adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...586A.138P (fulltext)
We present a quantitative analysis of the relative orientation in a set of nearby (pc) well-known MCs to quantify the role of the magnetic field in the formation of density structures on physical scales ranging from tens of parsecs to approximately one parsec in the nearest clouds. The present work is an extension of previous findings, as reported by the Planck collaboration, on their study of the polarized thermal emission from Galactic dust. Previous studies include an overview of this emission ( Planck Collaboration Int. XIX 2015 ), which reported dust polarization percentages up to 20% at low , decreasing systematically with increasing to a low plateau for regions with cm. Planck Collaboration Int. XX (2015) presented a comparison of the polarized thermal emission from Galactic dust with results from simulations of MHD turbulence, focusing on the statistics of the polarization fractions and angles. Synthetic observations were made of the simulations under the simple assumption of homogeneous dust grain alignment efficiency. Both studies reported that the largest polarization fractions are reached in the most diffuse regions. Additionally, there is an anti-correlation between the polarization percentage and the dispersion of the polarization angle. This anti-correlation is reproduced well by the synthetic observations, indicating that it is essentially caused by the turbulent structure of the magnetic field. Over most of the sky Planck Collaboration Int. XXXII (2016) analysed the relative orientation between density structures, which is characterized by the Hessian matrix, and polarization, revealing that most of the elongated structures (filaments or ridges) have counterparts in the Stokes and maps. This implies that in these structures, the magnetic field has a well-defined mean direction on the scales probed by Planck . Furthermore, the ridges are predominantly aligned with the magnetic field measured on the structures. This statistical trend becomes more striking for decreasing column density and, as expected from the potential effects of projection, for increasing polarization fraction. There is no alignment for the highest column density ridges in the cm sample. Planck Collaboration Int. XXXIII (2016) studied the polarization properties of three nearby filaments, showing by geometrical modelling that the magnetic field in those representative regions has a well-defined mean direction that is different from the field orientation in the surroundings. In the present work, we quantitatively evaluate the relative orientation of the magnetic field inferred from the Planck polarization observations with respect to the gas column density structures, using the histogram of relative orientations (HRO, Soler et al. 2013 ). The HRO is a novel statistical tool that quantifies the relative orientation of each polarization measurement with respect to the column density gradient, making use of the unprecedented statistics provided by the Planck
Paper: 2016MNRAS.459.4224C (acknowledgments)
We acknowledge the logistic support provided by Parkes operators. The Parkes radio telescope is part of the Australia Telescope National Facility, which is funded by the Commonwealth of Australia for operation as a National Facility managed by CSIRO. The American National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. We would like to thank A.D. Hincks, M. Hobson, A. Scaife for useful suggestions and comments. We thank the referee for providing constructive comments and help in improving the contents of this paper.
Paper: 2017ApJ...835L..17K (fulltext)
with mass loadings of . This leads to disks at high redshift with baryon fractions depleted relative to those at low redshift. This feedback effect is clear when a single galaxy, g 1536, is compared to the same galaxy simulated without SNe feedback. As Figure 3 shows, the redshift trend is nearly nonexistent without SNe feedback. Even at z = 2, the galaxy without feedback falls within the scatter of the SPARC observations, and within the scatter of the z = 0 MUGS2 relation. This tells us that we need not invoke feedback processes to explain the z = 0 SPARC RAR. Simple dissipational collapse of gas is sufficient to produce a similar relation. The evolution as a function of redshift is therefore dominated primarily by the stronger effect of feedback at higher redshift. Figure 2. The simulated relation is not constant with redshift. As this figure shows, at higher redshift the low g bar slope is much shallower than at z = 0. This shows that for high-redshift galaxies, their disks can be depleted of baryons compared with z = 0. We have focused on the low g bar end of the relation here, where the changes are most significant. Figure 3. The evolution seen in Figure 2 is primarily driven by feedback. This can be seen when looking at the same galaxy with and without feedback. Without feedback, the baryon fraction within the disk increases slightly from z = 0 to z = 2, but still roughly follows the RAR. At z = 2, strong outflows in the galaxy expel most of the baryons from the disk, flattening the acceleration relation. This effect is sensitive to the frequent merger-driven starbursts at high redshift, which can drive bursty outflows. 4. Discussion This Letter is the first work to show that the RAR’s acceleration scale and tight scatter, as reported by McGaugh et al. ( 2016 ), can arise from fully self-consistent hydrodynamical simulations. Efforts have been made in the past to explain related acceleration relations (the MDAR; the Baryonic Tully–Fisher, BTFR; McGaugh et al. 2000 , etc.) with analytic arguments (van den Bosch Dalcanton 2000 ; Kaplinghat Turner 2002 ), existing scaling relations (Di Cintio Lelli 2016 ), or hydrodynamical simulations (Santos-Santos et al. 2016 ). Analytic studies have found that the MOND-like scaling relations can arise as a consequence of exponential disks living within an NFW halo (van den Bosch Dalcanton 2000 ). van den Bosch Dalcanton ( 2000 ) showed that this can explain not only Tully–Fisher relation’s relatively tight scaling over a large range of masses and surface densities (McGaugh de Blok 1998 ), but even explain the appearance of a characteristic acceleration in disk galaxy rotation curves (McGaugh 1999 ). Subsequent studies have suggested that an even harder constraint for Λ CDM to match is the tight scatter in the MDAR (Wu Kroupa 2015 ) or the RAR (Milgrom 2016 ). A semi-empirical model recently published by Di Cintio Lelli ( 2016 ) showed that both the BTFR and the MDAR can arise as a result of galaxies that
Paper: 2020MNRAS.496.5052P (fulltext)
uncertain by an order of magnitude. Zane et al. ( 2011 ) performed the first detailed analysis of its spectrum. Among numerous fits tried by the authors, only those with the interstellar hydrogen column density N H fixed to the Galactic value 2.7 × 10 20 cm −2 gave an acceptable reduced chi-square statistic and a reasonable distance (other fits gave distances down to 175 pc, too large N H and tiny emitting area). A fit with a two-temperature atmosphere model (2NSA) gave eV/ eV, with radius of the cold component fixed at the realistic value R = 12 km. This fit gave L ∞ ∼ 6 × 10 32 erg s −1 at d ≈ 1.55 kpc. Other acceptable fits indicated d in the range from ∼1 kpc to ≈2.25 kpc. The values of the unabsorbed flux for various statistically acceptable fits implied L ∞ ranging from 5.5 × 10 32 to 9.2 × 10 32 erg s −1 , assuming d = 2 kpc. Shibanov et al. ( 2016 ) fitted the XMM–Newton and Chandra data using nsmax models of magnetized hydrogen atmospheres covering the entire surface, assuming a centred dipole field and the ensuing temperature distribution. They found that an additional absorption feature at energy E abs = 740 ± 30 eV improved the fit. In this case, the best-fitting model yields T ∞ = 1.18 ± 0.05 MK. The resulting normalization (the effective emitting radius) can be reconciled with R = 12 km (assumed in the nsmax model) at d ∼ 4 kpc. The corresponding luminosity is L ∞ = (3.0 ± 0.5) × 10 33 erg s −1 . Other fits, performed for different data sets with or without involving the absorption, are only slightly worse statistically. They yield T ∞ from 0.79 ± 0.03 MK to MK, corresponding to L ∞ ∼ 5 × (10 32 − 10 33 ) erg s −1 . Bogdanov et al. ( 2019 ) have performed an analysis of the spectrum obtained with NICER . They fixed d = 2 kpc and obtained k B T ∞ = 205 ± 3 eV, km ( erg s −1 ) for a BB plus power-law (BB+PL) fit with an absorption at E abs = 780 ± 20 eV. An alternative fit including a Gaussian absorption at E abs = 760 ± 10 eV and two BB components (the G2BB fit) gives eV, km, totalling to L ∞ = (5.4 ± 0.6) × 10 32 erg s −1 . The ranges of L ∞ and T ∞ in Table 2 cover the results obtained by Bogdanov et al. ( 2019 ) and by Shibanov et al. ( 2016 ) for the different instruments. 7. CXOU J160103.1–513353 is a CCO in the SNR G330.2+1.0. Proper motion of the SNR fragments indicates an age of 0.8–1 kyr, if one neglects deceleration; because of deceleration the actual age should be smaller (Borkowski et al. 2018 ; Williams et al. 2018 ). McClure-Griffiths et al. ( 2001 ) derived the minimum distance to this SNR of d = 4.9 ± 0.3 kpc from the observed velocities in the absorption spectrum; a hypothetical maximum distance is d 9.9 kpc. The X-ray spectrum of the CCO is well described with either single-component carbon or two-component hydrogen atmosphere models (Doroshenko, Suleimanov Santangelo 2018 ). In the latter case, the observed spectrum is dominated by the emission from a hot component with a temperature ∼3.9 MK, corresponding to
Paper: 2016ApJ...833..271S (fulltext)
The model includes galactic and isotropic diffuse emission components and all 3FGL sources within 8° of the position of interest. We optimize the parameters of the model with unbinned likelihood analysis using the gtlike tool. Normalization of the galactic and isotropic diffuse emissions and spectral parameters of the target source were considered as free parameters, while the spectral parameters of all neighboring sources were fixed at the 3FGL values. The spectral form for each source is a power law, a log-parabola, or a power law with exponential cutoff based on the 3FGL data. Next, using the gtsrcprob tool each photon is assigned a weight—the probability that it originates from the given pulsar candidate. For computational efficiency we keep 40,000 events with the highest weights for each source. Finally, the photon arrival times are converted to the barycentric frame using the gtbary tool. 3. METHOD The search for pulsations is performed with the time-differencing technique first proposed by Atwood et al. ( 2006 ) and subsequently refined by Pletsch et al. ( 2012a ). We scan over the pulsar’s frequency f and spin-down rate using the array of barycentric photon arrival times t a and corresponding weights w a . First, the arrival times are corrected to compensate for frequency evolution:where and t 0 = 286,416,002 (MJD 55225) is a reference epoch. The spectral density P m is obtained with a Fourier transform of the time differenceswhere , , and is the rectangular function that takes the value 1 for and 0 otherwise. Technically we consider only positive time differences and bin the time interval into bins. Within the cycle over all event pairs one calculates the sum of the product of weights in each bin. This prepares an input for the fast Fourier transform performed with the open source library fftw (Frigo Johnson 2005 ). 3 3 http://www.fftw.org We use the same value of T as Pletsch et al. ( 2012a ). The Nyquist frequency is , which determines the choice of N . The relatively high value of the Nyquist frequency is required to minimize the artificial binning noise. keeping in mind that some pulsars may be found at the second harmonic of the frequency. We scan over the parameter γ from 0 to with a step equal to . The range corresponds to a characteristic age of the pulsar greater than 1 kyr. The step is selected taking into account the computation time available for the project. The value used introduces a moderate loss of power for pulsations with frequencies higher than 10 Hz; see Equation (3) of Ziegler et al. ( 2008 ). The values of f and corresponding to the highest P m are used as a starting point for the final coherent scan. The latter is performed with maximization of the weighed H -test statistic (Jager et al. 1989 ) defined as follows:where is a Fourier amplitude of the l th harmonic The maximization is performed by a scan over f and γ with steps of and . The steps are chosen to ensure coherence over the whole time interval of
Paper: 2015MNRAS.447.1973B (acknowledgments)
We thank the anonymous referee for useful suggestions that improved the paper. AB and MLB acknowledge support from the European Research Council under the EC FP7 grant number 280127. MLB also acknowledges support from an STFC Advanced/Halliday fellowship. We thank K. Grainge for providing the SKA specifications, and F. Abdalla and E. Chapman for useful comments and suggestions.
Paper: 2016MNRAS.455.2936S (acknowledgments)
We thank Kaylea Nelson for providing the simulation data used in this work. DN thanks Max-Planck-Institut für Astrophysik for hospitality when this work is finished. This work was supported in part by NSF grant AST-1412768, the Research Corporation, and by the facilities and staff of the Yale Center for Research Computing.
Paper: 2017ApJ...834...80Z (acknowledgments)
We thank the anonymous referee for helpful comments and suggestions that improved the paper. We acknowledge the support of the National Natural Science Foundation of China (Grants No. 11125314 and No. U1531247) and the Strategic Priority Research Program (Grant No. XDB09000000) of the Chinese Academy of Sciences.
Paper: 2016ApJ...820....6C (fulltext)
are very restrictive, as η is likely to be less than unity. These limits can also accommodate the gamma-ray luminosity of 3FGL J1417.5–4402, 3 × 10 34 erg s −1 at a distance of 4.4 kpc (see Section 4 ). 3.5. Evolutionary Track With a binary period of 5.4 days, PSR J1417–4402 is on the evolutionary track of low-to-intermediate mass companions that started mass transfer onto a neutron star after they left the main sequence (Podsiadlowski et al. 2002 ), and have increasing periods. This distinguishes them from the black widows, redbacks, and other low-mass X-ray binaries that have binary periods day, began mass transfer on the main sequence, and evolve toward shorter periods. For 1 M secondaries, these classes are separated by a bifurcation period of hr at the onset of mass transfer. With M , the companion of PSR J1417–4402 must have already lost most of its initial mass, only a fraction of which, accreting onto the neutron star, is sufficient to spin it up to 2.66 ms. In the models of Podsiadlowski et al. ( 2002 ), PSR J1417–4402 is not far from the ends of the tracks that terminate when the binary period is in the range 6–20 days, and the companion is a He white dwarf of 0.2–0.3 M . The neutron stars in these calculations, assumed to begin with 1.4 M , end in the range 1.8–2.2 M . Exactly how far PSR J1417–4402 is along this evolution is difficult to determine, since its companion may or may not be filling its Roche lobe, may or may not be finished accreting onto the neutron star, and may be losing mass mostly through ablation by the pulsar wind, a process that is difficult to model. 4. CONCLUSIONS We have discovered a 2.66 ms pulsar in orbit around the 5.4 day optical variable discovered by Strader et al. ( 2015 ) in the error ellipse of the high-energy gamma-ray source 3FGL J1417.5–4402. The orbital dynamics require that the companion mass is M . The presence of radio pulsations suggests that the neutron star is not currently accreting (see Section 3.2 ). The pulsar is only detected a fraction of the time it is observed (although few observations exist at orbital phases Figure 2 ), and is likely eclipsed for more than half the orbit by outflowing material from the companion; this has so far prevented us from obtaining a phase-connected timing solution. The DM distance of 1.6 kpc is in conflict with the original estimate of , which was based on the assumption that the companion fills its Roche lobe. This ambiguity then affects most of the remaining interpretation, including the precise evolutionary state of the system, whether it currently has an accretion disk, and the masses, which depend on the modeled inclination angle. The current estimate of M should really be regarded as an upper limit. There are several claims of massive neutron stars in redbacks and black widows. Modeling these systems, however, is difficult, especially in the presence of significant heating of the secondary by the pulsar (see, e.g., Romani et al. 2015 ). So far
Paper: 2018MNRAS.474.2444S (fulltext)
for an unobscured AGN or for the Thomson-scattered emission of an obscured AGN, plus an APEC component representing the hot gas in the galaxy ( keV). We notice that the power-law component may also be partially attributed to emission from star formation and X-ray binaries, as discussed in Section 3.1.2 . The total absorption component is fixed to the Galactic value ( N H = 1.31 × 10 20 cm −2 , as given by the Colden Calculator with the NRAO data set 2 ). A similar value for the photon index () is obtained also when excluding the counts below 1.5 keV in order to avoid contamination by the hot gas. By fitting the 3.0–8.0 keV spectrum with a simple power law, we obtain a photon index . This low value is an indication that the source is heavily obscured and that the power law observed in the soft X-ray spectrum corresponds to the scattered component. However, due to the narrow energy interval considered, adding an obscured component to our fiducial model for the Chandra data does not improve the quality of the fit significantly, so that a combined fit with NuSTAR is needed to determine the real amount of obscuration. 3.1.2 Possible contribution from star formation IC 2497 is a luminous infrared galaxy (LIRG) with far-IR luminosity L FIR = 6.6 × 10 44 erg s −1 . Assuming the FIR–star formation rate relation of Kennicutt ( 1998 ), this corresponds to a star formation rate SFR ∼ 30 M ⊙ yr −1 . Using the relations of Ranalli et al. ( 2003 ) or Lehmer et al. ( 2016 ), the expected 2–10 keV luminosity is erg s −1 , which is comparable (within the large scatter of the correlations) to the observed 2–10 keV luminosity measured from the Chandra and NuSTAR spectra (we stress that all the used relations have a large scatter, so that the results have to be considered as an order of magnitude estimation). In addition, the low W1 - W2 colour, W1 - W2 = 0.3, suggests that the mid-IR emission is dominated by stellar emission (see Section 3.2.2 for a discussion of the mid-IR emission). Altogether, this points to a scenario where star formation provides an important contribution to the soft part of the X-ray spectrum. We will consider this component in the analysis below. 3.1.3 Phenomenological model Applying the unabsorbed power-law model described above to the 0.5–24.0 keV spectrum significantly underpredicts the hard X-ray counts observed with NuSTAR , especially in the 10.0–20.0 keV range (see Fig. 1 , left). This again is an indication that the source is highly obscured (e.g. Koss et al. 2016 ; Ricci et al. 2016a ). To infer the intrinsic column density N H and the X-ray luminosity of the source, we first fit the 0.5–24.0 keV spectrum with a phenomenological model which includes (1) an absorbed power law representing the intrinsic emission of the AGN, where we consider both photoelectric absorption and Compton scattering, (2) a second unabsorbed power law representing the scattered AGN component and the contribution from star formation, (3) the unabsorbed
Paper: 2020MNRAS.495.2582G (fulltext)
parameter being set using equation (8) of Dutton Macciò ( 2014 ). When using this method for NFW profiles, we do not correct the concentration parameter from that of Dutton Macciò ( 2014 ) as is done for Di Cintio et al. ( 2014 ) profiles. Figure 9. A comparison of the halo mass estimate from GCs and the dynamical mass estimates for VCC 1287. We plot the Beasley et al. ( 2016 ) GC tracer mass estimate (circle) and our stellar velocity dispersion dynamical mass measurement (star). A total halo mass estimate based on the GC number–halo mass relation of Burkert Forbes ( 2020 ) is plotted using two differing assumptions of halo profile (blue region – NFW cusp profile; red region – Di Cintio et al. 2014 halo core profile). For both dynamical mass measurements to be consistent with the halo mass estimate from the number of GCs ( N GC = 22 ± 8; Beasley et al. 2016 ), either a lower halo concentration parameter or the presence of a dark matter core is required. Theoretical efforts to simulate the formation of UDGs frequently rely either on the creation of a cored dark matter profile (e.g. Di Cintio et al. 2017 ) or on its pre-existence (e.g. Carleton et al. 2019 ). Jiang et al. ( 2019 ) also found UDGs in the nihao simulation to lie in cored dark matter profiles along with having concentration parameters lower than that expected based on dwarf galaxy and L * galaxy control samples. It is yet to be established whether these low concentration parameters are the cause of, or result of, UDG formation (Jiang et al. 2019 ). In both, the case of a cored halo or one with a lower concentration parameter, the enclosed mass of the profile would be biased towards lower values, causing NFW profiles to represent an upper limit to the enclosed mass. For this reason, we also choose to include the Di Cintio et al. ( 2014 ) halo profile as these models naturally produce cores, which may be more representative of the actual profile of UDGs. There exists a slight tension between the dynamical masses and the halo mass, as plotted, calculated with the Burkert Forbes ( 2020 ) relation. This tension may be the result of incorrect assumptions made in converting observations into dynamical masses or, more likely, incorrect halo profile assumptions made in plotting for the full halo mass inferred from GC counts (e.g. VCC 1287 may have a lower concentration parameter than we calculate with Dutton Macciò 2014 ). In order for the dynamical mass measurements to be consistent with the halo mass estimate from the Burkert Forbes ( 2020 ) relation, the dark matter halo of VCC 1287 must either be cored (e.g. the Di Cintio et al. 2014 profile of Fig. 9 ) or have a low-concentration parameter. We note that the total halo mass from the Burkert Forbes ( 2020 ) relation implies a halo more massive than what is expected from the stellar mass–halo mass relation similar to other UDGs in the Coma Cluster (Forbes et al. 2020 ). Indeed, both the stellar mass and GC system richness of VCC