adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.464.3972L (acknowledgments)
This work is supported by the Youth Programme of the Applied Basic Research Projects of Yunnan Province, China (2013FD056) and the Science Research Project of Education Department of Yunnan Province, China (2016ZZX230). We thank the REM team and we acknowledge the use of public data from the Swift data archive.
Paper: 2018A&A...617A.122K (fulltext)
a cooling break blueward of the X-rays. Finally, GF17 use modelling of the radio data ( Hancock et al. 2011 , 2012 ) to derive further lower limits on the circumburst density, again finding moderately high values contrasting our estimation given above under the assumption that the Ghirlanda relation is valid. The spectral shape of the radio detections is peculiar, which GF17 explain by interstellar scintillation. Alternatively, the anomalous spectrum may be created by the overlap of multiple components; for example, a reverse shock and a forward shock, as in the cases of GRB 130427A ( Laskar et al. 2013 ; Perley et al. 2014 ) and GRB 160625B ( Alexander et al. 2017 ; Troja et al. 2017 ). In such a case, scintillation would not be necessary to fully explain the deviation from the expected spectrum (though of course a lower amount of scintillation may still exist), the source size could be significantly larger and again, the limits on the circumburst medium density could be lower. As there are no further X-ray or radio observations, to our knowledge, however, neither of our alternative explanations can be pursued any further. Metzger et al. (2018) recently presented a study on the effects of fallback accretion on rapidly spinning proto-magnetars. They find that under certain conditions, the additional accretion can keep the magnetization within a certain regime which enables the launch of a relativistic jet for thousands of seconds, while at the same time also enabling additional energy injection into the late-time supernova, creating a viable solution to GRB 111209A/SN 2011kl. One caveat of this model is that it does not imply that the maximum extracted energy from the magnetar increases compared to the case without any additional accretion. The total rotational energy budget of a magnetar is where I ≈ 1.3 × 10 45 g cm 2 is the moment of inertia of the neutron star (assuming a typical mass M NS = 1.4 M ⊙), Ω 0 is the initial angular velocity and P 0 is the initial spin period. For a typical magnetar near break-up rotation speed ( P 0 ≈ 1 ms), it is E rot ≈ 3×10 52 erg, though Metzger et al. (2015) argue that neutron stars near the maximum possible mass (see Margalit Metzger 2017 ; Shibata et al. 2017 ; Ruiz et al. 2018 ; Rezzolla et al. 2018 , for recent research) and with larger radii and extreme spin periods could have up to an order of magnitude higher E tot . We note that GF17 argue that “the majority of this [energy] is carried away by neutrinos”. Such a mechanism would be expected in Type II core-collapse SNe, but in the case of a spindown-powered central engine, such as a magnetar, the energy is released as a Poynting flux which is converted into radiation and kinetic energy. While neutrinos may be produced in the dissipation region via p - γ interactions, the expected cross section and neutrino flux are very low ( Zhang Kumar 2013 ). Therefore a large fraction of the energy is available to drive the GRB, afterglow, and supernova. Without
Paper: 2020MNRAS.496.4931P (fulltext)
correct. A more detailed analysis of the disagreements between the classifiers (including the visual inspection) will be provided in a follow-up paper. However, we note that this superficial comparison between the classifiers should be enough to establish a classification strategy for new surveys. We recommend that all the available classifiers are used in a combined way. Because the different classifiers have completely different approaches to the classification problem, their agreement will reinforce the confidence of the classification. We currently recommend that all automated analysis are applied to a representative sample that is visually inspected in order to develop a decision tree. 7 SUMMARY AND CONCLUSIONS We have presented squeze , an algorithm that follows the human visual inspection to classify quasars and estimate their redshift. We have then tested the performance of this algorithm on visually inspected data from BOSS. We now summarize the key points we have discussed so far. squeze mimics the procedure of human visual inspection of the spectra to identify quasars and estimate their redshift. It performs a three step classification of quasars by identifying emission peaks, computing the redshift assuming an emission line, and selecting the correct quasar redshifts from the different possible values of z try . If we assume a confidence threshold of 0.32, we recover a purity of and a completeness of for the entire sample. This purity and completeness increases to 99.59 ± 0.06 and to , respectively, if we restrict to objects with z try ≥ 2.1. We can achieve virtually 100 per cent purity, if we assume a higher confidence threshold. For instance, for p min = 0.7, we recover a purity of and a completeness of for objects with z try ≥ 2.1. The analysis of a more realistic quasar redshift distribution produced by combining plates from BOSS and SEQUELS suggests that our results are slightly optimistic: for this analysis, and considering p min = 0.32, yields a purity and completeness of and , respectively. We find the standard deviation of our redshift errors to be 1500 km s −1 . We recommend the use of squeze redshift estimates as a prior to some additional redshift tuning. We discuss options to reduce these errors, but further study is required. Preliminary analysis suggests that the errors could be reduced to less than 1200 km s −1 . The performance of squeze presented here can be regarded as a conservative assessment since it is assumed that, where squeze disagrees with visual inspection, visual inspection is always correct. We will assess this in future papers in this series. We will also explore the versatility of squeze and its applicability to survey data that differs significantly from the BOSS spectra used here. APPENDIX A: OPTIMIZATION OF STEP 1 In Section 2.1 , we have explained squeze three-steps algorithm in detail. In particular, we have explained that the identification of emission peaks is done using a native peak finder.
Paper: 2020MNRAS.497.1443D (fulltext)
for the ground and metastable levels weighted by their experimental ion populations (76 per cent ground, 24 per cent metastable) is in good agreement with the experiment, particularly near the threshold and at higher energies. Interestingly, the area where the cross-section from this work lies outside the experimental uncertainty is at the energies just below the peak, where their experiment differs most with the adjusted Loch et al. results. Figure 5. Total collisional ionization cross-section for O v with 24 per cent metastable population; blue solid line: this work; blue dashed line: difference in cross-section owing to metastable population uncertainty; green dotted line: Dere (ground only); red dash–dotted line: Loch et al.; orange dashed line with circles: Fogle et al.; red circles: Loch et al. experiment; green triangles: Loch et al. experiment (adjusted); light blue squares: Fogle et al. experiment. 3.1.6 O <sc>vi</sc> The DI cross-section from this work is 5 per cent lower than Younger ( 1981a ) and 10 per cent lower than Dere ( 2007 ). The cross-section from Younger is obtained from the scaled cross-section for the isoelectronic sequence, while Dere uses FAC. Since FAC is used for the present work, also, and tests with different optimizations and configurations produces the same results, perhaps a possible explanation is that a different version of FAC may have been used here (v. 1.1.4) than used by Dere. Inner shell EA makes a notable contribution to the cross-section and has been clearly identified by experiment. The calculated EA cross-section is 25 per cent higher than Dere ( 2007 ), but this will not affect the rate coefficients substantially because the EA threshold is so much higher than that of DI. In comparison with experiment, the computed cross-section is favourable with the work of Rinn et al. ( 1987 ), lying close to it from threshold up to the peak, and thereafter the experiment is noticeably higher, as shown in Fig. 6 . The experiment of Defrance et al. ( 1990 ) has few data points at energies below the peak in cross-section, but this work does agree in that region. Above that, they lie higher than this work and their results are closer to Dere. Crandall et al. ( 1979 ) has a large uncertainty, which encompass all of the theoretical results in most cases. The rate coefficients are in very good agreement with Dere and Bell et al. ( 1983 ) for most of their range, and only differ at temperatures above 10 6 K, varying by 10–20 per cent. Figure 6. Total collisional ionization cross-section for O vi ground level; blue solid line: this work; blue dashed line: this work (DI only); green dashed line with circles: Dere; green dotted line: Dere (DI only); red dash–dotted line: Younger; green circles: Crandall et al. experiment; red triangles: Defrance et al. experiment; light blue squares: Rinn et al. experiment. 3.1.7 O <sc>vii</sc> and O <sc>viii</sc> The various DW calculations for He-like O vii are all in very
Paper: 2020MNRAS.492.5561M (acknowledgments)
ACKNOWLEDGEMENTS We wish to thank the anonymous reviewer for excellent suggestions and comments that have greatly improved this manuscript. UM and HBP acknowledge support from the Minerva center for life under extreme planetary conditions, the Israeli Science and Technology ministry Ilan Ramon grant and the ISF I-CORE grant 1829/12. HBP is supported by the Kingsley distinguished visitor program in Caltech.
Paper: 2017A&A...608A.126R (fulltext)
and the detailed shape of the break. In fact, this is what we show in Fig. 5 . Type-II galaxies with strong breaks (less influenced by radial redistribution, especially accretion) display steeper stellar age and metallicity profiles than type-II systems with weak breaks or even type-I galaxies. A consistent behaviour is also found in the case of the age profiles for type-III galaxies in which systems with weak breaks (less affected by accretion) display steeper profiles than galaxies with strong breaks; in which case, even positive gradients are displayed. However, in the case of the metallicity gradients, this trend is inverted. Type-III galaxies with weak breaks have shallower profiles than systems with strong breaks (which are more affected by accretion). This inverted trend can be explained by the characteristics of the accreted stars. In Ruiz-Lara et al. (2016a) we showed that accreted stars populating the RaDES galaxies are mainly old, metal-poor stars. As a consequence, the accumulation of accreted stars in the outer parts of type-III galaxies giving rise to these kind of systems causes the outer parts to be old (positive age gradients) and relatively metal-poor (producing steeper negative profiles). Based on Fig. 5 we can conclude that, not only does radial redistribution of material affect the shape of observed light distributions, but also accretion has to play a key role in shaping observed stellar age and metallicity profiles. All these results and the similarities between the observational results (TRL17) and the theoretical work presented in this paper seem to indicate that radial redistribution of material and accretion shape the SB profiles and generally flatten the age and the metallicity profiles with the exception of the metallicity profiles for type-III galaxies due to the accumulation of metal-poor, accreted stars in the outer disc. The greater the efficiency of this redistribution, the larger the flattening effect. As a consequence, we would expect steeper age and metallicity gradients at birth than what we observe presently due to the effect of the radial redistribution of material. To shed light on this aspect and the true role of this redistribution in shaping the age and metallicity profiles we have also analysed the inner gradients of these stellar parameters in the absence of radial motions, that is, considering birth locations (see Fig. 6 ). Figure 6 shows the same information as Fig. 4 but focusing on the effect of radial motion of stars on the stellar age and metallicity gradients. In this case we show mean values for type-I, II and III systems, not the values for all the individual galaxies. Grey circles represent the mean values of the stellar age and metallicity gradients (averaging among SB types) considering stars located at their current locations. Grey squares represent the same mean quantities but this time computed using stars located at their birth locations (thus avoiding the effect of radial
Paper: 2016ApJ...820..107W (fulltext)
These levels of abstraction are learned in an unsupervised (i.e., autonomous) fashion from a large catalog of input spectra. Once these features are learned from the data, a second, supervised learning stage is used to assign the learned features to their correct labels (e.g., H 2 O, CH 4 , etc.). Neural networks are now commonly used in complex classification tasks such as image recognition (e.g., Krizhevsky et al. 2012 ; Liu et al. 2014 ; Wang et al. 2016 ; Shen et al. 2015 ), speech and music recognition (e.g., Hung et al. 2005 ; Jaitly Hinton 2011 ; Zhang Wu 2013 ; Pradeep Kumaraswamy 2014 ), biology (e.g., Head-Gordon Stillinger 1993 ; Plebe 2007 ; Wu McLarty 2012 ; Spencer et al. 2015 ), and are finding increased use in the classification of galaxies and cosmology (e.g., Collister Lahav 2004 ; Agarwal et al. 2012 , 2014 ; Reis et al. 2012 ; Karpenka et al. 2013 ; Dieleman et al. 2015 ; du Buisson et al. 2015 ; Ellison et al. 2015 ; Huertas-Company et al. 2015 ). Whereas an in-depth derivation of DBNs is beyond the scope of this paper, we will briefly outline its underlying architecture and implementation. We refer the interested reader to Bengio ( 2009 ), Hinton ( 2012 ), and Fischer Igel ( 2014 ) for detailed derivations. 2.1. Restricted Boltzmann Machines Figure 1 shows a schematic of the the deep-belief network. The multi-layer DBN can be constructed from several Restricted Boltzmann Machines (Freund Haussler 1992 ; Bishop 2006 ; Le Roux Bengio 2008 ; Bengio 2009 , 2012 ; Lee et al. 2011a ; Hinton 2012 ; Montavon et al. 2012 ; Fischer Igel 2014 ) with the addition of a logistic regression layer at the top of the network. The RBM is a two-layer neural network able to learn the underlying probability distribution over its set of input values. It represents a particular kind of Markov Random Field (Davison 2008 ) consisting of one layer of binary or Gaussian stochastic visible units (the input data) and one layer of binary stochastic hidden units. In RBMs, all of the hidden units are connected to all of the visible units but have no intra-layer dependence. Hence, all of the hidden units given the visible units are statistically independent and we can write the probability of all visible units given all hidden units and vice versa as the product of the individual probabilities:where and are the column vectors of the visible and hidden units, respectively, and i and j are their corresponding indices. We now want to find a configuration of the hidden layers, , that allows us to reconstruct the input, , with minimal error. Since and are factorial, we can write the activation functions of the individual visible and hidden binary units aswhere ς is the sigmoid functionAssuming that both the visible and hidden units are binary, RBMs assign an energy term for each configuration of and :where and are the bias vectors for the visible and hidden units, respectively, and W is a matrix of connection weights between and . The probability over
Paper: 2019MNRAS.485.3457B (acknowledgments)
ACKNOWLEDGEMENTS LAB wishes to thank the National Research Foundation of South Africa for financial support. GMM acknowledges funding by the STFC consolidated grant ST/R000603/1. GH and SC gratefully acknowledge funding through grant 2015/18/A/ST9/00578 of the Polish National Science Centre (NCN). GAW acknowledges Discovery Grant support from the Natural Sciences and Engineering Research Council (NSERC) of Canada. ADU acknowledges the support of the Natural Science and Engineering Research Council of Canada (NSERC). This paper includes data collected by the TESS mission. Funding for the TESS mission is provided by the NASA Explorer Program. Funding for the TESS Asteroseismic Science Operations Centre is provided by the Danish National Research Foundation (Grant agreement no.: DNRF106), ESA PRODEX (PEA 4000119301) and Stellar Astrophysics Centre (SAC) at Aarhus University. We thank the TESS and TASC/TASOC teams for their support of this work. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. 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 NAS5-2655.
Paper: 2019A&A...622A.195K (fulltext)
the discrepancy is that the numerical values are obtained by fitting where V (1) and H (1) are considered independent. The second major difference is that the numerical data for sufficiently rapid rotation (Ω ⋆ ≳ 1) is incompatible with expressions of V and H which consider only terms proportional to sin 2 θ . Furthermore, in the foregoing analysis the anisotropy parameter a was kept as a free parameter and tuned such that in the slow rotation limit. It is also possible to compute a directly using Eq. (A14) in Appendix A of Kitchatinov Rüdiger (2005) by substituting and , and defining : where L is a length scale corresponding to large-scale inhomogeneity. Assuming L = L d and l corr = l gives l corr / L d = l/ L = k 1 / k f ≈ 0.1, allowing Eq. ( 49 ) to be solved with b as an input from simulations. The maximum anisotropy used in the bulk of the simulations is A V ≈ 0.52, which yields b ≈ 2.1 and a ≈ 2.7. This is somewhat greater than the values used in the fitting above. However, this discrepancy is mostly due to the freedom in choosing the value of ν t . 4.4. Dependence of Λ effect on Reynolds number The simulations in the preceding sections were made at low Reynolds numbers in comparison to the astrophysically relevant regime. Figure 6 shows the Λ coefficients for a representative case where the Coriolis number (Ω ⋆ ≈ 1.0) and turbulence anisotropy ( A V ≈ 0.3) were fixed in the range Re ≈ 0.6...99. The Coriolis number was chosen such that the higher-order coefficients V (1) , V (2) , H (2) , and M (2) did not appear according to the criteria in Sect. 3.3 . At low Reynolds numbers Re = 0.6...2 all of the coefficients are proportional to Re as expected from mean-field theory. At low Re the error bars for H (1) and M (0, 1) increase because the mean values of Q x y and Q x z are small. Furthermore, the coefficients level off beyond Re ≳ 10 where they are consistent with constants although with large error bars in particular for H (1) . These results suggest that the results obtained at Re ≈10 are representative of what can be expected in more turbulent cases. 4.5. Influence of large-scale magnetic fields Dynamically significant magnetic fields are ubiquitous in astrophysical objects where the Λ effect is thought to be important. In particular, stars with convection zones harbour dynamos that produce magnetic fields on various scales. The effects of large-scale magnetic fields on the Λ effect have been studied analytically by Kitchatinov et al. (1994b) and Kitchatinov (2016) . These studies indicate that large-scale magnetic fields tend to quench the Λ effect, but also that an additional H (0) effect arises in the presence of a horizontal mean magnetic field. Here the study of Kitchatinov et al. (1994b) is followed where the quenching formulae for V (0) and H (0) were calculated as functions of the magnetic field assuming the field to be horizontal. They found that where β = B 0 / B eq , and These equations correspond to the case where the
Paper: 2019ApJ...880..104P (fulltext)
it could be due to tidal stripping of the outer disk, or to “classical” accretion of dwarfs. The recent discovery of a substantial population of low-mass dwarfs associated with the Large Magellanic Cloud (Koposov et al. 2015 ; Drlica-Wagner et al. 2016 ) strengthens the latter scenario. The Clouds are not the only dwarfs with known satellites: dwarf galaxy companions have been found around various Magellanic analogs (Martínez-Delgado et al. 2012 ; MADCASH Survey: Carlin et al. 2016 ). A reasonable expectation is that a subset of these satellites will be tidally destroyed and accreted by their host halos, providing additional motivation to search for accreted stellar halos in dwarf galaxies. Stellar halos of galaxies are predicted and observed to be of very low surface brightness. This makes it challenging to study their presence quantitatively through diffuse light. Instead, the most tractable way to search for stellar halos is through deep, wide-field imaging now available on a number of large telescopes located at excellent sites. The isolated Local Group dwarf irregular IC 1613 is a good candidate to search for a stellar halo. It is a gas-rich, low-luminosity galaxy with an absolute magnitude of M V = −15.2 (McConnachie 2012 ), corresponding to a stellar mass of ∼10 8 M ⊙ . Its high Galactic latitude implies low foreground extinction, while it is near enough (725 ± 17 kpc; Hatt et al. 2017 ) that its bright stars are accessible in a reasonable exposure time from the ground. IC 1613 has been continuously forming stars throughout its lifetime, and hosts stars ranging from [Fe/H] ∼ −2 to −0.8 (Cole et al. 1999 ; Skillman et al. 2003 , 2014 ; Bernard et al. 2007 ; Weisz et al. 2014 ). A variety of studies sensitive to different stellar populations at different radii have found that the young stars are more centrally concentrated than the intermediate-age and old stars (Cole et al. 1999 ; Albert et al. 2000 ; Borissova et al. 2000 ; Skillman et al. 2003 ; Sibbons et al. 2015 ; McQuinn et al. 2017 ); though, there has been no definitive detection of a stellar halo as distinct from an extension of the primary body of IC 1613. The aim of this paper is to map IC 1613 with Subaru/Hyper Suprime-Cam (HSC) over a wider area and to fainter magnitudes than previous studies, generating the most sensitive map of its outer structure to date. The paper is organized as follows. The observations and information about how the data were reduced is presented in Section 2 . Section 3 describes the method of selecting different stellar tracers from the galaxy and goes on to explain the various analysis done with them. Section 4 compares our results with past works and places this project in a theoretical context. The conclusions are summarized in Section 5 . 2. Observations and Data Reduction IC 1613 was observed on 2015 October 16, with the HSC (Miyazaki et al. 2012 ) on the Subaru Telescope. The large aperture (8.2 m) of the telescope, along with the 15 field
Paper: 2015A&A...573A..84S (acknowledgments)
We would like to take the opportunity to thank the referee for his/her detailed reading of this paper and the useful comments provided.
Paper: 2017MNRAS.467.2673N (acknowledgments)
Acknowledgments The numerical simulations in this work were performed at the Centre for Intensive Computing Msocentre sigamm hosted by the Observatoire de la Cte dAzur. This work was supported by Brazil FAPESP Grant 2015/17962-5.