adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...628A..84F (fulltext)
our representations to this time span. Test calculations showed that our algorithm can lead to compact and precise representations for both degrees of freedom. With different procedure parameters, the resulting representations contain very different terms. This confirms the flexibility of our algorithm. In order to give representations as physical as possible, we resorted to the knowledge we have for the solution. Following Laskar et al. (2004) , we computed the fundamental frequencies of the secular solar system by applying the NAFF algorithm over time intervals of length 20 Myr for the proper modes , and 50 Myr for the proper modes 4 . From −35 Myr to 5 Myr with a step 0.1 Myr, we generated the samples of the fundamental frequencies ( g 1 , ..., g 8 , s 1 , ..., s 8 ) corresponding to these proper modes. The resulting nominal value of s 5 is about 0.00000015 arcsec yr −1 . The other frequency samples are shown in the panels of Fig. 9 , where the errors are estimated as the difference between the values of a frequency computed from the associated ephemeris and its quasi-periodic approximation. We also show in this figure the Chebyshev approximations of these fundamental frequencies. All of these Chebyshev approximations, the coefficients of which are listed in Table A.2 , were obtained by truncating those of degree 15. The truncation criterion was roughly that the discrepancy in a fundamental frequency should not induce an error in phase larger than 2 π /10 4 over several tens of million years. Fig. 9. Variations of major fundamental frequencies of the solar system over the time interval from −35 Myr to 5 Myr with origin at J2000. These frequencies (in arcsec yr −1 ) are computed by applying the NAFF algorithm to the proper modes of the secular solar system associated with major planets. The errors in the resulting frequency samples are estimated as the difference between the frequencies computed respectively from the original ephemeris and its quasi-periodic approximation. Also shown in this figure are the Chebyshev approximations of these varing fundamental frequencies (red curves), which are specified in Table A.2 . There are two important libration frequencies, r 1 = 0.251085 arcsec yr −1 of the resonance argument and r 2 = 0.117222 arcsec yr −1 of . To include them as additional fundamental frequencies, we expressed the main frequency as The d’Alembert characteristic, , was used to exclude non-physical frequency index vectors. To show to which degree our algorithm is practically useful, we discuss here the following two 100-term representations (R 100 for short), where with f = ( g 1 , ..., g 8 , s 1 , ..., s 8 , r 1 , r 2 ) and k = ( m 1 , ..., m 8 , n 1 , ..., n 8 , k 1 , k 2 ), the phase increment is associated with the main frequency ⟨ k , f ⟩. The way the residuals decrease with increasing number of representation terms in the two representation procedures of R 100 is shown in Fig. 10 . Fig. 10. Convergence property of the
Paper: 2018ApJ...861..122S (acknowledgments)
We thank the anonymous referee for detailed comments and suggestions. We would like to thank the members of the ENIGMA 14 14 http://enigma.physics.ucsb.edu/ group at the Max Planck Institute for Astronomy (MPIA) and UCSB for useful discussions and support. G.W. has been supported by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) under contracts 50 OR 1317 and 50 OR 1512.
Paper: 2018A&A...612A..20K (acknowledgments)
Acknowledgements This work was supported by grant GA ČR 13-10589S. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042) is greatly appreciated. The Astronomical Institute Ondřejov is supported by the project RVO:67985815. This research made use of the IUE data derived from the INES database and of the HST data derived from the MAST database using the SPLAT package.
Paper: 2016A&A...589A..13A (acknowledgments)
We thank the anonymous referee for his/her comments. T.A. is supported by an ESA Research Fellowship in Space Science.
Paper: 2016ApJ...833..101B (fulltext)
this, for the remainder of this paper we will treat the plasma density within the loops as approximately uniform in space and constant in time, with a value of n e ≈ 10 11 cm −3 . Figure 6. Histogram of plasma electron number densities (with bin size ) for reliable pixels located within the bullet. 4.5. Cooling Time As noted in Section 4.2 , the loop emission appears first in Fe xxi and subsequently appears ∼11 minutes later in Si iv and O iv . The similar spatial location and appearance of the intensity features discussed above in Figure 3 suggests that the Si iv emission originates from the same evaporated plasma as the Fe xxi emission, which has subsequently cooled into the cooler spectral lines. Further, the nearly constant density in the loops (noted above) suggests that the cooling process is approximately isovolumetric. To estimate the cooling time, we recall from Aschwanden ( 2005 ) that the radiative and conductive cooling times (in seconds) are given, respectively, byandwhere T is the plasma temperature (in K), n e is the electron number density (in cm −3 ), and L is temperature gradient length scale (in cm); we have also defined the proton mass m p , the heat capacity at constant volume c v , the conductive coefficient κ 0 = 1.1 × 10 −6 , and the radiative loss function (Aschwanden 2005 ). For our purposes, we will approximate the temperature gradient length scale as the loop half-length. From inspection of Figure 1 (c) (which best shows the coronal loops), we find that the footpoint separation for the loops that cross the slit is about 217. As this observation was relatively close to disk center at the time of the flare, for the purposes of this study we will adopt a conversion factor of 730 km arcsec −1 to obtain a footpoint separation of ∼15.8 Mm and a loop half-length (assuming a elliptical loop with eccentricity e = 0.8; see Section 5 for our explanation of this choice) of ∼10.1 Mm. From Equations ( 2 ) and ( 3 ) we note that, for a 10.1 Mm loop that begins at ∼11 MK at density ∼10 11 cm −3 , the conductive cooling time is significantly shorter than the radiative cooling time and will dominate the energy losses. As noted above, thermal conduction will quickly eliminate any initial temperature inhomogeneities that were present in the loop, resulting in the loop possessing a uniform temperature structure. Later, by the time the loop plasma has cooled sufficiently for significant Si iv and O iv emission to appear, it is the radiative energy losses that dominate. To estimate a timescale for the overall cooling from the ∼10 MK to ∼80,000 K, we equate these two timescales and solve for T . This yields an approximate crossover temperature, T *, at which the two energy-loss processes are approximately equal, given byFor a loop as described above, this yields a crossover temperature of T * ≈ 5.7 MK, and substituting back into Equations ( 2 ) or ( 3 ) above gives a timescale for the loop cooling from the Fe xxi temperatures to Si iv
Paper: 2019MNRAS.487.4939M (fulltext)
less concentrated (i.e. low n ) again. Note that error bars correspond to 1σ confidence intervals of the posterior distributions. 5 DISCUSSION 5.1 Morphological trends: two modes of star formation In nearby naked red nuggets stellar populations with different ages show different radial distributions (Fig. 4 ). In particular, we have shown how the effective radius remains almost constant up until ∼8 Gyr ago, but then it increases for younger ages. Note that individual points in Fig. 4 are not integrated values; i.e. they do not show how the galaxy would look at that time, but how the stars born at that time are distributed. The observed trend in R e , increasing with decreasing age of the stars, is effectively equivalent to the larger sizes of galaxies observed through bluer photometric bands (e.g. Evans 1994 ; Cunow 2001 ; Möllenhoff, Popescu Tuffs 2006 ; Kelvin et al. 2012 ) as younger stars will dominate the flux at shorter wavelengths. The peak in the Sérsic index evolution reveals a more complex scenario. The bottom panel in Fig. 4 suggests that a compaction phase takes place during the early live of red nuggets. This initial compaction is a well-known theoretical expectation, where gas is efficiently channelled towards the centre of dark matter haloes, leading to massive, gas-rich, compact, and star-forming discs (e.g. Gammie 2001 ; Dekel, Sari Ceverino 2009 ; Krumholz Burkert 2010 ; Cacciato, Dekel Genel 2012 ; Dekel et al. 2013 ; Dekel Burkert 2014 ; Zolotov et al. 2015 ; Dekel, Lapiner Dubois 2019 ). Observations of massive, compact and star-forming galaxies at high- z support this scenario (e.g. Barro et al. 2013 , 2014 , 2017 ; Huertas-Company et al. 2015 ; van Dokkum et al. 2015 ), but linking this population of galaxies to their more extended progenitors is not trivial. Our approach, tracing back in time the structural evolution of individual objects, shows that for our sample of galaxies an initial compaction took place, reaching a maximum ∼8 Gyr ago ( z ∼ 1). In order to explore the connection between SFR and compaction, we show in Fig. 5 the average evolution of sSFR–log Σ 1 in our sample, colour coded by the Sérsic index at that time (from Fig. 4 ). A clear connection can be seen between star formation activity and galaxy structure. The initial compaction takes place during the star-forming phase, as expected in a gas rich environment. Later, the actual quenching takes place when the compaction is maximal. This could be naturally explained if the gas brought towards the centre or the galaxy efficiently feeds the central supermassive black hole. The black hole would become therefore energetic enough to shut down star formation, likely leading to an inner bulge-like component (e.g. Silk Rees 1998 ; King 2003 ; Di Matteo et al. 2005 ; Sijacki et al. 2007 ; Silk 2013 ). Note also that the buildup of the central component is likely associated with a strong stellar feedback even, which would also favour the quenching process. The
Paper: 2020ApJ...889...72K (acknowledgments)
We acknowledge support from the project RVO-67985815 and GA ČR grants 17-16447S, 18-09072S, and 19-09489S. D.G. and B.C. acknowledge support from NSF grant AST-1910354, AGS-1654382, and NASA grants 80NSSC18K1128, 80NSSC19K0068, and NNX17AB82G to NJIT. This work was also supported by the Science Grant Agency project VEGA 2/0048/20 (Slovakia). Help from the Bilateral Mobility Projects SAV-18-01 of the SAS and CAS is acknowledged. This article was created in the project ITMS No. 26220120029, based on the supporting operational Research and development program financed from the European Regional Development Fund. We acknowledge the use of the Fermi Solar Flare Observations facility funded by the Fermi GI program ( http://hesperia.gsfc.nasa.gov/fermi_solar/ ).
Paper: 2020A&A...638L...8P (fulltext)
only by extending supernovae observations to high redshifts ( z ∼ 1) can a measurement of both Ω m, 0 and Ω Λ, 0 be obtained ( Goobar Perlmutter 1995 ). By contrast, through Eq. ( 10 ) we can estimate Ω Λ, 0 today, with no reference to Ω m, 0 , using the effect of Λ on galaxy-cluster scales rather than on the Universe as a whole, and based on low-redshift observations alone. We note that the independence of Eq. ( 10 ) from Ω m, 0 holds only at z = 0. Producing an estimate of Ω Λ, 0 using Eq. ( 10 ) requires measuring the present-day slope and intercept of Ω ta ( z ), ideally within a redshift range where Ω ta grows linearly with z . When data from higher redshifts are used, nonlinear terms will introduce some dependence between the Ω Λ, 0 and Ω m, 0 estimates, which, however, can be quantified based on Eq. ( 8 ). In practice, once measurements of Ω ta are obtained, a nonlinear fit will be performed on the predicted Ω ta ( z ) parameterized by Ω m, 0 and Ω Λ, 0 (see lower panel of Fig. 1 ), which will yield a measurement of both cosmological parameters (similar to the contours of Fig. 3 ). For the range z ≤ 0.3 considered in Sect. 3 , the deviation of Ω ta ( z ) from its linear approximation around z = 0 is 10% for any value of Ω Λ, 0 : small, but not subdominant compared to the statistical uncertainties that can be achieved using the large number of available clusters out to this distance. For this reason, the predicted shapes of the constraints from Ω ta ( z ) in Fig. 2 should be viewed as approximations. The technique discussed here depends on independent measurements of Ω ta in different structures and it does not require completeness of the sample of clusters used. For this reason, the same result can be obtained with measurements in only a fraction of the z ≤ 0.3 SDSS clusters, if the sample is optimized in terms of its redshift distribution. There is also margin for improvement in the accuracy of the measurement of Ω ta in individual clusters, for example, by applying quality cuts based on the absence of massive neighbors ( Korkidis et al. 2020 ), mass cuts ( Köhlinger et al. 2015 ), or galaxy-number cuts ( Karachentsev Nasonova 2010 ; Lee 2018 ). About 500 well-selected z 0.3 clusters, uniformly distributed over redshift, with Ω ta measured with 25% accuracy in each, would be enough to establish Ω Λ, 0 > 0 at the 5 σ level. In estimating the potential accuracy of constraints on Ω m, 0 and Ω Λ, 0 , we assumed all uncertainties to be statistical. However, before any statement on cosmology can be made based on measurements of Ω ta ( z ), systematic errors have to be carefully considered as well. For the proof-of-principle calculation of Sect. 2 we used the model of spherical collapse of a single structure in an otherwise uniform and isotropic background universe. Although Korkidis et al. (2020) have shown that the predictions of spherical collapse for Ω ta persist in N -body simulations, they have also reported a small systematic shift
Paper: 2016MNRAS.458..900C (acknowledgments)
EC would like to thank the Lady Davis foundation for financial support and Julio Navarro for the fruitful comments and interesting discussions. JS acknowledges support from the Alexander von Humboldt foundation. YH has been partially supported by the Israel Science Foundation (1013/12). SG and YH acknowledge support from DFG under the grant GO563/21-1. GY thanks MINECO (Spain) for financial support under project grants AYA2012-31101 and FPA 2012-34694. SP is supported by the Russian Academy of Sciences programme P-41. AK is supported by the Ministerio de Economía y Competitividad (MINECO) in Spain through grant AYA2012-31101 as well as the Consolider-Ingenio 2010 Programme of the Spanish Ministerio de Ciencia e Innovación (MICINN) under grant MultiDark CSD2009-00064. He also acknowledges support from the Australian Research Council (ARC) grants DP130100117 and DP140100198. He further thanks Isaac Hayes for shaft. We thank the anonymous referee for the useful comments and remarks. We also thank the Red Española de Supercomputación for granting us computing time in the Marenostrum Supercomputer at the BSC-CNS where part of the analyses presented in this paper have been performed.
Paper: 2017ApJ...838...87C (acknowledgments)
We thank Andrew Hearin and Peter Behroozi for useful discussions and the referee for detailed comments. Funding for PRIMUS has been provided by NSF grants AST-0607701, 0908246, 0908442, and 0908354 and NASA grant 08-ADP08-0019. A.L.C. acknowledges support from NSF CAREER award AST-1055081. We use data from the DEEP2 survey, which was supported by NSF AST grants AST00-71048, AST00-71198, AST05-07428, AST05-07483, AST08-07630, and AST08-08133. This study makes use of data from the AEGIS Survey and in particular uses data from GALEX , Spitzer , CFHT, and the Keck and Magellan telescopes.
Paper: 2020ApJ...891...90R (fulltext)
1, 2, and 3 are A10 , M12 , P17 , respectively; for M 2500 , the relations come from C11 , M12 , P17 , respectively. The agreement or disagreement of M HE with the other mass estimates also highlights a key procedural/mathematical difference between the methods of estimation: we calculate M HE at individual radii, which does not make use of knowledge of the pressure profile across all radii. However, the Y – M mass makes use of Y , a cumulative sum of spherically integrated pressure within R 500 (or R 2500 ). Similarly, M VT is fit across all pressure bins at radii larger than 100 kpc. Physically, we may expect M HE , M VT , and ( Y – M ) A10 (or ( Y – M ) C11 for M 2500 ) to be similar, as they directly or indirectly rely on the assumption of HE. The other Y – M relations, being calibrated to weak lensing or simulations, should encapsulate the total mass without regard to dynamical state. We expect mass estimates that assume HE underestimate the total mass due to the so-called hydrostatic mass bias, which itself encompasses several biases. The primary bias is not actually the HE assumption, but rather that the pressure gradient used only accounts for thermal pressure. Thus the lack of account for non-thermal pressure underestimates the mass by 10%–30% (see also Lau et al. 2013 ; Nelson et al. 2014a ; Biffi et al. 2016 ; Khatri Gaspari 2016 ; Hurier Angulo 2018 ; Ettori et al. 2019 ). 5.3. Residuals An underlying quadrupole is observed in our residual map (Figure 5 ). Modeling the ICM with the same centroid and an ellipsoidal ICM distribution results in very similar residual decrements and removes this quadrupole. Beyond ellipticity in the core of the cluster, the only observed SZ substructure is also in the core of the cluster. The corresponding integrated Y ( Y cyl ) due to the substructure (decrement −25 μ K) is 3.0 × 10 −7 h 70 2/3 Mpc 2 , whereas we find Y cyl ( R 500 ) = 7.3 × 10 −5 h 70 2/3 Mpc 2 for our NP fit; thus substructure accounts for only 0.5% of the SZ signal within θ 500 and is not a significant source of scatter in the mass estimates from Y – M relations for this cluster. Figure 5. Residual map (in μ K) of the Minkasi map with the SZ centroid marked by a red cross, the radio sources marked by tri_up (purple on white), and the Herschel 500 micron centroid by a blue cross; the beam is shown in the bottom left. The black contours are every 2 σ , excluding 0 ( σ = 12.8 μ K). As noted before, the central pressure profile and the central point-source amplitude are degenerate. In addition to the central radio source, a second, nearby radio point source is observed at 4.9 and 8.5 GHz (Giacintucci et al. 2014 ). We find that these two sources contribute to a mild increase in the uncertainties in the inner two pressure profile bins. The spatial coincidence of our residual SZ decrement and the radio minihalo (Figure 6 ) may be suggestive of an underlying link. It would be equally interesting if there is a link between the SZ
Paper: 2020ApJ...904...52N (fulltext)
Ia cluster rates with redshift has been used to explain the distribution of Fe in the ICM, as well as the SN Ia delay time distribution and respective predictions for single- versus double-degenerate progenitors (Sharon et al. 2010 ; Barbary et al. 2012 ; Friedmann Maoz 2018 ). Looking forward, assuming that the majority of short GRBs are linked to BNS merger progenitors, the true fraction of short GRBs that occur in galaxy clusters may similarly play a role in r -process element enrichment of the ICM and intracluster light (ICL). In addition, the large physical offsets of cluster short GRBs from their most probable hosts, relative to those associated with field galaxies, is of interest in understanding their origins. A natural explanation for the occurrence of short GRBs at tens of kiloparsecs away from their host galaxies, but within galaxy clusters, are the large systemic velocities that BNS systems may experience (Zevin et al. 2019 ), coupled with the older stellar population in clusters that contribute to longer delay times. Galaxy cluster studies have also shown that the ICL and BCGs compose ≈30%–50% of the cluster stellar mass (Zhang et al. 2019 ). In particular, the origins of the ICL and ICM have been linked to the disruption of dwarf galaxies (Gregg West 1998 ) or the tidal stripping of outer material from more typical galaxy cluster members (Gallagher et al. 1972 ; DeMaio et al. 2015 , 2018 ). Since 30% of short GRBs occur in the halos of their host galaxies (Fong Berger 2013 ; Zevin et al. 2019 ), beyond a few effective radii, the locations of these short GRBs are situated amid prime material for stripping to contribute to the ICL and ICM. 6. Conclusions We have presented identification of the early-type, quiescent host galaxy of GRB 161104A and a detailed investigation of the large-scale galaxy cluster environment surrounding GRB 161104A. We have also presented new modeling of 20 additional short GRB host galaxies, as well as the identification of the large-scale environment of GRB 090515 as a galaxy cluster. We summarize with the following conclusions: 1. The putative host of GRB 161104A is an early-type, quiescent galaxy at z = 0.793 ± 0.003. By jointly fitting the host photometry and spectroscopy from Magellan with Prospector , we determine a stellar mass of M ≈ 1.6 × 10 10 M ⊙ , mass-weighted age of t m ≈ 2.12 Gyr, metallicity of , dust extinction A V ≈ 0.08, and ongoing SFR ≈ 0.099 M ⊙ yr −1 . 2. Using deep optical and NIR observations, we determine that the host of GRB 161104A and most of the galaxies surrounding the XRT position are at similar redshifts of z ∼ 0.8. We confirm their membership in a cluster at a median redshift of z = 0.79 using DES cluster catalogs. We also show that GRB 090515 resides in a cluster at z ∼ 0.4. This makes GRB 161104A the fifth short GRB associated with a cluster and the highest-redshift short GRB known to be associated with a galaxy cluster. 3. We model the broadband SEDs of 20 additional
Paper: 2016MNRAS.457...38L (acknowledgments)
This research has made use of the NASA Astronomical Data System (ADS), the NASA Extragalactic Database (NED) and is based on observations obtained with XMM–Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. This research is also based on observations with the NASA/UKSA/ASI mission Swift . AL, SV and JR acknowledge support from the UK STFC.
Paper: 2018A&A...614A..56B (acknowledgments)
Acknowledgements We thank the anonymous referee for useful comments on the manuscript. We thank Jeffrey Chan for useful advice on the determination of the survey depth. We acknowledge financial support from “Programme National de Cosmologie and Galaxies” (PNCG) funded by CNRS/INSU-IN2P3-INP, CEA and CNES, France, and from “Projet International de Coopération Scientifique” (PICS) with Canada funded by the CNRS, France. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration and of the GOLDMine database ( http://goldmine.mib.infn.it/ ) ( Gavazzi et al. 2003b ). M. B. was supported by MINEDUC-UA projects, code ANT 1655 and ANT 1656. A. L. work was supported by the Sino-French LIA-Origin joint program. M. S. acknowledges support from the NSF grant 1714764 and the Chandra Award GO6-17111X. M. F. acknowledges support by the science and technology facilities council (grant number ST/P000541/1). E. W. P. acknowledges support from the National Natural Science Foundation of China through Grant No. 11573002. K. S. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC).
Paper: 2018ApJ...868..119K (acknowledgments)
We would like to thank Genavie Spence for her work on data analysis, and Samar Safi-Harb for insightful discussions. We would also like to thank the referee for helpful comments. Support for this work was provided by the National Aeronautics and Space Administration through Chandra Award Number GO3-14072, issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of the National Aeronautics Space Administration under contract NAS8-03060. Facilities: CXO , HESS .