adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.477L..35G (fulltext)
interlopers, selection biases, and metallicity diagnostics. These errors can easily overwhelm the chemical enrichment signal as a function of environment, particularly at high redshifts. In addition, the diverse definition of environment makes comparisons across different studies ambiguous (Ellison et al. 2009 ; Peng Maiolino 2014 ). Cosmological simulations with a full three-dimensional formation history of cluster galaxies are a powerful tool to overcome observational biases and understand the physics of the environmental dependence of the chemical evolution (Davé, Finlator Oppenheimer 2011 ; Bahe et al. 2016 ; Genel 2016 ). In this Letter, we explore the MZR evolution as a function of the formation history of galaxy groups and clusters in IllustrisTNG, a large-scale cosmological simulation. Using a novel method of separating progenitor cluster galaxies into accreted and infalling categories, we report for the first time a clear systematic signature of ‘chemical pre-processing’ in cluster galaxies over the past 10 Gyr. We show the effectiveness of pre-enriched gas inflows in driving the chemical evolution of cluster galaxies out to twice the virial radius. 2 METHODS Our results are based on the IllustrisTNG simulations (Marinacci et al. 2017 ; Naiman et al. 2018 ; Nelson et al. 2018 ; Pillepich et al. 2018a ; Springel et al. 2018 ). The galaxy formation model is an updated and extended version of the Illustris model (Vogelsberger et al. 2013 ; Torrey et al. 2014 ). Details of the model modifications are described in Weinberger et al. ( 2017 ) and Pillepich et al. ( 2018b ). We use the TNG100 simulation that has a volume of ∼(100 Mpc) 3 and m b = 9.4 × 10 5 M ⊙ h −1 , where m b is the baryonic mass per particle. From the TNG100 simulation, we select all haloes with M 200 c ≥10 13.0 M ⊙ h −1 , where M 200 c is total mass enclosed within R 200 c (the radius within which the mean density is 200 times the critical density of the Universe at the halo redshift). This leads to a selection of 127 galaxy clusters and groups at z = 0. The mean cluster halo mass of our sample is 10 13.4 M ⊙ h −1 . We define the boundary of a cluster as twice the R 200 c of the cluster halo, which is similar to the splashback radius (Diemer et al. 2017 ). Out of all friends-of-friends (FOF) subhaloes associated with the cluster at z = 0, all galaxies whether centrals or satellites residing within twice the R 200 c make up our cluster member galaxy sample. Note that we exclude flybys and satellite galaxies in the process of splashing back at z = 0 from our analysis. We restrict this analysis to a stellar mass range of 10 9 –10 10.5 M ⊙ h −1 . The lower stellar mass cut is to minimize numerical uncertainties such that there are at least 1000 stellar particles per galaxy (a single star particle in TNG100 has a mass of about 10 6 M ⊙ h −1 ). We impose the upper mass cut of 10 10.5 M ⊙ h −1 to reduce contaminations from active galactic nuclei (AGN) activities (Pillepich
Paper: 2021MNRAS.503.3610T (fulltext)
detected magnetic patches with red and blue lines (panel c). The intensity is normalized with the average intensity. The white and black magnetic fields indicate the positive and negative magnetic fields, respectively. The magnetic field is distributed in a network with a scale of several 10 Mm, and the structure of supergranulation can be seen. We note that although the maximum value of the colour bar for the magnetic field is 300 G, the maximum magnetic field strength exceeds 1000 G. The red and blue contours in panel (c) indicate positive and negative magnetic patches, respectively. Figure 3. The radiative intensity (panel a), the vertical magnetic field strength (panel b), and the detection of magnetic patches (panel c). The intensity is normalized by the average intensity I ⊙ . Panels (b) and (c) show the vertical magnetic field at τ = 0.01 surface. The red and blue lines indicate positive and negative magnetic patches, respectively. Fig. 4 shows the vertical magnetic field convolved with PSF and the detected features. The figure shows the raw simulated magnetic field (panel a), the magnetic field with the PSF of the Hinode /SOT (panel b), and the magnetic field with the PSF of SDO /HMI (panel c). SDO /HMI (panel c) shows the most diffused patches, and the field strength is weak on average. Figure 4. Magnetic field strength convolved with different kinds of PSF. The simulated raw magnetic field data (panel a), data convolved with the PSF of Hinode /SOT PSF (panel b), and data convolved with the PSF of SDO /HMI (panel c). Fig. 5 shows the probability density function (PDF) of the magnetic flux at the photosphere. Orange, green, and blue lines indicate the magnetic flux in the original raw data, the data with the PSF of Hinode /SOT, and the data with the PSF of SDO /HMI, respectively. The black line indicates the fitting result for a range that clearly shows the power-law distribution. The slope increases with increasing resolution, i.e. the raw simulation data show a steeper slope than the data with Hinode and SDO PSF. The power-law index of the PDF with Hinode ’s PSF (green line) is –1.848, consistent with Parnell et al. ( 2009 ), which showed –1.85 for the power-law index with the Hinode observation. This suggests that the simulated magnetic field data reproduce the real Sun nicely and support the appropriateness of the model in this study. Figure 5. The probability density function (PDF) at the photosphere. Orange, green, and blue lines indicate the PDF of the raw simulation data, the data with Hinode /SOT’s PSF, and the data with SDO /HMI’s PSF, respectively. The black lines are the fitted lines for their power-law distribution. Fig. 6 shows the results of our analysis for the detected magnetic field depths. |〈 B z 〉| means the absolute value of the vertical magnetic field averaged over each magnetic patches. We divide the horizontal axis into 100 bins. Each bin has the same size in logarithmic and linear scales for magnetic flux
Paper: 2017A&A...601A.139N (fulltext)
and , our CDF is, analogously with Eq. ( A.2 )and using Eq. ( A.1 ), (The use of is merely a matter of convenience for the integration limits.) The basis functions, that is, any CDF caused by all objects having given fixed and , are now easy to write in closed form. They are obtained by replacing and in the integrands by the fixed and , setting , and using the integration limits to describe the inequalities between and to define the piecewise function . This replaces the integral by a sum of such basis functions each multiplied by the corresponding weight of the and bin. The resulting basis functions are given in Sect. 2.1. The above assumption of most orbits to be close to the ecliptic plane is only approximate, and one can always define populations (especially those of near-Earth asteroids) for which it is not true even approximately. Thus the validity of this assumption should be checked for the targets used. However, as we show in Sect. 4, the assumption works quite well (given the large model error budget in any case) with typical asteroid populations for which there is some concentration of viewing geometries sufficiently near the ecliptic plane. Appendix B: Ill-posedness caused by two-point variation observables As earlier, we consider the case when is (approximately) the same for the pair. Now we have, for two rotation phases and , so, with , that is, (due to symmetry, we only need to consider the interval ), we define iso- contours in the plane (for given ) by so, to have viable solutions for from , we must have , , and so exist for given only if ; that is, Denoting our CDF is thus Again, we can include the -distribution by expanding the integral in the same way as with Eq. (A.4). The basis function for a given in the two-point brightness scatter case is, from Eq. ( B.1 ), Although the -integral can be given in terms of elliptic functions, this is best computed by evaluating the double integral numerically. The maximum value of is obtained at : Our basis functions are closed-form expressions of those computed by Monte-Carlo sampling in Szabo Kiss (2008) . These can be used to determine the -distribution, although we found the accuracy inferior to the solution based on the variation observable , which was to expected. In principle, we can expand to for a -grid in the same way that were expanded to . However, a notable difference between the two-index basis functions of - or - data is that the all reach their maxima at the same point since the two-point comparison can always contain two equal brightnesses for any and . Thus the -curves of the of a given form a curve family with the same abscissae for the minimum () and maximum (); that is, members of the family can easily be mimicked by a superposition of other members unlike in the case of . A number of simulations indeed confirmed that are not usable for solving the inverse problem in practice; that is, -information is not recoverable from -data. Adding prior assumptions on the
Paper: 2020ApJ...895L..40C (fulltext)
perpendicular to the mean magnetic field. The spectral hardness does not arise because the particle energy spectrum itself is hard, i.e., with , but rather because the distribution is anisotropic. Within the nonthermal tail of accelerated particles, lower energy electrons tend to be more aligned with the local magnetic field than higher energy electrons, so their synchrotron emission is comparatively weaker. This energy-dependent anisotropy has the effect of hardening the synchrotron spectrum. 3 3 We expect similar conclusions to hold for electrons accelerated by reconnection with a strong guide field, even in a nonturbulent environment. Indeed, in our simulations of magnetically dominated turbulence, the acceleration of particles attaining the strongest anisotropy is primarily due to reconnection (Comisso Sironi 2019 ). In laminar reconnection, the guide field would play the role of the mean field of our turbulent setup. Our results have important implications for the interpretation of the hard radio spectra of pulsar wind nebulae (PWNe), where the observed radio spectral index is typically (Gaensler Slane 2006 ; Reynolds et al. 2017 ). In the notable case of the Crab Nebula, the radio spectral index is (Hester 2008 ; Bühler Blandford 2014 ). Turbulence, which develops as an effect of MHD instabilities in PWNe (Porth et al. 2014 ), has been suggested to accelerate the radio-emitting electrons (Lyutikov et al. 2019 ; Xu et al. 2019 ). However, these studies simply invoked a hard particle spectrum () over some energy range, without addressing from first principles the origin of the accelerated particles. We have shown that producing hard radio spectra does not necessarily require an acceleration mechanism that transfers most of the available energy to a small fraction of particles (which is the case when ). Instead, hard synchrotron spectra may be produced by a relatively soft population of nonthermal particles with an energy-dependent anisotropy. Finally, note that our assumption of neglecting the effect of synchrotron cooling on the particle evolution is well justified in PWNe, where the cooling time of radio-emitting electrons is longer than the age of the system. For the Crab Nebula, it is generally quoted that the hard spectral slope persists over six orders of magnitude in frequency (for an extended discussion, see, e.g., Bietenholz et al. 1997 ). Since the hard range of our synchrotron spectra extends over four decades, contributions from turbulent regions with moderately different properties would be required. While a detailed comparison to the Crab Nebula radio emission is left for future work, we mention that in our model we would imply that (i) the total number of radio-emitting electrons is at least one order of magnitude higher than in the case with isotropic electrons. More precisely, the number of radio electrons increases by a factor of (ii) the initial magnetization of the plasma is relatively low and/or the predissipation
Paper: 2018A&A...615A.104G (fulltext)
defined as AGNs all galaxies beyond the left line in the BPT ([NII]/H α ) diagram. In specific cases we refer only to “pure” AGNs, namely galaxies above the curve defined by Kewley et al. (2001) in the BPT ([NII]/H α ) diagram. Also, galaxies classified as PAS or PSB (and RET in the WHAN diagnostic) are always held together. Fig. 3 Nuclear diagnostic diagrams for the whole HRS sample limited to emission-line objects with S ∕ N > 3. In the left BPT diagram panel , the broken separation line between AGNs (red) and TRAN (pink) galaxies is from Kewley et al. (2001) , while the dotted separation line between TRAN (pink) and HII region-like nuclei (blue) is from K03. In the second BPT diagram panel , the extreme starburst classification line is from Kewley et al. (2001) , while the separation between LINER (pink) and SEY (red) is from Kewley et al. (2006) . We note that the pink filled symbols refer to different types of objects in the three diagrams. The third panel reports the BPT diagram for the minority of galaxies (101) with [OI] in emission. In the WHAN diagram the separations between the different classes are from Gavazzi et al. (2011) . Fig. 4 Integrated diagnostic diagrams for emission-line objects with S ∕ N > 3. Brokenseparations in the diagrams are the same as in Fig. 3 . Despite the different populations and classification criteria, the WHAN and the BPT nuclear diagnostics give consistent results (see Table 2 ). Hereafter, two different percentages are always reported, one is the value resulting from one of the [NII]/H α or [SII]/H α BPT diagrams and the other is from the WHAN diagram. Between 53 and 64% of the nuclear spectra are HII region-like nuclei. The remaining percentage is divided between AGNs (21–27%) and PAS (15–21%). The “pure” AGNs are half of the AGNs percentage: 7–13%. These are galaxies ionized by a SMBH. Restricting to the 254 LTGs alone, the statistics is altered (see right columns of Table 2 ): the HII-regions increase to 67–77%; the AGNs become 21–27%; and the PAS spectra decrease to 2–7%. The latter class includes two PSB galaxies: HRS-116 and HRS-195. These two objects show a late-type morphology, with a central bulge and spiral arms, but the SF activity has been stopped suddenly. Also HRS-164 is a LTG with a PAS spectrum: its morphology shows a strong barred structure in the center, but it is an old and red object. A comparison between the nuclear and integrated spectral classifications can be performed among the subsample of HRS galaxies with available nuclear and integrated classification (see Figs. 5 , 7 , and 8 ); the subsample is reported in Table 3 . This comparison provides information about how the excitation properties of galaxies change from the center to the outskirts. As can be seen in the three figures, AGNs represent 18–26% in the nuclear spectra; while in the integrated they decrease to 4–11%. HII-regions are 59–71% in the nuclear classification, becoming 69–84% among the integrated spectra. PAS
Paper: 2020ApJ...892L...9A (fulltext)
alternatively imply incorrect assumptions about the underlying pulsar population properties. Figure 4. Eccentricities of DNSs identified within the LISA band with S/N > 7. Blue and green distributions are designed to match the low- and high-eccentricity DNSs in the MW, respectively. The two black distributions (different line styles) demonstrate the eccentricities expected from two ad hoc models for putative fast-merging DNSs. 4. Discussion and Conclusions Using the MW DNS merger rate of 210 Myr −1 derived from LIGO (Abbott et al. 2019 ), we find that a 4 yr (8 yr) LISA mission will detect on average 240 (330) DNSs. Approximately 25% of those will be “chirping,” allowing for their characterization as DNSs through their chirp mass. The remaining ≈75% will likely be indistinguishable from lower-mass double white dwarfs. Using a more pessimistic rate of 42 Myr −1 based on the Galactic population of DNSs, we find that LISA will detect 46 (65) DNSs for a 4 yr (8 yr) mission. However, the census of MW DNSs is incomplete as Doppler shifting of the radio waves that pulsars emit makes detecting DNSs with orbital periods ≲1 hr extremely challenging. If these systems exist in significant numbers, the nature of GW orbital decay implies that they were formed with similarly short orbital periods. The presence of such fast-merging DNSs can help resolve any difference between the DNS merger rates as determined by LIGO and the galactic population of DNSs. Because LISA bridges the orbital period gap separating DNSs detected with radio waves and by LIGO, it can detect DNSs that form with short orbital periods. As pointed out by Kyutoku et al. ( 2019 ), the sky position of a DNS as measured by LISA may allow for characterization by radio follow-up observations, provided the DNSs contain at least one radio bright pulsar. If, on the other hand, these systems are radio-quiet, the ability to measure orbital eccentricities as small as 10 −2 by LISA affords, perhaps, the only opportunity to measure and characterize the orbits of such systems, discerning between the different formation scenarios. The existence of such a fast-merging channel would have profound implications on the presence of r -process elements in the Universe. Recent studies have shown that, if r -process enrichment seen in the ultra-faint dwarf galaxies Reticulum II and Tucana III is formed from the merger of DNSs, then a fast-merging channel is required (Beniamini Piran 2016 ; Safarzadeh et al. 2019a ). Similar arguments are required to explain the existence of r -process enrichment in globular clusters (Zevin et al. 2019 ). These studies, which use binary population synthesis, suggest that such fast-merging DNSs could be formed through unstable Case BB mass transfer (Delgado Thomas 1981 ), in which a NS accretor enters a second common envelope when its stripped helium star companion evolves into a giant star. Dynamical formation provides an alternative evolutionary scenario that can explain the
Paper: 2019ApJ...885..105R (fulltext)
toward values of the order of τ c / τ IC ≈ 10 −3 –10 −2 for the relevant electron energies (see e.g., Pereira et al. 2016 ; del Palacio et al. 2018 for a modeling of AE Aur and other bow-shock systems). In particular, Pereira et al. ( 2016 ) claim that for the AE Aur system the energy density of the ambient radiation is largely dominated by dust emission, and found a value for the ratio τ c / τ IC roughly equal to 10 −2 . Finally, values of E max of the order of ≲1 TeV have been estimated in Pereira et al. ( 2016 ), implying that the logarithm in Equation ( 4 ) should be roughly of the order of ≈10. Given these fiducial values we can now estimate the IC flux in the X-ray band aswhich is indeed an upper limit because it is based on the most optimistic value for η e . Our estimate of the IC X-ray emission is consistent with the upper limit reported in Section 3.3 , but is in disagreement with previous and more optimistic estimates reported in the literature (e.g., López-Santiago et al. 2012 ; Pereira et al. 2016 ; del Palacio et al. 2018 ). The main reason for that is the different choice of the parameter η e , which in these previous works was assumed to be equal to η e ≈ 0.1, which implicitly implies that wind termination shocks were assumed to be much more effective than SNR shocks in accelerating electrons. In this respect, our estimate is thus more conservative: we assumed that wind termination shocks behave as SNR ones. To conclude, we stress that a similar reasoning can be adopted (referring to AE Aur) to interpret the radio upper limits. For the fiducial value of the magnetic field of ∼30 μ G, electrons of energy ≳1 GeV emit synchrotron radiation in the GHz domain. The characteristic synchrotron cooling time τ s for such electrons is of the order of several megayears. An estimate of the expected radio emission from the bow shock can then be obtained by substituting τ IC with τ s in Equation ( 5 ), and by noticing that , which is about two orders of magnitude smaller than τ c / τ IC . However, this is almost exactly compensated by the fact that the radio upper limits are much more stringent than those derived from X-ray observations. Therefore, we can conclude that also the nondetection of the bow shock of AE Aur in radio waves is consistent with the assumption that such shocks behave as SNR ones. Within this framework, the detection of radio synchrotron emission from the bow shock of the runaway star BD+43°3654 might be tentatively explained by the very large kinetic energy characterizing its stellar wind. For this system, estimates of the stellar mass-loss rate range from yr −1 (Peri et al. 2012 ) up to 1.6 × 10 −4 M ⊙ yr −1 (Kobulnicky et al. 2010 ), i.e., a factor of ≈30...800 larger than that estimated for AE Aur. Also, the estimated velocity of the wind of BD+43°3654 (2300 km s −1 ) is roughly a factor of two larger than that estimated fro AE Aur (Benaglia et al. 2010 ). Therefore, the wind kinetic energy is ≈10 2 ... 3 × 10 3
Paper: 2016ApJ...816...85L (fulltext)
(red crosses for the flux, yellow shadows for the flux error) is also plotted. We first subtract the stellar light in the SED by assuming a Rayleigh–Jeans tail scaled by the WISE W1 band data point and then fit the data points at 8–1000 μ m with the modified blackbody + MIR power-law model (namely, CMC fits; green lines). The final model SED is shown as dark gray lines. The T , α , and β parameters derived from the CMC fits are indicated in the top right corner of each panel. In a similar fashion as Rémy-Ruyer et al. ( 2013 ), a single modified blackbody is also fit to the Herschel data points for comparison (light gray line). Table 7 Low-metallicity Galaxies Used to Derive the Templates Note. Column (1): the galaxy name. Column (2): the metallicity 12 + log(O/H). Column (3): the dust temperature derived from the CMC fit (see text). Column (4): the FIR emission index. Column (5): the MIR power-law index. Column (6): the existence of the MIR Spitzer spectrum: good —the galaxy has a full range 5–35 μ m MIR spectrum; limited —the galaxy has an MIR spectrum with limited coverage; poor —IC 10 is quite extended, as judged from its MIPS 24 μ m image, making the Spitzer spectrum a poor reflection of the overall MIR continuum. Figure 10 shows the full IR SEDs (after adding the Spitzer spectra) for the 19 DGS galaxies. The MIR continua derived from the photometry fitting and those directly obtained from the MIR spectra are consistent. The MIR regions of these low-metallicity objects present substantial forbidden-line emissions and weak or no polycyclic aromatic hydrocarbon emission (e.g., Wu et al. 2006 , 2007 ), which contribute little to the continuum. For the final model SEDs of the 19 DGS galaxies, we discard the MIR portion of the fit SED continua based on WISE and Herschel photometry and replace it with scaled Spitzer spectra. Figure 10. Full IR SEDs of the 19 DGS galaxies that have Spitzer /IRS low-resolution spectra with high enough S/N, sequenced by their metallicity with the lowest at the bottom. The Spitzer spectra are scaled to match the continuum SEDs. A.3. Features of Low-metallicity Galaxy Templates Figure 11 compares the 19 low-metallicity templates with the normal SF galaxy templates in Rieke et al. ( 2009 ). The SEDs of these dwarf galaxies show a lot of variation. Compared with the solar-metallicity galaxies, the low-metallicity IR SEDs derived in this work tend to have the following features: 1. Higher FIR dust temperature. For the low-metallicity galaxies, the typical dust temperature is T dust = 34 ± 7.7. Our value is similar to the result by Rémy-Ruyer et al. ( 2013 ) ( T dust ∼ 32 K). Compared with the Herschel KINGFISH sample ( T dust ∼ 23 K; Rémy-Ruyer et al. 2013 ), which contains more metal-rich environments, the dust in these low-metallicity systems is generally warmer. 2. Steeply rising MIR continua. For low-metallicity galaxies in this work, the MIR continua slope α = 3.8 ± 0.8, which is much larger than typical