arXiv subjects

Anton Timur Jaelani

Publications and source records attributed to Anton Timur Jaelani.

5 recordsLinked to original sources

arXiv2026

Interplay of Compaction, Quenching, and Black Hole Growth in the Most Massive Galaxies since $z\sim5$: Insights from JWST and Chandra Data

The buildup of dense stellar cores is expected to mark an important transition in the star-formation and black-hole growth of massive galaxies. Using spatially resolved spectral energy distribution (SED) fitting of James Webb Space Telescope near-infrared imaging, combined with stacking analysis of Chandra X-ray data, we trace stellar mass buildup and average black hole accretion in the most massive galaxies at $z<5$, selecting 50 most massive galaxies per redshift bin at constant number density of $\sim4.4\times10^{-5}$ cMpc$^{-3}$. To robustly constrain central stellar populations, we separate active galactic nuclei (AGN) components affecting the photometry using multi-band morphological decomposition and SED analysis. We find that the sample selected with constant number density exhibits evolutionary trend of rapid central compaction at $z\sim4$, during which the median central 1 kpc stellar mass increases by $\sim0.60$ dex over $\sim400$ Myr. The majority of X-ray detected AGN ($63\%\pm12\%$) are hosted by galaxies undergoing the compaction, while we find neither individually detected X-ray sources nor a significant stacked X-ray signal at $z>4$, indicating that substantial average black-hole growth emerges primarily during, rather than before, the compaction. Following the compaction, central specific star formation rates (sSFR) decline by $\sim1.24$ dex over $\sim700$ Myr at $z\sim3$ while remaining elevated galaxy-wide, signaling the onset of inside-out quenching. Despite this central suppression, specific black hole accretion rate remains coupled to the total sSFR. Our results suggest that dense-core formation in the most massive galaxies marks the onset of inside-out quenching and a transition toward enhanced black-hole to stellar growth ratio.

astro-ph.GA↗

arXiv2025

Stellar Mass Assembly History of Massive Quiescent Galaxies since $z\sim4$: Insights from Spatially Resolved SED Fitting with JWST Data

Massive quiescent galaxies at high redshift show significantly more compact morphology than their local counterparts. To examine their internal structure across a wide redshift range and investigate potential redshift dependence, we performed spatially resolved SED fitting using pixedfit software on massive $(\log(M_*/M_\odot)\sim11)$ quiescent galaxies at $0 4$ kpc), while the central regions ($r \sim 1$ kpc) remain largely unchanged, with stellar mass surface density similar to local quiescent galaxies. The estimated star formation rates are too low to explain the stellar mass growth, indicating an additional stellar mass accumulation process, such as mergers, is necessary. We parameterize the size-mass relation of the most massive galaxies in our sample as $\log(R_{e,mass}) \propto α\log(M_*)$, and find $α= 2.67^{+1.14}_{-1.17}$ for $z\lessapprox2$, consistent with growth dominated by minor mergers, and $α= 0.91^{+0.20}_{-0.16}$ for $z\gtrapprox2$, consistent with growth dominated by major mergers. These results indicate that massive quiescent galaxies originate from compact quenched systems and grow through combinations of minor and major mergers.

astro-ph.GA↗

arXiv2023

Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks

Quasars experiencing strong lensing offer unique viewpoints on subjects related to the cosmic expansion rate, the dark matter profile within the foreground deflectors, and the quasar host galaxies. Unfortunately, identifying them in astronomical images is challenging since they are overwhelmed by the abundance of non-lenses. To address this, we have developed a novel approach by ensembling cutting-edge convolutional networks (CNNs) -- for instance, ResNet, Inception, NASNet, MobileNet, EfficientNet, and RegNet -- along with vision transformers (ViTs) trained on realistic galaxy-quasar lens simulations based on the Hyper Suprime-Cam (HSC) multiband images. While the individual model exhibits remarkable performance when evaluated against the test dataset, achieving an area under the receiver operating characteristic curve of $>$97.3% and a median false positive rate of 3.6%, it struggles to generalize in real data, indicated by numerous spurious sources picked by each classifier. A significant improvement is achieved by averaging these CNNs and ViTs, resulting in the impurities being downsized by factors up to 50. Subsequently, combining the HSC images with the UKIRT, VISTA, and unWISE data, we retrieve approximately 60 million sources as parent samples and reduce this to 892,609 after employing a photometry preselection to discover $z>1.5$ lensed quasars with Einstein radii of $θ_\mathrm{E}<5$ arcsec. Afterward, the ensemble classifier indicates 3080 sources with a high probability of being lenses, for which we visually inspect, yielding 210 prevailing candidates awaiting spectroscopic confirmation. These outcomes suggest that automated deep learning pipelines hold great potential in effectively detecting strong lenses in vast datasets with minimal manual visual inspection involved.

astro-ph.GA↗

arXiv2023

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data

Over the last two decades, around 300 quasars have been discovered at $z\gtrsim6$, yet only one has identified as being strongly gravitationally lensed. We explore a new approach -- enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion -- which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer.Our search method consists of two main parts: (i) the preselection of the candidates based on their spectral energy distributions (SEDs) using catalog-level photometry and (ii) relative probabilities calculation of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy-quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of $θ_\mathrm{E} \leq 1$ arcsec. Visual inspection is then performed for sources with CNN scores of $P_\mathrm{lens} > 0.1$, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs that can overcome the veto limitations of primarily dropout-based SED selection approaches.

astro-ph.GA↗

arXiv2018

Statistical Improvement in Detection Level of Gravitational Microlensing Events from their Light Curves

In Astronomy, the brightness of a source is typically expressed in terms of magnitude. Conventionally, the magnitude is defined by the logarithm of the received flux. This relationship is known as the Pogson formula. For received flux with a small signal to noise ratio (S/N), however, the formula gives a large magnitude error. We investigate whether the use of Inverse Hyperbolic Sine function (after this referred to as the Asinh magnitude) in the modified formulae could allow for an alternative calculation of magnitudes for small S/N flux, and whether the new approach is better for representing the brightness of that region. We study the possibility of increasing the detection level of gravitational microlensing using 40 selected microlensing light curves from 2013 and 2014 season and by using the Asinh magnitude. The photometric data of the selected events is obtained from the Observational Gravitational Lensing Experiment (OGLE). We found that the utilization of the Asinh magnitude makes the events brighter compared to using the logarithmic magnitude, with an average of about $3.42 \times10^{-2}$ magnitude and the average of the difference of error between the logarithmic and the Asinh magnitude is about $2.21 \times10^{-2}$ magnitude. The microlensing events, OB 140847 and OB 140885 are found to have the largest difference values among the selected events. Using a Gaussian fit to find the peak for OB140847 and OB140885, we conclude statistically that the Asinh magnitude gives better mean squared values of the regression and narrower residual histograms than the Pogson magnitude. Based on these results, we also attempt to propose a limit of magnitude value from which the use of the Asinh magnitude is optimal for small S/N data.

astro-ph.IM↗