adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.481.5073E (fulltext)
a method to infer the slope of the underlying DM halo at the average half-light radius , finding that both the Fornax and the Sculptor dSph must be embedded in DM haloes with constant-density cores, in conflict with the centrally divergent density cusps of haloes in DM-only simulations (the cusp/core problem ). Laporte, Walker Peñarrubia ( 2013 ) subsequently showed that also for triaxial DM haloes, the Walker Peñarrubia ( 2011 ) method gives reliable lower limits on the slope Γ. Sawala et al. ( 2015 ) use halo masses derived by Peñarrubia et al. ( 2008a ) for Milky Way dSphs using the Walker et al. ( 2009c ) estimator to motivate their corrections to stellar mass–halo mass relations obtained from abundance matching based on DM-only simulations. More recently, Fattahi et al. ( 2018 ) make use of the Wolf et al. ( 2010 ) estimator to study the tidal evolution DM haloes of the apostle simulations compatible with the observed kinematics of Milky Way dSphs. In this contribution, we study a virial mass estimator which is minimally affected by our ignorance on two key parameters: (i) the shape of the inner DM halo profile, and (ii) the spatial segregation of the stellar tracers within the DM halo. The use of the projected spherical virial theorem has several advantages over Jeans analysis for the construction of mass estimators: The virial theorem does not contain any functional dependence on β( r ) and thereby avoids the mass–anisotropy degeneracy. Also systematic biases of inferred enclosed masses and slopes Γ follow directly from the assumptions made on the DM and stellar density profiles and are independent of sample size. Furthermore, the average squared dispersion is a sum over all stars and does not require any binning of data. This makes mass estimators based on the virial theorem applicable also to systems with only few stellar tracers – carefully factoring in the uncertainties due to sample size on the measured squared velocity dispersion as pointed out by Laporte, Agnello Navarro ( 2018 ). This paper is structured as follows: in Section 2 we introduce the spherical virial theorem and discuss the systematics of mass estimators for dwarf galaxies consisting of a stellar tracer population embedded inside a DM halo. In Section 3 , we construct a mass estimator which is tailored to minimize the uncertainty introduced by our ignorance on the central slope of the underlying DM profile as well as on how deeply embedded the stellar component is within the DM halo. We do test this mass estimator on a catalogue of N -body mocks. Section 4 discusses how to obtain the total halo masses of dwarf galaxies, breaking the degeneracy between structural parameters constrained by use of the virial theorem with the help of controlled cosmological simulations. In Section 5 , we estimate the masses of Milky Way dwarf galaxies using the methods introduced in the previous sections, and discuss implications of the derived stellar mass–halo mass relation for
Paper: 2016ApJ...823...27N (acknowledgments)
T.N.-C., M.G.L., and A.V. are supported by NASA through the LWS program. T.N.-C. is supported by MFI/ Wind and SECCHI/STEREO NASA missions. A.V. is supported by JHU/APL internal funds. C.F. and W.Y. are supported by MNX13AP39G.
Paper: 2021ApJ...913..150C (fulltext)
We analyzed the burst spectra of the RB with the same model, and yielded an acceptable reduced χ 2 . However, the disk kT is up to 3 keV, which is too high for an NS X-ray binary. 4. Discussion and Summary We have analyzed the decay phase of the outburst experienced by the RB during 2017 observed by Insight-HXMT in a broad energy band. By stacking the type-II bursts with recurrence time of 100–300 s, we find for the first time a time delay of about 3 s at hard X-rays. The energy spectrum of the burst can be denoted by a Comptonization model, for which the temperature of the seed photons, the optical depth, and the temperature of the hot plasma are derived with values of 0.2 keV, 9–10, and 2–3 keV, respectively. These findings put more constraints upon the models at work for the RB. The tight relation between the burst strength and the waiting time (Lewin et al. 1976 ) leads to a series of models in history proposed via focusing on the sudden release of the accretion power in the disk. Type-II bursts can be caused by the instability of the disk on viscous (Lightman Eardley 1974 ) or thermal timescales (Shakura Sunyaev 1976 ), the magnetic barrier (D’Angelo Spruit 2010 , 2012 ), or the magnetic reconnection on the disk (Jiang et al. 2014 ). However, all models have their own shortages in covering all the features observed in the RB (Lewin et al. 1993 ). The possible breakthrough to discriminate different models may come from the joint observations carried out in 2015 by XMM, Swift/XRT, and NuSTAR (van den Eijnden et al. 2017 ). With this campaign, both the continuous and the mode-0 burst spectra were well measured in a rather broad energy band of 1–30 keV. It turns out that the inner radius keeps staying at round 41 R g ( R g = 2 GM /c 2 ) for both burst and pre-burst emissions, and the corona temperature of the pre-burst is 7.19 keV, which is significantly larger than 2.42 keV measured for the nonthermal temperature of the burst. Comparison for the corona temperatures between burst and pre-burst are always challenged by the contamination source 4U 1728–34, which is located only 05 away from the RB. We note that less change of the burst temperature with respect to the pre-burst was reported in ACSA (Mahasena et al. 2003 ) and BeppoSAX (Masetti et al. 2000 ), which is probably due to limited bandwidth (i.e., below 10 keV) of the former and the relatively poorer statistics of the latter that does not allow us to well constrain the parameters in the comptt model. If the burst is caused by the temporary accretion on the disk, via either disk instability or the magnetic barrier, the inner disk radius should evolve to be smaller than that of the pre-burst. Regarding the temperature difference of the corona between burst and pre-burst, the hot corona wherever it is located, either in the boundary layer or around the disk, can in principle be cooled by the seed photons of the burst shower. However, in history there are no hints for detecting such a
Paper: 2017MNRAS.469S.626J (acknowledgments)
Acknowledgements Rosetta is a European Space Agency (ESA) mission with contributions from its member states and the National Aeronautics and Space Administration (NASA). This work has made use of the Automated Multi Dataset Analysis (AMDA) and Rosetta Plasma Consortium Quicklook database, provided by a collaboration between the Centre de Données de la Physique des Plasmas (supported by Centre National de la Recherche Scientifique (CNRS), Centre national d'études spatiales (CNES), Observatoire de Paris and Université Paul Sabatier, Toulouse), and Imperial College London (supported by the United Kingdom Science and Technology Facilities Council). Support by the Swedish National Space board is acknowledged, including contracts 109/12, 135/13, 166/14 and 168/15. The work at the University of Oslo is partly supported by the Norwegian Research Council, grant number 240000. The contribution from the Space Research Institute of the Austrian Academie of Sciences was made possible by the funding of the Austrian Science Funds FWF P 28100-N36.
Paper: 2016ApJ...833...19P (fulltext)
to apply to large amounts of spectroscopic data even in the presence of large redshift errors in the standard pipeline. This algorithm selects a relatively small number of candidates from the large data set for visual inspection to confirm the correctness of the identification. While the false negative rate is very small based on a small test sample of known post-starburst galaxies, the false positive rate to be removed by visual inspection is between 100% and 300%. At this point, the amount of visual inspection required is still manageable, but a more optimized algorithm with a lower false positive rate will be critical when the next generation of surveys generate significantly larger samples. Examples of such surveys are the fourth phase of the SDSS-IV (Dawson et al. 2015 ), the Dark Energy Spectroscopic Instrument (Levi et al. 2013 ), and the Prime Focus Spectrograph (Takada et al. 2014 ). For SDSS and BOSS data in particular, it was found in retrospect that the redshift determination by the spectroscopic pipeline is very robust, with less than 5% incorrect redshifts even for this rare class of objects. Therefore, selecting the post-starburst galaxies from SDSS and BOSS assuming the pipeline redshifts to be reliable would have simplified the process and reduced the number of false positives significantly. In light of this, there are some improvements to incorporate the future work selecting post-starburst galaxies from large spectroscopic surveys. One such data set in the near future would be the entire BOSS (SDSS DR12). This phase of SDSS contains twice as many spectra than what we used in this paper, and it is now public (Alam et al. 2015 ). The first improvement might be to do the selection based on the stellar population properties, rather than empirical cuts on parameters such as A/Total or equivalent widths. Ideally, this approach is more preferable since it would lead to selection based on physically meaningful properties of the galaxy population such as stellar population age and metallicity. For example, one might define post-starburst galaxies to be populations that are older than 300 Myr but younger than 1 Gyr. Yesuf et al. ( 2014 ) fit stellar population models using multiwavelength data while taking AGNs and dust obscuration into account. Selection of these objects from the next generation of multiband photometric surveys is another possibility. Doing so requires distinguishing between the 3600 Å Balmer break and the 4000 Å break at slightly different redshifts. One can potentially resolve this ambiguity using multiwavelength information, for example, in near-UV (NUV) and near-IR (NIR), since the old population would have almost no flux in NUV but large flux in NIR, and conversely for the young population. Wild et al. ( 2014 ) studied galaxy SEDs in optical and NIR and found that post-starburst galaxies have unique, identifiable colors. However, the presence of dust and AGNs would make this task challenging. 7. CONCLUSION The
Paper: 2019ApJ...876..124C (fulltext)
that the age and metallicity distributions are Gaussian, with , σ T = 2 Gyr (e.g., Kalirai 2012 ), , and σ Fe/H = 0.5 (e.g., Xue et al. 2008 ). We model the resulting weighted CMD with a kernel density estimate (KDE), using a kernel bandwidth of 0.025. The resulting pdf’s for M F814W , for six different colors, are shown in Figure 11 . Figure 11. Normalized pdf’s for absolute magnitude for six different choices of m F606W − m F814W color. These pdf’s are derived from the KDE constructed from the VandenBerg et al. ( 2006 ) isochrones, weighted by a Salpeter IMF and the approximate age and metallicity distributions of the halo. Using the pdf for absolute magnitude as a function of color in conjunction with the halo density profile (Deason et al. 2011 ), we derive the pdf for the log distance distribution to our sample:where is the probability of log D given the Deason et al. ( 2011 ) density profile, and is the probability of object n having absolute magnitude M F814W (log D ) given its color m F606W, n − m F814W, n . 6 6 In the COSMOS field, in the area where we used additional catalogs for selection, we used the Subaru B and V photometric measurements from Capak et al. ( 2007 ) and converted these to STMAG F606W as directed by Sirianni et al. ( 2005 ). We used the F814W magnitudes as published in the Leauthaud et al. ( 2007 ) catalog. We then estimate the mean distance to each field . Each of the four fields has an average distance . Figure 12 shows the cumulative logarithmic distance pdf’s of our samples across the four fields, and average distances to each field are listed in Table 5 . We note that no kinematic information is incorporated into our distance estimate and that information from 3D kinematics will improve our distance estimates in subsequent work. Figure 12. Cumulative distance distributions for the four HALO7D fields. Distance distributions are computed as given by Equation ( 13 ), using colors and assuming an MW stellar density profile (Deason et al. 2011 ). All fields have . Figure 13 shows the LOS dispersions of the four HALO7D fields plotted as a function of mean galactocentric radius (where we have converted heliocentric distance to galactocentric radius ). Points indicate the median of the σ LOS posterior samples, and error bars indicate the 16th and 84th posterior percentiles. We compare our results to the velocity dispersion profiles measured in other studies. The figure also shows measured velocity dispersion profiles from SDSS BHB stars (Xue et al. 2008 ; black dashed line), BHB and blue straggler (BS) stars from the Hypervelocity Star Survey (Brown et al. 2010 ; gray dot-dashed line), and the SEGUE K -giant survey (Xue et al. 2016 ; connected black circles). The measured LOS velocity dispersions in the HALO7D fields using MSTO stars are consistent with other studies that have measured the LOS velocity dispersion profile over our distance range. Figure 13. LOS velocity dispersions of the four HALO7D fields, plotted
Paper: 2017A&A...597A.107S (acknowledgments)
The authors want to thank the referee for useful and constructive comments. We acknowledge the crucial contribution of the ESO staff for the management of service observations. In particular, we are deeply grateful to M. Hilker for his constant help and support of this program. Italian participation in VIPERS has been funded by INAF through PRIN 2008, 2010, and 2014 programs. L.G. and B.R.G. acknowledge support of the European Research Council through the Darklight ERC Advanced Research Grant (# 291521). O.L.F. acknowledges support of the European Research Council through the EARLY ERC Advanced Research Grant (# 268107). A.P., K.M., and J.K. have been supported by the National Science Centre (grants UMO-2012/07/B/ST9/04425 and UMO-2013/09/D/ST9/04030). R.T. acknowledge financial support from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement n. 202686. E.B., F.M., and L.M. acknowledge the support from grants ASI-INAF I/023/12/0 and PRIN MIUR 2010-2011. L.M. also acknowledges financial support from PRIN INAF 2012. Research conducted within the scope of the HECOLS International Associated Laboratory, supported in part by the Polish NCN grant Dec-2013/08/M/ST9/00664.
Paper: 2019MNRAS.486.5391D (acknowledgments)
ACKNOWLEDGEMENTS CC’s work is based upon research supported by the South African Research Chairs Initiative (SARChI) of the Department of Science and Technology (DST), the SKA SA, and the National Research Foundation (NRF). ND’s work is supported by a SARChI’s South African SKA Fellowship. LMW is supported by the Natural Sciences and Engineering Research Council of Canada through Discovery Grants. The numerical simulations were performed at the Centre for High Performance Computing.
Paper: 2017ApJ...842...21M (acknowledgments)
The authors thank the anonymous referee for useful comments and suggestions, which have improved this manuscript. Z.C.M. gratefully acknowledges support from the John F. Burlingame and the Kathryn McCarthy Graduate Fellowships in Physics at Tufts University. D.M. and Z.C.M. acknowledge the support of the Research Corporation for Science Advancement’s Cottrell Scholarship, the support from Tufts University Mellon Research Fellowship in Arts and Sciences. We thank Gabriela Calistro Rivera for her support on the implementation of AGNfitter . This study is partially based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under program ID 087.A-0514. Some of the data presented in this study 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. Keck telescope time was granted by NOAO, through the Telescope System Instrumentation Program (TSIP). TSIP is funded by NSF. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This study makes use of data from the NEWFIRM Medium-Band Survey, a multi-wavelength survey conducted with the NEWFIRM instrument at the KPNO, supported in part by the NSF and NASA. We thank the NMBS and COSMOS collaborations for making their catalogs publicly available. The combination of NEWFIRM Medium-Band Survey and Herschel data was supported by the National Aeronautics and Space Administration (NASA) under grant number NNX13AH38G issued through the NNH12ZDA001N Astrophysics Data Analysis Program (ADAP).
Paper: 2018ApJ...857....7Y (acknowledgments)
We thank the referee for suggestions that improved the manuscript, and Jeremy Darling, Jason Glenn, Serena Criscuoli, and Mihály Horányi for thoughtful guidance on the methodology and analysis. Facility: Apache Point Observatory (TripleSpec) . Software: Astropy (Robitaille et al. 2013 ), IPython (Perez Granger 2007 ), Matplotlib (Hunter 2007 ), molecular-hydrogen ( https://github.com/keflavich/molecular_hydrogen ), NumPy and SciPy (van der Walt et al. 2011 ).
Paper: 2018A&A...616A..46P (acknowledgments)
Acknowledgements Financial support by the Spanish Ministry of Economy and Competitiveness and the European FEDER Fund through projects AYA2014-60476-P (Solar magnetometry in the era of large solar telescopes) and AYA2014-60833-P (Spectropolarimetry: a window to stellar magnetism) is gratefully acknowledged. The German VTT is operated by the Kiepenheuer-Institut für Sonnenphysik at the Spanish Observatorio del Teide of the Instituto de Astrofísica de Canarias (IAC). This paper made use of the IAC Supercomputing facility HTCondor ( http://research.cs.wisc.edu/htcondor/ ). NSO/Kitt Peak FTS data used here were produced by NSF/NOAO.
Paper: 2016ApJ...821...68D (acknowledgments)
T.M.D. and G.S.C. were funded by STFC grant ST/J001260/1. P.A.B. acknowledges the support of the Australian Research Council via the Australian Laureate Fellowship scheme. We thank the developers of iSALE ( www.isale-code.de ) and are grateful to Fred Ciesla for discussions about the equilibration timescale calculations. We thank the anonymous reviewer for their valuable comments.
Paper: 2018ApJ...864..123M (fulltext)
bin with observed rest-frame wavelength. Here, we supplement the FUV and NUV results from this study with data used in our previous study (Taylor-Mager et al. 2007 ), which includes HST WFPC2 mid- to near-UV (F255W, λ c = 255 nm and F300W, λ c = 293 nm) and NIR data (F814W, λ c = 823 nm), as well as ground-based UBVR ( λ c = 360, 436, 540, and 634 nm, respectively) data from the VATT. Solid error bars indicate the error on the median, while dashed error bars give the 25%–75% quartile ranges, which indicate the spread of the data. The number statistics in the FUV and NUV are vastly improved in this study, which has resulted in much smaller error bars for each of the spiral, irregular, and peculiar galaxy type categories. Figure 5. Median CAS values in each type bin as a function of wavelength. Solid error bars indicate the error on the median, and dashed error bars indicate the 25%–75% quartile range of the data. The GALEX data (two left-most data points in each panel) are supplemented with HST and ground-based data. Circled data points: low GALEX S values are due to its poor (5 arcsec stellar FWHM) resolution. Disregarding this effect results in higher S values more in line with the trend displayed at longer wavelengths. In general, galaxies are more asymmetric and more clumpy at shorter rest-frame wavelengths, especially in the UV. This causes them to appear overall later type than they would at longer wavelengths. Sa-Sc galaxies appear less concentrated at shorter wavelengths, while Sd-Im and pec/mer galaxies do not exhibit a strong concentration versus wavelength relationship. The effect in the UV is particularly pronounced for elliptical/lenticular galaxies, which have little difference in their CAS parameters in the near-UV through the NIR, but appear significantly less concentrated, more asymmetric, and slightly more clumpy in the far-UV. For Figure 5 we reprocessed the Taylor-Mager et al. ( 2007 ) data through the newest version of the CAS code, as it treats background removal differently (utilizing the entire image with objects masked out via SExtractor catalogs versus a small user-defined box of empty sky). This had a minor impact on a small number of data points as compared to our 2007 results from the previous version of the code, but the overall trends with wavelength remain the same. This demonstrates that the difference in background subtraction methods does not have a significant impact on our conclusions. We rejected any median data points derived from fewer than five HST galaxies, as these low number statistics would produce unreliable results. Most panels in Figure 5 show a consistent general trend in CAS values as a function of rest-frame wavelength. A major exception is the clumpiness ( S ) parameter for all galaxy types except E/SO, where S increases significantly with decreasing wavelength, then drops dramatically between the HST /VATT UV and the shorter GALEX NUV wavelength (the second data point from the left in each