adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018A&A...610A..11I (fulltext)
different from those of normal SNe, and the study of the immediate environment of these events can provide important information on physical properties of their progenitors which so far have remained undetected. Fig. 1 Left panel : Pan-STARRS -band image of NGC 3191 with the PMAS (blue squares) and the MaNGA fields of view (red hexagon) superposed. Right panel : map of the H ii regions obtained with the code described in the text and applied to the PMAS cubes. The SLSN 2017egm position is marked with a red cross in both panels. The levels of the isophotes, from the outermost to the innermost, are 21.86, 21.11, 20.66, 20.22, and 19.91 mag/arcsec. Indeed, several studies of SLSN host galaxy samples have revealed significant differences in the properties of Type I and Type II hosts ( Leloudas et al. 2015 ; Perley et al. 2016 ; Lunnan et al. 2014 ; Schulze et al. 2016 ). Type I hosts are usually star-forming (SF) dwarf galaxies with low metallicities at the extreme end of the distribution in the Baldwin-Philips-Terlevich (BPT; Baldwin et al. 1981 ) diagram and likely even more extreme than gamma-ray burst (GRB) hosts (see e.g. Leloudas et al. 2015 ; Schulze et al. 2016 ; but see also Lunnan et al. 2014 ). In addition, they have been found in very young stellar populations, indicating that Type I SLSNe might be the first and most massive stars to end in a SN explosion after the onset of a new starburst episode in their galaxy or HII region ( Thöne et al. 2015 ). Type II hosts seem to be more metal rich and within the bulk of emission-line galaxies in the SDSS. At higher redshifts, Type I SLSN hosts evolve in line with the most star-forming galaxies towards higher-mass galaxies with higher absolute star formation rates (SFRs), but still seem to show a metallicity cut-off at 0.4 solar and a tendency towards low-mass hosts ( Schulze et al. 2016 ). In this paper, we present integral field unit (IFU) spectroscopic data of NGC 3191, the host galaxy of SN 2017egm, a Type I SLSN discovered by the Gaia satellite ( Delgado et al. 2017 ) on May 23, 2017. Detailed analysis of its early optical spectra revealed its Type I SLSN nature ( Dong et al. 2017 ; Nicholl et al. 2017 ). The SLSN reached its optical maximum on June 20, 2017, with an absolute magnitude of mag. As pointed out in Nicholl et al. (2017) , Bose et al. (2017) , and Chen et al. (2017b) , the host seems to be the first Type I host with super-solar metallicity. In this work, we analyse the properties of different regions in the host galaxy, and in the SLSN location, providing a more detailed analysis of the metallicity and the star formation rate distributions around the SLSN environment and several other H ii regions. Resolved spectral modelling across the host galaxy gives us important clues to the different underlying stellar populations, their ages, and their metallicities, allowing us a more detailed insight into this apparently very peculiar SLSN-I environment. 2. Observations We observed the
Paper: 2018MNRAS.478.3793D (fulltext)
Fig. 12 shows a time frame of each of the gasoline2 simulations compared to the gas surface density data of Corbelli et al. ( 2014 ) in the bottom right. Best fits were found via a combination of by eye and an automated routine using a structural similarity index (SSIM; built into python ’s scipy module) of images of the gas surface density. The simulated disc is first inclined at the orientation of M33, then rotated in increments to assess the suitability of different phase angles. These images are smoothed over a scale of 2 kpc to leave only the strongest features. The SSIM is then calculated for each time-stamp and phase angle (Δ t = 10 My, Δ θ = 10° using the time domain of 0.5–1.0 Gyr, resulting in 1800 images for each calculation). The best fitting of these low-resolution comparisons are then shown in Fig. 12 , now at their natural resolution. Figure 12. Best-fitting gasoline2 models compared to M33 gas distribution (lower right). In Fig. 12 we show one of the better-fitting snapshots of each simulation, orientated in the same manner as the observed M33. Certain features like the arms shown in Fig. 8 are seen in some of these models, with accompanying inter-arm voids. The lowest feedback model displays some very similar features; the upper-right arm structure, the irregularly shaped arm north-west of the centre, and the straight-arm segment in the edge of disc to the south-east. The nature of dynamic spiral features such as these allows for spiral arms that are less regular than the assumed fixed log-spiral features used by density wave-like potentials (Grand, Kawata Cropper 2012 ; Baba, Saitoh Wada 2013 ). Cavities in the gas seen in M33 are a simple result of inter-arm voids. However, the gas does not display the same smaller scale features, like the irregular patchy features seen in the inter-arms of M33. The 5 per cent feedback model shows a more disrupted spiral structure. Similar small arm features are seen as previously, though now the increased feedback stunts their growth considerably. This appears one of the poorer matches to the observational data, with both the feedback cavities and inter-arm regions too small to provide a good match for M33. The remaining higher feedback models provide a good match to the general structure of M33, though neither is a precise match. The 20 per cent model is very effective at creating large ISM cavities, much like the sphNG models, though this comes at the expense of creating very few strong arm features. The cavities in the 20 per cent model may even be too large and numerous, with the disc perhaps showing too great a degree of fragmentation compared to M33. The 10 per cent feedback model is a good middle ground, creating a handful of large cavities but also managing to maintain some elongated arm features. We found that many of the snapshots not shown here produced certain features very well, while missing others entirely. Enough realizations could feasibly eventually produce every feature
Paper: 2018MNRAS.475.2591S (acknowledgments)
ACKNOWLEDGEMENTS We thank the referee for going through the manuscript carefully and providing constructive comments, which improved the manuscript. All the observers of the 2-m HCT (operated by the Indian Institute of Astrophysics), who kindly provided part of their observing time for observations of the supernova, are gratefully acknowledged. We thank Takashi Hattori, Ji Hoon Kim and the staff at the Subaru Telescope for their excellent support of observations under S15A-078 (PI: K. Maeda). We thank Professor David Branch for clarifying some of the issues related to the spectrum synthesis code syn++ . We also thank Dr Kuntal Mishra for providing us with the code for fitting the bolometric light-curve. This research has been supported in part by the DST-JSPS grant DST/INT/JSPS/P-211/2016, the Grant-in-Aid for Scientific Research of JSPS (15H02075, 16H02168, 26400222, 26800100) and MEXT (25103515, 15H00788), and by the World Premier International Research Center Initiative, MEXT, Japan. This work has made use of the public data in the Swift data archive and the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA).
Paper: 2018ApJ...865...19W (fulltext)
panel), 95701-01-03-00 ( km, middle panel), and 91701-01-52-01 ( km, bottom panel), respectively. 3.2. Spectral Parameters The spectral fitting results are presented in Figure 5 . The unabsorbed power-law luminosity ( L PL ) and the disk luminosity increase simultaneously with the total luminosity ( L 3–30 keV ). The apparent inner disk radius ( R in ) is related to the disk normalization as , where D 10 is the distance in units of 10 kpc. Here we adopt the inclination angle θ = 66° (Fender et al. 1999 ), a color correction factor f = 1.9 (Neilsen et al. 2011 ), and a distance of 8.6 kpc (Reid et al. 2014 ) in the calculation. There are two branches shown in the diagrams of L 3–30 keV − R in and L 3–30 keV − kT : (1) The inner disk remains at a small radius ( R in ≤ 70 km, hereafter S branch), kT increases with the X-ray luminosity, and the X-ray emissions are fainter and softer (larger Γ). (2) The majority of data (189 out of 233) draw the other branch (L branch), the apparent inner edge of disk recedes, and the temperature increases slowly at high luminosity. Meanwhile, the photon index (Γ) of the nonthermal component is anticorrelated with the X-ray luminosity. Figure 5. Correlations between the spectral parameters and distribution of the reduced χ 2 with the fitting model of diskbb + powerlaw (right bottom panel). Open squares and filled cycles correspond to the data with R in being smaller and larger than 70 km, respectively. All parameters are plotted with their 1 σ errors. Intriguingly, there are the positive correlations between T rec , R in , and the X-ray luminosity as well. Additionally, T rec has an even tighter correlation with the power-law luminosity. Since the uncertainty on T rec is much larger than on L 3–30 keV , we carry out a power-law fit to the relation by taking the error on T rec into account, yielding an index of n = 1.10 ± 0.10. Meanwhile, T rec is proportional to . It is worth noting that the change of apparent inner disk radius can be interpreted as a varying color correction factor without moving the intrinsic inner disk radius (e.g., Merloni et al. 2000 ; Zoghbi et al. 2016 ). In order to check this scenario, we model the disk component with diskpn (Gierliński et al. 1999 ), in which the disk normalization is linked to the color correction factor: . The inner disk radius is fixed to the bottom limit 6 R g , although it is much smaller due to high spinning rate (McClintock et al. 2006 ). In addition, the power-law model is replaced by a physical Comptonization model ( tbabs*(diskpn + comptt) in XSPEC). We tie the seed temperature ( kT seed ) to the disk temperature and fix the plasma temperature to 50 keV because it cannot be constrained by our data. The data in the S branch are relatively soft, and the model described above cannot provide an adequate fit to them unless the kT seed varies freely and becomes hotter than the disk temperature; however, it is inconsistent. Henceforth, we only fit the data in the L
Paper: 2018MNRAS.474L..52S (fulltext)
one is a delta-function type in logarithmic scale given by where corresponds to the amplitude at the peak wavenumber k p , and another is a power-law type given by where is the amplitude at the normalization wavenumber k n . In this Letter, we set the normalization scale as k n = 1 Mpc −1 . First, we focus on the delta function type for the power spectrum. By substituting equation ( 8 ) into equation ( 3 ) with equation ( 7 ), we obtain the injected energy fraction to the CMB energy by the decaying PMFs as where a function of k p , is explicitly given by When k p is much larger than k D ( z i ) (> k D ( z μ )), the energy injection due to the decay of the PMFs becomes efficient before the BBN era. On the other hand, when k p is much smaller than k D ( z μ ), the energy injection from the PMFs occurs during the CMB distortion era. We focus on the case where the energy injection from the decaying of the PMFs is efficient after the BBN era and before the CMB distortion era, that is, the case where k p is set between the two decaying scales k D ( z i ) and k D ( z μ ). For such a case, we can approximately take (see Miyamoto et al. 2014 ). Combining equa-tion ( 2 ) and equation ( 10 ) yields a constraint on PMFs in the k p - plane. We present our new result obtained from magnetic reheating as a red line in Fig. 1 . Figure 1. The upper bound on the amplitude of the delta-function type for the power spectrum as a function of k p from the magnetic reheating. The limits from the BBN (Kawasaki Kusakabe 2012 ) and CMB distortions (Jedamzik et al. 2000 ) in blue and magenta lines, respectively, are shown in the same plot. For comparison, we plot limits from the BBN and CMB distortions in magenta and blue lines, respectively. We can see that our magnetic reheating constraint gives a tight limit on the PMFs on small sales from to 10 8 Mpc −1 . Next, we consider the power-law type of the power spectrum defined in equation ( 9 ). By using equation ( 3 ) with equation ( 7 ), we obtain Here, we focus on the magnetic reheating due to the fast-magnetosonic mode given in equation ( 5 ). In Fig. 2 , we plot a limit for the amplitude of the power-law type as a function of the spectral tilt n B in a red line. Figure 2. The upper bound on the amplitude of the power-law type for the power spectrum as the function of n B from the magnetic reheating of the fast-magnetosonic mode (red) and Alfvén-magnetosonic mode (blue). The constraint from the Planck (Planck Collaboration XIX 2016 ) is also shown. Note that n B = −3.0 corresponds to the scale-invariant power spectrum. We find that blue-tilted spectrum is strongly constrained compared with the scale-invariant spectrum ( n B = −3.0). It is shown that the causal mechanism of PMF generation predicts only a blue spectrum with n B ≥ 2 (Durrer Caprini 2003 ). Therefore, the constraint on the blue-tilted spectrum is suggestive for such a causal mechanism. So far we have considered the case that the Alfvén and
Paper: 2021MNRAS.500.4166S (fulltext)
given in Table 1 . Positive time lag hints that the radio emission precedes the X-ray emission but the lag is consistent with zero within Gaussian dispersion. Table 1. Best-fitting parameters of the Gaussian function fitted to the DCF in Fig. 3 according to equation ( 4 ). A is the amplitude of the Gaussian, T is the time-lag, and represents the width. The error in brackets of time-lag T represent the fit errors. Although using a conservative approach, the error on time-lag is given by the Gaussian width, . To further analyse the data, we work with the detrended light curves. The fractional variability amplitude of the radio frequencies is and that of the X-ray is . The normalized detrended flux for both the radio and X-ray emission (normalized with respect to the maximum flux density in their respective light curves) are shown in the top panel of Fig. 4 and a zoom-in of the same for the 5 h simultaneous observations is shown in the bottom panel of Fig. 4 . There is variability with varying amplitude. Qualitatively, the variability at both radio frequencies seems to coincide with the variability in the X-rays (see Fig. 4 ). We perform the cross-correlation analysis using the simultaneous X-ray and radio data of the detrended light curves. Fig. 5(a) shows the cross-correlation between the two radio bands. The DCF peaks at around zero time lag with a coefficient of 0.30 ± 0.04. There is also a correlation at negative time lag of 2.0 ± 0.1 h and positive time lag of 1.7 ± 0.1 h with DCF coefficients of 0.10 ± 0.04 and 0.13 ± 0.04, respectively. These secondary peaks hint at a possible periodicity in the radio data. The cross-correlation between the detrended radio and X-ray data is shown in Fig. 5(b) . The DCF of radio band with X-ray peaks around zero lag with a coefficient of 0.36 ± 0.10. The same DCF pattern is also evident when we correlate the X-ray data with the two independent radio bands (Figs 5c and d ). The DCF of both radio bands with X-ray peaks around zero lag with a coefficient of 0.45 ± 0.10 and 0.42 ± 0.10 for 13–15.5 GHz/0.3–10 keV and 15.5–18 GHz/0.3–10 keV, respectively. Figure 4. Detrended and normalized light curves for comparison. Top: For the entire duration of the observations of about 11 h with 13–15.5 GHz, 15.5–18 GHz, and 0.3–10 keV represented by the red circles, the black squares, and the blue diamonds, respectively. Bottom: Zoom-in of the light curves only for the simultaneous radio and X-ray observations of 5 h. Figure 5. Discrete cross-correlation of detrended light curves of (a) 15.5–18 and 13–15.5 GHz with secondary peaks representing possible periodicity, (b) 13–18 GHz/0.3–10 keV, (c) 13–15.5 GHz/0.3–10 keV, and (d) 15.5–18 GHz/0.3–10 keV. All correlations peak around zero time-lag. The blue-dotted line represents correlation coefficient of zero. As mentioned earlier, the radio and X-ray light curves show variability. The 5 h simultaneous observations of radio and X-ray data do not reveal significant features in
Paper: 2018A&A...609A..98T (acknowledgments)
The authors thank Rik Van Lieshout for fruitful discussions and an anonymous referee for an insightful review. Q.K. acknowledges funding from STFC via the Institute of Astronomy, Cambridge, consolidated grant. P.T. dedicates this paper to the memory of André Brahic.
Paper: 2020ApJ...892....7K (fulltext)
( M *) or the cumulative number density of massive galaxies () over 0.4 z 1.0 (even after accounting for the Eddington bias using the forward-modeling method). In this section, we further investigate the evolution of the SMFs derived using specific sets of M * measurements. We first consider how the SMF changes based on different assumptions for the SFH. In Figure 8 , we show galaxy SMFs derived using stellar masses from different SFHs, including the effects of bursts. It is clear that the effects of SFH, at least among the set of stellar mass estimates used here, on the SMF are minor. In Figure 11 , we illustrate this by comparing the SMFs based on FSPS models with bursts () with those with no bursts (). While there is a slight (≲0.1 dex) increase in characteristic mass derived from SFHs that allow bursts, this falls within the range of uncertainties, and we do not consider it significant. In both cases where we measure the SMF from stellar masses with SFHs that allow and disallow bursts, we find that the characteristic mass shows little evolution from z = 0.4 to 1.5. In Figure 12 , we show the cumulative comoving number density of massive galaxies () derived using stellar masses with different SFHs. In both cases, we normalize the results to the measurement at z = 0.4. Over 0.4 z 1.0, the normalized comoving number density of massive galaxies is approximately constant regardless of SFH. Similarly, the number densities drop by ≲0.3 dex from z = 1.0 to z = 1.5 relative to that at z = 0.4 if we use stellar masses from SFHs that allow bursts. We conclude that the systematic uncertainties arising from the choice of SFH contribute 0.1 dex to the error budget in the growth of the characteristic stellar mass of massive galaxies, which we determined from the combined assumption-average mass function. 6.3. Dependence on SPS Models In Figure 9 we evaluate how three choices for the stellar population models underlying iSEDfit M * estimates impact the derived SMFs and constraints on the growth of massive galaxies. In all cases, we compare only models with smoothly varying SFHs (e.g., no bursts). We show again the FSPS SMF in the left panel. We compare these to the SMFs based on Bruzual Charlot ( 2003 ) masses (, middle panel) and Maraston ( 2005 ) masses (, right panel). The different SPS models lead to different trends in terms of the redshift of the evolution of both the characteristic mass ( M *) of the SMF and comoving number density of galaxies with stellar mass >10 11 M ⊙ . There are competing claims as to the ability of the treatment of TP-AGB stars in the Maraston ( 2005 ) models to reproduce the colors of galaxies and star clusters. Kriek et al. ( 2010 ) found that the Maraston ( 2005 ) models could not simultaneously reproduce the rest-frame optical and near-IR portions of galaxy SEDs. Similarly, Conroy Gunn ( 2010a ) showed that the Maraston ( 2005 ) models produce redder colors at intermediate ages inconsistent with the colors of star
Paper: 2015ApJ...800...27R (acknowledgments)
We thank Tobi Heinemann, Ben Chandran, Phil Isenberg, and, in particular, Matt Kunz for fruitful discussions. E.Q. is supported in part by NASA HTP grant NNX11AJ37G, NSF grant AST-1333682, a Simons Investigator award from the Simons Foundation, the David and Lucile Packard Foundation, and the Thomas Alison Schneider Chair in Physics. M.R. thanks support from the Chilean Comisión Nacional de Investigación Científica y Tecnológica (CONICYT; Proyecto Fondecyt Iniciación No. 11121145). D.V. is supported in part by NASA HTP grant NNX11AJ37G and NASA grant NNX12AB27G.
Paper: 2019MNRAS.485.1008B (fulltext)
is much shorter than the dynamical time-scale of the system: To test this, we integrate equation ( A14 ) for Δ t = t 1 − t 0 = 1, and leave the rest of the parameters unchanged with respect to the fully adiabatic case shown in the left-hand panel of Fig. A3 . The right-hand panel of Fig. A3 shows the numerical solution in this case (solid black line). It is apparent that when the change in potential is impulsive, the solution to equation ( A14 ) obtained by numerical integration is no longer sensible, which confirms that we can no longer use the ansatz ( A9 ). Instead we need to resort to the arguments summarized in Appendix A1 and derived in Pontzen Governato ( 2012 ). A solution based on the latter is shown in the right-hand panel of Fig. A3 as a red dashed line. We caution the reader that a specific choice of phase underlies this solution, in particular one that maximizes the growth of the amplitude. APPENDIX B: STABILITY OF HALOES SIMULATED IN EQUILIBRIUM In order to assess the intrinsic differences between the adiabatic and impulsive core formation scenarios, we need to ensure that numerical artefacts are well under control in the central regions of the halo where core formation takes place. In particular, we want to ensure that two effects are negligible on the time-scales of our simulations within the region of interest for our work: (i) the growth of numerical errors coming from the initial set-up of a DM halo in equilibrium, and (ii) the development of collisionless relaxation due to particle discreteness. In order to assess the magnitude of these two effects, we look at the stability of the halo set-up in equilibrium in a self-consistent way in Section 3.1 (referred to as the equilibrium case throughout this work). Regarding (i) above, the method we have used (developed in Kazantzidis et al. 2004 ) has been used and tested extensively and we can be confident that numerical errors are under control as long as the amount of simulation particles sampled in the central region is large enough, which is connected to (ii), the issue of particle discreteness. Regarding collisionless relaxation, the relaxation time-scale is given by (Binney Tremaine 1987 ): where N is the number of collisionless particles and t cross is the crossing time. To ensure that an N -body representation of a DM halo does not suffer from gravitational two-body relaxation, the relaxation time needs to be longer than the simulation time. In order to achieve that, we need to avoid very large accelerations arising due to close encounters between simulation particles. Choosing an appropriate softening length ensures this (Power et al. 2003 ). In particular, by demanding that the maximal gravitational force due to two-body encounters (equal to the maximum stochastic softened force) is smaller than the minimum mean field force in the simulation (which occurs at the virial radius), we get From this, it follows that a lower limit to the softening length required to