arXiv subjects

Aryana Haghjoo

Publications and source records attributed to Aryana Haghjoo.

11 recordsLinked to original sources

arXiv2026

Surveying the Universe in 4D: Beating Cosmic Variance with Wide-Field Slitless Spectroscopy from HST, JWST, Euclid, Roman, and Beyond

We summarize strategies, lessons learned, and future directions from the Space Telescope Science Institute workshop Surveying the Universe in 4D: Beating Cosmic Variance with Wide-Field Slitless Spectroscopy from HST, JWST, Euclid, Roman, and Beyond, held August 24--28, 2026. The workshop examined scientific results, observational and data analysis challenges, extraction tools, and future opportunities. Discussions highlighted (1) the transformative potential of WFSS for the study of transient phenomena, galaxy evolution --both spatially-resolved and within the broader context of the cosmic web--, and rare populations and (2) the synergies among Euclid and Roman surveys, Rubin-LSST monitoring, JWST WFSS, and high-resolution integral-field observations. Participants identified advances in forward modeling and physics-informed machine learning as essential for addressing spectral overlap, crowded fields, and upcoming, very large data volumes. Realizing WFSS's full potential will require community-wide infrastructure, science-ready data products, accessible cloud-based analysis tools, and robust benchmarking of reduction pipelines. Crucially, participants called for systemic changes to properly recognize early-career researchers who invest significant efforts in pipeline, code, and calibration developments that enable WFSS science, and stressed that progress requires collaborative, multidisciplinary practices that optimize the participation and benefits of the next generation.

astro-ph.GA↗

arXiv2026

Extended Components of Little Red Dots in the Rest-Frame Optical

Recent JWST observations have revealed a population of red, compact, high-redshift objects called Little Red Dots (LRDs), whose host components have remained largely unconstrained, possibly due to their extreme compactness. Current morphological studies suggest the presence of extended emission in LRDs at rest-frame ultraviolet wavelengths. However, in the rest-frame optical regime, investigations have been limited by small sample sizes and insufficient imaging depth, hindering reliable separation between point-like and potential extended components. Here we perform the image stacking analysis of 217 LRDs in four NIRCam bands, a large and homogeneous sample observed with the COSMOS-Web survey. Our results reveal the detection of faint extended emission in the F444W band, with a typical size of ~200 parsecs and magnitude of ~27.7 AB at z~6.5. We perform four-band photometric spectral energy distribution fitting based on galaxy templates and derive an average stellar mass of log(M*/M_sun) = 9.02 +0.20/-0.18. Given this stellar mass, the host galaxy is compact, that is, ~2.5 times smaller than star-forming galaxies of similar mass at comparable redshifts. This work provides direct observational evidence for the existence of LRD host galaxies at rest-frame optical wavelengths and offers new insights into the stellar buildup of these systems within the first billion years after the Big Bang.

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arXiv2026

Discovery and Analysis of a Type II Supernova Candidate at z = 3.19 from JWST's COSMOS-Web Survey

The launch of the James Webb Space Telescope (JWST) has enabled the discovery of a small but increasing sample of high-redshift core-collapse supernovae (CC SNe), which provide new tests of massive star evolution in the early Universe. In this study, we report the discovery of SN 2023aeaf in COSMOS-Web survey observations, which at $z = 3.195$ has one of the highest SN spectroscopic redshifts to date. Using two epochs of JWST photometry separated by $\sim$1 month in the rest frame, we photometrically classify SN 2023aeaf by comparing the JWST photometry to spectrophotometric CC SN and Type Ia (SN Ia) models and UV observations of SNe from the Swift telescope, finding that SN 2023aeaf is highly likely to be a Type II SN. A spectrum of the SN$+$host galaxy was also obtained $\sim$30 rest-frame days after discovery but shows no clearly identifiable SN features, with H$α$ emission from the host potentially masking emission from the SN. Although the limited photometric coverage prevents strong constraints on the explosion properties, we find that the data are most consistent with a $\sim$12$M_\odot$ progenitor with $\sim$0.5$M_{\odot}$ of circumstellar material. We next use the host-galaxy spectrum and photometry to model the host spectral energy distribution (SED) using the Prospector Bayesian inference framework. We find that the host is a star-forming galaxy with a sSFR of $ \log_{10}(\rm sSFR/yr^{-1})= -10.17^{+0.13}_{-0.10}$, a stellar mass of $\log(M_\star/M_\odot) = 9.04^{+0.03}_{-0.04}$, and a gas-phase metallicity of $12 +{\rm log_{10}}({\rm O/H}) = 7.82\pm0.02$. SN 2023aeaf joins a growing sample of early Universe CC SNe with high luminosities, dense CSM, and low-metallicity environments.

astro-ph.HE↗

arXiv2026

Learning to See Sharper: A Physics-Informed Artificial Intelligence Framework for Super-Resolving Galaxy Spectra

The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral super-resolution that enhances low-resolution galaxy spectra by a factor of $\sim$10 in resolving power ($R\sim100$ to $R\sim1000$). The model is trained on 1,187 paired JWST/NIRSpec observations from the JADES program, where low-resolution prism spectra are matched with medium-resolution grating spectra (G140M, G235M, G395M) combined into a unified reference covering 1-5 $μ$m. Our three-stage architecture performs an initial super-resolution, infers the redshift from the coarse reconstruction, and then applies a physics-informed residual refinement that uses attention across emission-line tokens to learn inter-line relationships and predict parametric line profiles, alongside a convolutional branch for continuum corrections. Evaluated on a 20% held-out sample, the model achieves noise-limited residuals over most of the spectral range and systematically improves the signal-to-noise ratio of key diagnostic lines including [OII], H$β$, [OIII], and H$α$, often by factors of several. The super-resolved spectra successfully deblend features that are entirely unresolved at prism resolution, such as the [OIII] $λ\lambda4959,5007$ doublet and H$β$. As a proof of concept using JWST data, this approach is readily extensible to the low-resolution grism spectroscopy that will be delivered by Euclid and the Roman Space Telescope, potentially enabling population-level diagnostics across millions of galaxy spectra that would otherwise be inaccessible at grism resolution.

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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

Through Thick and Thin: The Cosmic Evolution of Disk Scale Height

To investigate the formation and evolution of vertical structures in disk galaxies, we measure global $\operatorname{sech}^2$ scale heights, averaging thin and thick components when present, for 2631 edge-on disk galaxies with $M_*>10^{10} M_\odot$ at $0 1.5$, but rises to $4.0\pm0.4$ at $z=0.25$. These results imply that the high-redshift progenitors of present-day thick disks were of intermediate thickness, neither thin nor thick, yet dynamically hot and dense. The observed radial variation of scale height is consistent with the artificial flaring expected from observational effects, disfavoring minor mergers as the primary mechanism of disk thickening. Instead, we suggest that the high-redshift intermediate-thickness disks were single-component systems that increased their vertical scale height through decreasing surface mass density and/or violent gravitational instabilities, eventually producing thick disks. Thin-disk growth begins at $z\approx2$ and dominates at $z\lesssim1$, yielding a vertically more compact system with decreasing scale heights from $z\approx1$ to $0$. The inferred thin-disk mass fraction increases from $0.1\pm0.03$ at $z=1$ to $0.6\pm0.1$ at $z=0$. Together, these findings reveal a continuous evolutionary link between high-redshift single-component disks and present-day thick thin disk systems.

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.

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arXiv2025

Large-Scale Structure in COSMOS-Web: Tracing Galaxy Evolution in the Cosmic Web up to $z \sim 7$ with the Largest JWST Survey

We present a reconstruction of the large-scale structure using the James Webb Space Telescope's (JWST) COSMOS-Web program to trace environmentally driven galaxy evolution up to $z\sim7$. We applied a weighted kernel density estimation method to 160,000 galaxies with robust photometric redshifts. We find that stellar mass has a positive correlation with density at all redshifts, stronger for quiescent galaxies (QGs) at $z\lesssim2.5$, while at higher redshifts ($2.5\lesssim z\lesssim5.5$) this trend is confined to extreme overdense environments, consistent with early mass assembly in proto-clusters. The star-formation rate (SFR) shows a negative trend with density for QGs at $z\lesssim1.2$, reversing at $z\gtrsim1.8$, while star-forming galaxies (SFGs) show a mild positive correlation up to $z\sim5.5$. The specific SFR remains nearly flat for SFGs and declines with density for QGs at $z\lesssim1.2$. Moreover, mass and environmental quenching efficiencies show that mass-driven processes dominate at $z\gtrsim2.5$, the two processes act with comparable strength between $0.8\lesssim z\lesssim2.5$, and environmental quenching becomes stronger for low-mass galaxies ($M_\star\lesssim10^{10} M_\odot$) at $z\lesssim0.8$. These findings reveal that large-scale structure drives galaxy evolution by enhancing early mass assembly in dense regions and increasingly suppressing star formation in low-mass systems at later times, establishing the environmental role of the cosmic web across cosmic history. COSMOS-Web, the largest JWST survey, provides accurate and deep photometric redshifts, reaching 80% mass completeness at $\log(M_\star/M_\odot)\sim8.7$ at $z\sim7$, enabling the first view of how environments shaped galaxy evolution from the epoch of reionization to the present day.

astro-ph.GA↗

arXiv2025

The stellar mass function of quiescent and star-forming galaxies and its dependence on morphology in COSMOS-Web

We study the stellar mass function (SMF) of quiescent and star-forming galaxies and its dependence on morphology in 10 redshift bins at $0.2 0.6$) dominate the quiescent SMF at ${\rm log}(M_{\star}/{\rm M_{\odot}})>10$ at all redshifts, while disks ($B/T<0.2$) dominate at ${\rm log}(M_{\star}/{\rm M_{\odot}})<9$. However, most bulge-dominated galaxies are star-forming, with their fraction increasing with redshift and decreasing mass, consistent with being progenitors of quiescent bulges. We find evidence for environmental quenching onset at $z\sim3$ from the upturn in the quiescent SMF at ${\rm log}(M_{\star}/{\rm M_{\odot}})<9.5$, contributed by disk-dominated galaxies consistent with satellite quenching that retains disk morphologies. Number densities of ${\rm log}(M_{\star}/{\rm M_{\odot}})>10$ quiescent galaxies are lower than recent literature by $0.1-0.7$ dex, but agree well with simulations at $2 3$, simulations increasingly underpredict observations. Finally, we build an empirical model describing galaxy number density evolution by parametrizing quenching rates, baryon conversion efficiency, and bulge formation. Our model supports a scenario where star-forming galaxies grow central bulges before quenching in massive halos.

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arXiv2025

The ALPINE-CRISTAL-JWST Survey: NIRSpec IFU Data Processing and Spatially-resolved Views of Chemical Enrichment in Normal Galaxies at z=4-6

We present a statistical study of spatially resolved chemical enrichment in 18 main-sequence galaxies at $z=4$--6, observed with \jwst/NIRSpec IFU as part of the ALPINE-CRISTAL-\jwst\ survey. Performing an optimized reduction and calibration procedure, including local background subtraction, light-leakage masking, stripe removal, and astrometry refinement, we achieve robust emission-line mapping on kiloparsec scales. Although line-ratio distributions vary across galaxies in our sample, we generally find mild central enhancements in [O\,\textsc{iii}]/H$β$, [O\,\textsc{ii}]/[O\,\textsc{iii}], [S\,\textsc{ii}]$_{6732}$/[S\,\textsc{ii}]$_{6718}$, H$α$/H$β$, and $L_{\rm Hα}/L_{\rm UV}$, consistent with elevated electron density, dust obscuration, and bursty star formation accompanied by reduced metallicity and ionization parameter. These features point to inside-out growth fueled by recent inflows of pristine gas. Nevertheless, the median metallicity gradient is nearly flat over a few kpc scale, $Δ\log({\rm O/H}) = 0.02 \pm 0.01$ dex kpc$^{-1}$, implying efficient chemical mixing through inflows, outflows, and mergers. From pixel-by-pixel stellar and emission-line characterizations, we further investigate the resolved Fundamental Metallicity Relation (rFMR). Metallicity is described by a fundamental plane with stellar mass and SFR surface densities, but with a stronger dependence on $Σ_{\rm SFR}$ than seen in local galaxies. Our results indicate that the regulatory processes linking star formation, gas flows, and metal enrichment were already vigorous $\sim$1 Gyr after the Big Bang, producing the nearly flat metallicity gradient and a stronger coupling between star formation and metallicity than observed in evolved systems in the local universe.

astro-ph.GA↗

arXiv2025

Machine Learning Classification of COSMOS2020 Galaxies: Quiescent vs. Star-Forming

Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may struggle to capture complex galaxy properties. This study aims to develop a robust and efficient machine learning (ML) classification method to identify quiescent and star-forming galaxies within the Farmer COSMOS2020 catalog. We utilized JWST wide-field light cones from the Santa Cruz semi-analytical modeling framework to train a supervised ML model, the CatBoostClassifier, using 28 color features derived from 8 mutual photometric bands within the COSMOS catalog. The model was validated against a testing set and compared to the SED-fitting method in terms of precision, recall, F1-score, and execution time. Preprocessing steps included addressing missing data, injecting observational noise, and applying a magnitude cut (ch1 < 26 AB) along with a redshift range of 0.2 < z < 3.5 to align the simulated and observational datasets. The ML method achieved an F1-score of 89\% for quiescent galaxies, significantly outperforming the SED-fitting method, which achieved 54%. The ML model demonstrated superior recall (88% vs. 38%) while maintaining comparable precision. When applied to the COSMOS2020 catalog, the ML model predicted a systematically higher fraction of quiescent galaxies across all redshift bins within 0.2 < z < 3.5 compared to traditional methods like NUVrJ and SED-fitting. This study shows that ML, combined with multi-wavelength data, can effectively identify quiescent and star-forming galaxies, providing valuable insights into galaxy evolution. The trained classifier and full classification catalog are publicly available.

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