arXiv subjects

Nicole Drakos

Publications and source records attributed to Nicole Drakos.

7 recordsLinked to original sources

arXiv2026

Hidden in Pixels. I. Discovery of dual "little red dots" indicates excess clustering on kilo-parsec scales

``Little Red Dots'' (LRDs) are an abundant high-redshift population newly discovered by the James Webb Space Telescope (JWST) and considered to be an early growth phase of supermassive black holes (SMBHs). Using a method of pixel-by-pixel color selection and relaxing the compactness criteria, we identify four dual LRD candidates in the COSMOS-Web survey with projected separations of $0.\!\!^{\prime\prime}2$-$1.\!\!^{\prime\prime}2$. A comparison between existing LRD samples and mock data reveals that the projected separations of these dual LRD candidates are unlikely to result from chance projections of objects at different redshifts. Furthermore, two of the four systems are covered by COSMOS-3D slitless spectroscopy, and a single-line detection at the same observed wavelength for each LRD in a pair strongly supports that they are at identical redshifts. Assuming that the detected lines are H$α$ based on their high equivalent width and broad profile, the spectroscopic redshifts of $z=5.822$ and $5.464$ for the two pairs are consistent with their photometric redshifts, yielding projected separations of $1.64$ and $7.36\,{\rm kpc}$. These discoveries suggest that the angular auto-correlation function (ACF) of LRDs exhibits an excess ($\sim20$-$30$ times) on sub-arcsec (kilo-parsec) separations compared to an extrapolation of a power-law ACF of JWST-found AGNs measured over $10^{\prime\prime}$-$100^{\prime\prime}$. Our sample is likely to represent precursors of mergers between LRDs, and such mergers may be one of the mechanisms that can drive the rapid growth of SMBHs in their early evolutionary stages.

astro-ph.GA↗

arXiv2026

Photometric Super-Resolution for Improving Galaxy Morphological Measurements using Conditional Generative Adversarial Networks

The measurement of galaxy morphological parameters from astronomical images features in a wide range of modern analyses, including galaxy evolution and cosmological weak lensing studies. The precision and accuracy of morphological parameter estimation can be influenced by several key factors. The effective seeing of the image, summarized by the point spread function (PSF), limits how galaxy features or light profiles are resolved. The pixel scale of the detector also influences the resolution and the amount of statistical information available for a given object. The depth of the observations determines the signal-to-noise ratio of the image. Improving each of these factors is very costly, either in terms of detector upgrades, observatory design, or observing time. Here, we develop a conditional generative adversarial network, called Neo, trained to transform existing ground-based images into sharper, finer-scale images comparable to space-based image quality. We demonstrate that Neo improves the accuracy of measured morphological parameters by factors of $2$-$10$ when trained to translate Subaru Hyper Suprime-Camera (HSC) images to approximate Hubble Space Telescope (HST) data. Neo is designed for applicability to ongoing, large-scale surveys such as the Legacy Survey of Space and Time (LSST) conducted by Vera C. Rubin Observatory in combination with space telescopes such as HST, James Webb Space Telescope, and Nancy Grace Roman Space Telescope. These results suggest that Neo could be used to improve both cosmological and galaxy evolution analyses based on massive, ground-based survey datasets like LSST. The model code is open source and available at https://purl.archive.org/neo/code.

astro-ph.IM↗

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.

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arXiv2025

On Soft Clustering For Correlation Estimators

Properly estimating correlations between objects at different spatial scales necessitates $\mathcal{O}(n^2)$ distance calculations. For this reason, most widely adopted packages for estimating correlations use clustering algorithms to approximate local trends. However, methods for quantifying the error introduced by this clustering have been understudied. In response, we present an algorithm for estimating correlations that is probabilistic in the way that it clusters objects, enabling us to quantify the uncertainty caused by clustering simply through model inference. These soft clustering assignments enable correlation estimators that are theoretically differentiable with respect to their input catalogs. Thus, we also build a theoretical framework for differentiable correlation functions and describe their utility in comparison to existing surrogate models. Notably, we find that repeated normalization and distance function calls slow gradient calculations and that sparse Jacobians destabilize precision, pointing towards either approximate or surrogate methods as a necessary solution to exact gradients from correlation functions. To that end, we close with a discussion of surrogate models as proxies for correlation functions. We provide an example that demonstrates the efficacy of surrogate models to enable gradient-based optimization of astrophysical model parameters, successfully minimizing a correlation function output. Our numerical experiments cover science cases across cosmology, from point spread function (PSF) modeling efforts to gravitational simulations to galaxy intrinsic alignment (IA).

astro-ph.IM↗

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 Sérsic 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 $R_e \propto (1+z)^{-α}$, with $α= 1.11 \pm 0.07$ (star-forming) and $1.40 \pm 0.09$ (quiescent); (3) star formation surface density ($Σ_{\mathrm{SFR}}$) increases with redshift and shows stronger evolution for massive BGGs ($\log_{10}(M_\ast/M_\odot) \geq 10.75$); (4) in the $Σ_*$--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.

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arXiv2023

The Web Epoch of Reionization Lyman-$α$ Survey (WERLS) I. MOSFIRE Spectroscopy of $\mathbf{z \sim 7-8}$ Lyman-$α$ Emitters

We present the first results from the Web Epoch of Reionization Lyman-$α$ Survey (WERLS), a spectroscopic survey of Lyman-$α$ emission using Keck I/MOSFIRE and LRIS. WERLS targets bright ($J<26$) galaxy candidates with photometric redshifts of $5.5\lesssim z \lesssim 8$ selected from pre-JWST imaging embedded in the Epoch of Reionization (EoR) within three JWST deep fields: CEERS, PRIMER, and COSMOS-Web. Here, we report 11 $z\sim7-8$ Lyman-$α$ emitters (LAEs; 3 secure and 8 tentative candidates) detected in the first five nights of WERLS MOSFIRE data. We estimate our observed LAE yield is $\sim13$%, broadly consistent with expectations assuming some loss from redshift uncertainty, contamination from sky OH lines, and that the Universe is approximately half-ionized at this epoch, whereby observable Lyman-$α$ emission is unlikely for galaxies embedded in a neutral intergalactic medium. Our targets are selected to be UV-bright, and span a range of absolute UV magnitudes with $-23.1 < M_{\text{UV}} < -19.8$. With two LAEs detected at $z=7.68$, we also consider the possibility of an ionized bubble at this redshift. Future synergistic Keck+JWST efforts will provide a powerful tool for pinpointing beacons of reionization and mapping the large scale distribution of mass relative to the ionization state of the Universe.

astro-ph.GA↗

arXiv2020

The GOGREEN and GCLASS Surveys: First Data Release

We present the first public data release of the GOGREEN and GCLASS surveys of galaxies in dense environments, spanning a redshift range $0.8<z<1.5$. The surveys consist of deep, multiwavelength photometry and extensive Gemini GMOS spectroscopy of galaxies in 26 overdense systems ranging in halo mass from small groups to the most massive clusters. The objective of both projects was primarily to understand how the evolution of galaxies is affected by their environment, and to determine the physical processes that lead to the quenching of star formation. There was an emphasis on obtaining unbiased spectroscopy over a wide stellar mass range ($M\gtrsim 2\times 10^{10}~\mathrm{M}_\odot$), throughout and beyond the cluster virialized regions. The final spectroscopic sample includes 2771 unique objects, of which 2257 have reliable spectroscopic redshifts. Of these, 1704 have redshifts in the range $0.8<z<1.5$, and nearly 800 are confirmed cluster members. Imaging spans the full optical and near-infrared wavelength range, at depths comparable to the UltraVISTA survey, and includes \textit{HST}/WFC3 F160W (GOGREEN) and F140W (GCLASS). This data release includes fully reduced images and spectra, with catalogues of advanced data products including redshifts, line strengths, star formation rates, stellar masses and rest-frame colours. Here we present an overview of the data, including an analysis of the spectroscopic completeness and redshift quality.

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