adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.481.4123K (fulltext)
for non-degenerate companion stars to SNe Ia (Ruiz-Lapuente et al. 2018 ). Woods et al. ( 2017 , 2018 ) have searched for the extended, ionized nebulae around Galactic and LMC SN remnants that are expected for an SSS progenitor to SNe Ia, and similar to analysis of pre-explosion X-ray imaging, they rule out the most luminous and hottest of these sources. Also similar to our analysis of NGC 4696, analysis by Gilfanov Bogdán ( 2010 ) and Johansson et al. ( 2016 ) do not find a large population of SSS in elliptical galaxies that could produce SNe Ia at their observed rates, suggesting that the SSS phase is either very short-lived, occurs at low X-ray luminosities, is significantly obscured by circumstellar material, or does not precede a large fraction of SNe Ia in elliptical galaxies. Combined with the growing sample of optical and X-ray limits in the literature (Maoz Mannucci 2008 ; Nelemans et al. 2008 ; Li et al. 2011 ; Nielsen et al. 2013 , 2014 ; Kelly et al. 2014 ), our SN 2017ejb limits rule out interesting regions in WD temperature and luminosity for plausible progenitor systems. In particular, we can rule out most systems near the Chandrasekhar limit, assuming they were stably accreting for years before explosion. Combined with the limits from SNe 2011fe, 2012fr, and 2014J, we conclude at the 95 per cent confidence level that 47 per cent of SNe Ia explode from systems involving a stably accreting Chandrasekhar-mass SSS. With the growing body of evidence against typical SSS phases in particular and many single-degenerate scenarios in general as the progenitors of SNe Ia, theoretical and observational focus must shift towards the evolutionary pathways and detectable signatures of binary WD systems as the progenitors of SNe Ia (such high-velocity WD runaways from the dynamically driven double-degenerate double-detonation scenario; Shen et al. 2018 ). Future analysis of pre-explosion imaging for all SNe Ia can be used to verify or constrain expectations for the configuration of their progenitor systems and explosion scenarios. In light of the wide variety of SN Ia explosion models, this type of analysis provides one of the most promising lines of inquiry for resolving the SN Ia progenitor problem. 6 CONCLUSIONS We analyse post-explosion imaging and spectroscopy of SN 2017ejb and pre-explosion HST and Chandra imaging of its explosion site. In summary, we find: SN 2017ejb is a low-luminosity SN Ia with strong C ii absorption features in its pre-maximum spectra. Photometrically, it has a low peak luminosity, it declines quickly, and it lacks a secondary i -band maximum. Spectroscopically, it is similar to SN 1986G, but with relatively weak Ti ii bands. Overall, it is most similar to low-luminosity SNe Ia such as SNe 1986G and 1991bg. We do not detect any counterpart to SN 2017ejb in pre-explosion Chandra imaging. Assuming that any pre-explosion Chandra source resembles a blackbody obscured by Milky Way extinction, our limits correspond to at
Paper: 2020ApJ...897...74Z (fulltext)
The expanding H ii region may overrun and compress these nearby preexisting clumps/cores, causing them to collapse and form new stars/clusters. This is the so-called radiation-driven implosion model (Bisbas et al. 2009 ). As the ionization front “boiled off” the outer parts of those dense clumps/cores, the rest of the neutral gas is stretched out into a cometary globule, with a dense head surrounding the newly formed stars, a bright rim pointing toward the ionizing star, and a cometary tail pointing away from the ionizing star (Bisbas et al. 2011 ). Appearances of cometary globules and bright-rimmed clouds inside H ii region are thought to be the evidence of the radiation-driven implosion model (Bisbas et al. 2011 and references therein). Considering that expanding bubbles interact with surrounding materials and trigger star formation, it is reasonable to expect that bubbles of larger size will have much more time to sweep around the surrounding materials, and thus have more chances to trigger star formation and then show more evidence of triggered star formation (Yuan et al. 2014 ), and vice versa for bubbles with smaller size. Hence we plan to study the molecular cloud and star formation around bubbles with larger and smaller sizes separately in a large sample, compare them with each other, and get evidence of triggered star formation. We found a sample of 226 bubbles from the catalog of Simpson et al. ( 2012 ) in the region covered by the 12 CO(3–2) survey performed with the James Clerk Maxwell Telescope (JCMT) submillimeter telescope (Dempsey et al. 2013 ). The 12 CO(3–2) data makes it possible for us to study the molecular gas around these bubbles in a relatively high angular resolution. As a pilot study, we first investigate G15.684-0.29 as one example of the largest bubbles in our sample, and G13.899-0.011 as one example of the smallest bubbles in our sample in the next paper. G15.684-0.29, which has a diameter of 31.4 pc (Section 3 ), is an ideal bubble for studying triggered star formation. Similar to RCW 79 and RCW 120 (Zavagno et al. 2006 , 2007 ), G15.684-0.29 has a simple morphology (circular ionized regions surrounded by dust emission rings in the mid-IR) and hosts signposts of massive-star formation at their peripheries (luminous IR sources, ultracompact radio sources; Section 3 ). Near-IR imaging gives information about the stellar nature of the luminous IR point sources observed at the borders of the ionized regions. In this work, we study the large bubble G015.684-0.29, and see if it is easy for the large bubble to trigger star formation. We describe our data in Section 2 and give a simple introduction of G15.684-0.29 in Section 3 . We present the results and discussions in Section 4 and a summary in Section 5 . 2. Archival Data 2.1. The GLIMPSE and MIPS Survey The Galactic Legacy Infrared Mid-Plane Survey Extraordinaire (GLIMPSE) is a mid-infrared survey (3.6, 4.5, 5.8, and 8.0 μ m) of the Inner Galaxy performed
Paper: 2020A&A...634L...9W (fulltext)
of the solar-like stars for which rotational periods could not be determined exhibit near-solar photometric variability, R var . Here, we have provided one possible explanation for a decreased detection fraction for these stars. We investigated the compensation of the facular and spot contributions to the rotational signal in stellar photometric light curves. This compensation hampers the detection of stellar rotational periods, and contributes to a decreased detection rate for solar-like stars. Our results show that the detectability of rotational periods in stars with solar-like photometric variability crucially depends on metallicity. Hence, metallicity is one additional important parameter to consider when investigating solar-like stars, and more accurate measurements of metallicity are needed. Appendix A: Kepler rotation periods Fig. A.1. Period detection for the star KIC 10797342. Top panel : part of the full 4 yr light curve showing photometric variability. Middle panel : Lomb-Scargle periodogram showing several peaks above the 5 σ threshold (blue line). Bottom panel : auto-correlation function showing one pronounced peak indicated by the red asterisk. Table A.1. Stellar fundamental parameters and rotation periods of the 97 periodic stars in our sample. Notes. In the last two columns the apparent Kepler magnitude Kp and the absolute Gaia magnitude M G are given. We analysed stars from Table 2 in McQuillan et al. (2014) satisfying our selection criteria (i.e. the non-periodic sample) to search for potentially undetected rotation periods. The Kepler data used in this study were reduced with the Presearch Data Conditioning (PDC) pipeline (version 8.0-9.2). We appended all available Kepler quarters by dividing each light curve by its median and subtracting unity. Then we rebinned the data from 30-minute to 6-hour cadences, and analysed the full 4-year time series using Lomb-Scargle periodograms and auto-correlation functions. The Lomb-Scargle periodogram return peaks of different power on a period grid. The peak height is equivalent to the goodness of sine fit to the data, i.e. the more sinusoidal the signal, the higher the peak at this period. At the same time, the auto-correlation function (ACF) searches for self-similarity of the light curve signal, independently of the shape of the signal, and returns a peak at the best time lag (i.e. the ACF period). Since the time series of these slowly rotating solar-like stars are usually non-sinusoidal, we use the ACF period as initial period guess. For both the periodogram and the ACF, we searched for cases where both methods returned consistent peaks, with periods of up to 50 days. The term “consistent” covers different cases. In case a) the periodogram and the ACF both show a pronounced peak at roughly the same period (i.e. the periods may differ by up to five days). This case is considered the easiest one, and we do not show an example here. In case b) the ACF shows a pronounced peak and the
Paper: 2015ApJ...813...18S (acknowledgments)
This research was conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. This scientific work makes use of the Murchison Radio-astronomy Observatory, operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. The International Centre for Radio Astronomy Research (ICRAR) is a Joint Venture between Curtin University and the University of Western Australia, funded by the State Government of Western Australia and the Joint Venture partners. Some of the results in this paper have been derived using HEALPix (Górski et al. 2005 ). We would like to thank André Offringa for customizing AO-flagger software to work with BIGHORNS data. We would also like to thank Matthew Francis for generating and sharing STEC data for the MRO.
Paper: 2018A&A...614L...1L (fulltext)
either because the source istoo faint or, for example, because of non-ideal weather. The flux density scale is set by observations of DR 21. Sources NGC 7027, 3C 274, and 3C 84 are used as secondary calibrators. A detailed description of the data reduction and analysis is given in Teräsranta et al. (1998) . The error estimate in the flux density includes the contribution from the measurement rms and theuncertainty of the absolute calibration. Observation statistics are available in Table A.1 . Columns 1 – 3 give the name and the coordinates of the sources (in J2000). The number of detections and observations at 37 GHz isshown in Col. 4. and the detection percentage in Col. 5. Cols. 6 and 7 give the maximum flux density at 37 GHz and the 1.4 GHz flux density from FIRST. A table containing 37 GHz flux densities and their errors is available at the CDS. Table A.1 Basic data and statistics of the 37 GHz observations. Notes. ( a ) This source has been detected also by LOFAR at a very low frequency of 150 MHz with a flux density of 3.6 mJy. We also performed test observations of a source with a descrepancy in its classification. In Järvelä et al. (2015) J090113.23+465734.7 is listed as radio loud. A search radius of 1 arcmin was used to obtain the radio data, and while there is no detected radio source at the source coordinates (the mean epoch of the FIRST observations is 1997.2), there is a radio source in its close proximity at coordinates RA 09h01m09.225s, Dec +46d57m21.19s. This is MJV 09737, which has no listed redshift or observationsat other wavelengths. Their separation is 0.72 arcmin and it is therefore probable that the radio data in Järvelä et al. (2015) originate in this source, and J090113.23+465734.7 is apparently radio silent. The source has been detected at 37 GHz only once, and the detection raised concern about the possible contamination by MJV 09737 since it also lies within our antenna beam (2.4 arcmin at 37 GHz). The flux density of MJV 09373 at 1.4 GHz is 1.55 mJy and it has not been detected at higher radio frequencies; thus it is likely to be faint at 37 GHz. To ensure this we ran a set of simple tests to check how another radio source lying in the antenna beam might affect our observations, by observing the sources in pairs several times. All observations of MJV 09737 were non-detections so its contributionto 37 GHz observations seems negligible, but this test is not precise enough to certainly rule out possible contamination. Appendix B <italic>Fermi</italic>–LAT data reduction Since the 37 GHz detections suggest the presence of jets, it is possible that these sources are also gamma-ray emitters. Therefore we obtained their Fermi gamma-ray fluxes in the energy range 0.1–300 GeV by analysing the Fermi –LAT data from 2008 August 04 to 2017 August 10 using the Fermi Science Tools 1 version v10r0p5. We applied the data selection recommendation 2 for the Pass 8 data to select events in the source class, while excluding those
Paper: 2017ApJ...842...47V (acknowledgments)
We thank the referee for useful comments that improved the manuscript. We thank K. Schmidt, L.I. Mas-Ribas, and R. Amorin for useful discussion. E.V. and S.C. acknowledge financial support from PRIN-MIUR 201278X4FL, Evolution of Cosmic Baryons: Astrophysical Effects and Growth of Cosmic Structures. C.G. acknowledges support by VILLUM FONDEN Young Investigator Programme through grant no. 10123. K.C. acknowledges funding from the European Research Council through the award of the Consolidator Grant ID 681627-BUILDUP. L.C. is supported by Grant DFF 4090-00079. Based on observations collected at the European Southern Observatory for Astronomical research in the Southern Hemisphere under ESO programmes P095.A-0653, P094.A-0115 (B) and ID 094.A-0525(A). M.N., M.M., A.M., and P.R. acknowledge financial support from PRIN-INAF 2014 1.05.01.94.02.
Paper: 2021MNRAS.505.5862W (fulltext)
measured Eu is likely due to the current detection limit for Eu (Ishimaru et al. 2004 ). Overall, the result for case 1 (a mean delay of 0.1 Gyr in the lognormal term for NSMs) appears in qualitative agreement with those in Ishimaru et al. ( 2015 ) and Ojima et al. ( 2018 ) with a fixed delay of 0.1 Gyr (see Appendix A for additional tests). The top-right panel of Fig. 5 shows the result for case 2, in which the delay-time distribution of NSMs is ∝ t −1 . We find a slower increase of [Eu/Fe] with [Fe/H] and a smaller [Eu/Fe] at high [Fe/H] than those for case 1. This is due to an absence of the early (lognormal) component in the delay-time distribution. As a whole, however, the evolution of [Eu/Fe] including its scatter is similar between cases 1 and 2. The bottom-left panel of Fig. 5 shows the result for case 3, in which a CCSN-like, little delay time for NSMs is assumed (Fig. 1 ). We find that the evolution of [Eu/Fe] with [Fe/H] appears incompatible with the measured stellar abundance. The model predicts stars with [Fe/H] −3.5, at which few stars with measured Eu have been found. Note this cannot be attributed to the detection limit for Eu, because the predicted stars have high [Eu/Fe] (>0.5). It should also be noted that the reason cannot be the rarity of stars with [Fe/H] −3.5. The building blocks more massive than M * = 10 6 (orange curve in the bottom-left panel of Fig. 5 ), which generate the bulk of stars in the halo (the right-hand panel of Fig. 3 ), have already experienced NSM events (〈 N NSM 〉 > 1) at [Fe/H] −3.5. Moreover, the model does not predict stars with [Eu/Fe] 0 at [Fe/H] ∼ −3, at which a large number of stars with measured Eu exist (see also Section 4.4 and Appendix A ). The result for case 4 is shown in the bottom-right panel of Fig. 5 . The smaller SFE ( K in Table 1 ) results in the appearance of Eu at too low metallicity ([Fe/H] ∼ −4) to be compatible with the observation. However, the lowest metallicity at which Eu starts increasing depends on t min in the delay-time distribution of NSMs (equation ( 13 ); see Appendix A ). Another problem is that the contribution of SNe Ia starting at low metallicity inhibits the increase of Eu above [Eu/Fe] ∼ 0. It should be noted, however, that the yield of Eu from NSMs and their event rate, the product of which determines the average [Eu/Fe], are somewhat uncertain. Overall, therefore, our model is (at least marginally) compatible with the observational trend of [Eu/Fe] in the MW halo, regardless of the explored variations in the delay-time distributions (except for case 3). Note that a reduction of the NSM (or rare CCSN-like) event rate (or B in Section 2.2 ), which also reduces 〈 N NSM 〉, will enhance [Eu/Fe] at low metallicity in the low-mass building blocks with 〈 N NSM 〉 1. However, this will not substantially change the probabilistic distribution of stars on the [Eu/Fe]–[Fe/H] plane (colour scale in Fig. 5 ) as presented in Appendix A (Fig. A4 ) because of the resulting
Paper: 2015ApJ...805..133J (acknowledgments)
We gratefully thank J. Zhao for providing his inversion results from HMI data, T. Hartlep for providing his meridional flow simulation data, and an anonymous referee for an excellent numerical suggestion. This material is based upon work supported by the National Science Foundation under Grant Number 1351311. A.S. acknowledges support from grant FA-F02-F028 of the Uzbek Academy of Sciences. S.K. was supported by NASA’s Heliophysics Grand Challenges Research grant 13-GCR1-2-0036. This work utilized data obtained by the Global Oscillation Network Group (GONG) program, managed by the National Solar Observatory, which is operated by AURA, Inc. under a cooperative agreement with the National Science Foundation.
Paper: 2016MNRAS.455.3864A (fulltext)
temperatures and metallicities is fair, with our temperatures being an average of 161 K cooler (σ = 181 K), and our metallicities an average of 0.12 dex lower (σ = 0.15 dex). The surface gravities show a similar offset, ours being lower by 0.18 dex, and an rms scatter σ = 0.16 dex, but the discrepancies seem to be largest (by up to 0.5 dex) for the coolest and more metal rich stars in the sample. Figure 2. Upper panel: observed (solid black line) and best-fitting model (broken red line) for four representative stars in our sample of proposed flux standards. Bottom panel: ratio between model and observed fluxes. Table 1. Derived parameters and angular diameters (θ) for the proposed bright spectrophotometric A-type standards. The scatter given corresponds to the standard deviation in θ for the spectral data points in the 400–800 nm range. The uncertainty σ(θ) is derived by adding the scatter in quadrature with a 0.4 per cent error that corresponds to propagating the uncertainty in the zero-point of the flux scale (0.8 per cent or 0.008 mag). Both the atmospheric parameters from the NGSL library (Lindler Heap 2008 ) and those derived in this paper are provided. We estimate our uncertainties are about 120 K, 0.2 dex and 0.1 dex (random) and 150 K, 0.2 dex and 0.1 dex (systematic), for T eff , log g and [Fe/H], respectively (but see text for a caveats about log g for some stars). The V -band photometry derived from the NGSL spectra, and those measured from the ground and compiled by Mermilliod, Mermilliod Hauck ( 1997 ) are in the last two columns. Our conclusion about the systematics in our derived surface gravities is reinforced by comparing our results with Allende Prieto Lambert (1999), who compared the V -band absolute magnitudes, computed from Hipparcos ’ parallaxes, and B − V colours, with models of stellar interiors. Their surface gravities for eight stars in common are an average of 0.33 larger than ours (σ = 0.18 dex), and show a similar trend as we found with the NGSL values, with larger discrepancies for cooler more metal-rich stars. The comparison with metallicities from the PASTEL bibliographical catalogue (Soubiran et al. 2010 ), which compiles stellar atmospheric parameters determined from high resolution, high signal-to-noise spectra in the literature, shows, for eight stars in common, a mean offset of 0.02 dex, ours being lower, and significant scatter (σ = 0.48 dex). Our gravities are also lower than those in PASTEL by an average of 0.08 dex (σ = 0.21 dex). Lastly, our comparison with the parameters published by Koleva Vazdekis ( 2012 ) shows the largest spread, with standard deviations of 702 K in T eff , 0.5 dex in log g and 0.6 dex in metallicity. To evaluate the effect of neglecting reddening in our analysis, we have tested the consequences of adopting instead the reddening estimates provided by Lindler Heap (2008) as part of the NGSL, correcting the spectra for reddening using the mean Galactic extinction curve given by
Paper: 2018ApJ...853...59C (acknowledgments)
J.R.A.D. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1501418. C.B. acknowledges support from the Alfred P. Sloan Foundation. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate. Some/all of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non- HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. Facilities: Kepler , Keck:II (NIRC2).