noise in this
Paper: 2017ApJ...847..113H (acknowledgments)
I thank Dr. Miho Janvier for a critical review of the manuscript and several helpful suggestions. I also gratefully acknowledge the stimulating discussions on related topics from the team on magnetic waves in solar flares led by Alexander Russell and hosted and organized by the International Space Science Institute ISSI.
Paper: 2018MNRAS.481.2841H (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank to Dr. G.B. Rusu from the USAMV, Iasi, Romania for its help during the electron microscopy imaging. Our thanks also go to Dr. R. Jijie, for her contribution during the preliminary phases of the study, and to our colleagues, Dr. G. Borcia, Dr. A. Chiper and Prof. G. Popa, for careful reading of the manuscript and helpful suggestions. This work was supported by the Romanian Space Agency (ROSA) under the project STAR CDI ID 486/2017–2018. The authors also acknowledge the support from the COST action CM1401 (Our Astro-Chemical History).
Paper: 2017A&A...598A.126L (acknowledgments)
The authors thank the anonymous referee for constructive comments that helped to improve the manuscript and presentation of the results. The research work was funded by the Austrian Science Fund (FWF): P27765 and P23618. The model calculations have been carried out at VSC (project P70068, H. Muthsam). We thank Luis Bellot Rubio from the Instituto de Astrofisica de Andalucia (CSIC) supplying the inverted SUNRISE IMaX data used in this paper.
Paper: 2019MNRAS.485.2532H (acknowledgments)
ACKNOWLEDGEMENTS We are grateful to an anonymous referee whose suggestions helped improve the manuscript. PH acknowledges financial support from the Natural Sciences and Engineering Research Council of Canada and thanks NAOC for hospitality during a sabbatical visit made possible by support from the Chinese Academy of Sciences (CAS President’s International Fellowship Initiative, 2017VMA0013). We also acknowledge support from the National Natural Science Foundation of China under grant numbers 11733007 and 11673037.
Paper: 2017MNRAS.469.4705C (fulltext)
possible powering source could be the spin-down of a magnetar (Kasen Bildsten 2010 ; Inserra et al. 2013 ; Nicholl et al. 2013 ). Another possibility is the interaction between the ejecta and an H-poor CSM (Chatzopoulos, Wheeler Vinko 2012 ; Sorokina et al. 2016 ; Vreeswijk et al. 2017 ). A third scenario could be a pulsational pair-instability SN (Woosley, Blinnikov Heger 2007 ; Woosley 2017 ), or pair-instability explosion of a very massive star (with a core of ≥50 M ⊙ , e.g. Gal-Yam et al. 2009 ; Dessart et al. 2013 ). The latter is perhaps limited only to slowly declining H-poor SLSNe, SLSNe-R, with a declining rate consistent with the radioactive decay of 56 Ni and 56 Co. Until recently, models predicted a long rise in contradiction to observations (Chen et al. 2015b ), but Kozyreva et al. ( 2017 ) showed that they can also be fast rising. The long rise times predicted by pair-instability supernova models have been seen in at least one SLSN (Lunnan et al. 2016 ). However, spectroscopic models predict spectra that are different from observations (Jerkstrand, Smartt Heger 2016 ). SLSNe-R are a potential subclass of SLSNe-I. The existence and characterization of this class is discussed in Gal-Yam ( 2012 ) and De Cia et al. (in preparation). While the debate on the powering source of SLSN-I is ongoing, the general agreement is that these explosions are associated with the death of massive stars, given their total radiated energies ( E > 10 51 erg, Gal-Yam 2012 ). The study of their host galaxies has revealed that SLSN-I require extreme hosts, e.g. with low-metallicity ( Z ≤ 0.5 Z ⊙ ) and a high rate of star formation (SFR) for their low mass (Neill et al. 2011 ; Lunnan et al. 2014 ; Thöne et al. 2015 ; Chen et al. 2016 ; Perley et al. 2016 ; Schulze et al. 2016 ), and strong emission lines (Leloudas et al. 2015 ). However, Perley et al. ( 2016 ) argue that a high rate of star formation is not required, since there are many counterexamples of SLSNe with low SFRs, based on slit spectroscopy. High-resolution observations with Hubble Space Telescope (HST) showed that the host galaxies of hydrogen-poor SLSNe are usually irregular dwarf galaxies with asymmetric morphology or multiple peaks (Lunnan et al. 2015 ; Angus et al. 2016 ). Gamma-ray bursts (GRBs) are another energetic phenomenon likely associated with deaths of massive stars (Hjorth et al. 2003 ). Their host galaxies show similarities to the hosts of SLSNe (Lunnan et al. 2014 ), although the latter are more extreme (Vreeswijk et al. 2014 ; Leloudas et al. 2015 ; Perley et al. 2016 ; Schulze et al. 2016 ). Fruchter et al. ( 2006 ) and Blanchard, Berger Fong ( 2016 ) showed that GRBs are more concentrated in the brightest regions of their host galaxies. Similarly, if SLSNe are indeed associated with very massive stars, and their hosts are unusual for the young stellar content (Leloudas et al. 2015 ; Thöne et al. 2015 ), one natural expectation is that they may explode in blue, UV bright,
Paper: 2018A&A...620A..95K (fulltext)
non-thermal electrons with energy 30 keV are depositing their energy and the plasma densities are sufficient to make 0.4 THz emission optically thick. The observation of ribbon-like HXR emission ( Liu et al. 2007 ) is often viewed as an additional indication that UV emission is caused by the energy deposition of HXR producing electrons. 4.3. Time variability of sub-THz emission Relatively dense plasma n > 10 11 cm −3 heated by energetic electrons to temperatures 0.1–1 MK leads to enhanced radiation ( Raymond et al. 1976 ; Johnson et al. 2011 ; Scullion et al. 2016 ), so that the radiation losses would lead to effective cooling. The radiative cooling time is ( Cox Tucker 1969 ; Raymond et al. 1976 ; Bian et al. 2016 ) where the radiative loss function is calculated using the CHIANTI ( Dere et al. 1997 ) function rad_loss.pro 2 . The radiative loss can be estimated using the approximation Λ( T )≃1.2 × 10 −19 T −1/2 , where T is in units of kelvin and Λ( T ) in units of [erg cm 3 s −1 ], giving Λ( T )≃4 × 10 −22 for T = 10 5 K. The radiative loss time for plasma temperatures between T = 10 4 − 10 6 K is shown in Fig. 3 . The results suggest that the plasma can quickly cool if the heating time is greater than the radiation loss time. The observations by Kaufmann et al. (2009a) show that the flux changes with a characteristic time of 0.1 − 1 s. This suggests that the interplay between non-thermal electron heating and the radiative cooling of dense plasma can explain the observed variability of sub-THz emission. Quasi-synchronised broadband solar flare rapid pulsations at sub-second timescales are observed at HXR and centimetre-millimetre wavelengths ( Takakura et al. 1983 ; Cornell et al. 1984 ; Kaufmann et al. 2000 ). These short-timescale features are often called “elementary bursts” following de Jager de Jonge (1978) . In particular, efforts to search for small-scale structures have also been made in optical wavelengths, typically H α observations ( Wang et al. 2000 ; Trottet et al. 2000 ; Kurt et al. 2000 ; Altyntsev et al. 2008 ; Fleishman et al. 2016 ) For example, Wang et al. (2000) reported fine temporal structures of the order of 0.3 − 0.7 s in H α off-band emission, and it is found that these fine structures only occur at flare kernels whose light curves are closely correlated with HXR emission. This close correlation suggests that the sub-THz pulsations are caused by fast electron energy deposition or by thermal conduction in the transition region and heated chromosphere. In such a scenario, the finite temperature of the ambient plasma plays an important role in determining the energy deposition ( Kontar et al. 2015 ; Jeffrey et al. 2015 ). An alternative way proposes heating by fast electrons accelerated in situ in the chromosphere due to the magnetic Rayleigh-Taylor instability ( Zaitsev Stepanov 2015 , 2017 ). 5. The sub-THz emission model During solar flares the transition region defined as the plasma between the chromosphere and
Paper: 2018MNRAS.474.5114C (acknowledgments)
Acknowledgements We thank the anonymous referee for insightful comments and suggestions. We thank Vincent van Eylen and Subo Dong for useful discussions. This work was supported by the Netherlands Research Council NWO [grant #621.016.701 (LGM2)] and by the Netherlands Research School for Astronomy (NOVA). This research was supported by the Interuniversity Attraction Poles Programme (initiated by the Belgian Science Policy Office, IAP P7/08 CHARM) and by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 671564 (COMPAT project).
Paper: 2017MNRAS.470.4739S (acknowledgments)
Acknowledgments We thank the anonymous referee for their comments, which helped us to improve the manuscript. We thank Alessandro Bressan for his invaluable suggestions and useful discussions. MM and MS acknowledge financial support from the Italian Ministry of Education, University and Research (MIUR) through grant FIRB 2012 RBFR12PM1F and from INAF through grant PRIN-2014-14. MM acknowledges support from the MERAC Foundation. Part of the Numerical calculations have been made possible through a CINECA-INFN agreement, providing access to resources on GALILEO and MARCONI at CINECA.
Paper: 2018A&A...614L...4K (fulltext)
is particularly the case when considering stars with (quasi)periodic amplitude/phase change, commonly known as the Blazhko phenomenon ( Blazhko 1907 , see also Shapley 1916 ). Most of the progress made on this issue comes from observations, largely attributed to the space missions CoRoT and Kepler (e.g., Benkő et al. 2014 ; Szabó et al. 2014 ). Nevertheless, even these highly accurate data have been unable to provide the grounds for a physically sound (and working) scenario 1 . There are also contradictory figures on the mere occurrence rates of the Blazhko (hereafter BL) stars. These rates (for fundamental mode stars) range from ~ 12% to ~ 50% (see Kovacs 2016 and Smolec 2016 and references therein). Interestingly, analyses of the high-precision space observations yield apparently quite similar rates to those derived from classical ground-based data ( Benkő et al. 2014 ; Jurcsik et al. 2009 ). This, and the apparent lack of application of modern methods of time series analysis in this field, has prompted us to examine the occurrence rates of the BL stars from a subset of the available space data. Even without knowing the underlying physical cause of the BL phenomenon, it is clear that this single number may help to distinguish between various ideas. For example, models based on the resonance between radial modes will likely face difficulties if amplitude/phase modulation is indeed as common as the present study shows. 2. Datasets and the method of analysis In establishing a solid, but not too extensive dataset for this introductory work, we opted for the Kepler two-wheel (K2) survey. Unfortunately, there are not many well-documented (star-by-star) RR Lyrae surveys available for this mission. However, the variability survey on Campaigns 0–4 by Armstrong et al. (2016) has proven to be extensive enough, suitable for the purpose of this work. We have chosen not to include C0 due to the short observational time span devoted to this campaign in the early phase of K2. A few basic parameters of the datasets used are listed in Table 1 . As this table shows, there are several miss-classifications in the original list 2 . Nevertheless, the number of the remaining stars is large enough to use them as a statistically significant sample in assessing the occurrence rate of the BL phenomenon. There are some four or five stars belonging to the globular cluster M4 in C02; otherwise the rest are apparently Galactic field/halo stars (many of them are rather faraway objects); see Fig. 1 for the distribution of the 237 targets analyzed in this paper over the Kepler field of view during the four campaigns. Fig. 1. Distribution of the stars analyzed in this letter (see Table 1 ) in the field of view of Kepler . Table 1. Summary of the datasets used in this paper. Notes. N tot stands for the total number of stars classified as RRab stars by Armstrong et al. 2016 . N RR is for the actual number of RRab stars. The time series data were downloaded for C01 and C02 from
Paper: 2019A&A...624A..87B (acknowledgments)
Acknowledgement We thank C. Dominik for his extremely valuable input on infrared excesses of circumstellar disks and the anonymous referee for providing useful feedback that helped to improve the quality of this article. The research of A.J.B. and F.S. leading to these results has received funding from the European Research Council under ERC Starting Grant agreement 678194 (FALCONER). M.B. acknowledges funding from ANR of France under contract number ANR-16-CE31-0013 (Planet Forming disks). Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This publication makes use of VOSA, developed under the Spanish Virtual Observatory project supported from the Spanish MINECO through grant AyA2017-84089. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France ( Wenger et al. 2000 ). To achieve the scientific results presented in this article we made use of the Python programming language 4 , especially the SciPy ( Jones et al. 2001 –), NumPy ( Oliphant 2006 ), Matplotlib ( Hunter 2007 ), scikit-image ( Van der Walt et al. 2014 ), scikit-learn ( Pedregosa et al. 2012 ), photutils ( Bradley et al. 2016 ), and astropy ( Astropy Collaboration 2013 , 2018 ) packages.
Paper: 2017MNRAS.466.3507L (fulltext)
b ) and Nardiello etal. ( 2015 ). To look for signatures of SP stars along M4 AGB stars in the V C UBI plane, we follow a standard procedure similar to the one outlined in Monelli etal. ( 2013 ). We first cleaned the CMD from foreground/background contamination, i.e. cluster membership has been assigned on the basis of the source position in the ( B I ) versus ( U V ) plane (see fig. 2 in Monelli etal. 2013 ). As a first step, we defined AGBs as shown in the top-left panel of Fig. 2 . Then, we plotted the selected stars in other CMDs that provide the cleanest separation of the evolved sequences to help verify the identification (see Fig. 2 ). To this aim, we cross-correlated our UBVI photometry with the 2MASS catalogue 3 to carefully inspect the position of selected AGB stars in CMDs including near-infrared colours. All selected stars are compatible with being AGBs in all diagrams displayed in Fig. 2 . This demonstrates that they cannot be RGBs migrated to the AGB sequence due to photometric errors. Indeed, no random errors can explain that the selected stars are always located in a well-defined sequence bluer of the main body of the RGB in different CMDs obtained from independent measurements/data sets. From all the above, we conclude that the selected stars are indeed AGBs. We finally note that the observed AGB/RGB ratio ( R AGB/RGB 0.44 0.23) agrees within the errors with the theoretical one ( R AGB/RGB 0.37). This by itself is obviously not a proof that all candidate AGBs are indeed AGB stars, but just a consistency check for our empirically derived value of R AGB/RGB . Figure 2. Selection of AGB star candidates in different CMDs. From top-to-bottom: V , B V ; V , B I ; and U , U I . Empty cyan symbols are AGB stars. Magenta asterisks mark candidate AGB stars in common with Marino etal. ( 2008 ). To assess whether some significant residual contamination may still be present in our AGB sample after the field-star subtraction, we used the code TRILEGAL (Girardi etal. 2005 ). We determined the fraction of Galactic field stars within a box 3030arcmin 2 (i.e. comparable to the FoV of our photometric catalogue) centred on the cluster centre that survive our cluster AGB selection. We found one star with colours and magnitudes consistent with being on the cluster AGB, to be compared with the two AGB objects rejected with our field-subtraction procedure. This implies that any residual field contamination is very likely negligible. Fig. 3 shows the V C UBI diagram, with AGB stars highlighted. As already shown by Monelli etal. ( 2013 ), the RGB shows a large C UBI spread at fixed V , that exceeds the spread due to photometric errors, and a hint of bimodality can also be observed. Figure 3. V C UBI diagram is shown. AGB stars and stars in common with the Marino etal. ( 2008 ) study are marked with the same symbols as in Fig. 2 . We display also a fiducial reference line (dashdotted line) employed to quantify the broadening of the RGB and AGB sequences