Recovering stellar population parameters via different population models and stellar libraries

Three basic ingredients are required to generate a simple stellar population (SSP) library, i.e., an initial mass function (IMF), a stellar evolution model/isochrones, and an empirical/theoretical stellar spectral library. However, there are still some uncertainties to the determination and understanding of these ingredients. We perform the spectral fitting to test the relative parameter offsets between these uncertainties using two different stellar population models, two different empirical stellar libraries, two different isochrones, and the Salpeter and Chabrier IMFs. Based on these setups, we select five SSP libraries generated with the Galaxev/STELIB and Vazdekis/MILES models, and apply them to the pPXF full-spectrum fitting of both MaNGA and mock spectra. We find that: 1) Compared to the Galaxev/STELIB model, spectral fitting qualities with the Vazdekis/MILES model have significant improvements for those metal-rich (especially over-solar) spectra, which cause better reduced $\chi^2$ distributions and more precisely fitted absorption lines. This might due to the lack of metal rich stars in the empirical STELIB library, or code improvement of the Vazdekis model. 2) When applying the Vazdekis/MILES model for spectral fitting, the IMF variation will lead to not only a systematic offset in $M_*/L_r$, but also offsets in age and metallicity, and these offsets increase with increasing stellar population ages. However, the IMF-variation caused metallicity offsets disappear in the case of Galaxev/STELIB based libraries. 3) The Padova2000 model provides a better match to the MaNGA galaxy spectra at [M/H]$_L<-1.0$, while the BaSTI model match the local galaxy spectra better at [M/H]$_L>-1.0$. Current tests suggest that spectral fitting with the Vazdekis/MILES+BaSTI combination would be a better choice for local galaxies.Comment: 19 pages, 17 figures, accepted for publication in MNRA

Recovering stellar population parameters via two full-spectrum fitting algorithms in the absence of model uncertainties

Using mock spectra based on Vazdekis/MILES library fitted within the wavelength region 3600-7350\AA, we analyze the bias and scatter on the resulting physical parameters induced by the choice of fitting algorithms and observational uncertainties, but avoid effects of those model uncertainties. We consider two full-spectrum fitting codes: pPXF and STARLIGHT, in fitting for stellar population age, metallicity, mass-to-light ratio, and dust extinction. With pPXF we find that both the bias in the population parameters and the scatter in the recovered logarithmic values follows the expected trend. The bias increases for younger ages and systematically makes recovered ages older, $M_*/L_r$ larger and metallicities lower than the true values. For reference, at S/N=30, and for the worst case ($t=10^8$yr), the bias is 0.06 dex in $M_*/L_r$, 0.03 dex in both age and [M/H]. There is no significant dependence on either E(B-V) or the shape of the error spectrum. Moreover, the results are consistent for both our 1-SSP and 2-SSP tests. With the STARLIGHT algorithm, we find trends similar to pPXF, when the input E(B-V)<0.2 mag. However, with larger input E(B-V), the biases of the output parameter do not converge to zero even at the highest S/N and are strongly affected by the shape of the error spectra. This effect is particularly dramatic for youngest age, for which all population parameters can be strongly different from the input values, with significantly underestimated dust extinction and [M/H], and larger ages and $M_*/L_r$. Results degrade when moving from our 1-SSP to the 2-SSP tests. The STARLIGHT convergence to the true values can be improved by increasing Markov Chains and annealing loops to the "slow mode". For the same input spectrum, pPXF is about two order of magnitudes faster than STARLIGHT's "default mode" and about three order of magnitude faster than STARLIGHT's "slow mode".Comment: Accepted for publication in MNRAS. 17 pages, 17 figure

A Time Efficient Approach for Decision-Making Style Recognition in Lane-Change Behavior

Fast recognizing driver's decision-making style of changing lanes plays a pivotal role in safety-oriented and personalized vehicle control system design. This paper presents a time-efficient recognition method by integrating k-means clustering (k-MC) with K-nearest neighbor (KNN), called kMC-KNN. The mathematical morphology is implemented to automatically label the decision-making data into three styles (moderate, vague, and aggressive), while the integration of kMC and KNN helps to improve the recognition speed and accuracy. Our developed mathematical morphology-based clustering algorithm is then validated by comparing to agglomerative hierarchical clustering. Experimental results demonstrate that the developed kMC-KNN method, in comparison to the traditional KNN, can shorten the recognition time by over 72.67% with recognition accuracy of 90%-98%. In addition, our developed kMC-KNN method also outperforms the support vector machine (SVM) in recognition accuracy and stability. The developed time-efficient recognition approach would have great application potential to the in-vehicle embedded solutions with restricted design specifications

Broad-line region configuration of the supermassive binary black hole candidate PG1302-102 in the relativistic Doppler boosting scenario

PG1302-102 is thought to be a supermassive binary black hole (BBH) system according to the periodical variations of its optical and UV photometry, which may be interpreted as being due to the relativistic Doppler boosting of the emission mainly from the disk around the secondary black hole (BH) modulated by its orbital motion. In this paper, we investigate several broad emission lines of PG1302-102 using archived UV spectra obtained by IUE, GALEX, and Hubble, to reveal the broad-line region (BLR) emission properties of this BBH system under the Doppler boosting scenario. We find that the broad lines Ly$\alpha$, NV, CIV, and CIII] all show Gaussian profiles, and none of these lines exhibits obvious periodical variation. Adopting a simple model for the BLR, we perform Markov chain Monte Carlo fittings to these broad lines, and find that the BLR must be viewed at an orientation angle of $\sim33^{\circ}$, close to face-on. If the Doppler boosting interpretation is correct, then the BLR is misaligned with the BBH orbital plane by an angle of $\sim51^\circ$, which suggests that the Doppler boosted continuum variation has little effect on the broad-line emission and thus does not lead to periodical line variation. We further discuss the possible implications for such a BLR configuration with respect to the BBH orbital plane.Comment: 9 pages, 6 figures, matches A&A version (only minor changes