arXiv subjects

Atousa Kalantari

Publications and source records attributed to Atousa Kalantari.

6 recordsLinked to original sources

arXiv2026

Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves

Microlensing can reveal populations of faint compact objects that are otherwise difficult to detect. Depending on their design, all-sky surveys have the potential to search for these objects across the sky. The Transiting Exoplanet Survey Satellite (TESS), primarily designed to detect transiting exoplanets, also provides near all-sky coverage with high cadence. In this work, we use TESS data to search for microlensing candidates using both traditional and machine-learning methods and to identify associated false positives in high-cadence surveys. Microlensify is a physics-informed, transformer-based variational autoencoder trained on simulated single-lens microlensing light curves and real TESS Sector 12 data. The model classifies events, reconstructs light curves, and estimates microlensing event durations. Applied to $\sim 5.6$ million TESS light curves, it identified between $0.036\%$ and $1.89\%$ as microlensing candidates across different TESS pipelines. After applying microlensing detection metrics and cross-matching with SIMBAD, we obtained a final list of candidates and identified false positives including long-period variables, Mira variables, cataclysmic variables, red giants, and transients. We also found Gaussian-like peaks caused by asteroid crossings, a potential source of false positives in high-cadence microlensing surveys. The model also predicts event duration with an accuracy of $R^2 = 0.97$. The model was further tested on published events from different ground-based microlensing surveys, confirming 92.7% as microlensing, demonstrating its applicability across surveys with different cadences.

astro-ph.IM↗

arXiv2025

Physical properties of galaxies and the UV Luminosity Function from $z\sim6$ to $z\sim14$ in COSMOS-Web

We present measurements of the rest-frame ultraviolet luminosity function (UVLF) in three redshift bins over $z\sim5.5$-14 from the JWST COSMOS-Web survey. Our samples, selected using the dropout technique in the HST/ACS F814W, JWST/NIRCam F115W, and F150W filters, contain a total of 3099 galaxies spanning a wide luminosity range from faint ($M_{\rm UV}\sim-19$ mag) to bright ($M_{\rm UV}\sim-22.5$ mag). The galaxies are undergoing rapid star formation, with blue stellar populations. Surprisingly, their median UV spectral slope $\beta$ does not evolve at $z>8$, suggesting minimal dust, or physical separation of dust and star formation at early epochs. The measured UVLF exhibits an excess at the bright-end ($M_{\rm UV}<-21$ mag) compared to pre-JWST empirical results and theoretical predictions of an evolving Schechter function, with the excess beginning at $z\sim9$ and becoming increasingly prominent toward $z\sim12$. Our analysis suggests that reproducing the observed abundance of UV-bright galaxies at high redshift requires a combination of physical processes, including elevated star formation efficiencies, moderate levels of stochasticity in galaxy luminosities, and minimal dust attenuation.

astro-ph.GA↗

arXiv2025

Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies

Unveiling the evolutionary history of galaxies necessitates a precise understanding of their physical properties. Traditionally, astronomers achieve this through spectral energy distribution (SED) fitting. However, this approach can be computationally intensive and time-consuming, particularly for large datasets. This study investigates the viability of machine learning (ML) algorithms as an alternative to traditional SED-fitting for estimating stellar masses in galaxies. We compare a diverse range of unsupervised and supervised learning approaches including prominent algorithms such as K-means, HDBSCAN, Parametric t-Distributed Stochastic Neighbor Embedding (Pt-SNE), Principal Component Analysis (PCA), Random Forest, and Self-Organizing Maps (SOM) against the well-established LePhare code, which performs SED-fitting as a benchmark. We train various ML algorithms using simple model SEDs in photometric space, generated with the BC03 code. These trained algorithms are then employed to estimate the stellar masses of galaxies within a subset of the COSMOS survey dataset. The performance of these ML methods is subsequently evaluated and compared with the results obtained from LePhare, focusing on both accuracy and execution time. Our evaluation reveals that ML algorithms can achieve comparable accuracy to LePhare while offering significant speed advantages (1,000 to 100,000 times faster). K-means and HDBSCAN emerge as top performers among our selected ML algorithms. Supervised learning algorithms like Random Forest and manifold learning techniques such as Pt-SNE and SOM also show promising results. These findings suggest that ML algorithms hold significant promise as a viable alternative to traditional SED-fitting methods for estimating the stellar masses of galaxies.

astro-ph.GA↗

arXiv2025

COSMOS-Web: Estimating Physical Parameters of Galaxies Using Self-Organizing Maps

The COSMOS-Web survey, with its unparalleled combination of multiband data, notably, near-infrared imaging from JWST's NIRCam (F115W, F150W, F277W, and F444W), provides a transformative dataset down to $\sim28$ mag (F444W) for studying galaxy evolution. In this work, we employ Self-Organizing Maps (SOMs), an unsupervised machine learning method, to estimate key physical parameters of galaxies -- redshift, stellar mass, star formation rate (SFR), specific SFR (sSFR), and age -- directly from photometric data out to $z=3.5$. SOMs efficiently project high-dimensional galaxy color information onto 2D maps, showing how physical properties vary among galaxies with similar spectral energy distributions. We first validate our approach using mock galaxy catalogs from the HORIZON-AGN simulation, where the SOM accurately recovers the true parameters, demonstrating its robustness. Applying the method to COSMOS-Web observations, we find that the SOM delivers robust estimates despite the increased complexity of real galaxy populations. Performance metrics ($\sigma_{\mathrm{NMAD}}$ typically between $0.1$--$0.3$, and Pearson correlation between $0.7$ and $0.9$) confirm the precision of the method, with $\sim$ $70\%$ of predictions within 1$\sigma$ dex of reference values. Although redshift estimation in COSMOS-Web remains challenging (median $\sigma_{\mathrm{NMAD}} = 0.04$), the overall success of the highlights its potential as a powerful and interpretable tool for galaxy parameter estimation. A key advance of this work is the use of JWST/NIRCam photometry, particularly the F444W band, which enhances SOM training and allows more accurate estimation of stellar mass, SFR, and age compared to previous studies using IRAC/Spitzer filters.

astro-ph.GA↗

arXiv2025

COSMOS Web: Morphological quenching and size-mass evolution of brightest group galaxies from z = 3.7

We present a comprehensive study of the structural evolution of Brightest Group Galaxies (BGGs) from redshift $z \simeq 0.08$ to $z = 3.7$ using the \textit{James Webb Space Telescope}'s 255h COSMOS-Web program. This survey provides deep NIRCam imaging in four filters (F115W, F150W, F277W, F444W) across $\sim 0.54~\mathrm{deg}^2$ and MIRI coverage in $\sim 0.2~\mathrm{deg}^2$ of the COSMOS field. High-resolution NIRCam imaging enables robust size and morphological measurements, while multiwavelength photometry yields stellar masses, SFRs, and S\'ersic parameters. We classify BGGs as star-forming and quiescent using both rest-frame NUV--$r$--$J$ colors and a redshift-dependent specific star formation rate (sSFR) threshold. Our analysis reveals: (1) quiescent BGGs are systematically more compact than their star-forming counterparts and exhibit steeper size--mass slopes; (2) effective radii evolve as $R_e \propto (1+z)^{-\alpha}$, with $\alpha = 1.11 \pm 0.07$ (star-forming) and $1.40 \pm 0.09$ (quiescent); (3) star formation surface density ($\Sigma_{\mathrm{SFR}}$) increases with redshift and shows stronger evolution for massive BGGs ($\log_{10}(M_\ast/M_\odot) \geq 10.75$); (4) in the $\Sigma_*$--sSFR plane, a structural transition marks the quenching process, with bulge-dominated systems comprising over 80\% of the quiescent population. These results highlight the co-evolution of structure and star formation in BGGs, shaped by both internal and environmental processes, and establish BGGs as critical laboratories for studying the baryonic assembly and morphological transformation of central galaxies in group-scale halos.

astro-ph.GA↗

arXiv2024

Simulating Gravitational Microlensing Events by TESS: Predictions on Statistics and Properties

We study the statistics and properties of microlensing events that can be detected by the Transiting Exoplanet Survey Satellite(TESS) based on Monte Carlo simulations. We simulate potential microlensing events from a sample of the TESS Candidate Target List(CTL) stars by assuming different observational time spans(or different numbers of sectors for each star) and a wide range of lens masses, i.e., $M_{\rm l}\in [0.1M_{\oplus},~2 M_{\odot}]$. On average, the microlensing optical depth and the event rate for CTL stars are $\simeq 0.2\times 10^{-9}$, and $\Gamma_{\rm{TESS}}\simeq0.6\times10^{-9}$ per star per day, respectively. The microlensing optical depth decreases by increasing the CTL priority, whereas the efficiency for detecting their microlensing signals enhances with the priority. Additionally, we simulate the microlensing events from the TESS Full-Frame Images(FFIs) stars extracted from the \texttt{TESS}-\texttt{SPOC} pipeline. The optical depth and event rate for these stars are on average $\simeq 1$-$3\times 10^{-9}$, and $\Gamma_{\rm{TESS}}\simeq 1$-$4\times 10^{-9}$ per star per day, and their highest values occur for sector $12$. The total number of microlensing events for the CTL stars is $N_{\rm e, \rm{tot}}\sim0.03$, whereas for the FFIs' stars number of events per star during $27.4$-day observing windows is $\hat{N}_{\rm e, \rm{tot}}\simeq1.4 \times 10^{-6}$. Based on four criteria we extract the detectable microlensing events and evaluate the detection efficiencies. The highest efficiency for detecting microlensing events from the TESS data occurs for the lens mass $\log_{10}[M_{\rm l}(M_{\odot})] \in [-4.5$,~$-2.5]$, i.e., super-Earth to Jupiter-mass Free Floating Planets(FFPs). The detectable microlensing events from the TESS stars are significantly affected by both finite-source and parallax effects.

astro-ph.EP↗