Search arXivSearch

arXiv · 2405.00481

Main sequence of star formation and colour bimodality considering galaxy environment

Abstract

This study involves the use of friend-of-friend method on the volume limited samples constructed from the Sloan Digital Sky Survey Data Release 12 (SDSS DR12) to classify the galaxies into isolated and non-isolated environments hence to investigate the influence of the galaxy environment on the main sequence of star formation, and colour bimodality. We classified the galaxies into the luminous volume-limited sample with $ -22.5\leq M_r \leq -20.5$ (mag), and the faint volume-limited sample with $-20.5 \leq M_r \leq -18.5$ (mag). Using the WHAN diagnostic diagram we assignedthe samples into star-forming, strong AGN, weak AGN, and retired galaxies based on their environment (isolated and non-isolated). The friend-of-friend method was successful in producing consistent results regarding the stellar mass-SFR and stellar mass-colour known relations. Apart from that the decrease in the slope of the main sequence for star-forming galaxies by $0.04$ dex and intercept by $0.39$ dex for the luminous sample was observed while the faint sample a decrease of $0.08$ dex in slope and $0.74$ dex in intercept was observed between isolated and non-isolated galaxies. A significant difference on the number of galaxies between isolated and non-isolated galaxies within, above and below the main sequence by $7.47\%$, $28.51\%$, $14.59\%$ for the luminous sample while for the faint sample by $16.15\%$, $32.60\%$, $35.23\%$ on average, respectively are observed. A significant difference in the number of galaxies in the blue cloud, green valley, and red sequence by $10.30\%$, $20.61\%$, $5.74\%$ for luminous sample while for faint sample by $28.46\%$, $41.36\%$, $8.95\%$ on average, respectively was observed. The study concludes that the galaxy environment influences the shaping and positioning of galaxies along the star formation main sequence and colour bimodality.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pius Privatus, Umananda Dev Goswami. 2025-01-11. Main sequence of star formation and colour bimodality considering galaxy environment. https://doi.org/10.1016/j.dark.2024.101802

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