adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...840...28K (fulltext)
≤ 2 redshift range. (b) The same as (a) for 10 examples of each of our three stochastic SFH families. Except for the two very lowest metallicities, increases with redshift in a nearly linear fashion, with little overlap between the tracks for the three SFH families. 5. Summary We combined SSP SEDs from the Starburst99 and BC03 codes for young and old stellar ages, respectively, and generated arrays of CSP SEDs for a large variety of exponentially declining and stochastic SFHs by stacking SSP SEDs as a function of the adopted time-dependent SFRs. For our large suite of models with stochastic SFHs, we take the average metallicity evolution as a function of cosmic age into account. We calculated the intrinsic flux ratios between rest-frame visible–near-IR and rest-frame mid-IR 3.5 μ m on a grid of six metallicities and 16 ages of stellar populations for 13 different SFH families, for 29 visible–near-IR filters, and 5 mid-IR filters with central wavelengths near 3.5 μ m. We find that taking metallicity evolution into account results in significantly higher values at higher redshifts and for higher-metallicity stellar populations at those redshifts. We also provide the range of and for different Hubble types of nearby galaxies, and confirm that our models agree with the observed values. We demonstrated that the method can infer to a level of fidelity that is comparable to that of more established methods. We conclude that the method and its extension, the method presented here, are valid as a first-order dust-correction method, when using the morphology and size of a galaxy as broad a priori constraints to its SFH and redshift, respectively. The method will be applicable to large samples of galaxies for which resolved imagery is available in one rest-frame visible–near-IR filter and one rest-frame λ ∼ 3.5 μ m bandpass. We make our CSP synthesis models and all our results available via a dedicated website in the form of FITS and PNG maps, and ASCII data tables of values as a function of age and metallicity. Appendix A A Set of Distinguishable SSPs We construct our combined model SSP SEDs for a manageably small, yet comprehensive set of parameters that span the range of representative stellar population properties and dust extinction. We consider six metallicity and 16 age values of the stellar population, four extinction laws, and eight V -band extinction values (see Table 6 ). We adopt a grid of 16 stellar population ages between 8 8 In Planck 2015 cosmology (Planck Collaboration et al. 2016 ), the Hubble time t H ≃ 13.8 Gyr. Hence, t = 13.25 Gyr corresponds to 0.55 Gyr after the Big Bang or , consistent with the Planck Collaboration (Paper 2016) results of τ = 0.058 and , so this maximum age is a deliberate choice. 3 Myr and 13.25 Gyr in steps that roughly double for ages between 10 Myr and 10 Gyr. This allows us to follow the rapid evolution of massive stars, without reserving too many SEDs for the slow evolution of low-mass stars. Because the MW
Paper: 2016ApJ...828...37F (fulltext)
spectrum would be expected from diffusive shock accelerations, but harder spectra are possible when the particle energy is converted from the electromagnetic energy of the sources, for example, in pulsar magnetospheres (Arons 2003 ), or in reconnection processes (Guo et al. 2014 ). Besides, an interplay between the acceleration and dissipation processes at the acceleration site could also lead to a hard injection spectrum (Dermer et al. 2012 ; Murase et al. 2012 ). Murase et al. ( 2016 ) and Bechtol et al. ( 2015 ) demonstrated that a pp scenario with a spectrum normalized to the IceCube datapoint at ∼10 TeV would overproduce γ rays above the flux of the non-blazar component of the isotropic γ -ray background. In this work, we will instead consider the case of an injection spectrum index equal or smaller than two, but normalized to the IceCube datapoint at 30 TeV (which is about three times lower than that at 10 TeV). Specifically, in Section 3 , we focus on a benchmark case of an injection spectrum and maximum energy . We discuss the effects of different choices of dN / dE and in Section 4 . 2.2. Cosmic-Ray Diffusion Galaxy clusters are known as efficient containers of cosmic rays (Völk et al. 1996 ; Berezinsky et al. 1997 ). Depending on the particle’s Larmor radius r L and the field’s coherence length l c , the propagation of cosmic rays in the cluster turbulent magnetic field can be divided into semi-diffusive () and diffusive () regimes (Kotera Lemoine 2008 ). When a particle’s energy reaches , the particle enters the semi-diffusive regime with a diffusion coefficient ofHowever, most particles have energy much lower than , and thus stay in the diffusive regime. The diffusion coefficient in this regime can be written as (Brunetti Jones 2014 )where is the spectrum index for Kolmogorov diffusion, and are the typical amplitude and scale of magnetic field fluctuations in massive clusters (Brunetti Jones 2015 ). The diffusion time of cosmic rays in the cluster magnetic field can be estimated asCosmic rays with diffusion time longer than the Hubble time would be completely confined by the cluster magnetic field, defining the lower limit of the energy of cosmic rays we expect to observe from the cluster as Particles with would have a better chance to escape from the cluster magnetic field. To estimate this escape probability, we first write down the probability that a particle with diffusion coefficient D reaches radius r after time t :assuming that the particle starts from r = 0 and the field is homogenous. The probability that a particle can successfully escape from a cluster of size within the Hubble time is then (Kotera Lemoine 2008 ) Cosmic rays accelerated in the strong shocks in the accretion region are expected to have a spectrum index . The flux of high-energy cosmic rays from a cluster with mass at redshift z = 0.1 can be calculated aswhere d L is the luminosity distance, and f π is the pion production rate that will be introduced
Paper: 2019ApJ...879...30L (fulltext)
(denoted by the cyan diamond) where the spectra of Si iv , C ii , and Mg ii are fairly bright and show some blueshifts. Figure 1. Observation overview of the B1.6 flare event on 2016 December 6. Top left panel: an AIA 131 Å image during the rise phase of the flare. The green dotted line and the purple contours mark the IRIS slit position and the flaring footpoints, respectively, which are the same as in the SJI 1400 Å image (bottom left panel). Top right panel: light curves of GOES 1–8 Å (black), AIA 131 Å (blue, arbitrary scale), and SJI 1400 Å (red, arbitrary scale) from the flaring region. The vertical black line represents the peak time of the flare. The blue and red dashed lines indicate the times when the AIA 131 Å and SJI 1400 Å images were observed, respectively. The two red dotted lines mark the time range (10:37:00–10:39:00 UT) as shown in Figures 3 and 4 . Bottom right panel: spectra of Si iv , C ii , and Mg ii along the slit at 10:37:45 UT. The vertical dotted line represents the reference wavelength for each of the spectral lines. The cyan diamond denotes the footpoint location that is cut across by the slit, as also shown in the SJI 1400 Å image. The two horizontal lines mark the space range (10′′) that is plotted in Figure 3 . 3.1.2. Blueshifts in the Si <sc>iv</sc>, C <sc>ii</sc>, and Mg <sc>ii</sc> Lines Figure 2 shows the Si iv , C ii , and Mg ii line profiles at the footpoint location denoted by the diamond (see Figure 1 ) for two times. It is seen that all the three lines are blueshifted, especially at an earlier time (10:37:34 UT, top panels). The blueshift velocities are measured to be 20, 13, and 6.3 km s −1 for the Si iv , C ii , and Mg ii lines, respectively, namely decreasing as the line formation temperature decreases. A few seconds later (10:37:45 UT, bottom panels), all the velocities decrease nearly by half. Note that some of the Mg ii (and also C ii ) line profiles are almost singly peaked, i.e., without a central reversal, which is a common feature for flaring footpoints. Figure 2. Si iv , C ii , and Mg ii line profiles at the footpoint location denoted by the cyan diamond as shown in Figure 1 for two times. The dotted line in each panel represents the reference wavelength of the spectral line. For the Si iv line, we adopt a single Gaussian fitting (the red curve) to derive the Doppler velocity ( V g ). For the optically thick C ii and Mg ii lines, we make a moment analysis to obtain the Doppler velocity ( V m ). The spatio-temporal variations of the total intensity, Doppler velocity, and line width of the Si iv and Mg ii lines are plotted in Figure 3 . From the intensity map we can see that the bright footpoints (the contours) show some apparent motion toward the north. Interestingly, at the footpoints (e.g., the ones denoted by the diamond), both of the Si iv and Mg ii lines exhibit blueshifts particularly at an early time. Such a blueshift in cool lines has scarcely been reported before. A similar
Paper: 2020MNRAS.493.5218S (fulltext)
To estimate the effective area correction between the different detectors of Fermi and BAT, we adopted the following methodology (Page et al. 2009 ): (i) To find the best-fitting model: Each of the time-resolved spectra of all detectors (BAT, GBM: n6, n7, n9, and b1) was jointly analysed using different spectral functions such as power law, cutoff-power law, and Band, without including the constant factor correction. In this exercise, the time-averaged and time-resolved spectra were found to be better fitted by cutoff-power-law function. (ii) Finding the normalization-offsets: We then simultaneously fitted the five bright time-resolved spectra with the best-fitting model, cutoff-power law, by multiplying the model by a constant of normalization for each instrument. The constant of BAT data was frozen to unity, while those of the GBM detectors were kept free. In addition, these constants were also tied across the respective data sets of the different time-resolved intervals. The relative normalizations were found as, for n6 detector, for n7 detector, for n9 detector, and for b1 detector. These constant factors are consistent with the expected intercalibration uncertainty of up to and hence were used for the subsequent spectral analyses. 3.2 Parameters evolution For characterizing the spectra, we fit the brightest time intervals in both episodes with several phenomenological models including power law with an exponential cutoff (cutoff-power law), blackbody + cutoff-power law, and traditional Band functions. In Fig. 2 , the reduced χ 2 obtained for the different models in the different time intervals are shown. We note that in the brightest time interval (bin 5) of the first episode, blackbody + cutoff-power law model is statistically favoured (i.e reduced χ 2 ∼ 1). On the addition of blackbody component to the cutoff-power law, we also find an improvement in Akaike Information Criterion (AIC; Akaike 1974 ) and Bayesian Information Criterion (BIC; Schwarz et al. 1978 ) by 15 and 7, respectively (see Appendix B ). At the same time, in all other time intervals, this model is also found to give comparable statistics when compared with other models. The Monte Carlo (MC) simulations for estimating the statistical significance of the additional blackbody component with the cutoff-power-law model are performed for the brightest time interval. The difference of χ 2 -statistics between cutoff-power-law model and blackbody + cutoff-power-law model is found to be 19. Here, we assume cutoff-power-law model as the null hypothesis model and blackbody + cutoff-power-law model as the alternate model and simulations are run for realizations. We find that the probability of observing Δ χ 2 > 19 is 10 −2.52 , which corresponds to ∼3 σ significance level. Thus, by considering the above fit statistics and MC simulations obtained in the brightest time bin, we consider blackbody + cutoff-power-law model to be the best-fitting model for all the time-resolved intervals
Paper: 2021MNRAS.505.2349S (fulltext)
only 13 SNe are removed as a result. The latter is much more impactful, however, removing ∼700 spectra (mostly from the SED Machine; Blagorodnova et al. 2018 ) and nearly as many SNe. In fact, this amounts to the most severe cut on SNe and the second most severe cut on spectra (after the ‘overlap’ cut). With future improvements to deepSIP it may be possible to revisit and lessen the extremity of this cut, but this will have to wait until those improvements can be realized. Irrelevant photometric passbands: After the cuts described above, the 60 140 photometric points that remain are in one of 62 different passbands, some of which are irrelevant to our analysis and must therefore be removed. By far, the primary factor in rendering observations in a given passband ‘irrelevant’ is if that band is too broad. For example, 4923 Gaia G -band (Jordi et al. 2010 ), 2104 Pan-STARRS w -band (Chambers Pan-STARRS Team 2018 ), 612 ATLAS c -band or o -band (Tonry et al. 2018 ), and many other wide-band observations are removed, along with a further 5061 unfiltered photometric points. A second factor in determining the relevance of a passband is the wavelengths of light that it passes. As our SDM uses a WLR implementation that is trained at optical and near-infrared wavelengths, we must discard passbands that lie outside this domain, thereby removing some Swift observations (Brown et al. 2014 ) among others. All told, 15 624 photometric points are removed, and those that remain are homogenized down to 21 distinct passbands. Photometric uncertainties: Of the remaining 44 516 photometric points, 10 429 do not include a symmetric uncertainty in the reported magnitude. We are able to ‘fix’ 6120 of these by taking the larger of their (asymmetric) lower and upper magnitude uncertainty estimates, but 4393 remain which have no identifiable quantitative uncertainty indicator. We take the conservative approach of removing these data, leaving us with just under 1200 SNe covered by ∼2900 spectra and ∼ 40 000 photometric points. 2 + Distinct passbands: As the SDM requires, at a minimum, two photometric points in different passbands (and a spectrum), we remove SNe that fail this requirement. This reduces the sample to 747 SNe with ∼2400 spectra and ∼ 35 000 photometric points. Literature search for photometric systems: While we have taken care above to homogenize the passbands in our sample (in the sense of resolving the differing names given to identical filters), we must also understand the photometric systems to which they belong. To do so, we visit the primary source provided by the OSC for each remaining point in our photometry sample (amounting to 248 sources) and attempt to determine the photometric system. We find that 26 SNe (covered by 5719 photometric points) are either not an SN Ia or are not an SN Ia subclass that can be handled by our WLR. These are dropped, as are an additional 7220 photometric points whose photometric system is either not clear or not
Paper: 2016MNRAS.457.4536W (fulltext)
3. Enclosed gas mass profiles as a function of radius for the known potential Galactic YMCs precursors. The transition point from solid to lower opacity indicates the beam resolution of the data. The triangle markers correspond to the initial conditions used by Banerjee Kroupa ( 2013 ) to simulate the monolithic formation of the R136 YMC. The circular markers correspond to the initial conditions used by Banerjee Kroupa ( 2014 ) to simulate the monolithic formation of the NGC 3603 YMC. The star markers correspond to the initial conditions given in the fifth row of table 1 in Assmann et al. ( 2011 ) to simulate a monolithically forming star cluster. As these simulations are 3D models, and the data that we discuss in this paper are 2D projections, we must take care to ensure that we are making a fair comparison to these models. In Banerjee Kroupa ( 2013 , 2014 ), they present their initial conditions as initial cluster mass, initial gas mass and half-mass radius. Using their assumed ∼33 per cent SFE, we combine these masses to obtain the total gas mass prior to star formation. We also use the following relation to convert the half-mass radius to the Plummer radius (Heggie Hut 2003 ) – Assmann et al. ( 2011 ) already provide the Plummer radius. We then take the total 3D gas mass for all of the models and convert this to a projected mass (Heggie Hut 2003 ) – Overall Fig. 3 shows that the initial projected masses within a given radius for these various models generally too high, particularly at smaller radii. We note that the Banerjee Kroupa ( 2014 ) model for NGC 3603 appears consistent with observations at radii > 0.5 pc. However, it is overdense on smaller spatial scales. Whilst the models generally predict mass surface densities that are greater than the observed clouds, this is not unambiguously the case. The highly star-forming clouds such as Sagittarius B2, W49 and W51 all lie very close to these models. Though we reiterate that the mass estimates, particularly in the highly star-forming clouds, are upper limits. Accounting for temperature effects would create an even larger disparity between the observations and simulations. We also note that the simulated models assume that both the gas and stars are initially distributed according to a Plummer distribution, which is not true for the gas in the clouds in our sample (see Section 4 ). This is a critical difference between the true conditions in the ISM and those assumed in these models. The gravitational potential of the gas is dominant until a significant number of stars have formed within the gas, and it is clear that the initial potential does not arise from a Plummer-like gravitational potential. 4 DISCUSSION In Walker et al. ( 2015 ), we used a sample of YMCs and possible YMC precursor clouds at the Galactic Centre to attempt to distinguish between two commonly argued modes of YMC formation – popping or conveyor-belt modes. We did this simply by comparing their mass surface density
Paper: 2017A&A...604A.109S (fulltext)
5 . Nevertheless, the resulting statistical errors of shear, as opposed to its bias, are larger by roughly the factor 1.7 with the uniform prior, which reflects the larger a-priori uncertainty on . This implies that a shear bias due to an incorrect prior is negligible in this experiment. 5. Discussion Unlike galaxy ellipticities defined in terms of unweighted moments, GLAM provide a practical definition of ellipticity with useful properties for any chosen adaptive weight : they (i) are identical with the ellipticity of isophotes of elliptical galaxy images; (ii) under the influence of shear they behave exactly like defined with unweighted moments; (iii) they are unbiased estimators of reduced shear; (i)(iii) assume ideal conditions without pixellation, PSF convolution, or pixel noise. These effects fundamentally limit our ability to determine the GLAM ellipticity (see below). Under ideal conditions, see SS97, it is known that for unweighted moments already obeys (iii). However, unweighted moments are formally infinite for realistic galaxy images because of pixel noise so that weighting is a necessity which is done adaptively for GLAM . For adaptive weights, we have shown (i) and (ii) in Sects. 2.2 and 2.3 . Their relevance as unbiased estimators for reduced shear, statement (iii), follows thereafter from (ii) and the conclusions in SS97 for unweighted moments. We emphasise that GLAM do not require “deweighting” in a lensing analysis ( Melchior et al. 2011 ): the ellipticity in term of the weighted moments is actually unbiased. In particular, we do not have to devise the same weight function for all galaxies as any weight function equally obeys (ii). If the minimum of the functional ( 3 ) uniquely exists, the moments of the best-fitting template will, for a constant scalar , be the adaptively weighted moments of the galaxy light profile for the weight (Appendix A ). This means that the radial profile of the weight at separation from the centroid is for a template profile . We exploited this relation between adaptive moments and moments of a best-fitting elliptical profile to analyse limits to adaptive moment-based methods under the loss of image information. Namely, for realistic images subject to pixellation and PSF effects, underfitting bias can be present for GLAM if estimated from the post-seeing image. It potentially arises because of a loss of information on the pre-seeing galaxy image, fundamentally due to pixellation. To explore the limitations, we assume noise-free images and express in Sect. 2.4 the mapping between a pre-seeing and post-seeing image by the linear operation . The pre-seeing adaptive moments are equivalent to a least-square fit with residual of an elliptical profile to the pre-seeing image . If estimated from the post-seeing image whilst ignoring residuals, the inferred ellipticity is biased for and a singular . The latter indicates a loss of information on the pre-seeing image. For noise-free images and the estimator
Paper: 2021MNRAS.503.5232R (fulltext)
unaffected by the Milky Way potential. We see in the lower panel that even for V peak the relation is broadly flat with significant scatter. For example, without including pericentre information, the V peak error bars for CVenI and Draco overlap, suggesting that these two galaxies with similar stellar masses could have formed within haloes of the same size. The revised estimate suggest that Draco I likely formed within a halo that was more massive. Improving constraints on the dynamical mass of the dSphs at present combined with better constraints on their pericentres from Gaia can provide deeper understanding of the past evolution and the mass of the progenitor haloes of MW dwarf spheroidal galaxies. Among the galaxies we have considered, Draco and Ursa Minor are affected the most by including pericentre information. These, the densest dSph satellites, have relatively small pericentres ( r peri ≲ 50 kpc) and this pushes their inferred V max values lower than they would have been without pericentre information. We find that both Draco and Ursa Minor appear to inhabit subhaloes with V max ≃ 20–27 km s −1 (at one sigma) and are less dense than would be expected for the most massive subhaloes with small pericentres (see Fig. 1 ). This exacerbates the traditional TBTF problem (Boylan-Kolchin et al. 2011 , 2012 ). The inferred range of V peak values is also tightened for these galaxies. Both galaxies have inferred peak maximum circular velocities (prior to infall) that put them in range to have been significantly affected by reionization, V peak ≃ 25–37 km s −1 (e.g. Bullock, Kravtsov Weinberg 2000 ; Gnedin 2000 ; Benítez-Llambay et al. 2017 ; Fitts et al. 2017 ). It is worth considering whether our results are affected by numerical resolution. There is a fair amount of evidence to suggest that simulations such as ours do not accurately track halo survival after they have lost a significant () amount of mass, with ∼3000 particles appearing to be a critical limit below which haloes disrupt unphysically (e.g. Van den Bosch Ogiya 2018 ; Errani Navarro 2020 ). In terms of halo counts alone, this effect should not affect current cosmological zoom simulations significantly, since accreted halo mass functions rise steeply with increasing mass, d n /d M ∼ M −1.8 (e.g. Giocoli, Tormen van den Bosch 2008 ). This means that for every halo of mass M that was accreted long ago and has lost 99 per cent of its original mass (100 M ), there are as many as ∼4000 haloes of mass M that were accreted more recently and that can be accurately counted. As discussed in Kelley et al. ( 2019 ), convergence tests suggest that our halo counts are mostly complete down to V max > 4.5 km s −1 , or only ∼150 particles. Of particular relevance to this work is whether or not internal halo structures, e.g. V max / V peak distributions at fixed V max , are tracked appropriately. We have limited our analysis to haloes with V max > 10 km s −1 , or ≳ 3000 particles. According to the