Search arXivSearch

arXiv · 2110.10190

Modelling simple stellar populations in the near-ultraviolet to near-infrared with the X-shooter Spectral Library (XSL)

Abstract

We present simple stellar population models based on the empirical X-shooter Spectral Library (XSL) from NUV to NIR wavelengths. The unmatched characteristics of relatively high resolution and extended wavelength coverage ($350-2480$ nm, $R\sim10\,000$) of the XSL population models bring us closer to bridging optical and NIR studies of intermediate and old stellar populations. It is now common to find good agreement between observed and predicted NUV and optical properties of stellar clusters due to our good understanding of the main-sequence and early giant phases of stars. However, NIR spectra of intermediate-age and old stellar populations are sensitive to cool K and M giants. The asymptotic giant branch, especially the thermally pulsing asymptotic giant branch, shapes the NIR spectra of $0.5-2$ Gyr old stellar populations; the tip of the red giant branch defines the NIR spectra of populations with ages larger than that. We construct sequences of the average spectra of static giants, variable-rich giants, and C-rich giants to include in the models separately. The models span the metallicity range $-2.2<[Fe/H]<+0.2$ and ages above 50 Myr, a broader range in the NIR than in other models based on empirical spectral libraries. Our models can reproduce the integrated optical colours of the Coma cluster galaxies at the same level as other semi-empirical models found in the literature. In the NIR, there are notable differences between the colours of the models and Coma cluster galaxies. The XSL models expand the range of predicted values of NIR indices compared to other models based on empirical libraries. Our models make it possible to perform in-depth studies of colours and spectral features consistently throughout the optical and the NIR range to clarify the role of evolved cool stars in stellar populations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kristiina Verro, S. C. Trager, R. F. Peletier, A. Lançon, A. Arentsen, Y. -P. Chen, P. R. T. Coelho, M. Dries, J. Falcón-Barroso, A. Gonneau, M. Lyubenova, L. Martins, P. Prugniel, P. Sánchez-Blázquez, A. Vazdekis. 2022-02-17. Modelling simple stellar populations in the near-ultraviolet to near-infrared with the X-shooter Spectral Library (XSL). https://doi.org/10.1051/0004-6361%2F202142387

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Spatially Resolved Physical Properties of Young Star Clusters and Star-forming Clumps in the Brightest z>6 Galaxy, the Strongly Lensed Cosmic Spear at z=6.2

We present spatially resolved analysis of stellar populations in the brightest $z>6$ galaxy known to date (AB mag 23), the strongly lensed MACS0308$-$zD1 (dubbed the ``Cosmic Spear'') at $z_{\rm spec}=6.2$. New JWST NIRCam imaging and high-resolution NIRSpec IFU spectroscopy span the rest-frame ultraviolet to optical. The NIRCam imaging reveals bright star-forming clumps and a tail consisting of three distinct, extremely compact star clusters that are multiply-imaged by gravitational lensing. The star clusters have delensed effective radii of $R_{\rm{eff}} \lesssim 8$ pc, stellar masses of $M_{*} \sim 10^{6}-10^{7}\,M_{\odot}$, and high stellar mass surface densities of $Σ_{*} \gtrsim 2\times 10^{4}\,M_{\odot}~\rm{pc}^{-2}$. While their stellar populations are very young ($\sim 6-11$ Myr), their dynamical ages exceed unity, consistent with the clusters being gravitationally bound systems. Placing the star clusters in the size vs.~stellar mass density plane, we find they occupy a region similar to other high-redshift star clusters within galaxies observed recently with JWST, being significantly more massive and denser than local star clusters. Spatially resolved analysis of the brightest clump reveals a compact, intensely star-forming core. The ionizing photon production efficiency ($ξ_{\rm{ion}}$) is slightly suppressed in this central region, potentially indicating a locally elevated Lyman continuum escape fraction facilitated by feedback-driven channels.

astro-ph.GA

Predicting Supermassive Black Hole-Host Mass Offsets from Broadband Photometry Across Cosmological Simulations with Forecasts for LSST

The possibility of over-massive black holes suggested by James Webb Space Telescope photometric discoveries of 'little red dots', may disfavor light supermassive black hole (SMBH) seeds. However, what should constitute the mass (range) of 'heavy' seeds remains relatively unconstrained. Moreover, Vera C Rubin Observatory's Legacy Survey of Space and Time will photometrically characterize galaxies without direct black hole mass measurements. We forward-model the SIMBA, IllustrisTNG, and EAGLE cosmological simulations into the photometric bands of LSST to train an ensemble machine learning classifier. Our framework achieves $91\%$--$94\%$ accuracy across SIMBA and IllustrisTNG in distinguishing between over-massive and under-massive SMBH growth regimes under LSST magnitude limits, using only broadband photometry. Furthermore, cross-simulation transfer experiments (training on one cosmological simulation and evaluating on another using rank-normalized features) achieve $83\%$--$89\%$ accuracy. This suggests the relative photometric ordering of growth regimes is largely preserved even across fundamentally different sub-grid SMBH feedback prescriptions. Signal decomposition shows our classification is driven by host galaxy colors ($82\%$--$87\%$ accuracy) and, relatedly, the accretion-state's spectral energy distribution shape as opposed to an inversion of our forward model's analytical luminosity prescription. Given that the evaluated simulations employ heavy seed prescriptions ($\geq 10^{4}~M_\odot$), our methodology establishes a validated baseline for classifying post-seeding growth regimes.

astro-ph.GA

Cross Subtype Transferability of Machine Learning Photometric Redshift Relations in Low Redshift Seyfert AGN

Photometric redshift estimation for active galactic nuclei (AGN) is complicated by the combined effects of host-galaxy light, nuclear emission, dust attenuation, and broadband spectral diversity. We investigate whether machine learning photo-z relations trained on one low-redshift Seyfert subtype remain valid when transferred to another, and whether probabilistic subtype classification can be used to identify sources for which a specialised regressor is reliable. Using spectroscopically selected Seyfert I and Seyfert II samples from SDSS, matched to AllWISE photometry over 0 < z_spec <= 0.6, we constructed a common 45-feature representation from SDSS ugriz and WISE W1-W4 data. Random Forest and XGBoost regressors were evaluated within each subtype, followed by controlled cross-subtype transfer tests, redshift and sample size-matched experiments, feature ablations, and an independent classifier-gated regression test. The subtype specific models achieved strong within-sample performance, with the Seyfert II model reaching R2 = 0.965 and sigma_NMAD = 0.0169. However, transfer between Seyfert I and Seyfert II produced a clear and asymmetric degradation in accuracy that persisted after matching the samples and restricting the photometric inputs. A probabilistic Seyfert classifier further identified subsets for which the Seyfert II regressor was more reliable, while extrapolation beyond the redshift range represented in training produced systematic underestimation. These results demonstrate that AGN photo-z performance depends strongly on the population and redshift domain represented in the training data, supporting subtype-aware calibration and applicability-based source selection.

astro-ph.GA