adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...904...82Q (fulltext)
no notable difference in the spatial distribution of variable stars compared to the unclassified (nonvariable) sources according to Gaia DR2. 5.6. P-L Relations Accurately determined periods have been the means to distinguish variable stars, particularly pulsating variables (LPV, Cepheids, RR Lyrae, RV Tauri, etc.) within Gaia (Gaia Collaboration et al. 2019 ). Figure 13 displays the distribution of periods available from Gaia for sources in the local sample. It can be noted that the whole sample contains LPV stars with a wide range of periods, but that those within the solar neighborhood are restricted to stars with periods of ≲1250 days, presumably Mira or SR variables, as classified by Gaia (Molnár et al. 2018 ; Mowlavi et al. 2018 ). Figure 13. Period distributions obtained for variable stars in Gaia DR2 for the Gaia–BAaDE and local samples (see Figure 5 and Section 5.6 ). It has been established that P-L relations are a very powerful tool to distinguish AGB stars of different natures (Wood et al. 1999 ; Ita et al. 2004 ; Lebzelter et al. 2019 ). By recognizing that Mira variables pulsate dominantly in the fundamental mode, they can be promising candidates for distance determinations of remote galaxies, using empirical relations based on the LMC (Whitelock et al. 2008 ). With 2MASS K magnitudes, Gaia DR2 parallaxes, extinction maps, and periods for a subsample of the local sample ( period sample), we are able to make a comparison of the BAaDE targets with previously studied variable stars. In Figure 14 , we present the P-L relation for those BAaDE stars in the local sample with measured Gaia periods ( period sample), where there is a spread in the magnitude, resulting from uncertainties in the apparent K magnitude, extinction, and distance (indicated by the error bars) and IR variability (not indicated). A comparison is made of the P-L distributions with known variability sequences, associated with distinct pulsation modes, that have been derived from Gaia DR2 data for LMC populations as discussed by Lebzelter et al. ( 2019 ). These sequences have been transformed to , using the LMC distance modulus in that work (18.49 mag). Moreover, the established P-L relations for Miras from Whitelock et al. ( 2008 ) and Matsunaga Team ( 2006 ) in the LMC are added. Figure 14. Period–luminosity relations found for the variable stars within the local sample. The sequences marked as C , C ′, and D represent different known variability sequences, associated with distinct pulsation modes derived for the LMC based on Gaia data (Lebzelter et al. 2019 ). Note that the sequences were corrected for a distance modulus of 18.49 mag. Period–luminosity relations reported for LMC using other surveys are also shown (Matsunaga Team 2006 ; Whitelock et al. 2008 ). The local sample appears to be much flatter than the empirical P-L relations for the LMC, and it is clear that most of the stars in the local sample fall below the LMC P-L relations (Matsunaga Team 2006 ;
Paper: 2018MNRAS.476.2278H (fulltext)
at the redshifts of interest) and that the seeing was good (0.65 arcsec), slit losses are likely ≲30 per cent. Moreover, because we are primarily concerned with redshifts rather than line luminosities, aperture corrections to the line fluxes are unnecessary. The spectra were obtained using the standard two-position ‘mask nod’ in which the telescope position was dithered ±1.5 arcsec along the slit direction, with individual integration times of 120 s ( H band) or 180 s ( K band). Total integration times of 2880 s were obtained, with 24 and 16 individual exposures in the H and K bands, respectively. The MOSFIRE data were reduced using the publicly available data reduction pipeline developed by the instrument team; 12 see Steidel et al. ( 2014 ) for a detailed description of the procedure. One-dimensional spectra were extracted, flux-calibrated, and analysed using the mospec package (Strom et al. 2017 ). 2.1.2 GNIRS observations Near-infrared spectra of resolved components of two multicomponent single-dish submm sources (UDS292 and UDS306) were obtained using the cross-dispersed mode of the Gemini Near-Infrared Spectrograph (GNIRS 13 ) on the Gemini North 8.1 m telescope. This configuration provides continuous spectral coverage from 0.84 to 2.48 m at a spectral resolution of R ≈ 1500 with a spatial scale of 0.15 arcsec pix −1 . The slit dimensions were 1.0 arcsec × 7.0 arcsec. The observations used an ABBA pattern, nodding along the slit to keep the galaxy on slit at all times. Eight individual on-source integrations of 240 s each were performed for each source. Observations of standard stars were obtained before and after each set of SMG observations. These were used to correct the spectra for telluric absorption. The spectral reduction, extraction, and wavelength and flux calibration procedures were performed using the Gemini iraf package and pyraf . 14 Briefly, the processing consists of removing cosmic ray-like features, dividing by flat-fields taken with infrared and quartz halogen lamps, subtracting sky emission using exposures taken at a different point in the dither pattern, and finally rectifying the tilted, curved spectra using pinhole flats. Wavelength calibration is performed using Argon arc spectra, and then a spectrum of each order is extracted, divided by the standard star observation to cancel telluric absorption lines, and roughly flux-calibrated using the telluric standard star spectrum. The different spectral orders for each extraction window are merged into a single 1D spectrum from 0.84 to 2.48 m. In all cases, the agreement in flux between the overlapping regions of two consecutive orders was very good, and scaling factors of only 3 per cent or less were necessary. 2.1.3 XSHOOTER observations One target was observed on 2015 March 4 with the XSHOOTER echelon spectrograph (Vernet et al. 2011 ) on the ESO VLT-UT2 (Kueyen) as part of programme 094.A-0811(A), providing near-continuous spectroscopy from 0.3 to 2.48 m with a 1.2
Paper: 2017ApJ...849...47L (fulltext)
of the initial rotational energy. Other energies increase the irreducible mass of the BH. For a • (0) = 0.5, M • = 0.98 M • (0), or 2% of the initial mass can be used to power a GRB. 10 10 Atteia et al. ( 2017 ) found the maximum isotropic energy of GRBs when they studied the GRB energy distribution within redshifts z = 1–5. Jet break measurements are needed to derive the beaming-corrected energy, which can be compared with our model predictions. As the magnetic field on the BH is supported by the surrounding disk, there are some relations between B • and . In a hyperaccreting flow in a GRB, it is possible that a magnetic flux is accumulated near the BH horizon. Considering the balance between the magnetic pressure on the horizon and the ram pressure of the innermost part of the accretion flow (e.g., Moderski et al. 1997 ), one can estimate the magnetic field strength threading the BH horizon , where is the radius of the BH horizon. One thus hasInserting it into Equation ( 31 ), we obtain the magnetic power and torque as a function of the mass accretion rate and BH spin, i.e., where . It is found that X (0) = 1/6 and X (1) = π − 2. Both neutrino-annihilation and magnetic powers depend on the disk mass accretion. In Figure 3 , we present the BZ power as a function of accretion rate for different BH spins and compare it with the neutrino-annihilation power. We find that (1) the magnetic power is much greater than the neutrino-annihilation power for a moderate to high-spin BH, (2) the neutrino-annihilation power dominates over the BZ power for BHs with a very small spin at high accretion rates, and (3) compared with the magnetic power, is much more sensitive to the mass accretion rate . Therefore, if the disk accretion rate is variable, the jet driven by the neutrino-annihilation process should be highly variable. However, the MHD jet is usually subject to instabilities, such as kink instability (Wang et al. 2006 ) and magnetic reconnection (e.g., the Internal-Collision induced Magnetic Reconnection and Turbulence, ICMART, in Zhang Yan 2011 ). These MHD processes will add complexity to the GRB light curves. Figure 3. Magnetic power as a function of accretion rate for different BH spins, a • = 0.01 (thick red solid line) and 0.99 (thick blue solid lines). The dashed lines show the neutrino-annihilation power calculated with Equation ( 22 ) for a • = 0.01 (red dashed line) and 0.99 (blue dashed line). The thin blue line is produced with the analytic expression of Tchekhovskoy et al. ( 2011 ) for a • = 0.99, where the average magnetic flux and κ = 0.044 are taken based on the numerical simulation Model A0.99f. In the magnetic model, baryons from the disk will be suppressed by the strong magnetic field lines. For , the baryon-loading rate for the BZ-driven jet is (Paper I )For , the dependence of on will be . In Equation ( 39 ), f p is the fraction of protons and r z is the distance from the BH in the jet direction, which is normalized to 10 11 cm.
Paper: 2017ApJ...840...59C (acknowledgments)
We thank Borja Anguiano (Macquarie), Jonathan Bird (Vanderbilt), Sven Buder (MPIA), Sofia Randich (INAF), and Kevin Schlaufman (Carnegie Observatories). Software: The Cannon (Casey et al. 2016b ), Astropy (Astropy Collaboration et al. 2013 ), TOPCAT (Taylor 2005 ). This research made use of NASA’s Astrophysics Data System Bibliographic Services. This work was partly supported by the European Union FP7 programme through ERC grant number 320360. A.R.C. thanks King’s College of the University of Cambridge for their support. Funding for K.H. has been provided through the Simons Foundation Society of Fellows and the Marshall Scholarship. Funding for RAVE has been provided by the Australian Astronomical Observatory; the Leibniz-Institut fuer Astrophysik Potsdam (AIP); the Australian National University; the Australian Research Council; the French National Research Agency; the German Research Foundation (SPP 1177 and SFB 881); the European Research Council (ERC-StG 240271 Galactica); the Istituto Nazionale di Astrofisica at Padova; The Johns Hopkins University; the National Science Foundation of the USA (AST-0908326); the W. M. Keck foundation; the Macquarie University; the Netherlands Research School for Astronomy; the Natural Sciences and Engineering Research Council of Canada; the Slovenian Research Agency (research core funding No. P1-0188); the Swiss National Science Foundation; the Science Technology Facilities Council of the UK; Opticon; Strasbourg Observatory; and the Universities of Groningen, Heidelberg and Sydney. The RAVE web site is https://www.rave-survey.org .
Paper: 2016ApJ...820...91G (acknowledgments)
We thank the Juno science team, Eli Tziperman, and Adam Showman for useful discussions and comments. This research has been supported by the Israeli ministry of Science under grants 3-11481 and 45-851-641, and the Helen Kimmel Center for Planetary Science at the Weizmann Institute of Science.
Paper: 2016ApJ...830..129A (acknowledgments)
We acknowledge the support from the following agencies: US National Science Foundation–Office of Polar Programs, US National Science Foundation–Physics Division, University of Wisconsin Alumni Research Foundation, the Grid Laboratory Of Wisconsin (GLOW) grid infrastructure at the University of Wisconsin–Madison, the Open Science Grid (OSG) grid infrastructure; US Department of Energy, and National Energy Research Scientific Computing Center, the Louisiana Optical Network Initiative (LONI) grid computing resources; Natural Sciences and Engineering Research Council of Canada, WestGrid, and Compute/Calcul Canada; Swedish Research Council, Swedish Polar Research Secretariat, Swedish National Infrastructure for Computing (SNIC), and Knut and Alice Wallenberg Foundation, Sweden; German Ministry for Education and Research (BMBF), Deutsche Forschungsgemeinschaft (DFG), Helmholtz Alliance for Astroparticle Physics (HAP), Research Department of Plasmas with Complex Interactions (Bochum), Germany; Fund for Scientific Research (FNRS–FWO), FWO Odysseus program, Flanders Institute to encourage scientific and technological research in industry (IWT), Belgian Federal Science Policy Office (Belspo); University of Oxford, United Kingdom; Marsden Fund, New Zealand; Australian Research Council; Japan Society for Promotion of Science (JSPS); the Swiss National Science Foundation (SNSF), Switzerland; National Research Foundation of Korea (NRF); Villum Fonden, Danish National Research Foundation (DNRF), Denmark.
Paper: 2019MNRAS.486.1377D (acknowledgments)
ACKNOWLEDGEMENTS The authors would like to thank the anonymous referee for extremely valuable comments and suggestions. A special thank goes to Kristoffer Stensbo–Smidt for all the work and the precious suggestions he put in reviewing and commenting this work. MB acknowledges the INAF PRIN-SKA 2017 program 1.05.01.88.04 and the funding from MIUR Premiale 2016: MITIC . GL and MDV acknowledge the UE funded Marie Curie ITN SUNDIAL that partially supported this work. SC acknowledges support from the project ‘ Quasars at high redshift: physics and Cosmology ’ financed by the ASI/INAF agreement 2017-14-H.0. Topcat has been used for this work (Taylor 2005 ). C 3 has been used for catalogue cross-matching (Riccio et al. 2017 ). DAMEWARE has been used for ML experiments (Brescia et al. 2014a ).
Paper: 2018ApJ...856..180C (fulltext)
on the neutron star. All MeerKAT observations were processed in a homogeneous way using PSRCHIVE. The first few days of Parkes and MeerKAT observations (see Figure 1 (b)) were used to improve the rotational ephemeris thereafter used to fold all MeerKAT observations. Radio frequency interference (RFI) was excised in a multi-step process: first, 10% of the recorded band was removed due to bandpass roll-off, yielding the nominal 900–1670 MHz MeerKAT L band; next, a mask with known persistent RFI signals (e.g., GSM, GPS) was applied to the data; finally, PSRCHIVE tools and clean.py, an RFI-excision script provided by the CoastGuard data analysis pipeline (Lazarus et al. 2016 ), were used to remove remaining RFI. More than 400 MHz of clean band was typically retained after this flagging. We summed all frequency channels, integrations, and polarizations for each observation to obtain total-intensity profiles. We then obtained times-of-arrival (TOAs) for each observation (i.e., two or three per day) by cross-correlating individual profiles with a standard template based on a very high signal-to-noise ratio observation. The same procedure was applied to MeerKAT observations of PSR J1644−4559. Using the TEMPO software we confirmed that the timing solution derived for PSR J1644−4559 was consistent with published parameters (see Manchester et al. 2005 ). Like many magnetars, PSR J1622–4950 displays unsteady rotation (e.g., Dib Kaspi 2014 ). Nevertheless, we were able to obtain a simple phase-connected timing solution for the three week period spanning the first two sets of paired Chandra and NuSTAR observations (Table 1 ), fitting the TOAs with TEMPO to a model containing only pulse phase, rotation frequency ( ν ), and frequency derivative (). This solution is presented in Table 2 . Over the five month span of all the radio timing observations presented here, the for PSR J1622–4950 changed by a factor of 7, and changed sign. In order to probe the evolution of the spin-down torque (), we fit short-term overlapping timing models where a fit for ν and proves adequate, i.e., with featureless timing residuals. Each of these short-term timing solutions spanned approximately one week. Figure 3 shows the run of from these solutions spanning five months. Figure 3. Time evolution of properties of PSR J1622–4950. Top panel: frequency derivative measured from short-term timing solutions (spanning horizontal error bars) obtained from MeerKAT data (see Section 3.3 ). Bottom panel: 0.3–10 keV absorbed X-ray flux as measured from the best-fit blackbody model (see Table 3 ). Swift observations are shown as open squares and combined Chandra and NuSTAR observations are represented simply by their error bars. The X-ray flux appears to be decaying exponentially with a time constant of 111 ± 19 days (see Section 4.4 ). All error bars represent 1 σ confidence levels. The gray band in both panels represents the timespan during which the X-ray outburst and radio revival most
Paper: 2019MNRAS.484..196T (fulltext)
where A and B are the regression coefficients (intercept and slope), and ε is the intrinsic random scatter about the regression. and are the 1σ uncertainty of L 6 and L X , respectively. We conducted a Markov chain Monte Carlo (MCMC) simulation, where the Markov chain is created using the Metropolis–Hastings algorithm. We assumed uniform prior distributions for the regression parameters, and estimated the linear correlation coefficient between and . The best-fitting regression parameters and correlation coefficient with uncertainties are provided by calculating the mean and standard deviation from the posterior probability distributions of the model parameters with 10 000 iterations from the MCMC sampler (see also Mateos et al. 2015 ; Chen et al. 2017 ). The resultant linear relation is as follows: and the correlation coefficient is r = 0.84 ± 0.01, confirming again a tight correlation between L 6 and L X for our type 1 AGN sample. This linear L 6 – L X correlation is roughly consistent with that reported by Mateos et al. ( 2015 ) and Chen et al. ( 2017 ) in the low-luminosity region for log L X 44.5−45. We also performed a partial correlation analysis between log L 6 and log L X , with the correlation component due to redshift removed. The resultant partial correlation coefficient is 0.61, which is still indicating a tight correlation. We conclude that our type 1 AGN sample with RASS allows us to see a tight correlation for a wide luminosity range. However, it cannot explain the L 6 – L X relations of hyper-luminous quasars selected from the SDSS/WISE (e.g. Hot DOGs, ERQs, and WISSH quasars); these objects show a negative offset with respect to the linear relation. To describe this non-linear behaviour seen over a wide luminosity range, a luminosity-dependent relation would be required, as Stern ( 2015 ) and Chen et al. ( 2017 ) suggested. 3.2 Dependence of <italic>L</italic><sub>X</sub>/<italic>L</italic><sub>6</sub> on Eddington ratio In Section 3.1 , we confirmed that hyper-luminous quasars tend to be ‘X-ray weak’ with respect to their MIR luminosities. Some possibilities were suggested in previous works, as the origin of this X-ray deficit in hyper-luminous quasars. For example, Chen et al. ( 2017 ) reported that the different L 6 – L X relations provided in the literature were due to the different X-ray flux limits. Other possibilities to explain the deficit may be a luminosity-dependent covering factor ( C f ) and may be a dependence of the MIR to X-ray luminosity ratio on hydrogen column density ( N H ). However, Martocchia et al. ( 2017 ) concluded that the observed X-ray deficit in hyper-luminous quasars cannot be explained by a luminosity-dependent C f (see also Ichikawa et al. 2017 ), and Mateos et al. ( 2015 ) found no clear dependence of the MIR to X-ray luminosity ratio on N H , in AGNs with log N H 24 cm −2 (see also Lutz et al. 2004 ; Gandhi et al. 2009 ; Ichikawa et al. 2012 ). As described in Section 1 , the X-ray emission
Paper: 2017A&A...605A..80C (fulltext)
it may not be the case for () point statistics. For instance, peak-peak correlation functions on small scales are not very well reproduced by this approximation even for large contrasts because the contribution from the other critical points actually dominates at small distance; there is at least one saddle point between two peaks. However, in the context of this work, we impose the peak constraint in one location only and therefore the rare peak approximation is expected to be accurate for . As an illustration, Fig. 2 shows the Gaussian mean number density of minima, saddle points, and peaks ( Longuet-Higgins 1957 ; Adler 1981 , and later generalised to weakly non-Gaussian fields by Pogosyan et al. 2011 ) and compares the latter to the high- approximation (related to the genus) which can be easily computed The relative error between the number density of peaks and its high- approximation is shown on the right-hand panel of Fig. 2 . Fig. 4 Zero lag annulus correlation function in units of for a central peak with height and eigenvalues and in for a Gaussian random field with power spectrum smoothed with a Gaussian filter (similar to Fig. 3 when the two points on the annulus are the same). The separation is given in units of the smoothing length. 2.4. Density correlations on the circle surrounding a central peak with given geometry The expected product of projected density in two locations of space and such that and and given a peak in of height and curvatures along the first and second coordinates can be analytically computed. For instance for a power-law power spectrum with (and ), we get where is the trace of the density Hessian at the location of the peak, is the ellipticity of the peak and the unconstrained correlation function is To start with, Fig. 4 shows the zero lag contribution to the annulus correlation function (i.e. when the two points are at the same location on the annulus, ) in the frame of the central peak. As expected the amplitude of fluctuations around the peak have an ellipsoidal shape that is more elongated along the smallest curvature . This zero-lag annulus correlation is dominated by the square of the mean density profile at small separations and by the fluctuations at larger separations. Figure 3 then shows the full constrained correlation function on the annulus. We use different orientations of the pair () with regard to the axis of smaller curvature (corresponding to ) of the central peak. The orientation of is described by the angle which is taken to be 0, and from the left-hand to the right-hand panel. On each plot, the angle between and , namely , vary between 0 and and the separation to the central peak is described by the value . We show the result for two different peak heights: the most likely value (top panels) and a rarer case (bottom panels), which is more relevant to our study. In each case respectively, we fix the peak curvatures to their most likely values and , respectively (see Appendix A for a