adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.504.4865C (fulltext)
B ), which can be used not only to capture complicated patterns between variables and to keep a good balance between bias and variance, but also to identify the most important variables that explain model predictions. We address the second problem by using a linear dimension reduction algorithm, the principal component analysis (PCA; see Appendix A ), which can effectively reduce the dimension of the halo AH and the galaxy SFH and yet retain large amounts of information of the histories for the empirical modelling. Finally, we address the third problem by building a deep model that incorporates components of both dimension reduction and ensemble regressor and classifier. Each component in the model is motivated physically and can be optimized separately. This approach makes the model capable of dealing with complex patterns in parameter space, and yet transparent to interpret. The ensemble regressor and linear dimension reduction method has already been used to study the relationship among halo properties in Chen et al. ( 2020 ). Here, we extend it to studying the galaxy–halo connection. As the first in a series, this paper focuses on central galaxies in dark matter haloes. We identify important ingredients that should be included in an empirical model, and demonstrate the limit such a model can reach in describing the stellar masses and SFHs of individual galaxies. Our model is built on the inferences from two hydrodynamic simulations, the Illustris-TNG (e.g. Nelson et al. 2019 ) and EAGLE (e.g. The EAGLE team 2017 ). This paper is organized as follows. In Section 2 , we describe the simulation data we use, and define the halo properties and samples used in our analysis. In Section 3 , we use both the GBDT and PCA to study the relations of galaxy stellar mass and SFR with halo properties for both star-forming galaxies and quenched galaxies. We also identify halo properties that cause a galaxy to quench. In Section 4 , we build an empirical model that predicts the SFH of galaxies in dark matter haloes, testing its performance in several steps. We summarize and discuss our results in Section 5 . 2 THE DATA 2.1 The Illustris-TNG and EAGLE simulations The Illustris-TNG simulation (Marinacci et al. 2018 ; Naiman et al. 2018 ; Nelson et al. 2018 , 2019 ; Springel et al. 2018 ; Pillepich et al. 2018b ) is a suite of cosmological, hydrodynamical simulations implemented with the moving-mesh code Arepo (Springel 2010 ). The cosmological parameters are taken from the Planck 2015 results (Planck Collaboration XIII 2015 ): Hubble constant with h = 0.6774, cosmological constant Ω Λ,0 = 0.6911, matter density Ω M,0 = 0.3089, baryon density Ω B,0 = 0.0486, and initial power spectrum with normalization σ 8 = 0.8159 and index n s = 0.9667. The simulated physical processes for galaxy formation include gas cooling, star formation, stellar feedback, metal enrichment, black hole feedback, and so on. The details can be found in the two method papers, Weinberger
Paper: 2018ApJ...853..110R (acknowledgments)
We want to thank Chris Carilli for assistance on the simulations of the ngVLA observations and for providing us with the ngVLA antenna position files, and Giovanni Dipierro for helpful comments on the manuscript. This work was supported in part by the ngVLA Community Studies program, coordinated by the National Radio Astronomy Observatory, which is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. A.I. and L.R. acknowledge support from NSF grant No. AST-1535809. A.I., S.L., and H.L. acknowledge the support from the Center for Space and Earth Sciences at LANL, and H.L. acknowledges the support from the LANL/LDRD program. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan) and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Paper: 2018MNRAS.481.3196C (fulltext)
very well separated components () and with marginal distributions and pairwise scatter plots as shown in the diagonal and lower triangle of the matrix of plots in Fig. 3(a) . The upper triangle of the matrix displays the correlation between each pair of variables. Thus, the first two of the three variables have a very high (almost linear) pairwise correlation of 0.98, while the other two pairs have little to modest correlations between them. It is tempting to surmise that one of the first two dimensions are nearly redundant and that dropping one of them would not have much effect on the quality of clustering. We test this assertion by performing GMMBC (with BIC used to determine the optimal number of groups) on the dataset using each of the three distinct pairs of variables. Figs 3(b) –(d) display the results of clustering each pair of coordinates, with colours representing the obtained clustering and character denoting the true grouping. Including either one of the two seemingly redundant variables with the third variable identifies only two groups and a partitioning (Figs 3b and c ) that hardly matches the true with an adjusted Rand index of 0.37. Interestingly, clustering the two seemingly redundant variables (and ignoring the third) does better, identifying four groups and with . However, this is still a far cry from the perfect partitioning that is obtained when all three coordinates are used in GMMBC with BIC to determine the optimal number of groups. Therefore, it is important to consider the relevant variables in clustering. This fact becomes more important in the light of attempts by many researchers to cluster GRBs using, for instance, only the duration variables. This example also demonstrates that two variables having correlations even as high as 0.98 are not necessarily redundant and should be carefully analysed before arriving at decisions on their inclusion or exclusion in analysis. Raftery Dean ( 2006 ) proposed a method for selecting variables containing the most relevant clustering information by recasting the variable selection problem in terms of model selection where comparison of the models is done via BIC (refer to Chattopadhyay Maitra 2017 for a thorough review on variable selection). Figure 3. Result of clustering (a) data simulated from a three-dimensional five-component GMM , using (b, c, d) two and (e) all three variables. 2.3 Measuring distinctiveness of partitioning through the overlap Chattopadhyay Maitra ( 2017 ) explain that an overlap measure can be used to indicate the extent to which clusters obtained through a method are distinct from one another. We refer to that paper for further details, but note that they adopt Maitra Melnykov ( 2010 )’s definition of the pairwise overlap between two groups as the sum of their misclassification probabilities. To provide a sense of these pairwise overlap measures, we illustrate three two-dimensional examples in Fig. 4 . (For ease of display and understanding, we use two
Paper: 2021ApJ...915L...1D (fulltext)
of the Ramaty ( 1969 ) model fit to the radio spectra. Both spectral indices are listed on Table 2 . As can be seen from these results, the spectral indices of the energy distribution of the radio emitting electrons agree reasonably well with that of the hard X-ray producing electrons, when considering photons with energies higher than 400–700 keV or, conversely, electrons with energies ≥1 MeV. The reconstructed energy spectra of the accelerated electrons can be represented by a broken power law with break energy around 500 keV, and spectral index δ = 4 below this energy and δ = 2 above (see Figure 6 ). The photons detected below energies of ∼500 keV (representing E 2 ) are produced by lower-energy electrons with an energy distribution index of δ ≈ 4, whereas the higher-energy hard X-rays and radio emission (microwaves and millimeter waves) are generated by electrons with energies ≥1 MeV and δ ≈ 2. Previous works, such as Silva et al. ( 2000 ), showed that the spectral indices for the population of accelerated electrons emitting the radio and the X-rays do not match, with the radio index being harder than that of the X-rays by approximately 2 units. The authors explained this discrepancy by the fact that the radio emission is produced by higher-energy electrons than the X-rays, and that the common accelerated electron population has a power-law energy distribution that breaks up above 300 KeV. Since the hard X-rays analyzed in Silva et al. ( 2000 ) had energies lower than ∼200 keV, the results are in agreement with those presented here for the SOL2013-05-13 flare, as well as the two discrepancies in the spectral indices of hard X-rays and radio. Moreover, Vestrand ( 1988 ) and Dennis ( 1988 ) report giant flares observed in hard X-rays with energies larger than 1 MeV that showed a breakup in their photon spectra at ∼300 keV. More recently, from the analysis of an X8.2 solar flare detected in hard X-rays by RHESSI, Ning et al. ( 2019 ) found a broken-up power-law spectra originating from a coronal source. However, the break energy was only about 50 keV and the harder spectral photon index was ∼3. There are basically three possible explanations for electrons to exhibit such a broken-up power-law energy distribution. The first is that there are two components, or sources, of energetic electrons that were produced by two different acceleration mechanisms (Silva et al. 2000 ; Krucker et al. 2008 ). However, to produce a similar temporal evolution in radio and hard X-ray emission, there must be a close link between these two acceleration mechanisms. Alternatively, the second possibility is that the difference in spectral indices is due to physical characteristics of a certain acceleration mechanism and/or a transport process of electrons such as diffusive shock acceleration (Li et al. 2013 ), where a hardening of the spectrum occurs around 500–600 keV, or stochastic acceleration due to wave–particle interactions in the presence of Coulomb collisions
Paper: 2019MNRAS.490.5860S (fulltext)
cube is the removal of the continuum and determination of the stellar kinematics. Because both the MUSE and SINFONI spectral resolutions are only moderate, we only fit for the stellar velocity and velocity dispersion. We also included an additive fourth degree polynomial in the fit to model any emission from the central AGN. To create the continuum-subtracted MUSE cube, we renormalized the best-fitting binned spectra to the median continuum level of each individual spectrum before subtraction. Fig. 4 shows the results of our ppxf fits for the MUSE cube. Figure 4. Results from fitting the MUSE cube with ppxf . The top row plots the stellar velocity ( left ) and dispersion ( right ). The bottom row plots the mean light-weighted stellar population age ( left ) and mean light-weighted metallicity ( right ). In all frames, North is up and East is to the left. 2.6.2 Emission line fitting To measure the bulk distribution and kinematics of the molecular and ionized gas in NGC 5728, we fit single Gaussians to the relevant emission line tracers of the gas. In particular, we fit the CO (2–1) line (ν rest = 230.538 GHz) in the ALMA cube; the H β, [O iii ] doublet, [O i ], [N ii ] doublet, H α, and [S ii ] doublet lines in the MUSE cube; and the [Fe ii ]λ1.64 m, [Si vi ], H 2 (1–0) S(1), and Brγ lines in the SINFONI cube. This set of lines represent the strongest emission lines observed in nearby galaxies and AGNs and provide diagnostic power into the state of gas in a range of phases from cold molecular gas [CO (2–1)] to warm molecular gas [H 2 (1–0) S(1)] to hydrogen recombination lines (H β, H α, Br γ) and forbidden lines ([O iii ], [O i ], [N ii ], [S ii ], [Fe ii ], [Si vi ]) from ionized gas in a range of ionization levels. For each line, we cut out the relevant sections of the continuum-subtracted spectra that contain the single line or multiple lines in the case of the [O iii ] doublet, H α and [N ii ] blended region, and [S ii ] doublet. We fixed the velocity and velocity dispersion to be the same for all doublets and fixed the intensity ratio to the expected theoretical value of 3 for the [O iii ] and [N ii ] doublets. For the fit of the [Si vi ] line, we also included Gaussian components for the nearby H 2 (1–0) S(3) and Br δ lines. As an estimate of the local noise around each line, we measured the root mean square (RMS) of the line-free regions. Using the RMS, we produced 100 simulated spectra for every emission line by randomly perturbing the original spectra assuming a Gaussian distribution with a standard deviation equal to the RMS. Each of the simulated spectra were fit in the same way as the original spectra and uncertainties on the integrated flux, velocity, and velocity dispersion were calculated from the standard deviation of the best-fitting values from the simulated spectra. Finally, all velocity dispersions derived from MUSE and SINFONI data were corrected for instrumental broadening. For lines from MUSE, we assumed an
Paper: 2015MNRAS.453.2830M (acknowledgments)
We would like to thank the anonymous referee for the useful comments and suggestions which helped improve this paper. NPM and FAG would like to thank Monica Valluri for the very interesting discussion and useful comments that help to improve this manuscript. NPM, FAG, PMC and CMG are grateful to Carles Simó for his illuminating discussions, comments and suggestions. We are also grateful to Volker Springel, Simon White and Carlos Frenk for generously allowing us to use the Aquarius simulations. Finally, NPM wants to thank D. D. Carpintero, F. Bareilles and E. Suárez for their assistance with the lp-vicode as well as with the pre-processing of the data. NPM, PMC and CMG were supported with grants from the Consejo Nacional de Investigaciones Científicas y Técnicas (IALP UNLP–CONICET) de la República Argentina (CCT-La Plata) and the Universidad Nacional de La Plata (FCAG UNLP). FAG and BWO acknowledge support from the US NSF Office of Cyberinfrastructure by grant PHY-0941373 and by the Michigan State University Institute for Cyber-Enabled Research (iCER). BWO was supported in part by NSF grants PHY-0822648 and 1430152: Physics Frontiers Center/Joint Institute for Nuclear Astrophysics (JINA). APC is supported by a COFUND/Durham Junior Research Fellowship under EU grant [267209].
Paper: 2017MNRAS.466.4672F (fulltext)
Zr The neutron-capture element Zr is predominately produced by AGB stars and is a first s -process peak element. The s -process contribution to Zr in the Solar system is 80percent (e.g. Simmerer etal. 2004 ; Sneden, Cowan Gallino 2008 ; Bisterzo etal. 2014 ). By determining the abundance of Zr, we expand on the measurements of Y, another first s-process peak element whose abundances were previously obtained by NS11 . Fig. 3 (a) shows [Zr/Fe] as a function of [Fe/H]. The dispersion in [Zr/Fe] is 0.12dex, which is larger than the error of 0.04dex. When considering the two populations, the scatter in [Zr/Fe] is smaller for the low- stars with a dispersion of 0.06dex compared to a dispersion of 0.13dex when combining the high- and TD stars. In addition to the difference in the dispersion of [Zr/Fe], the low- stars have a systematically lower [Zr/Fe] abundance compared to the high- and TD stars. Figure 3. Four s -process elements (Zr, La, Ce and Nd) versus [Fe/H]. The red circles indicate low- stars, the blue open circles indicate high- stars and the plus signs indicate TD stars. The 1 error is shown in the bottom left of each plot. This possible separation in the high- and low- populations is in agreement with the slight separation in abundance measurements of [Y/Fe] for the two populations seen in NS11 . As Zr and Y are both first-peak s -process elements, they are expected to follow the same trend. Fig. 4 demonstrates the difference in the Zr abundance for a low- star (HD 194598) and a high- star (HD 111980) at nearly the same metallicity (see Table 1 ). The low- star has a weaker Zr ii line than the high- star. While the effective temperature differs between these two stars by approximately 150K, all other lines in this region exhibit very similar strengths. Figure 4. MIKE spectra around the Zr line at 4208.98 for a low- star (HD 194598) and a high- star (HD 111980). A comparison between [Y/Fe] as measured by NS11 and [Zr/Fe] is presented in Fig. 5 . The abundance of Zr follows the abundance of Y, which is expected if Y and Zr have a common origin. While there is a systematic offset between [Y/Fe] and [Zr/Fe] that may be due to errors in the gf values of the lines, the important point is that the slope of the data is close to 1. The high- stars have a tighter correlation between [Y/Fe] and [Zr/Fe] compared to the low- stars. Figure 5. Comparison of [Y/Fe] (taken from NS11 ) and [Zr/Fe]. The red circles indicate low- stars, the blue open circles indicate high- stars and the plus signs indicate TD stars. The dashed line shows the one-to-one correspondence line. The 1 error is shown in the top left. 3.2 La, Ce and Nd We extend upon the Ba measurements of NS11 by determining abundances for other elements belonging to the second s -process peak, namely La, Ce and Nd. The s -process contribution to La in the Solar system is 75percent and there are a number of advantages with using La as an s -process indicator compared to Ba (e.g. Simmerer etal.
Paper: 2016ApJ...832..111J (acknowledgments)
We thank the anonymous referee for a thorough report and many constructive comments, which helped us improve the presentation of this work. This work is supported by the 973 program (2015CB857005, 2012CB821804), the NSFC (11522324,11421303), the Strategic Priority Research Program “The Emergence of Cosmological Structures” of the Chinese Academy of Sciences, grant No. XDB09010400 and the Fundamental Research Funds for the Central Universities. H.J.M. would like to acknowledge the support of NSF AST-1517528. The numerical calculations were done on the supercomputing system in the Supercomputing Center of the University of Science and Technology of China.
Paper: 2016A&A...585A.162M (fulltext)
the spectra we used the multi-component approach of Kosenko et al. (2010) , who extensively studied the XMM-Newton and Chandra data. First, one NEI component with LMC abundances accounts for circumstellar medium (CSM) or ISM emission. Then, one NEI component for each (group of) element(s) having detected lines: oxygen, silicon and sulphur, argon and calcium, and iron. In the latter case two NEI components with distinct parameters are used, as the spectrum evidently includes both medium temperature and very hot iron. Due to the low count rate, and therefore statistical weight, of the Fe K blend, the hot iron component was driven to fit lower energy lines instead. To alleviate this issue we fitted the high-energy part of the spectrum separately with this component, then froze the best-fitting parameters in the global fits. Residuals around 0.72 keV (lines of Fe xvii ) were fitted with an additional Gaussian line. 0509<inline-formula specific-use="simple-math">−</inline-formula>67.5 (MCSNR J0509<inline-formula specific-use="simple-math">−</inline-formula>6731): XMM-Newton observed the SNR for ks in 2000, with pn operated in Large Window mode. This dataset is presented in Kosenko et al. (2008) , while Warren Hughes (2004) reported the spectral and imaging analysis of a Chandra observation. Finally, Badenes et al. (2008) attempted to reproduce spectra from both instruments using a grid of hydrodynamical models and an X-ray emission code. Inconsistencies between pn and MOS spectra were found, with lines in the pn spectrum (red-)shifted relative to those in MOS spectra by about 1%. This is likely a gain issue of the pn instrument. We discarded spectra from the MOS instruments, as they were operated in Small Window mode, for which no FWC data are available. To get the spectral model to match the observed energies of atomic lines, we freed the “redshift” parameter available in XSPEC models, which allows an ad hoc change of the energy scale. Satisfying results were obtained for a shift of 1%, which is the measured pn/MOS discrepancy Kosenko et al. (2008) . As for J05196902, lines from heavy elements are prominent, and we used a multi-component model. Si, S, and Ar were grouped in a NEI component, and shared the same temperature and ionisation age. Another NEI component modelled the continuum+lines emission from the CSM/ISM. No Si, S, Ar, or Ca were included in this component. Iron was included in two NEI components, one with a medium temperature (1.4 keV) and a high- one (11 keV) that reproduces the strong Fe K line. The latter component also includes calcium. Even with this model, residuals remained around Fe lines (0.72 keV and 1.22 keV), which we fitted with two Gaussian lines. The very high temperature of the second Fe component is atypical for SNRs, but was also suggested in Chandra data by Warren Hughes (2004) . The SNR exhibits a high-energy continuum tail, which previous studies tried to reproduce with non-thermal models. This tail can also
Paper: 2021MNRAS.500.2908W (fulltext)
λλ1907, 1909, C iv 1548, 1551, and He ii λ1640. They find seven objects with [C iii ], C iii ] λλ1907, 1909 emission, some of them reaching extremely high equivalent width values (). Finally, they find that C iv 1548, 1551 and He ii λ1640 have equivalent widths below 2 Å and conclude that the detection of C iv 1548, 1551 atÅ may require extremely metal poor stars and gas. In SBS 0335−052E, which is more metal-poor that the previous sample, C iv 1548, 1551 shows a weak P-Cygni profile and nebular components. The EWs of the nebular [C iii ], C iii ] λλ1907, 1909 and C iv doublet emissions are 5 and 1.7 Å, respectively. The largest C iv 1548, 1551 EW which has been reported for a nearby starburst galaxy is Å. This is for galaxy J104457, which has 12+log(O/H) = 7.45 (Berg et al. 2019b ) and is at a very similar redshift (0.013) compared to SBS 0335−052E. Senchyna Stark ( 2019 ) present HST COS UV observations of six star-forming XMPs with 12+log(O/H) between 7.4 and 7.7, i.e. lower than in the work of Senchyna et al. ( 2017 ). They find stellar C iv 1548, 1551 in five of the galaxies and high [C iii ]+C iii ] equivalent widths (Å) in two objects. The highest EWs of C iv 1548, 1551 and He ii which they find are 4.4 and 1.7 Å, respectively. For SBS 0335−052E we find EWs of 1.7 Å for C iv 1548, 1551 and He ii λ1640. Although nebular C iv 1548, 1551 emission is present in some very high sSFR nearby systems, nearby systems do not reach the equivalent widths of C iv which are observed in individual lensed systems at z >6. Undisputed detections of nebular emission in He ii in predominantly star-forming systems at z >6 remain elusive (Senchyna Stark 2019 ). Objects located at z = 2-4 with high values of EWs(C iii ]), i.e. ≥10 Å, are analysed in Nakajima et al. ( 2018 ), who find that EWs(C iii ])>20 Å can only be modelled with a combination of photoionization by stars and an AGN. 6.2 C/O Berg et al. ( 2016 ) and Berg et al. ( 2019a ) studied the C/O ratio derived via UV collisionally excited emission lines of a sample of 12 low metallicity galaxies with significant detections of carbon and oxygen lines. At low metallicity, (12+log(O/H)8.0), they find that no clear trend is evident in C/O versus O/H. We find log(C/O) ∼ −0.45 via tests 2 and 3, and 12+log(O/H) = 7.48 / 7.42 via tests 4/5, respectively. This is very similar to what Berg et al. ( 2016 ) report for galaxy J120122, which is log(C/O) ∼ −0.45 and 12+log(O/H) = 7.45. 6.3 JWST observation As a simple exercise, we placed SBS 0335−052E at redshift z = 10, and used the COS G130M + G160M + G185M spectrum to simulate an observation of this galaxy with the near-infrared spectrograph (NIRSpec) onboard JWST . For this purpose, the spectrum’s flux was diluted using the luminosity distance for cosmological parameters H 0 = 69.6, Ω M = 0.286, and Ω vac = 0.714. In addition, we accounted for IGM absorption of UV photons using Madau ( 1995 ) attenuation. We degraded the spectrum to match NIRSpec’s prism