adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.493.4936W (fulltext)
higher post-outburst luminosities lead to shorter durations (∼hours to days) when DNB contributes significantly to the total luminosity and surface temperature. The total amount of energy available from burning hydrogen is set by the initial hydrogen column, and corresponds to the integral of the elevated luminosity cooling curves, which is constant in all cases with the same initial hydrogen column. For the initial hydrogen column of y H ∼ 1.2 × 10 6 g cm −2 , the total amount of energy that is released from hydrogen burning is 10 38 erg (i.e. ∼7 MeV nucleon −1 ). Without residual accretion or a large helium buffer that may slow down the depletion of hydrogen, the times when DNB significantly increases the observed surface temperature for an H–C envelope are ∼0.1–80 d from the end of the accretion outburst, depending on the initial hydrogen column and cooling luminosity. For larger initial cooling luminosities (and thus higher surface temperatures), the initial change in surface temperatures increases, while the relevant duration decreases, as the available hydrogen is consumed more rapidly. 4.2 Effect on cooling curves due to changing composition In addition to releasing energy through nuclear reactions, DNB can alter the observed cooling after accretion outbursts by altering the light element column in the envelope over time. In Section 2.1 , we show the effect on the T s – T b temperature relations for varying hydrogen column sizes in H–C envelopes (see Fig. 1 ). The decreasing hydrogen column due to DNB will only affect the cooling curve when the hydrogen column is varied in the transition region (as shown in Fig. 2 ). At the relevant temperatures for NSs in quiescence (with initial post-outburst surface temperatures T s > 8 × 10 5 K), this means that the same boundary temperature (at the bottom of the envelope) corresponds to different surface temperatures when the hydrogen column varies in the range 10 4 ≲ y H ≲ 10 7 g cm −2 . When the initial hydrogen column after the accretion outburst is smaller than y H ∼ 10 4 g cm −2 , a further decreasing hydrogen column will not affect the cooling curve. When the initial hydrogen column is larger than y H ∼ 10 4 g cm −2 , the cooling curve transitions from relatively hydrogen-rich envelope temperature relations towards those for a hydrogen-poor envelope. We show this effect in Fig. 8 , where we ignore the effect of heat released by DNB and only show the effect of a changing envelope composition. The dashed cooling curves in Fig. 8 correspond to fixed hydrogen columns, while the solid cooling curves correspond to the scenario where the hydrogen column decreases over time due to DNB. When DNB is taken into account, all the plotted cooling curves (irrespective of initial column density) correspond to that of a hydrogen-poor envelope after ∼50 d. Note that for larger post-outburst luminosities, it can take less time (∼1 d) to decrease the hydrogen column below y H ∼ 10 4 g cm −2 (see e.g. the
Paper: 2019MNRAS.488.3003B (fulltext)
(Barnes et al. 2018 ). 5 THE LINK TO AGN EFFICIENCY The inclusion of ATC leads to an increased CC fraction, increased central cumulative gas fraction, and flattens the CC fraction redshift evolution relative to simulations that neglect it. The temperature profiles and the gas fraction profiles both suggest that the central black hole injects less energy into the ICM when thermal conduction is included. Therefore, we now examine the energy injected by the central black hole and how this energy couples to the ICM. We define the central black hole of each cluster as the most massive black hole within of the potential minimum at z = 0. Its properties are then traced as a function of redshift. During the formation of the ICM ( z ≤ 2) the low accretion rate, kinetic mode feedback dominates (Weinberger et al. 2018 ). In Fig. 9 , we plot the median cumulative energy injected by the central black hole in the kinetic mode feedback for simulations with and without ATC. At z = 0, the inclusion of ATC results in a 24 per cent reduction in the energy injected by the low accretion rate kinetic feedback mode, which agrees with the results from the temperature and gas fraction plots. Figure 9. Median cumulative energy injected in the kinetic mode (solid lines) as a function of redshift for the central black hole compared to the energy lost via radiative cooling within r 500 (dash–dot-dotted lines) for simulations with (red) and without (blue). For simulations with ATC, we plot the conductive heating (short dashed line) within as a function of redshift. The right scale shows the median stellar mass (long-dashed) and black hole mass (dash-dotted) with (dark red) and without (dark blue) ATC. When ATC is included the central black hole injects less energy, but the stellar and black hole masses are unchanged. Conductive heating only contributes a small fraction of the energy difference. The right axis of Fig. 9 shows the median stellar mass of the brightest cluster galaxy, defined as the mass within twice the stellar half-mass radius for the most massive subhalo, and the median central black hole mass as a function of redshift. Despite the reduction in energy produced by the inclusion of ATC, the masses of the galaxy and black hole have a negligible difference for simulations with and without ATC, especially for z ≤ 1. Therefore, the central black hole is still able to self-regulate and control its growth and the growth of the galaxy. One explanation of the difference in injected energy with and without ATC is that there is significant heat transfer from outer radii into the core of the cluster, providing the additional energy required to offset radiative losses. To investigate this we define the conductive heating with a sphere by its conductive luminosity. This is defined as the heat flux integrated over the surface of the sphere where we chose a sphere of radius to account for the collapse and growth of the cluster with cosmic time. Only gas that is
Paper: 2017MNRAS.468.2189Z (fulltext)
Fig. 9 favour periodic models more strongly when the light curve is sinusoidal and favour QPO models more strongly when the light curves are sawtooth like. We introduced the higher order p 4 QPO models with periods locked in a 2:1 ratio and a common coherence time to explore if this difference was driven by the assumption of sinusoidal waveforms in the one-component QPO model. We show in Fig. 10 that using the two-component QPO models halves the number of RRab and RRd variables that prefer a QPO compared to the one-component model. The increase in periodic preference going from a one-component to the two-component QPO models for the more sinusoidal RRc and RRe sources is marginal. An even larger shift is seen for the fundamental mode Cepheids. These results are in agreement with the behaviour we found in Section 3 for the experiments comparing artificial light curves with sawtooth or sinusoid waveforms. It is not surprising, therefore, that a higher order QPO model results in a greater preference for periodicity. The two-component QPO is a better approximation to the sawtooth-like light curves of RRab, RRd and fundamental mode Cepheids, so the coherence time can act more like the desired physical parameter, and less as a proxy for light-curve shape. Figure 10. Distribution of the RRab+RRd, RRc+RRe, fundamental Cepheids and first overtone Cepheids in the AIC likelihood difference for the QPO and periodic models when using either the standard QPO model (dotted) or the higher order model (solid). The changes are small for the more sinusoidal RRc+RRe and first overtone Cepheids, but shift strongly towards the periodic case for the more sawtooth-like RRab+RRd and fundamental mode Cepheids. In addition to light-curve shape, we also noted in Section 2 that mischaracterized errors can bias the results, irrespective of light-curve shape. If some of the noise level assumed in the model is less than the true noise, the extra variance will be interpreted as signal. The natural way to model this extra variance is to move the model away from periodic to model the mischaracterized noise as stochastic variability. If we take the best-fitting model and re-estimate the errors so that it has 2 /dof1, then the fraction of Cepheids and RRLyrae favouring the periodic model increase significantly, from 70 and 10percent to 90 and 65percent in the case of first overtone and fundamental Cepheids, respectively, and increasing from 80 and 15percent among RRc/RRe and RRab/RRd to 95 and 90percent, respectively. 4.3 Variable distributions in QPO parameters The lesson from Section 3 is that the QPO parameter cannot simply be interpreted as a measure of coherence because it is significantly affected by light-curve shape. This is, however, no barrier to using the QPO parameters to classify variables. Fig. 11 shows the distribution of all the sources in the P , P and QPO parameter spaces. We see that the P plane maps physically distinct variable types to nearly unique spaces.
Paper: 2016ApJ...832..175T (fulltext)
of dust extinction. Generally, the single-band SFRs are in agreement or are slightly lower than those derived from SED modeling with MAGPHYS. If we are unable to use either of the first two methods and we have at least one detection in another filter, we scale the SEDs from the DLA counterparts that were fit with the first method to match the detected host galaxy flux. We then use the scaled SEDs to estimate the rest-frame UV flux and use the median and standard deviation of the scaled SEDs to calculate the rest-frame UV SFR. Finally, if there are no detections in any band but there are upper limits in the rest-frame UV band, we calculate SFR upper limits using the second method. Photometric measurement were made using the aperture photometry technique, using the HST point-spread function (PSF) for GRB-DLA counterparts with HST data and the DCT 12 PSF for the ground-based data (corresponding to ∼2 and ∼17 kpc diameter apertures respectively). The large difference in apertures comes from the fact that HST is able to resolve the host galaxy. We assume that the light from unresolved sources is solely from the host galaxy and background sky. All SFRs are calculated from dust-corrected observations unless otherwise stated. The host extinction, A V , is taken either from SED models or from GRB afterglow measurements using a Small Magellanic Cloud (SMC)-like extinction law which has been shown to best depict the GRB explosion environment (e.g., Schady et al. 2012 ). We assume the host extinction is the same as the GRB line-of-sight extinction, which Perley et al. ( 2013 ) has shown is fairly consistent within a factor of two to three. If the host extinction is an upper limit, we use that value in all dust-corrected calculations and report SFR upper limits. In Table 1 , we report these GRB-DLAs with SFR error estimates but treat these as SFRs upper limits in all plots using dust-corrected SFRs. Our host extinction is, in general, higher than the reported for DLAs in the SDSS survey for our sample’s column densities (Murphy Bernet 2016 ). This may likely be because GRB-DLAs are found at a smaller impact parameter of (Blanchard et al. 2016 ) than the general DLA population of (Fumagalli et al. 2015 ) or more simply because our DLA sample traces, in general, metal-rich, and likely dust rich, systems (for example, see the correlation between and metal line equivalent widths in Murphy Bernet 2016 ). 3.1.1. SED Fitting Star Formation Rate We use MAGPHYS with the HIGHZ extension (da Cunha et al. 2008 , 2015 ), to model the host galaxy SEDs from photometry. MAGPHYS models templates to the data and returns an SED with fitted parameters which include SFR, stellar mass ( M ), dust mass ( M ), and A V . This particular package is well suited for galaxies and takes into account bursty star formation which is appropriate for GRB host galaxies as suggested by Hunt et al. ( 2014 ). MAGPHYS uses a continuous model of star formation with superimposed random bursts that
Paper: 2017ApJ...842..134B (fulltext)
image for illustration purposes and to help explain the caveats of the algorithm (compare it to the segmentation obtained for COR2B). Figure 7. CME event observed on 2008 December 12 on COR2A (upper panels) and COR2B (bottom panels) FOVs. The red line indicates the CME segmentation obtained by CORSET. Earlier stages of the CME development in the FOV of the COR2 instruments are not shown in Figure 7 . At these earlier time instances, CORSET was able to segment the CME; however, these results had to be ignored because they led to NARD equal to zero on the application of CORSET3D. The radial speeds derived by CORSET3D as a function of latitude and longitude are shown in Figure 8 . The CORSET3D reconstruction shows that this CME event propagates in the inner portion of northwest quadrant of the solar corona (as seen from Earth, see Figure 8 ) up to at least a heliocentric distance of 16 solar radii (de-projected distance by the time the event reached the end of the time period selected, i.e., 13:37 UT). Figure 8 indicates that the radial speed is lowest at the northernmost portion of the reconstructed CME front. Further analysis is necessary to assess whether the speed differences along the front are artifacts resulting from the segmentation or a true effect. Byrne et al. ( 2010 ) also studied this event using a technique based on triangulation and found a speed of about 400 km s −1 between 10 and 15 solar radii. Using the GCS model, Möstl et al. ( 2014 ) found that the initial speed of this CME was about 497 km s −1 . Figure 8. Radial speeds of the front of the CME event observed on 2008 December 12 as a function of latitude and longitude (in the Stonyhurst coordinate system), as derived by CORSET3D. If we assume that this event continues propagating radially after 13:37 UT, it would be observed at Earth with its center in the negative side of the y axis of the Geocentric Solar Ecliptic (GSE) coordinate system. This assumption seems to be in agreement with the magnetic flux estimated by Howard DeForest ( 2014 ). According to these authors, the corresponding magnetic structure reached Earth first in the negative portion of the y axis, i.e., westward of the Sun–Earth line. A previous study from Liu et al. ( 2010 ) used triangulation in both STEREO /COR2 and STEREO /Heliospheric Imagers’ FOVs to track this CME event along its evolution to Earth (see, e.g., Figure 9 in Liu et al. 2010 ). They identified two structures in the time-elongation maps constructed from running difference images along the ecliptic plane. They called these two structures “feature 1” and “feature 2,” although they seem to be part of one complex CME structure, which is the one CME event considered in this work. Liu et al. ( 2010 ) found that the speed of both features ranged between 300 and 600 km s −1 , developing westward during their entire evolution from the Sun to the Earth. Lugaz ( 2010 ) computed the direction of this complex event with respect to the Sun–Earth line
Paper: 2019ApJ...871....1M (fulltext)
for the freedom in the low-order gravity field, because the likelihood function used to obtain our posterior distribution of interior models assumed an inflated variance on J 2 to accord with the numerical precision of our theory of figures implementation (see Section 2.1 ). Because the resulting distribution included a diversity of heavy-element and helium distributions, envelope transition locations, and core masses, the seismology suggests a tension with the Voyager rotation rate commonly assumed for Saturn’s interior that different three-layer interior models seem unlikely to resolve. This conclusion based on the ring seismology adds support to the notion that periodicities in Saturn’s magnetospheric emission (e.g., Desch Kaiser 1981 ; Gurnett et al. 2005 ; Giampieri et al. 2006 ) may not be consistently coupled to the rotation of Saturn’s interior (e.g., Gurnett et al. 2007 ; Read et al. 2009 ). The present model is potentially oversimplified in two major ways. First, the model is not suited to address the close multiplets of waves observed to have the same azimuthal wavenumber m , namely, the multiplets of waves in the C ring with m = 2 and m = 3. The bulk of these seem naturally explained by the model of Fuller ( 2014 ), wherein avoided crossings between the f -modes and deep g -modes of higher angular degree give rise to a number of strong perturbations with the same m value. However, in the wealth of new OLR and OVR wave patterns that have been measured from increasingly low signal-to-noise VIMS data since Hedman Nicholson ( 2014 ), it seems that only two waves add to the mixed-mode picture, both with m = 2: the close-in W76.44 wave, and the Maxwell ringlet wave whose frequency and m number were predicted by Fuller ( 2014 ). The f -modes of higher angular degree have less amplitude in the deep interior and so are less likely to undergo degenerate mixing with any deep g -modes strongly. Indeed, there is not yet any direct evidence for f -modes with l > 3 undergoing avoided crossings with deep g -modes, although the outlying (5, 4) OVR warrants closer scrutiny in the mixed-mode context. The second major simplification of the present model is the assumption that Saturn rotates rigidly. While upper limits can be established for the depth of shear in Jupiter or Saturn’s envelopes on magnetohydrodynamic grounds (Liu et al. 2008 ; Cao Stevenson 2017 ), evidence gathered from spacecraft indicates that zonal wind patterns do penetrate to significant depths (Smith et al. 1982 ; Kaspi et al. 2018 ). It has been proposed that the insulating molecular regions of these planets may be rotating differentially on concentric cylinders (Busse 1976 ; Ingersoll Pollard 1982 ; Ingersoll Miller 1986 ), the zonal winds being the surface manifestation of these cylinders of constant angular velocity.The mode identifications made in Section 5 and Table 1 reveal that the seismological data set now samples a variety of radial (via the angular degree l ) and
Paper: 2020A&A...641A..37K (fulltext)
of the burst spectrum (see Lewin et al. 1993 ; Strohmayer et al. 2006 , for a review). We scrutinised the NICER, XMM-Newton EPIC-MOS/PN, Swift -XRT, and NuSTAR data for the presence of thermonuclear bursts at different time scales (1, 10, and 100 s), but we found none. Next we focused on INTEGRAL JEM-X (3–35 keV) data and identified one thermonuclear burst candidate. We describe the analysis of the JEM-X data covering the event below. 6.1. Properties of the type-I X-ray burst We searched INTEGRAL/JEM-X data for bursts within revolutions 1986–2009, following the same approach as Mereminskiy et al. (2017) . We focussed on one of the two JEM-X cameras, JEM-X1; JEM-X2 was operating simultaneously, but this instrument seems to be experiencing reduced sensitivity in recent years. We identified one candidate thermonuclear (type-I) burst from IGR J17591−2342, in Scw-500010 of INTEGRAL revolution 2001 on Sept. 19, 2018; 23:07:33 (MJD 58380.96358 TT). The key properties of the burst are listed in Table 3 . Based on its intensity, this burst should have also been detectable by ISGRI; however, due to the loss of sensitivity over time in the lower ISGRI energy band, 18–27 keV, we did not detect this event in those data. Table 3. Parameters of the type-I X-ray burst observed by INTEGRAL JEM-X during the outburst of IGR J17591−2342 in 2018. Notes. (a) 58380.96358 MJD. (b) The τ fit has been measured after a plateau of ∼10 s. (c) Burst peak flux in 0.1–40 keV energy band. (d) Burst peak temperature. (e) Pre-burst unabsorbed flux in 0.1–250 keV energy range. (f) Burst fluence in 0.1–40 keV energy band. The type-I X-ray burst occurred ∼60 d after the outburst commenced, just after the source was at the highest X-ray luminosity (see Fig. 2 ). We defined the burst start time as the time at which the X-ray intensity of the source first exceeded 10% of the burst peak flux (above the persistent intensity level). The event showed a fast (∼1–2 s) rise time, followed by a ∼10 s long plateau at its peak (Fig. 8 ). Therefore, we conclude that most likely a photospheric radius expansion took place (see e.g. Falanga et al. 2007 ; Galloway et al. 2008 ; Bozzo et al. 2010 ). After the plateau, the burst decay profile could be well fitted with an exponential function and the correspondingly derived e-folding time is τ fit ∼ 9 s. The total duration of the burst was ∼40 s. Fig. 8. Light curve of the type-I X-ray burst detected by JEM-X and reported for the first time in this paper. The burst start time was 58380.96358 MJD. The JEM-X light curve was extracted in the 5–25 keV energy range with a time bin of 2 s. For the burst spectral analysis, we first determined the persistent flux before the burst onset by converting the ISGRI counts from Rev-2001 to the total broadband spectrum obtained from Revs. 1994–1995 (see Sect. 5 ). We note that during the outburst, the ISGRI data in the 20–150 keV band alone were fitted by a power-law model, and the spectral parameters were
Paper: 2016A&A...595A..56S (fulltext)
in star formation: both in short bursts (during close passages of the satellite) as well as sustained high star formation rates lasting several Gyrs. In Starkenburg et al. (2016) we show that the general properties of our simulated dwarf systems compare very well to a large sample of dwarf irregular galaxies and blue compact dwarfs from the literature. In this paper we provide a quantitative characterization of the morphological and kinematic properties of the dwarf systems during the minor merger events and thereby facilitate a more detailed comparison to observations. For the analysis we used morphological descriptions that have been applied to characterise where galaxies lie along the Hubble sequence, to disentangle interacting from isolated systems, and to describe the stellar distributions in major mergers of or larger spiral galaxies, such as the CAS (concentration, asymmetry and smoothness) and GM (Gini coefficient and M20) indicators (see e.g. Conselice et al. 2000 ; Conselice 2003 ; Abraham et al. 2003 ; Lotz et al. 2004 , and references therein). These have also been applied to describe the stellar components of isolated irregular dwarf galaxy samples ( Conselice 2003 ; Lotz et al. 2004 ), to characterise the gas distribution in starbursting dwarf galaxies ( Lelli et al. 2014 ) and in simulations of major mergers ( Holwerda et al. 2011a ), as well as a variety of observational samples ( Holwerda et al. 2011b , c , d , e , 2012 , 2013 , 2014 ). This paper is organised as follows. The hydrodynamical simulations are described concisely in Sect. 2 , while in Sect. 3 we focus on one specific simulated system and highlight key tidal features as the merger takes place. In Sect. 4 we introduce the morphological and kinematic indicators used and compare the results to some observational samples. We present a brief comparison to two dwarf galaxies with peculiar properties, namely IC10 and NGC 6822 in Sect. 5 . The summary and conclusions are given in Sect. 6 . 2. Method We analyse the structural properties of dwarf galaxies during minor mergers with dark satellites for a suite of hydrodynamical simulations recently presented in Starkenburg et al. (2016) . The simulations span a range of initial conditions for the dwarf galaxy, its satellite and a variety of orbital configurations for the interaction. They were performed using the OWLS ( Schaye et al. 2010 ) version of Gadget-3 (based on Springel et al. 2001 ; Springel 2005 ) with implementations for star formation and feedback as described in Schaye Dalla Vecchia (2008), Dalla Vecchia Schaye (2008) . Table 2 Parameters for the orbits. Notes. (a) The apo-to-peri ratio is an average for the first pericentric passage for different main and satellite masses and inclinations. Fig. 1 Evolution of the model-A dwarf galaxy with , merging with a 20% mass ratio dark satellite with on a co-planar relatively radial orbit (the intermediate orbit in Table 2 ). All figures show an inclined view of the