adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...854...37S (fulltext)
flux toward the Lyman limit. The consequence of this is that at high redshift (), SLSNe-I have a characteristic red observer-frame color and long-duration light curves. The identification of SLSNe-I at requires information on the UV spectrum and evolution. As highlighted in Section 3.5 , data in this wavelength range are sparse, with only DES16C2nm, Gaia16apd, PS1-11bam, and DES15E2mlf having data that extend to . Figure 9 shows the luminosity and color evolution of SLSNe-I at maximum light inferred from these spectra as a function of redshift. The spectra have been normalized and adjusted to the peak magnitude and color of each event. These spectra are then redshifted between 0 and 4, and integrated through the DECam r and z passbands. Figure 9 shows the evolution in luminosity and color for these spectra, along with other events in the literature. Where required, K -corrections have been determined from the spectrum of Gaia16apd. The single-epoch 5 σ magnitude limits of the DES deep and shallow fields are shown along with those expected for LSST wide-field and deep-drilling fields (Scovacricchi et al. 2016 ; D. Scolnic 2017, private communication). These limits suggest that in a single epoch, DES can detect SLSNe-I that are spectroscopically similar to Gaia16apd and DES16C2nm, to z = 3.2 in z -band observations of its deep fields or z = 3.8 for objects that are spectroscopically similar to DES15E2mlf. For the LSST deep-drilling fields, the maximum redshifts in the z band are 3.5 for the DES16C2nm spectrum and 4.3 when considering the DES15E2mlf spectrum. Figure 9. Observer-frame evolution of SLSNe-I. Upper panel: the apparent r -band evolution of SLSNe-I as a function of redshift. DES16C2nm is shown as a purple star, with literature SLSNe-I in orange and the photometrically confirmed objects of Cooke et al. ( 2012 ) shown as black crosses. Values determined from the maximum-light spectrum of Gaia16apd, normalized to the peak magnitude of Gaia16apd, are shown in blue, with the corresponding DES16C2nm spectrum from VLT in red and DES15E2mlf in green. K -corrections for literature events have been determined using the Gaia16apd spectrum. The magnitude limits for the DES deep and shallow fields and LSST wide-field and deep-drilling fields are also shown. Second panel: as above, only for the z band. Third panel: the J -band evolution of SLSNe-I as a function of redshift. The magnitude limits for the Euclid and WFIRST shallow, medium, and deep surveys are also shown. Lower panel: the r − z color evolution of SLSNe-I as a function of redshift as inferred from the upper two panels. A horizontal line of mag highlights the difference between low () and high () redshift events. This analysis can be extended to the observer-frame NIR, where surveys such as Euclid (with 5 σ limiting magnitude per visit in the deep fields and an irregular cadence of 10 visits to each of the two fields in a 6-month observing window for 5 years; Inserra et al. 2018a )
Paper: 2019ApJ...885..156E (fulltext)
; Livadiotis McComas 2012 , 2013b ; Livadiotis 2016 ). Therefore, the IBEX maps may be consistent with the radial trend in γ sw detected with NH -SWAP. If we find that γ sw behaves in a systematic way using Voyager 2 observations, we may be able to monitor γ sw with NH and forecast when NH will reach the termination shock or at least determine if the location of the termination shock is moving dramatically inward or outward. Many theoretical and simulation studies have explored how the process of the solar wind picking up interstellar material can heat the solar wind (Matthaeus et al. 1999 ; Smith et al. 2001 ; Isenberg et al. 2003 ; Adhikari et al. 2017 ; Zank et al. 2018 ). Given that when we account for the radial variation of γ sw , we are then able to obtain excellent agreement between the inferred slowing from the amount of pickup and the amount of observed slowing, we conclude our work provides further observational evidence that the solar wind picking up interstellar pickup ions likely plays a role in heating the solar wind and altering the T – n relationship with increasing radial distance. However, additional work combining simulations and data should be performed to determine exactly why the solar wind T – n relationship systematically varies in a way that produces a linear decrease in γ sw with increasing distance from the Sun. Possibly, solar wind turbulence or wave particle interactions generated by the pickup process heat the solar wind altering the T – n relationship with increasing radial distance. In the inner heliosphere compressions, shocks, and shears are strong, but in the outer heliosphere all of those are much weaker. Therefore, those are not likely to impact the changes in the T – n relationship to the degree that waves and turbulence related to the waves could in the outer heliosphere. Our solar wind polytropic index radial profile γ sw ( r ) for the outer heliosphere, our initial analysis of the evolution of the total polytropic index ( γ ) from the inner to outer heliosphere, and the total polytropic index ( γ ) in the heliosheath from IBEX provide substantial metrics for outer heliospheric simulations. This work will become more relevant as NH gets closer to the termination shock heading in the direction of the ribbon and higher-resolution maps of the ENA ribbon from the Interstellar Mapping and Acceleration Probe ( IMAP ) mission become available (McComas et al. 2018 ). Below, we summarize our main findings and conclusions. 1. In the outer heliosphere (21–43 au), we found the density radial profile to decrease at either nearly or slightly less than the spherical expansion rate. 2. As NH moves farther away from the Sun, we can assess if picking up of interstellar material hinders the expansion of the solar wind altering the solar wind density profile. 3. In the outer heliosphere (21–43 au), the temperature profile is well above what is expected for an adiabatic profile. 4. The relationship between the solar wind
Paper: 2018ApJ...861....8C (fulltext)
lower. If the unknown energy is from dark energy, this implies the existence of inhomogeneity of the dark energy (de Putter et al. 2010 ; Blomqvist 2008 , 2010 ). Appendix A NPB Correction for Patches with Beam Convolution Fourier transform presumes that the signal is periodic such that the two ends are continuous; thus, when one deals with real data, it is inevitable to encounter jump discontinuity, which induces extra power in the power spectrum. Although the data are mostly discrete, the NPB effect nevertheless persists and should be taken into account as systematic error. A patch taken from a full-sky map has such an intrinsic effect, which is uncorrectable because NPB is part of the morphology. The effect, however, is further aggravated when there is beam convolution. As the statistics and comparisons in this paper are made between beam-convolved Planck data and simulations with the best-fit ΛCDM model (without beam convolution), it is necessary to study the effect because the power spectrum of a beam-convolved patch has more induced power than the smoothed power spectrum of the nonperiodic patch. To estimate the correction, we simulate 50 full-sky CMB maps and take 30 deg 2 patches centered at the equator (in total 600 patches). We take the central 20 deg 2 part directly from the 30 deg 2 patches and denote their mean power spectrum as . Then we use 5′ to convolve the 30 deg 2 patches and choose the central 20 deg 2 part, the mean power spectrum of which is denoted . The correction is therefore , where b is the beam transfer function. One finds that the extra power due to 5′ beam convolution is a rather smooth curve and can be best fitted to be ( μ K 2 ) from l ≥ 300. And for the 143 GHz band map with beam transfer function 730 (Adam et al. 2016a ) the correction for NPB is ( μ K 2 ). Appendix B XPS on the <italic>Planck</italic> Data Throughout the paper we use XPS on the Planck data: four CMB maps, the 143 GHz channel map, and the foreground-subtracted maps at 100, 143, and 217 GHz from the SEVEM method, to extract the peak position. The XPS is a quadratic estimator between two maps that can provide an unbiased estimate of the underlying power spectrum of the correlated signals and, at the same time, reduce the uncorrelated ones. We take from the Planck Legacy Archive the FULL MISSION RINGHALF1 and FULL MISSION RINGHALF2 maps for SMICA, NILC, SEVEM, Commander, and the 143 GHz map. Since the patches from RINGHALF1 (denoted α ) and RINGHALF2 (denoted β ) contain the same CMB signal but uncorrelated noise, we can employ the XPS on their Fourier modes and ,where ∗ denotes complex conjugate and the angle brackets denote average over all for integer k . However, as Chiang Chen ( 2011 , 2012 ) pointed out, lack of an ensemble and nonzero chance correlation with the so-called “uncorrelated” signal results in some residual from the XPS, which has the following relation:where and are the (uncorrelated) noise power spectrum from RINGHALF1 and
Paper: 2016MNRAS.458.2928C (fulltext)
this work we do not see a wedge structure due to the incomplete treatment of instrumental frequency dependence. This will be addressed in future work. We have however included two theoretical lines relating to the wedge limit on all figures – a dashed line which assumes sources in the entire field of view (in our case 10 deg) will leak into the main field and an optimistic solid line which assumes only sources within the chosen central 4 deg (approximately the full width at half-maximum – FWHM – of the station beam) will affect the image. Further work on the contamination within the wedge is ongoing in the literature and as such recovery below the theoretical wedge line should be treated with caution. We refer to the combination of the noise, dirty foregrounds and dirty cosmological signal as the ‘total input signal’. We refer to the foreground model estimated by GMCA as the ‘reconstructed foregrounds’ and the difference between the reconstructed foregrounds and the total input signal as the ‘residuals’, which will contain the cosmological signal, noise and any foreground fitting errors. The foreground fitting errors can be defined as the difference between the simulated foregrounds and the reconstructed foregrounds and will contain any cosmological signal or noise which has been fitted as foregrounds as well as an absence of any foregrounds not modelled. 5 RESULTS Unless otherwise stated we present results for cylindrical power spectrum calculated over a 10 MHz bandwidth segment centred at 165 MHz, where the variance of the cosmological signal peaks in our simulation. This bandwidth is formed using an extended Blackmann–Nuttall window, though other windows are possible, as discussed in Section 5.1 . We 3D Fourier transform the data segment and bin the data according to the values of k perp , k los . The power at any particular k los , k perp , P ( k los , k perp ) = 〈δ( k los , k perp )δ*( k los , k perp )〉 is the average power of all the uv cells in the bin centring on k los , k perp . We choose to plot the ‘dimensionless’ power spectrum, , which has units of mK 2 throughout this paper: where V is the volume of the 10 MHz bandwidth in Mpc. We first construct cylindrical power spectrum for the different signal contributions in order to understand how the EoR window is constructed. In Fig. 1 , we see the Galactic synchrotron, Galactic free–free and unresolved extragalactic foregrounds while in Fig. 2 we see the dirty cosmological signal and dirty foregrounds (with all three foreground contributions summed). The action of the PSF can be clearly seen as a loss of power on scales above this (i.e. for k perp > 0.65 Mpc −1 ). Note that, since we are seeing power spectrum over a 10 MHz bandwidth only we do not see the evolution of the cosmological signal over redshift. We see clearly the area of k -space where the foregrounds appear to be at their strongest is at low k los , however, it should be noted that there is appreciable foreground
Paper: 2021ApJ...911..114S (fulltext)
of the neck radius, a , determined by the numerical simulation and laboratory experiments. The spring constant increased almost linearly with neck radius, and the results from the numerical simulation (circles) and laboratory experiments (triangles and squares) were observed to overlap. Figure 6. Results of FEM simulation (circles) and laboratory experiments (triangles and cubes) for compression. The solid line denotes the semianalytical formula (Equation ( 4 )). Triangles and cubes are determined using the data between 90 to 95 and 40 to 90 N, respectively. The error bars of laboratory experiments are the standard deviation of three samples. The upper curve is from SU17 . The inverse triangle is the FEM simulation result for the a = R cylinder (without shrinkage due to sintering). Two sets of laboratory experiment results are shown in Figure 6 ; the difference between these sets is the force range used to determine the spring constant. Figure 7 displays the applied force as a function of displacement as measured in the laboratory experiment. This relationship was not linear, particularly for small forces; thus, the spring constant is dependent on the range of the applied force. In contrast, the FEM simulation was based on a linear response. The spring constant at the commencement of the compression was small compared to that at a large compressive force. There are two possible causes for this nonlinear behavior. First, the misalignment of the two end planes led to a nonlinear behavior because the force required to enable deformation until perfect fitting to the autograph was small compared to the force required after the perfect fitting. Second, if there is a bump on the end surface, the compression force changes at the commencement of compression. Figure 7. An example of a laboratory experiment result. At the beginning, the force increases nonlinearly, and then increases almost linearly after reaching 20 N. The neck shape was adopted from the second curve from the bottom in Figure 2 . The spring constant was determined using the data in the linear region of the force-displacement curve. For comparison, two ranges of the compression force were used; 40–90 and 90–95 N. Although there was a considerable scatter in the data, the results from the laboratory experiments were consistent with the numerical simulation results. The maximum radius in Figure 6 is 2.375 mm, corresponding to the original radius of the sphere prior to sintering. However, this radius was not realized by sintering, because the volume of the sphere decreased as a result of sintering. The maximum radius () following sintering was . The solid curve in Figure 6 is the model in SU17 . In contrast to the experimental and numerical results, the spring constant of the model increased as the neck radius decreased. In this model, the deformable region was restricted to the neck between the spheres, and the length of the neck decreased with the neck radius. As a result, the
Paper: 2016MNRAS.455.2363H (fulltext)
mergers might be detectable for longer (e.g. through stellar streams, bright cores) than gas-poor galaxies. CO surveys of protocluster and identically selected field galaxies would allow one to compare the gas mass fractions of these two populations. Protocluster galaxies might be expected to have higher gas fractions due to efficient supply of pristine intergalactic gas through filamentary accretion, but this is a picture yet to be empirically tested. Lyα blobs are generally found in high- z protoclusters and indicate the presence of large volumes of cold gas around forming galaxies. Fakhouri Ma ( 2009 ) examined the role of environment on the halo merger rate in N -body simulations, using the friend-of-friends group finder (Davis et al. 1985 ) within the millennium simulation (Springel, Matteo Hernquist 2005 ) to trace the merger histories of dark matter sub-haloes out to z = 2. A key finding was a correlation between environment and merger rate, such that sub-haloes in the densest environments underwent merger rates 2–2.5 times higher than those in the lowest density regions. With the caveat that galaxies are biased tracers of the matter field, it is reasonable to assume that there will be a similar trend for the galaxies themselves. It is interesting that the level of enhancement of the merger rate found by Fakhouri Ma ( 2009 ) in the simulations is broadly in agreement with the enhancement we find for SSA22. Our analysis traces the rest-frame ultraviolet emission which is very sensitive to dust obscuration; for example, extended/clumpy star-forming regions could be hidden and the merger fraction understated, or a clumpy dust distribution could lead to very high extinction to certain stellar populations, giving the stellar emission overall a clumpy morphology, that could possibly be misinterpreted as a merger. We do not expect the use of rest-frame UV observations to lead to a significant bias in our interpretation unless there is a significant difference between the obscuration properties of galaxies in the protocluster and the field, since we have applied an identical analysis to both. Nevertheless, we are now obtaining near-infrared HST -WFC3 observations that will allow us to repeat our analysis based on the morphology of the rest-frame optical flux. Similarly, we have ALMA follow-up observations of LBGs along the ‘merger sequence’ in SSA22 that will allow us to directly detect the thermal emission from cold dust responsible for extinction in the ultraviolet/optical light. Our result is marginal mainly due to small number statistics, which dominate the size of the uncertainties on our merger fractions. This arises from the lack of a complete LBG catalogue covering the whole SSA22 field and sparse HST coverage of SSA22. It would be useful to extend our analysis to additional protoclusters and fields at a similar redshift to overcome the potential issues of small number statistics and cosmic variance, but high- z protoclusters are
Paper: 2019ApJ...871L...9H (fulltext)
SNe roughly match the required luminosity and timescale, but we would not expect their progenitors to explode two years later as SNe Ia (Bildsten et al. 2007 ). The fast-declining light curves of SNe Ibn also indicate that very little 56 Ni is produced in the explosion (Foley et al. 2007 ; Pastorello et al. 2008a ), which may challenge white dwarf explosion models. One way of avoiding these issues is to allow PS1-12sk and SN 2006jc to come from different types of stars. SNe Ibn would then be a mixed class, with some low- and some high-mass progenitors. This could potentially explain some of the spectral diversity we see among SNe Ibn (Hosseinzadeh et al. 2017b ). The five spectra that we have of PS1-12sk look almost identical to spectra of SN 2006jc, 12 12 SN 2006jc was discovered after peak, so the phases of these spectra are not well constrained. but perhaps any explosion into helium-rich CSM would look the same. This situation would be analogous to the discovery that some events initially classified as SNe IIn are actually the explosions of white dwarfs with hydrogen-rich CSM (now called SNe Ia-CSM; Dilday et al. 2012 ; Silverman et al. 2013 ). In fact, this may be the reason that events classified as SNe IIn are less correlated with star formation than other classes of core-collapse SNe (Anderson et al. 2012 ; Habergham et al. 2014 ), despite some having luminous progenitor detections. 5. Summary and Conclusions We have presented a very deep limit on the star formation rate at the site of the SN Ibn PS1-12sk, nominally ruling out a massive star origin. We present three alternative proposals for how this SN could have occurred in such a unique environment. 1. The progenitor of PS1-12sk was a massive star ejected or tidally stripped from a nearby ultra-compact dwarf galaxy. 2. The progenitor of PS1-12sk was a rare massive star in a region with a very low star formation rate. 3. The progenitor of PS1-12sk was a low-mass star. Our result only deepens the mystery surrounding PS1-12sk, in that none of these possibilities is fully satisfactory in explaining the origins of SNe Ibn. Given that there is not much more data to collect on this particular event, the resolution will likely come from a statistical study of SN Ibn host galaxies. Although this is currently the only SN Ibn discovered in a non-star-forming environment, we still suffer from a small sample size of only tens of events. Current and future large-scale surveys will be much better suited to find these rare, fast-evolving transients, allowing us to better understand the statistical properties of their host environments. Appendix A Extinction Sanders et al. ( 2013 , p. 4) claimed that “the SED of [PS1-12sk] is not consistent with significant reddening given reasonable assumptions about the photospheric temperature” and neglect extinction throughout their analysis. However, as an additional check, we calculate an upper limit on the host galaxy extinction based on the nondetection of
Paper: 2018ApJ...865....2H (fulltext)
find (their Table 1, with ). On the other hand, Hoang et al. ( 2018 ) find much higher merger fractions of up to for their nominal GC model, which are higher than the merger fractions of Petrovich Antonini ( 2017 ) and our merger fraction by 2 orders of magnitude. This large difference can be ascribed to different initial conditions. About half of the binaries in Hoang et al. ( 2018 ) merge due the gravitational wave emission only (i.e., even in the absence of the torque of the MBH). Moreover, Hoang et al. ( 2018 ) considered binaries much closer to the MBH ( to (∼10 −2 )) for which the octupole-order terms are important, in contrast to our simulations. The merger rate predicted by Hoang et al. ( 2018 ) is ∼1–, which is one (two) order(s) of magnitude larger than our GC rate estimate without VRR assuming a top-heavy (canonical) IMF. This can be ascribed to the following. 1. The merger fraction adopted by Hoang et al. ( 2018 ) is , which is 0.15/0.005 = 30 times larger than our adopted fraction. 2. The “conversion” factor from the merger fraction to the rate adopted by Hoang et al. ( 2018 ) is . We assumed a “conversion” factor of and for the canonical and top-heavy IMFs, respectively. These two points combined explain the different merger rates. In calculating their rates, Hoang et al. ( 2018 ) assumed that there exists a steady-state population of BH–BH binaries in the central 0.1 pc, with a number of corresponding to a merger rate of . This requires replenishment of the BH–BH binaries on a timescale of 100 Myr, which would be consistent with in situ star formation in a disk of massive O/B stars close to the MBH (e.g., Bartko et al. 2009 ). In our rate calculation, we assumed a global influx of compact objects, which is more appropriate in our situation given that we considered a binary population at larger distances from the MBH. VanLandingham et al. ( 2016 ) were the first to consider the combined effects of VRR and LK evolution by means of hybrid N -body methods. Due to computational limitations, the MBH masses were limited to up to . VanLandingham et al. ( 2016 ) found high merger fractions in their simulations. This is consistent with our results, in the sense that we find that the enhancement due to VRR is most significant for low MBH masses. More quantitatively, VanLandingham et al. ( 2016 ) found a merger fraction of in their simulation with (10k stars). This is very similar to our merger fraction of for the case and γ = 2 (adding the semisecular and non-semisecular fractions). This apparent agreement may be fortuitous, however, given that we did not consider a number of potentially important effects (see Section 6.1 ). In particular, DF is an important process in the simulations of VanLandingham et al. ( 2016 ); prior to their merger, most merging binaries sink inwards to the MBH. DF could therefore be important for bringing a binary from a regime with , where LK–VRR coupling is unimportant, to a region in which , where LK–VRR