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Sogol Sanjaripour

Publications and source records attributed to Sogol Sanjaripour.

8 recordsLinked to original sources
arXiv2026

COSMOS-Web: does halo mass alone shape the clustering of star-forming and quiescent galaxies?

While stellar mass correlates strongly with halo mass, it remains unclear whether halo mass alone governs galaxy star-formation activity, or whether secondary halo properties and environment also play a role. We investigate these effects beyond halo mass by measuring the auto- and cross-correlations of star-forming and quiescent galaxies in the COSMOS-Web survey from $z = 5$ to the present day. To isolate environmental contributions, we introduce a method that matches the halo mass distributions of both populations using the UniverseMachine model. We find that quiescent galaxies remain more strongly clustered than star-forming systems by at least $0.5-1$ dex at all redshifts, even after controlling for halo mass. At $z \le 2$, this excess clustering increases towards lower stellar masses, with the most clustered objects being $\log(M_\star/{\rm M}_\odot) \le 9.5$ quiescent galaxies. This points to environmental quenching significantly affecting low-mass galaxies at $z \le 2$, likely driven by ram-pressure stripping or the suppression of cold gas accretion, as these objects show disky morphologies. Cross-correlations further reveal one-halo conformity up to $z \simeq 2$: low-mass (or satellite) quiescent galaxies are more strongly clustered around massive (or central) quiescent galaxies than around star-forming centrals of the same halo mass. This signal may arise from quenching mechanisms affecting both centrals and satellites, correlated assembly histories prior to infall, or dependencies on secondary halo properties. Both environmental quenching and conformity appear to vanish between $z \simeq 5$ and $2$. Together, these results challenge the common assumption that clustering and star-formation activity depend solely on halo mass.

astro-ph.GA↗
arXiv2026

The Galaxy's Guide to the Tokenizer: A Benchmark for Scientific Foundation Models

Tokenization is central to adapting scientific data for transformer-based foundation models, yet its impact on learned representations remains poorly understood. We compare four tokenization strategies, Affine, AIM, JetFormer, and VQ-VAE, within a unified transformer framework for astronomical imaging. Using 640,000 galaxy images from the DESI Legacy Survey and a shared AstroPT backbone, we evaluate each method on reconstruction fidelity and prediction of physical properties. Our results reveal trade-offs across approaches. The flow-based JetFormer achieves higher reconstruction quality, while VQ-VAE yields strong probe performance for galaxy physical properties. Affine and AIM better preserve localized morphological information. We find that reconstruction and representation quality are decoupled, and no single method consistently performs best across the tasks considered here. By grounding our evaluation in independently measured physical quantities, we hope this study serves to highlight the potential of scientific data as a basis for constructing interpretable benchmarks for foundation models.

astro-ph.IM↗
arXiv2026

Expanding the High-z Supernova Frontier: "Wide-Area" JWST Discoveries from the First Two Years of COSMOS-Web

Transient astronomy in the early Universe (z > 2) remains largely unexplored, lying beyond the rest-frame optical spectroscopic reach of most current observatories. Yet this regime promises transformative insights, with high-redshift transients providing direct access to the early Universe and enabling studies of how stellar populations and cosmology evolve over cosmic time. JWST is uniquely equipped to probe these redshifts efficiently in the rest-frame optical and near-IR. We present results from an initial pathfinder search, covering an area of ~133 arcmin^2 (~0.037 deg^2) independently imaged by the PRIMER and COSMOS-Web (hereafter COSMOS) extragalactic surveys. Although neither program was designed for time-domain astronomy, combining their data results in difference images separated by roughly one year, leading to the discovery of 68 supernovae (SNe) with host photometric redshifts reaching z < 5. For most SNe, only a single epoch is available, but the combination of host redshift, classification, color, and magnitude enables us to prioritize candidates for detailed photometric and spectroscopic follow-up. Among the most notable sources are a relatively bright, blue CCSN at z > 3 (SN 2023aeab) and a young, normal SN Ia at z > 2 (SN 2023aeax). The sample distribution highlights the increasing likelihood that a wide-area JWST program can uncover younger, bluer, and potentially more extreme explosions. While this pathfinder effort is limited in cadence and number of filters, it demonstrates the strong potential of a dedicated, well-planned time-domain survey with JWST to obtain the sample sizes and rate measurements needed to chart SN populations deep into the early Universe.

astro-ph.HE↗
arXiv2026

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 ($σ_{\mathrm{NMAD}}$ 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$σ$ dex of reference values. Although redshift estimation in COSMOS-Web remains challenging (median $σ_{\mathrm{NMAD}} = 0.04$), 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↗
arXiv2026

Reducing the Dimensions of AGN Lightcurve Manifolds

The Active Galactic Nuclei (AGN) glossary is vast and complex. Depending on selection method, observing wavelength, and brightness, AGNs are assigned distinct labels, yet the relationship between different selection methods and the diversity of time-domain behavior within and across classes remains difficult to characterize in a unified framework. Changing-look AGNs (CLAGNs), which transition between classifications over time, further complicate this picture. In this work, we learn a data-driven, low-dimensional representation of multi-wavelength photometric light curves of AGNs, in which the structure of the projected manifold correlates with AGN class and independent spectroscopic properties. Using the NASA Fornax Science Platform, we assemble light curves from ZTF, Pan-STARRS, Gaia, and WISE/NEOWISE for two samples: (1) a heterogeneous set of $\sim$2000 AGNs spanning $z \lesssim 1$, including SDSS quasars, variability-selected sources, and CLAGNs; and (2) a homogeneous sample of $\sim$65000 narrow-line AGNs at $z \approx 0.1$ with well-characterized optical emission-line measurements. Without using class labels during training, the learned manifolds organize variability-selected AGNs into coherent regions of the low-dimensional space, distinguish between turn-on and turn-off CLAGNs, and place tidal disruption events in distinct regions. Manifold coordinates correlate with key spectroscopic and host-galaxy properties, including stellar mass, [OIII] luminosity, and D$_n$(4000), demonstrating that heterogeneous multi-band variability can be combined in a purely data-driven manner to recover correlations with independent physical diagnostics, without requiring explicit physical modeling. These results show that manifold learning offers a practical, assumption-light approach for integrating time-domain surveys and prioritizing spectroscopic follow-up.

astro-ph.GA↗
arXiv2025

Selection of Dwarf Galaxies Hosting AGNs: A Measure of Bias and Contamination using Unsupervised Machine Learning Techniques

Identifying AGNs in dwarf galaxies is critical for understanding black hole formation but remains challenging due to their low luminosities, low metallicities, and star formation-driven emission that can obscure AGN signatures. Machine learning (ML) techniques, particularly unsupervised methods, offer new ways to address these challenges by uncovering patterns in complex data. In this study, we apply Self-Organizing Maps (SOMs) to explore the SED manifold of dwarf galaxies and evaluate AGN selection biases across diagnostics. We train a 51 by 51 SOM on 30,344 dwarf galaxies (redshift less than 0.055 and stellar mass below 10 to the 9.5 solar masses) from the NSA catalog using nine-band photometry from near-UV to mid-infrared. A set of 438 previously identified dwarf AGNs, selected via various methods, was mapped onto the SOM. AGNs identified by different methods occupy distinct and partially overlapping regions in SED space, reflecting selection biases tied to host properties. BPT selected AGNs cluster in higher-mass regions, while X-ray and variability-selected AGNs show broader distributions. WISE-selected AGNs are concentrated in lower-mass regions and form two clumps: one associated with bluer, starburst-like systems and the other with redder, more AGN-like SEDs. This separation may help distinguish true AGN hosts from starburst contaminants in WISE-selected samples. AGNs selected via traditional emission-line, broad-line, and WISE methods tend to avoid SOM regions linked to strong star formation. In contrast, a subset of AGNs in low-mass galaxies occupy regions indicative of high AGN luminosity relative to stellar content, highlighting luminous AGNs in faint hosts. These results demonstrate the utility of manifold learning for improving AGN selection in the low-mass regime.

astro-ph.GA↗
arXiv2024

Application of Manifold Learning to Selection of Different Galaxy Populations and Scaling Relation Analysis

The growing volume of data produced by large astronomical surveys necessitates the development of efficient analysis techniques capable of effectively managing high-dimensional datasets. This study addresses this need by demonstrating some applications of manifold learning and dimensionality reduction techniques, specifically the Self-Organizing Map (SOM), on the optical+NIR SED space of galaxies, with a focus on sample comparison, selection biases, and predictive power using a small subset. To this end, we utilize a large photometric sample from the five CANDELS fields and a subset with spectroscopic measurements from the KECK MOSDEF survey in two redshift bins at $z\sim1.5$ and $z\sim2.2$. We trained SOM with the photometric data and mapped the spectroscopic data onto it as our study case. We found that MOSDEF targets do not cover all SED shapes existing in the SOM. Our findings reveal that Active Galactic Nuclei (AGN) within the MOSDEF sample are mapped onto the more massive regions of the SOM, confirming previous studies and known selection biases towards higher-mass, less dusty galaxies. Furthermore, SOM were utilized to map measured spectroscopic features, examining the relationship between metallicity variations and galaxy mass. Our analysis confirmed that more massive galaxies exhibit lower [OIII]/H$β$ and [OIII]/[OII] ratios and higher H$α$/H$β$ ratios, consistent with the known mass-metallicity relation. These findings highlight the effectiveness of SOM in analyzing and visualizing complex, multi-dimensional datasets, emphasizing their potential in data-driven astronomical studies.

astro-ph.GA↗