adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.494.3014A (fulltext)
respectively. For each of these frequency windows we compute the power spectral density. We selected windows of 16 h to compute each bin. In order to avoid segments containing magnetic experiments or events as the ones reported in Tables 2 and 1 , we apply a mask to the data. To do so we take as a figure of merit the amplitude spectral density in the range . Those segments where this figure of merit is exceed by five sigma are discarded from our analysis. Following this criterion, we exclude 9 segments out of 300. This analysis allows a generic description of the statistical behaviour of the fluctuations without any previous assumption on its stationarity. Fig. 4 shows the results for both frequency ranges. In agreement with our previous analysis, the amplitude of the fluctuations for the low-frequency bin is coherent and follows the same statistical distribution for all four magnetometers. Bins are statistically distributed with similar median values, namely These values are based on the 16th, 50th, and 84th percentiles of the histogram. Also, in this case we can derive a common mean value for the fluctuations in this frequency range of . On the other hand, the noise power in the higher frequency bins show a different amplitude spectral density for each magnetometer. The median values in this case are In the latter we observe that the distribution of the median values is narrower and not overlapping between them. Both behaviours are clearly distinguished in the histograms of Fig. 4 , which characterizes the variability of the magnetic field fluctuations on-board for the two frequency regimes that we have previously identified. Figure 4. Time evolution and statistical distribution of the X-component of the magnetic field fluctuations as measured on-board, for two different frequency regimes. Colours correspond to magnetometers: PX (blue), MX (red), PY (yellow), and MY (green). Time is indicated in DAL, while ASD stands for Amplitude Spectral Density. Top: Fluctuations in the frequency range . Bottom: Fluctuations in the frequency range . In the framework of future gravitational wave detectors in space, the variability in the spectra of magnetic field fluctuations is particularly relevant in the low-frequency regime. There are two main reasons for that: first, the mHz band is the main objective of a gravitational wave detector in space, since these observatories are designed to study the gravitational wave universe in this frequency band. Second, at the same time, the low-frequency fluctuations will be precisely the main contribution to the magnetic induced force noise in the free-falling test masses. According to Armano et al. ( 2016 ), magnetic induced forces could contribute to a maximum of 3 of the measured 12 at 0.1 mHz of the differential acceleration measured on LPF test masses. Although the precise determination of the magnetic noise contribution is still pending, the current estimate is expected to be of the order of other
Paper: 2018A&A...614A..16C (fulltext)
SPHERE data We also re-reduced the SPHERE observations of HD 4113 published in Moutou et al. (2017) (Program 096.C-0249, PI: Moutou), taken on 2015-10-08. These observations were taken in IRDIFS-EXT mode following an observing strategy that is similar to our observationsas described above, and are summarized in Table 2 . The IRDIS data were taken in the K1-K2 mode ( λ K 1 = 2.1025 μ m, λ K 2 = 2.2255 μ m), while the IFS data cover a range of wavelengths from 0.97–1.66 μ m with an average spectral resolution of 34.5. These data include a maximum field rotation of 51 deg between coronagraphic frames. Our data reduction procedure was identical to that of the IRDIFS data described in the previous section. 4 Results 4.1 SPHERE 2016 high-contrast imaging data The source HD 4113C was detected at high significance in both IRDIS H2 and IRDIS SDI reductions, but was not recovered in the H3 filter, suggesting the presence of strong methane absorption. The measured relative photometry and astrometry are given in Table 3 , while the IRDIS PSF-subtracted images are shown in Fig. 2 . We adopt a lower limit on the contrast of 14.8 mag for the H3 filter, using the 3 σ contrast curve at the location of the H2 detection. Table 3 Measured astrometry and photometry of HD 4113C from the SPHERE observations. In the IFS data using PCA alone, no significant detection was found in any individual wavelength channel or in the mean taken over wavelength. After we applied SDI to the IFS data, we clearly detected the companion, with 14 of the wavelength channels showing an excess of flux above 1.5 σ significance at the location of the IRDIS detection. A weighted-mean image is shown in Fig. 2 . The data reduction for wavelengths below 1.02 μ m suffer from considerably more residual noise in the SDI+PCA reduction and are not included in the subsequent analysis. The measured contrast ratios from IFS and IRDIS were converted into apparent flux measurements through multiplication by a BT-NextGen spectral model ( Allard et al. 2012 ) with T eff = 5700 K, [Fe/H] = 0.3 dex and log g = 4.5, close to the values determined in Sect. 2 . The model spectrum was flux-scaled using the distance and radius of HD 4113A. The extracted spectrum is shown in Fig. 4 . 4.2 SPHERE archival high-contrast imaging data After processing the 2015 data, we were unable to unambiguously recover the companion HD 4113C in the IRDIS images. Close to the location of the 2016 detection, we find an excess of flux with 2–3 σ significance in the SDI reduction. However, because of the lower S/N, our reduction of the IRDIS data does not provide substantial constraints on the position or flux of the companion, and we have excluded it from the following analysis. In the IFS SDI reduction, we also find an excess of flux in wavelength channels corresponding to the J - and H -band peaks found in the2016 data, again with lower significance. We find six channels at which the companion is visible at S ∕ N > 4, and by
Paper: 2017MNRAS.465.3889R (acknowledgments)
Acknowledgments The research leading to these results has received funding from the European Union Seventh Framework Programme FP7-2011 under grant agreement no 284405. WASP-South is hosted by the South African Astronomical Observatory, and Super-WASP-North by the Isaac Newton Group and the Instituto de Astrofisica de Canarias; we are grateful for their ongoing support and assistance. Funding for WASP comes from consortium universities and from the UKs Science and Technology Facilities Council. This publication makes use of VOSA, developed under the Spanish Virtual Observatory project supported from the Spanish MICINN through grant AyA2008-02156.
Paper: 2017MNRAS.468.1616P (fulltext)
in hadronic scenario, with spectrum and emissivity directly related to the spectrum of highest energy CRs. The simplified model of gamma-ray evolution, used in our paper, is not strongly influenced by the shape of the spectrum as it approximates it with E 2 . 2.1 Hydrodynamic modelling The dynamical evolution of an SNR was modelled by numerically solving the time-dependent HD equations of mass, momentum and energy conservation: where is the mass density, is the flow velocity, P is the thermal pressure, is the unit vector, E is the total energy density and is the adiabatic index. The total energy density is the sum of the thermal and kinetic components: We adopt the pluto code (Version 4.2; Mignone etal. 2007 , 2012 ) to solve the system of HD conservation laws by using a cell-centred finite-volume approach based on Godunov-type schemes. The code design enables efficient usage of massively parallel computers through the message passing interface standard (MPI) for interprocessor communications. We do not include radiative cooling and thermal conduction in our HD equations, describing only the free and Sedov expansion phases of the SNR evolution in a tenuous, collisionless medium. The transition time from Sedov to radiative phase for an SNR is described with the approximation (e.g. Blondin etal. 1998 ; Petruk 2005 ; Orlando etal. 2011 ) where E 51 E 0 /(10 51 erg) and E 0 is the initial total explosion energy, contained mostly in the form of kinetic energy. In our set of simulations, t tr 0.5Myr and therefore our modelled SNRs never reach the radiative phase. We will compute the radio emission from the SNR but with assumption that the radiation has no impact yet on its dynamical evolution. Throughout our modelling, we do not activate the MHD solver of pluto because it is in any case powerless in describing the generation of magnetic turbulence by CRs upstream of the shock and corresponding MFA. Such an amplified magnetic field is dominant compared to the field compressed only due to fluid compression, especially for young SNRs where non-linearity is very pronounced. NLDSA module, which runs parallel with pluto HD code, simultaneously performs calculations of MFA, synthesize the global radio emission in this amplified field and also accounts for its impact on hydrodynamics. We used the following set of pluto algorithms in our simulations: linear interpolation with default limiter, HLLC Riemann solver and RK2 algorithm for the time evolution. Additionally, we used MULTID shock flattening algorithm for the numerical dissipation near the strong shocks. Our three-dimensional (3D) computations were carried out in spherical coordinates ( r , , ), by using a static logarithmic grid, with mesh size increasing with the SNR radius. Detection and tracking of the SNR shock waves in the fluid, travelling in some direction x , is based on two standard numerical conditions, namely and , where p represents parameter, setting the shock strength. We modified pluto
Paper: 2016A&A...585A.162M (fulltext)
in the Bar regions are primarily on the near side (low fraction), lending support to the foreground location of the Bar. Finally, we compared the populations of SNRs in Local Group galaxies via their X-ray luminosity function. The XLF of SNRs in the SMC, M31, and M33 are relatively homogeneous over all the observed luminosity range, although that of the SMC is flatter. The LMC XLF is remarkable by its prominent bright end. The largest population of SNRs brighter than erg s is found in the LMC (13 SNRs vs. 8 and 7 in M31 and M33, respectively). This is possibly an effect of the lower metallicity in the LMC: massive stars have smaller mass loss rates (less heavy elements to drive stellar winds) and the interaction of SN ejecta with less massive CSM shells produce brighter remnants. The number of SNRs brighter than erg s in the LMC is comparable to that in M31 and M33, likely owing to its high recent SFR and high current type Ia SN rate. The LMC XLF flattens significantly because of incompleteness: many X-ray-faint SNRs have been missed so far, due to the incomplete coverage of the LMC with sensitive X-ray instruments (i.e. Chandra or XMM-Newton ). This work presents the state of the art on X-ray emission of SNRs in the LMC. However, it is clear that the current sample is incomplete, as evidenced by the flattening of the X-ray luminosity function of LMC SNRs (Sect. 7 ). In the last 15 years, new SNRs were confirmed or discovered in the LMC at an almost constant rate (one or few per year), principally using X-ray observations. There is no indication that this trend will stop in the near future, so that more observations of the LMC will increase the sample of SNRs. Nevertheless, the observing time of major observatories is limited and expensive. We conclude this work by offering several strategies to maximise the chance of finding “missing” SNRs: As shown in Sect. 8 , star formation is intensearound the SGS LMC 4, and the edges of the shellabound in SNRs. Many LMC SGS have not been (fully) surveyedby XMM-Newton , for instance (in the notationof Kim et al. 1999 )SGS 3 and 6 in the north, SGS 2 and5 in the west, and SGS 4 in the south. Targeting inparticular SGSs associated to star formation (e.g. withH ii region along the rims) warrants successfulSNR searches. The follow-up observations of X-ray-selected candidates (usually ROSAT sources) with XMM-Newton have been extremely successful. Such programmes should be continued until completion of the list of candidates. Even the ROSAT (targeted) survey of the LMC was not covering the LMC up to its outskirts. To find SNRs in these regions, the future eROSITA survey ( Merloni et al. 2012 ) will be most useful, covering the full sky in the 0.58 keV band. The LMC is located close to the South Ecliptic Pole and will be observed with a deeper exposure than the rest of the sky. Looking for new SNR candidates, especially evolved X-ray-only SNRs, will be of special interest. Even in existing data, some SNRs
Paper: 2019ApJ...885L..22S (fulltext)
with other techniques for measuring half-mass radii are presented in Suess et al. ( 2019 ). Suess et al. ( 2019 ) also show that the half-mass radii recovered using our technique are not significantly biased by the galaxy’s half-light radius, stellar mass, or redshift. In Figure 1 , we show the strength of galaxy color gradients (i.e., r e,mass / r e,light ) as a function of stellar mass for two redshift slices in our new low-redshift sample. We divide the sample into star-forming and quiescent populations using the UVJ diagram and the quiescent definition from Whitaker et al. ( 2012 ). In Suess et al. ( 2019 ), we found that there was a significant trend between color gradient strength and stellar mass for galaxies at 1.0 ≤ z ≤ 2.5. Here, we find that this trend continues to lower redshifts. The only exception is in the lowest-redshift quiescent bin, where our sample is quite small. In the lower panels of Figure 1 , we also show how the best-fit relation between r e,mass / r e,light and stellar mass varies as a function of redshift for our full 0 ≲ z ≤ 2.5 sample. Figure 1. Top: color gradient strength as a function of stellar mass for two redshift intervals. Small light blue points and light red squares show individual star-forming and quiescent galaxies; large blue points and red squares show a running median. The blue and red lines show best-fit linear relations to each trend; lines are outlined in black if the slope of the relation is inconsistent with zero, and outlined in gray if the slope is consistent with zero. Dashed vertical blue and red lines show the mass completeness of the star-forming and quiescent samples. Bottom: best-fit relation between color gradient strength and stellar mass for star-forming (left) and quiescent (right) galaxies as a function of redshift. Dashed lines represent fits whose slopes are consistent with zero. We see clear redshift evolution in the best-fit color gradient-mass relations for both quiescent and star-forming galaxies. In Figure 2 , we show the median strength of color gradients for all galaxies with , the completeness limit of our full sample. In Suess et al. ( 2019 ), we showed that the strength of galaxy color gradients decreases sharply between z ∼ 2.5 and z ∼ 1. Here we find that this evolution may flatten below z ∼ 1; r e,mass / r e,light for both quiescent and star-forming galaxies remains roughly constant at a value of ∼0.7. Figure 2. Median half-mass to half-light radius ratio for star-forming and quiescent galaxies (blue circles and red squares) as a function of redshift. r mass / r light traces the strength of radial color gradients; values less than one indicate negative color gradients, where the center of the galaxy is redder than the outskirts. Only galaxies with , where our sample is complete, are included. Error bars show the central 68% of 500 bootstrap samples. The strong color gradient evolution previously observed at z ≳ 1 appears to flatten at z ≲ 1. 3. The Growth of Quiescent
Paper: 2017ApJ...838...48M (acknowledgments)
We are grateful to the referee whose comments led to an improved version of the paper. We acknowledge useful discussions with Tobias Heinemann, Oliver Gressel, and Leonardo Krapp. We are grateful to Thomas Berlok for help with the simulations and for useful comments on the manuscript. The research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP/2007-2013) under ERC grant agreement 306614.
Paper: 2019ApJ...871...30I (acknowledgments)
This work is supported by the ERC Synergy Grant “BlackHoleCam: Imaging the Event Horizon of Black Holes,” grant 610058. We thank the National Science Foundation (AST-1126433, AST-1716536) and the Gordon and Betty Moore Foundation (GBMF-5278) for financial support of this work. This work was supported in part by the black hole initiative at Harvard University, which is supported by a grant from the John Templeton Foundation. M.K. acknowledges the financial support of JSPS KAKENHI grant Nos. JP18K03656 and JP18H03721. R.-S.L. is supported by the National Youth Thousand Talents Program of China and the Max-Planck Partner Group. L.L. acknowledges the financial support of DGAPA, UNAM (project IN112417), and CONACyT, México. I.C. acknowledges the financial support of the National Research Foundation of Korea (NRF) via a Global PhD Fellowship Grant (NRF-2015H1A2A1033752). This paper makes use of the following ALMA data: ADS/JAO.ALMA2016.1.00413.V. ALMA is a partnership of the ESO (representing its member states), NSF (USA), and NINS (Japan), together with the NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by the ESO, AUI/NRAO, and NAOJ. This research has made use of data obtained with the Global Millimeter VLBI Array (GMVA), which consists of telescopes operated by the Max-Planck-Institut für Radioastronomie (MPIfR), IRAM, Onsala, Metsahovi, Yebes, the Korean VLBI Network, the Green Bank Observatory, and the Long Baseline Observatory (LBO). The VLBA is an instrument of the LBO, which is a facility of the National Science Foundation operated by Associated Universities, Inc. The data were correlated at the DiFX correlator of the MPIfR in Bonn, Germany. This work is partly based on observations with the 100 m telescope of the MPIfR at Effelsberg. This work made use of the Swinburne University of Technology software correlator (Deller et al. 2011 ), developed as part of the Australian Major National Research Facilities Programme and operated under license. Software: AIPS (Greisen 2003 ), DiFX (Deller et al. 2011 ), HOPS (Whitney et al. 2004 ; L. Blackburn et al. 2019, in preparation), KORAL (Sadowski et al. 2013 , 2014 , 2017 ), PolConvert (Martí-Vidal et al. 2016 ), eht-imaging library (Chael et al. 2016 ), Stochastic Optics (Johnson 2016 ).
Paper: 2018MNRAS.481.5580F (acknowledgments)
ACKNOWLEDGEMENTS The authors would like to gratefully acknowledge Eric Emsellem and Cheng Li for useful discussions on the subject, and the anonymous referee for useful comments that improved this paper. AFM acknowledges support from STFC. MAF is grateful for financial support from the CONICYT Astronomy Program CAS-CONICYT project No. CAS17002, sponsored by the Chinese Academy of Sciences (CAS), through a grant to the CAS South America Center for Astronomy (CASSACA) in Santiago, Chile. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org . SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.
Paper: 2019ApJ...876...41T (fulltext)
(NFO, including corrections). The resultant light curves are displayed in Figure 2 . Figure 2. Photometric observations of V506 Oph from the URSA and NFO telescopes, along with the residuals. NFO is displaced vertically for clarity. Enlargements of the minima are shown at the bottom. Our adopted model discussed in Section 5 is overplotted. Table 5 URSA Observations of V506 Oph (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. Table 6 NFO Observations of V506 Oph (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. 5. Light-curve Analysis The URSA and NFO photometry of V506 Oph was analyzed using version 2013 of the Wilson–Devinney LC program (Wilson Devinney 1971 ; Wilson 1979 , 1990 ) called within a Markov chain Monte Carlo (MCMC) scheme. Our method of solution used the emcee 5 5 http://dfm.io/emcee/current code of Foreman-Mackey et al. ( 2013 ), which is a Python implementation of the affine-invariant MCMC ensemble sampler proposed by Goodman Weare ( 2010 ). We typically used 100 walkers and uniform priors within suitable limits for all fitted quantities. As the system is well detached, we used the LC program in mode 2, along with the option of simple reflection and synchronous rotation of both components (see Section 6 ). The ephemeris and mass ratio of the binary were held fixed at the values in Table 4 , and the primary temperature was set to 6840 K (Section 2 ). The main parameters of the fit were the inclination angle, i ; the temperature of the secondary, T eff,2 ; the surface potentials, Φ 1 and Φ 2 ; a phase shift, Δ φ ; and the out-of-eclipse magnitude difference at phase 0.25, m 0 . We assumed initial measurement errors for the URSA and NFO observations of 0.02 mag, and a scale factor, f (with a log-uniform prior), was included as an additional adjustable parameter, which we solved for self-consistently and simultaneously with the other parameters (see Gregory 2005 ). Convergence of the chains was checked visually, with the additional requirement of a Gelman–Rubin statistic of 1.05 or smaller for each parameter (Gelman Rubin 1992 ). The URSA and NFO data sets were initially analyzed separately. Tests indicated the best results were obtained by solving also for the linear limb-darkening coefficients of each star ( x 1 , x 2 ), as well as the gravity-darkening exponents ( β 1 , β 2 ). More complicated limb-darkening laws did not provide any improvement. The albedos for both components were held fixed at a value of 0.5, commonly adopted for convective stars, as experiments with other values gave poorer results. No significant third light was detected, consistent with the fact that the Gaia /DR2 catalog (Gaia Collaboration et al.