adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...832..114M (fulltext)
χ 2 ), constrained by all of the photometric detections and upper limits from NIR to radio wavelengths (Figure 4 ). Parameters are analyzed under a Bayesian approach generating a posterior probability distribution function. The SED fitting failed to tightly constrain fracAGN given the lack of mid-IR photometric points (which differentiate between AGN and star-forming galaxies) and the loose constraint from the NIR. The posterior probability distribution suggests that the AGN component contributes at most ∼20% to the total IR luminosity, with the highest probability being assigned to a contribution of 0%–5%. This is consistent with the estimation from the L X / L FIR ratio in Section 3.1 . The model SED with the maximum 20% AGN fraction is also plotted in Figure 4 , which is inconsistent with the 100 μ m Herschel /PACS upper limit. Figure 4. Best-fit SED (black) from CIGALE PYTHON. Different components are color-coded. The data points from left to right are HST /WFC3 F110W+F160W, Spitzer /IRAC 3.6 μ m + 4.5 μ m, Herschel /PACS 100 μ m + 160 μ m, Herschel /SPIRE 250 μ m + 350 μ m + 500 μ m, APEX/LABOCA 870 μ m, SPT 1.4 mm + 2.0 mm, and ATCA 5.5 GHz + 2.1 GHz. We do not include the ATCA 9.0 GHz upper limit, which is less constraining, because the source may be resolved at this frequency. The inset plot shows the normalized probability distribution function of the AGN fraction in the total IR luminosity. The AGN most probably contributes less than 5%. The dash–dotted line is the model curve with the maximum 20% AGN contribution to the IR. In this model, the AGN contribution does not extend to the radio part of the SED. The ATCA radio continuum upper limits at 2.1 and 5.5 GHz are consistent with the FIR-to-radio correlation for star-forming galaxies. The radio part of the SED for radio-loud AGN would be at least a factor of ∼2 higher (e.g., Rush et al. 1996a , 1996b ; Morić et al. 2010 ). 3.3. Theoretical Expectations We have provided evidence that SPT0346-52 is star formation dominated in the IR. Since our SFR is mainly constrained by the FIR photometric data, L IR being almost all from star formation suggests that the inferred SFR reflects the true SFR. We examine whether this observed high SFR can be physically explained in the framework of “maximum” starbursts (Elmegreen 1999 ) where a substantial fraction of available gas is consumed to make stars. Following Tacconi et al. ( 2006 ), the maximum (“Schmidt-law”) SFR can be written aswhere Elmegreen ( 1999 ) defines as the star formation efficiency, f g is the gas fraction, M tot is the total (dynamical) mass of the system, t dyn is the dynamical time, and v c is the circular velocity in km s −1 . We use the definition in Tacconi et al. ( 2006 ), v c = 0.67 × v FWHM = 410 km s −1 ( v FWHM = 613 ± 30 km s −1 measured from the low- J CO(2-1) line; Aravena et al. 2016 ). Assuming a gas fraction of 0.3–0.8, we utilize the observed SFR of 3600 ± 300 M yr −1 to 19 19 Here we adopt the SFR
Paper: 2017MNRAS.465..811N (fulltext)
( 2010 ). They have also shown that when these are combined with lower limits required to avoid the compactness problem, values of Γ 0 are in the range 10 2 –10 3 . Concerning direct estimates (rather than limits) of Γ 0 , a spectral break in the prompt component has been observed only in a few cases (Ackermann et al. 2011 ; Tang et al. 2015 ). Most of the available estimates of the value of Γ 0 have been inferred from the detection of an early peak in the afterglow lightcurve. Ghirlanda et al. ( 2012 ) collected all GRBs with known redshift and with an early peak in the optical lightcurve, and inferred Γ 0 under the assumption that the peak corresponds to the blast wave deceleration time. The Γ 0 values have been derived both for a constant and wind-like medium, and are shown in Fig. 4 as star symbols (the green colour refers to optical lightcurves, while the yellow colour refers to a similar analysis applied to GeV lightcurves of LAT GRBs with temporally extended GeV emission). The most stringent limits derived in this work lie above most of the values inferred from GRBs with an optical peak. This implies that the non-detection of synchrotron afterglow radiation is consistent with the simplest model, and there is no evidence that mechanisms producing a suppression of the GeV flux (see Section 2.2 ) are at work. The possibility of testing the relevance of these processes is however limited by the instrument sensitivity. We can conclude that present instrument capabilities are not pointing to the need for a relevant suppression of the high-energy afterglow synchrotron flux. On the other hand, the upper limits lie not far from (and sometimes below) the estimated values of Γ 0 . This suggests that the LAT should be able to detect the synchrotron afterglow component for those GRBs with the largest bulk Lorentz factors and largest energetics. A fraction of the LAT-detected GRBs are indeed characterized by the presence of an emission above 100 MeV lasting much longer than the prompt radiation, whose flux decays in time as a PL (Ackermann et al. 2013 ). These are the brightest GBM GRBs, and a large Lorentz factor Γ 0 > 500 has been inferred for them. An association with synchrotron afterglow radiation has been claimed to be consistent also with their spectral and temporal properties (Kumar Barniol Duran 2009 , 2010 ; De Pasquale et al. 2010 ; Ghirlanda, Ghisellini Nava 2010 ; Ghisellini et al. 2010 ; Lemoine, Li Wang 2013 ; Nava et al. 2014 ; Beniamini et al. 2015 ), although photons with particularly large energies (>10 GeV) detected at late times (>10 2 s) are in excess of the synchrotron limit and require a different explanation (Piran Nakar 2010 ; Wang, Liu Lemoine 2013 ; Ackermann et al. 2014 ). Finally, we comment on the dependence of these results on the unknown parameters ε e and η γ , with reference to a homogeneous density medium (similar considerations hold also for a wind-shaped density medium). In the first regime, where observations
Paper: 2016MNRAS.459.3104S (fulltext)
pulsars. Timing noise manifests as a red-noise (time-correlated) process in the TOAs, and is typically described by a wide-sense stationary stochastic process (Groth 1975 ), modelled as either random walks in the pulsar spin parameters, or with a more general power spectrum. The origin of this noise is unclear. While some of it may be caused by variable torques associated with changes in the pulsar magnetosphere state (Lyne et al. 2010 ), a significant fraction is likely associated with rotational irregularities interior to the star (Melatos Link 2014 ). The properties of timing noise vary markedly across the pulsar population, with its strength depending on the pulsar spin frequency ν and frequency derivative (Shannon Cordes 2010 ), and likely other factors because of the large dispersion levels of timing noise between pulsars. For example, the most stable MSP, PSR J1909−3744 shows no evidence for instabilities with phase variations limited ≲100 ns (≈0.3 per cent of pulse phase) over 11 yr (Shannon et al. 2015 ). In contrast, the timing noise in young pulsars and magnetars can contribute many cycles of pulse phase on week to month time-scales (Livingstone et al. 2011 ), both making it difficult to find a phase-coherent solutions in poorly sampled data and presenting challenges to TOA-modelling algorithms. While finding the origin of timing noise is important for understanding neutron stars, it is also necessary to account for timing noise as part of a general timing model for the pulsar. This is necessary to eliminate, or at least mitigate, bias in the estimation of other parameters in the model (Coles et al. 2011 ; van Haasteren Levin 2013 ; Lentati et al. 2014 ; Kerr et al. 2015 ). In addition to exhibiting timing noise, pulsars can experience glitch events (e.g. Espinoza et al. 2011 ; Yu et al. 2013 ), in which they are observed to suddenly change spin state, with the most significant component being increases in spin frequency that can exceed 1: 10 5 (Manchester Hobbs 2011 ). Glitches have been modelled with permanent changes in ν and , as well as transient components, in which changes in ν are modelled to decay (typically exponentially) on time-scales τ. Occasionally multiple glitch-decay components (with different time-scales) are invoked, particularly when high cadence (daily or higher) observations (e.g. Dodson, McCulloch Lewis 2002 ). Glitch components are often identified and characterized by searching for variation of spin frequency and frequency derivative in subsets of the data (Lyne et al. 2015 ). This method is suboptimal because it does not utilize the phase coherence of the pulse arrival times. The study of glitches is further complicated by the presence of timing noise. There are two prevailing theoretical models for glitches. In the first, glitches are associated with the transfer of angular momentum between the superfluid interior and solid crust of the neutron star (Anderson Itoh 1975 ; Alpar et al. 1984 ). A
Paper: 2021MNRAS.505.5753F (fulltext)
power. In AGN and LI(N)ER galaxies, the molecular component is more dominant than in star-forming galaxies. There is, however, significant variation from galaxy to galaxy. Studying the relative contribution of different phases to the outflow rate and the kinetic power as a function of SFR or α bol revealed no clear trends (see e.g. Section B1 ). Figure 6. Relative contribution of the molecular (blue), ionized (green), and neutral atomic (red) phases to the total kinetic power of the outflow. The galaxies whose names are in red are star-forming galaxies. Next we investigate the mass outflow rate of the three different phases, ionized, neutral atomic, and molecular for galaxies which have measurements (or upper limits) for at least two phases. This comparison can be seen in Fig. 7 , where we plot the different mass outflow rates against each other. The samples in the three plots are slightly different. To increase the statistics, in each plot we show the galaxies with measurements in both of the phases plotted. We distinguish between high-redshift sources ( z > 1, green) and low-redshift sources ( z 0.2, black). The data points with a red edge have no reliable extinction correction for the ionized phase and thus should be treated as lower limits. These figures clearly show that the ionized mass outflow rate is much smaller (up to 2–3 orders of magnitude smaller) than the neutral atomic or molecular mass outflow rate. A major caveat is that some ionized outflow rates might be underestimated because of an uncertain extinction correction. But even if these points are ignored, the ionized outflow rates are significantly lower (cf. Fig. 5 , which only includes galaxies with proper extinction correction). This confirms previous findings which estimate the ionized phase to contribute only minimally (Rupke Veilleux 2013 ; Carniani et al. 2015 ; Ramos Almeida et al. 2019 ). The figure on the top right highlights that outflow rates in the molecular and neutral atomic phase are similar (usually within 1 dex). This picture might be slightly skewed, however, as we include upper limits in the molecular and ionized, but not in the atomic neutral outflow phase. However, the relative contribution of the ionized outflow rate to the total outflow rate varies from galaxy to galaxy (from ≤ 1 to 10’s of ) and a simple prescription as in Fluetsch et al. ( 2019 ) might not be fully appropriate to determine the total outflow budget. If electron densities from auroral or trans-auroral lines are a better estimate than the densities based on the [S ii ] doublet (as suggested by Davies et al. 2020 , see Section 3.2.1 ), then the contribution of the ionized phase would drop even further by additional 1–2 orders of magnitude and hence it would become negligible also in star-forming galaxies. Figure 7. Comparison between the outflow rates in the ionized, molecular, and neutral gas phases. The black symbols represent the low-redshift ( z 0.2) sources while the green symbols
Paper: 2019MNRAS.485.2942N (fulltext)
normalized to unity at 1 keV, this is seen as a decrease of the reflection amplitude. Figure A2. An example illustration of the effect of the emission of the bottom lamp and of the emission of the top lamp circling around the BH (i.e. crossing twice the equatorial plane). The blue solid and red dashed curves show the observed reflection spectra computed with reflkerr_lp for δ = 0 and 1, respectively, for h = 2, a = 0.998, r in = 4.9, r out = 1000, Γ = 1.7, kT e = 200 keV, ξ = 1, kT bb = 50 eV, and θ o = 9° (thick curves) and 30° (thin curves). The black dotted curve shows the observed primary spectrum. We see the actual reflected spectra may be much weaker with respect to the observed primary emission than those in the approximate (δ = 0) case. In the shown case, this is due mostly to the primary emission of the bottom lamp gravitationally focused toward the observer, which enhances the observed primary flux by as much as a factor of 10 in the case of θ o = 9°. Since our plotted primary spectra are normalized to unity at 1 keV, this enhancement is seen here as a decrease of the amplitude of the reflection. We emphasize that properly assessed reflection amplitude gives an important constraint on the lamppost model, where both the fraction and the radial distribution of photons illuminating the disc surface are fully determined by the height of the primary X-ray source. Then, the radial emission profile is strictly related to assumed geometry, and treating the fraction of reflected photons as a free parameter leads to results which are not self-consistent. Still, our model allows the user to make it free. APPENDIX B: <monospace>hreflect</monospace> We describe here details of our hybrid reflection model. In particular, we apply rescaling of the xillver spectra to account for an angular distribution of reflection predicted by this model, which is different than that of ireflect (with the reflection strength in ireflect larger by a factor of ≃1.5 for edge-on directions). In some cases, we apply also a minor rescaling related with differences of the incident spectra. We explain the criteria which we use to establish the rescaling factor, denoted below by f . We follow an approach similar to that of Done Gierliński ( 2006 ). We use xillver , which applies a more accurate absorption model, as a reference for the spectral shape below ∼20 keV, and ireflect , which correctly describes the angular distribution of the reflected radiation using the exact Klein–Nishina cross-section, as a reference for the reflection amplitude. The absorption model applied in ireflect assumes that the absorption coefficient above 12 keV is ∝ E −3 , then, the depth of its absorption edge at these energies can be matched to that predicted by xillver by modifying any parameter (we choose ξ) affecting the absorption level at 12 keV. As can be seen below, the shapes of ireflect spectra with such an adjusted ξ typically agree very well with those of xillver in the ∼10–30
Paper: 2017ApJ...846...11M (fulltext)
constrain the escape fraction of the ionizing radiation and/or the presence of faint satellite galaxies during the late stages of cosmic reionization. We have predicted radial surface brightness profiles that include the contribution from (i) fluorescent emission powered by ionizing radiation leaking from the central galaxy (as in Mas-Ribas Dijkstra 2016 , for two different CGM prescriptions), and (ii), nebular emission from faint satellite sources that possibly resided within the halo of the central galaxies (as in Mas-Ribas et al. 2017 ). We have compared our predictions with observations of Ly α halos at z = 5.7 and z = 6.6, and have also predicted H α and visible continuum surface brightness profiles may be detectable within future JWST observations. We present and discuss our findings below. 1. Our comparison of models to observations favors the model with a very low escape fraction of ionizing radiation, , from galaxies within the range of magnitudes . This scenario implies that these galaxies do not contribute significantly to the reionization process. Therefore, if galaxies are the major contributors to the reionization process, as recently restated by Parsa et al. ( 2017 ), a large population of faint sources with a high ionizing escape fraction is necessary. 2. The presence of faint satellite sources is unclear. Two out of four comparisons with current Ly α data indicate that satellite sources might contribute to the extended Ly α emission, but the effect of systematics and uncertainties in the observations do not allow for a clear conclusion. Given the required existence of faint sources driven by the low escape fraction results, if the presence of satellite sources around brighter galaxies is ruled out by the observations, this might imply that the population of faint objects is spread out within the IGM, making its detection more challenging. 3. JWST will be able to probe the ionizing escape fraction from one bright, galaxy up to distances of a few tens of pkpc from the center. Fainter galaxies and/or a high S/N for the continuum observations require the observation and stacking of several objects. We stress that we have adopted and extrapolated two CGM models that were derived to match observables at redshifts around . The validity of our results therefore depends on the ability of these two models to realistically describe the medium surrounding galaxies. While a more detailed parameterization of the CGM at high redshift (if currently feasible) is beyond the scope of our work, future observations of extended emission, together with absorption studies (e.g., Dijkstra Kramer 2012 ; Hennawi Prochaska 2013 ; Prochaska et al. 2013 , see also Steidel et al. 2010 , 2011 ), will clearly enable us to use our proposed method to further constrain the escape fraction. The prospects for doing this experiment at lower redshifts, , are interesting, as the surface brightness for emission lines depends on redshift as . However, the contribution
Paper: 2018MNRAS.481..860R (fulltext)
a low self-interaction cross section). However, it seems unlikely that this same model could then be responsible also for the much larger dark matter core reported in Fornax (e.g. Goerdt et al. 2006 ; Walker Peñarrubia 2011 ; Cole et al. 2012 ; Pascale et al. 2018 ) and the similarly large cores reported in nearby gas-rich isolated dwarf irregulars (e.g. Moore 1994 ; Flores Primack 1994 ; Read et al. 2017 ). By contrast, models in which dark matter is heated by bursty stellar feedback could naturally account for such a diversity of central dark matter densities, at least in principle. Recall that whether or not a dark matter core can form from such ‘heating’ depends on: (i) the pre-infall dark matter halo mass, M 200 ; (ii) the halo concentration parameter, c 200 ; (iii) the total stellar mass, M * ; and (iv) the size of the dark matter core (e.g. Di Cintio et al. 2014 ; Read et al. 2016a ; Contenta et al. 2018 ; Bermejo-Climent et al. 2018 ). (Smaller cores require less energy to form and form more rapidly.) Dark matter cores are also easier to form at high redshift when the star formation rates are high and the halo masses are smaller (Madau, Shen Governato 2014 ). This suggests that small (pc) dark matter cores may indeed form very early in the Universe even in ultra-faint dwarfs. This effect could be ubiquitous, or it could be stochastic, depending on the merger history, spin and/or concentration of any given dwarf (e.g. Laporte Peñarrubia 2015 ). We will return to this issue in more detail in a forthcoming paper where we present GravSphere models for all of the Milky Way classical dwarfs. 8 CONCLUSIONS We have used a new mass modelling method, GravSphere , to measure the central dark matter density profile of the Draco dwarf spheroidal galaxy. Our key findings are as follows: Using mock data with sampling, binary star population, and foreground contamination similar to that for the real Draco dwarf, we showed that GravSphere is able to successfully recover the dark matter density profile of a tidally stripped Draco-like dwarf within its 95 per cent confidence intervals (Fig. 4 ). However, while we were able to distinguish the amplitude of the central density, , of our cored and cusped mocks at 95 per cent confidence, the logarithmic slope of the density profile, , depended on our choice of priors. This sensitivity to the prior diminishes with improved spectroscopic sampling (Appendix B ). We then applied GravSphere to the real Draco data. We inferred a high central density of with logarithmic slope at 95 per cent confidence, consistent with expectations from pure-dark matter structure formation simulations in ΛCDM (Fig. 7 ). We tested the robustness of this result, showing that even using a rather extreme prior on γ DM that biases us towards cored models, our GravSphere models still favour a logarithmic slope of at 95 per cent confidence, steeper than that of a uniform-density core. Dark matter models with a high central density and a
Paper: 2019ApJ...872...67G (fulltext)
sampling rate. 4. Discussion 4.1. Prominences From the H α tomogram, we can estimate the size and location of the satellite emission that we associate with prominences by assuming that they are corotating with the secondary star. The projected distance of these features from the center of mass (CM) of the binary is simply r = v /Ω, where v is the inclination-corrected velocity amplitude of the emission and Ω is the angular velocity of the secondary. The orbital inclination is expected to be ∼75° based on our assumed stellar masses and the mass function measured by Marsh et al. ( 2016 ). Therefore, the correction between the radial velocity and space velocity is less than 10%. For the orbital period from Marsh et al. ( 2016 ), the inverse angular velocity is 2043 s rad −1 . Assuming a mass for the WD of 0.8 M ⊙ , then the mass ratio of the system is q ≈ 0.3 for a secondary with a mass typical for an M5 dwarf. The radius of the secondary’s Roche lobe is (Eggleton 1983 ). Since the secondary is probably close to filling its Roche lobe, we use this as the stellar radius and compare it to the size of the prominences. The brightest emission from the prominences is seen around 500 km s −1 , corresponding to 4.4 Roche radii from the CM. The prominences precede and trail the secondary by ± 0.15 in phase, meaning that the prominences extend 3 stellar radii from the surface of the secondary. The faint emission behind the secondary at 1000 km s −1 corresponds to 8.7 stellar radii from the CM, or about 5 stellar radii from the backside of the red dwarf. In isolated magnetized stars, the corotation radius is where the gravitational pull of the star balances the centrifugal acceleration of plasma trapped in the field. Prominences found at or beyond the corotation radius are called “slingshot” prominences (Steeghs et al. 1996 ). In binary systems the balance between centrifugal and gravitational forces is found at the Lagrangian points. The L 4 and L 5 points are actually rather broad regions that make an equilateral triangle with the primary and secondary stars. For the assumed parameters of the AR Sco binary, the L 4 and L 5 points are 370 km s −1 from the CM. The prominences extend more than 30% farther than L 4 and L 5 points. The trailing prominence differs in phase from the L 5 point by 20°. The L 2 point is expected to be at 4.5 Roche radii from the CM, or a velocity of 500 km s −1 , while the faint prominence behind the secondary is seen at twice that distance. We conclude that the prominence emission extends well beyond the outer Lagrangian points. The stability of slingshot prominences has been modeled by Ferreira ( 2000 ). Such prominences can be stable out to ∼5 stellar radii if the dipole surface magnetic field is larger than ∼100 G, and out to 10 stellar radii for a 500 G field. A quadrupole field would require ∼500 G for prominence stability at 5 stellar radii. The WD field at the secondary is expected to have an average field strength of order