arXiv subjects

Suchetha Cooray

Publications and source records attributed to Suchetha Cooray.

17 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↗

arXiv2026

Estimating Galaxy Star Formation Rates from Murchison Widefield Array Radio Continuum Emission

We investigate the frequency dependence of the low-frequency infrared (IR)--radio correlation for nearby galaxies drawn from the {\sl Herschel} Reference Survey (HRS), using radio continuum observations from the GaLactic and Extragalactic All-sky MWA (GLEAM) survey obtained with the Murchison Widefield Array (MWA). The GLEAM survey provides densely sampled flux densities across 20 sub-bands spanning 72--231 MHz, allowing detailed characterization of the low-frequency radio spectral energy distribution (SED). We analyze 18 nearby galaxies detected by GLEAM and examine the frequency dependence of the IR--radio correlation coefficient $q_ν$. Among these galaxies, three sources show Seyfert or LINER signatures and may contain non-negligible AGN contributions to the radio continuum emission. We therefore use the remaining 15 predominantly star-forming galaxies as the primary sample for deriving the main calibration results. For the majority of the sample, the low-frequency radio continuum is well described by a single power-law spectral model from the GLEAM frequency range up to 1.5 GHz, with a median spectral index of $γ=-0.63$. The ensemble-averaged $q_ν$ relation and its intrinsic scatter provide a practical calibration for converting low-frequency radio luminosities into star formation rates (SFRs), while explicitly quantifying the associated systematic uncertainty. Our results indicate that the low-frequency IR--radio correlation remains stable across the MWA bands for nearby star-forming galaxies, supporting the use of low-frequency radio continuum as a dust-unbiased tracer of star formation in the local Universe. These results provide a local benchmark for interpreting low-frequency radio continuum surveys and for future studies of radio-based SFR tracers with next-generation facilities such as the Square Kilometre Array (SKA).

astro-ph.GA↗

arXiv2026

A Path to an All-Sky Survey with Roman

A deep, space-based, all-sky near-infrared survey carried out with the Nancy Grace Roman Space Telescope would constitute a foundational astronomical infrastructure for decades to come. In this white paper, we present a concrete and feasible path to imaging the entire sky at $\sim0.1''$ resolution, beginning with high-impact fields in Cycle 1 and scaling to ultra-wide coverage within the nominal mission. This first-epoch survey will reach $\mathrm{H}\sim25.5$ AB mag (5$σ$) and maximize synergies with contemporaneous observatories, while preserving substantial time for other ambitious Roman programs. We outline representative scheduling scenarios and an example Cycle 1 program that triples early Roman-LSST overlap and delivers high-value community data products such as LSST forced photometry, joint \textit{Gaia}-Roman astrometry, and catalogs of Galactic substructure, stong lenses, and other rare systems. The Cycle 1 program will lay the foundation for an eventual all-sky survey, while also delivering high-impact early science. We invite broad community participation in shaping and carrying out both the initial program and the long-term vision of an all-sky Roman survey.

astro-ph.IM↗

arXiv2025

ReplicationBench: Can AI Agents Replicate Astrophysics Research Papers?

Frontier AI agents show increasing promise as scientific research assistants, and may eventually be useful for extended, open-ended research workflows. However, in order to use agents for novel research, we must first assess the underlying faithfulness and correctness of their work. To evaluate agents as research assistants, we introduce ReplicationBench, an evaluation framework that tests whether agents can replicate entire research papers drawn from the astrophysics literature. Astrophysics, where research relies heavily on archival data and computational study while requiring little real-world experimentation, is a particularly useful testbed for AI agents in scientific research. We split each paper into tasks which require agents to replicate the paper's core contributions, including the experimental setup, derivations, data analysis, and codebase. Each task is co-developed with the original paper authors and targets a key scientific result, enabling objective evaluation of both faithfulness (adherence to original methods) and correctness (technical accuracy of results). ReplicationBench is extremely challenging for current frontier language models: even the best-performing language models score under 20%. We analyze ReplicationBench trajectories in collaboration with domain experts and find a rich, diverse set of failure modes for agents in scientific research. ReplicationBench establishes the first benchmark of paper-scale, expert-validated astrophysics research tasks, reveals insights about agent performance generalizable to other domains of data-driven science, and provides a scalable framework for measuring AI agents' reliability in scientific research.

cs.CL↗

arXiv2025

Insights into the dependence of galaxy properties on the environment with explainable machine learning models

Galaxies reside within dark matter halos, but their properties are influenced not only by their halo properties but also by the surrounding environment. We construct an interpretable neural network framework to characterize the surrounding environment of galaxies and investigate the extent to which their properties are affected by neighboring galaxies in IllustrisTNG300 data ($z=0$). Our models predict galaxy properties (stellar mass and star formation rate) given dark matter subhalo properties of both host subhalo and of surrounding galaxies, which serve as an explainable, flexible galaxy-halo connection model. We find that prediction accuracy peaks when incorporating only the nearest neighboring galaxy for stellar mass prediction, while star formation rate prediction benefits from information from up to the third-nearest neighbor. We determine that environmental influence follows a clear hierarchical pattern, with the nearest neighbor providing the dominant contribution that diminishes rapidly with additional neighbors. We confirm that central and satellite galaxies, as well as different galaxy categories based on mass and star-forming activity, exhibit distinct environmental dependencies. Environmental dependence for low-mass galaxies ($\log(M_*/M_\odot) < 10$) shows 35-50% environmental contribution compared to just 8-30% for massive centrals, while satellite galaxies experience consistently stronger environmental effects than centrals across all populations. Furthermore, we find that the most significant attribute from neighboring subhalos for predicting target galaxy properties is its distance to the nearest neighboring galaxy. These quantitative results offer guidance for constructing more sophisticated empirical and semi-analytic models of galaxy formation that explicitly include environmental dependence as a function of galaxy type and mass.

astro-ph.GA↗

arXiv2025

How does feedback affect the star formation histories of galaxies?

Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the CAMELS suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at $z\sim0$ across the IllustrisTNG, SIMBA and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affects the efficiency of baryon cycling and the rates at which central black holes grow, while effects of varying AGN feedback depend on model-dependent implementations of black hole seeding, accretion and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power-law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.

astro-ph.GA↗

arXiv2025

sOPTICS: A Modified Density-Based Algorithm for Identifying Galaxy Groups/Clusters and Brightest Cluster Galaxies

A direct approach to studying the galaxy-halo connection is to analyze groups and clusters of galaxies that trace the underlying dark matter halos, emphasizing the importance of identifying galaxy clusters and their associated brightest cluster galaxies (BCGs). In this work, we test and propose a robust density-based clustering algorithm that outperforms the traditional Friends-of-Friends (FoF) algorithm in the currently available galaxy group/cluster catalogs. Our new approach is a modified version of the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, which accounts for line-of-sight positional uncertainties due to redshift space distortions by incorporating a scaling factor, and is thereby referred to as sOPTICS. When tested on both a galaxy group catalog based on semi-analytic galaxy formation simulations and observational data, our algorithm demonstrated robustness to outliers and relative insensitivity to hyperparameter choices. In total, we compared the results of eight clustering algorithms. The proposed density-based clustering method, sOPTICS, outperforms FoF in accurately identifying giant galaxy clusters and their associated BCGs in various environments with higher purity and recovery rate, also successfully recovering 115 BCGs out of 118 reliable BCGs from a large galaxy sample. Furthermore, when applied to an independent observational catalog without extensive re-tuning, sOPTICS maintains high recovery efficiency, confirming its flexibility and effectiveness for large-scale astronomical surveys.

astro-ph.CO↗

arXiv2024

Katachi: Decoding the Imprints of Past Star Formation on Present Day Morphology in Galaxies with Interpretable CNNs

The physical processes responsible for shaping how galaxies form and quench over time leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; SFH) tracers that we use to study galaxies. While the morphology-SFR connection is well studied, the correlation with past star formation activity is not as well understood. To quantify this we present Katachi, an interpretable convolutional neural network (CNN) framework that learns the connection between the factors regulating star formation in galaxies on different spatial and temporal scales. Katachi is trained on 9904 galaxies at 0.02$<$z$<$0.1 in the SDSS-IV MaNGA DR17 sample to predict stellar mass (M$_*$; RMSE 0.22 dex), current star formation rate (SFR; RMSE 0.31 dex) and half-mass time (t$_{50}$; RMSE 0.23 dex). This information allows us to reconstruct non-parametric SFHs for each galaxy from \textit{gri} imaging alone. To quantify the morphological features informing the SFH predictions we use SHAP (SHapley Additive exPlanations). We recover the expected trends of M$_*$ governed by the growth of galaxy bulges, and SFR correlating with spiral arms and other star-forming regions. We also find the SHAP maps of D4000 are more complex than those of M$_*$ and SFR, and that morphology is correlated with t$_{50}$ even at fixed mass and SFR. Katachi serves as a scalable public framework to predict galaxy properties from large imaging surveys including Rubin, Roman, and Euclid, with large datasets of high SNR imaging across limited photometric bands.

astro-ph.GA↗

arXiv2024

High Dimensional Statistical Analysis and its Application to ALMA Map of NGC 253

In astronomy, if we denote the dimension of data as $d$ and the number of samples as $n$, we often meet a case with $n \ll d$. Traditionally, such a situation is regarded as ill-posed, and there was no choice but to throw away most of the information in data dimension to let $d < n$. The data with $n \ll d$ is referred to as high-dimensional low sample size (HDLSS). {}To deal with HDLSS problems, a method called high-dimensional statistics has been developed rapidly in the last decade. In this work, we first introduce the high-dimensional statistical analysis to the astronomical community. We apply two representative methods in the high-dimensional statistical analysis methods, the noise-reduction principal component analysis (NRPCA) and regularized principal component analysis (RPCA), to a spectroscopic map of a nearby archetype starburst galaxy NGC 253 taken by the Atacama Large Millimeter/Submillimeter Array (ALMA). The ALMA map is a typical HDLSS dataset. First we analyzed the original data including the Doppler shift due to the systemic rotation. The high-dimensional PCA could describe the spatial structure of the rotation precisely. We then applied to the Doppler-shift corrected data to analyze more subtle spectral features. The NRPCA and RPCA could quantify the very complicated characteristics of the ALMA spectra. Particularly, we could extract the information of the global outflow from the center of NGC 253. This method can also be applied not only to spectroscopic survey data, but also any type of data with small sample size and large dimension.

astro-ph.IM↗

arXiv2023

Extracting an Informative Latent Representation of High-Dimensional Galaxy Spectra

To understand the fundamental parameters of galaxy evolution, we investigated the minimum set of parameters that explain the observed galaxy spectra in the local Universe. We identified four latent variables that efficiently represent the diversity of high-dimensional galaxy spectral energy distributions (SEDs) observed by the Sloan Digital Sky Survey. Additionally, we constructed meaningful latent representation using conditional variational autoencoders trained with different permutations of galaxy physical properties, which helped us quantify the information that these traditionally used properties have on the reconstruction of galaxy spectra. The four parameters suggest a view that complex SED population models with a very large number of parameters will be difficult to constrain even with spectroscopic galaxy data. Through an Explainable AI (XAI) method, we found that the region below 5000\textupÅ and prominent emission lines ([O II], [O III], and H$α$) are particularly informative for predicting the latent variables. Our findings suggest that these latent variables provide a more efficient and fundamental representation of galaxy spectra than conventionally considered galaxy physical properties.

astro-ph.GA↗

arXiv2023

Photometric Observations and Period Analysis of an SU UMa-type Dwarf Nova, MASTER OT J004527.52+503213.8

MASTER OT J004527.52+503213.8 (hereafter MASTER J004527) is a dwarf nova discovered by the MASTER project in 2013. At 18:20 UTC on 24 October 2020, brightening of this object was reported to vsnet-alert (24843 by Denisenko). This was the second report of a superoutburst after its discovery. Photometric observations were made using the 23.5-cm Schmidt-Cassegrain telescope at Okayama University of Science observatory soon after the alert through 4 November 2020. In this work, we present the photometric data from our observation, and the analysis of the light curves of MASTER J004527 during the 2020 outburst. We propose a method to determine the period of superhumps by polynomial fitting, which can be applied to a light curve with many missing data. In addition to our own data, we incorporate other all sky survey data of the outburst to better understand the properties of the superhumps. Based on our observations, we conclude that MASTER J004527 is an SU UMa-type dwarf nova, since no early superhumps occurred.

astro-ph.SR↗

arXiv2022

Galaxy Manifold: Characterizing and understanding galaxies with two parameters

We report the discovery of a two-dimensional Galaxy Manifold within the multi-dimensional luminosity space of local galaxies. The multi-dimensional luminosity space is constructed using 11 bands that span from far ultraviolet to near-infrared for redshift < 0.1 galaxies observed with GALEX, SDSS, and UKIDSS. The two latent parameters are sufficient to express 93.2% of the variance in the galaxy sample, suggesting that this Galaxy Manifold is one of the most efficient representations of galaxies. The transformation between the observed luminosities and the manifold parameters as an analytic mapping is provided. The manifold representation provides accurate (85%) morphological classifications with a simple linear boundary, and galaxy properties can be estimated with minimal scatter (0.12 dex and 0.04 dex for star formation rate and stellar mass, respectively) by calibrating with the two-dimensional manifold location. Under the assumption that the manifold expresses the possible parameter space of galaxies, the evolution on the manifold is considered. We find that constant and exponentially decreasing star formation histories form almost orthogonal modes of evolution on the manifold. Through these simple models, we understand that the two modes are closely related to gas content, which suggests the close relationship of the manifold to gas accretion. Without assuming a star formation history, a gas-regulated model reproduces an exponentially declining star formation history with a timescale of $\sim$1.2 Gyrs on the manifold. Lastly, the found manifold suggests a paradigm where galaxies are characterized by their mass/scale and specific SFR, which agrees with previous studies of dimensionality reduction.

astro-ph.GA↗

arXiv2022

Wavelets and sparsity for Faraday tomography

Faraday tomography through broadband polarimetry can provide crucial information on magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters. However, the limited wavelength coverage of the instruments requires that we solve an ill-posed inverse problem when we want to obtain the Faraday dispersion function (FDF), a tomographic distribution of the magnetoionic media along the line of sight. This paper explores the use of wavelet transforms and the sparsity of the transformed FDFs in the form of wavelet shrinkage (WS) for finding better solutions to the inverse problem. We recently proposed the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT; Cooray et al. 2021), a new flexible algorithm that showed significant improvements over the popular methods such as Rotation Measure Synthesis. In this work, we introduce CRAFT+WS, a new version of CRAFT incorporating the ideas of wavelets and sparsity. CRAFT+WS exhibit significant improvements over the original CRAFT when tested for a complex FDF of realistic Galactic model. Reconstructions of FDFs demonstrate super-resolution in Faraday depth, uncovering previously unseen Faraday complexities in observations. The proposed approach will be necessary for effective cosmic magnetism studies using the Square Kilometre Array and its precursors.

astro-ph.IM↗

arXiv2020

An Iterative Reconstruction Algorithm for Faraday Tomography

Faraday tomography offers crucial information on the magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters, by observing its magnetoionic media. The observed linear polarization spectrum is inverse Fourier transformed to obtain the Faraday dispersion function (FDF), providing us a tomographic distribution of the magnetoionic media along the line of sight. However, this transform gives a poor reconstruction of the FDF because of the instrument's limited wavelength coverage. The current Faraday tomography techniques' inability to reliably solve the above inverse problem has noticeably plagued cosmic magnetism studies. We propose a new algorithm inspired by the well-studied area of signal restoration, called the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT). This iterative model-independent algorithm is computationally inexpensive and only requires weak physically-motivated assumptions to produce high fidelity FDF reconstructions. We demonstrate an application for a realistic synthetic model FDF of the Milky Way, where CRAFT shows greater potential over other popular model-independent techniques. The dependence of observational frequency coverage on the various techniques' reconstruction performance is also demonstrated for a simpler FDF. CRAFT exhibits improvements even over model-dependent techniques (i.e., QU-fitting) by capturing complex multi-scale features of the FDF amplitude and polarization angle variations within a source. The proposed approach will be of utmost importance for future cosmic magnetism studies, especially with broadband polarization data from the Square Kilometre Array and its precursors. We make the CRAFT code publicly available.

astro-ph.IM↗

arXiv2020

A Study on the Baryon Acoustic Oscillation with Topological Data Analysis

(abridged) The scale of the acoustic oscillation of baryons at the baryon-photon decoupling is imprinted on the spatial distribution of galaxies in the Universe, known as the baryon acoustic oscillation (BAO). The correlation functions and power spectrum are used as a central tool for the studies on the BAO analysis. In this work, we analyzed the spatial distribution of galaxies with a method from the topological data analysis (TDA), in order to detect and examine the BAO signal in the galaxy distribution. The TDA provides a method to treat various types of "holes" in point set data, by constructing the persistent homology (PH) group from the geometric structure of data points and handling the topological information of the dataset. We can obtain the information on the size, position, and statistical significance of the holes in the data. A particularly strong point of the persistent homology is that it can classify the holes by their spatial dimension, i.e., a 0-dim separation, 1-dim loop, 2-dim shell, etc. We first analyzed the simulation datasets with and without the baryon physics to examine the performance of the PH method. We found that the PH is indeed able to detect the BAO signal: simulation data with baryon physics present a prominent signal from the BAO, while data without baryon physics does not show this signal. Then, we applied the PH to a quasar sample at $z <1.0$ from extended Baryon Oscillation Spectroscopic Survey in Sloan Digital Sky Survey Data Release 14. We discovered a characteristic hole (a hollow shell) at a scaler $\sim150\ [{\rm Mpc}]$. This exactly corresponds to the BAO signature imprinted in the galaxy/quasar distribution. We performed this analysis on a small subsample of 2000 quasars. This clearly demonstrates that the PH analysis is very efficient in finding this type of topological structures even if the sampling is very sparse.

astro-ph.CO↗

arXiv2020

A Method for Unmasking Incomplete Astronomical Signals: Application to CO Multi-line Imaging of Nearby Galaxies Project

Photometric surveys have provided incredible amounts of astronomical information in the form of images. However, astronomical images often contain artifacts that can critically hinder scientific analysis by misrepresenting intensities or contaminating catalogs as artificial objects. These affected pixels need to be masked and dealt with in any data reduction pipeline. In this paper, we present a flexible, iterative algorithm to recover (unmask) astronomical images where some pixels are lacking. We demonstrate the application of the method on some intensity calibration source images in CO Multi-line Imaging of Nearby Galaxies (COMING) Project conducted using the 45m telescope at Nobeyama Radio Observatory (NRO). The proposed algorithm restored artifacts due to a detector error in the intensity calibration source images. The restored images were used to calibrate 11 out of 147 observed galaxy maps in the survey. The tests show that the algorithm can restore measured intensities at sub 1% error even for noisy images (SNR = 2.4), despite lacking a significant part of the image. We present the formulation of the reconstruction algorithm, discuss its possibilities and limitations for extensions to other astronomical signals and the results of the COMING application.

astro-ph.IM↗

arXiv2019

CO Multi-line Imaging of Nearby Galaxies (COMING) IV. Overview of the Project

Observations of the molecular gas in galaxies are vital to understanding the evolution and star-forming histories of galaxies. However, galaxies with molecular gas maps of their whole discs having sufficient resolution to distinguish galactic structures are severely lacking. Millimeter wavelength studies at a high angular resolution across multiple lines and transitions are particularly needed, severely limiting our ability to infer the universal properties of molecular gas in galaxies. Hence, we conducted a legacy project with the 45 m telescope of the Nobeyama Radio Observatory, called the CO Multi-line Imaging of Nearby Galaxies (COMING), which simultaneously observed 147 galaxies with high far-infrared flux in $^{12}$CO, $^{13}$CO, and C$^{18}$O $J=1-0$ lines. The total molecular gas mass was derived using the standard CO-to-H$_2$ conversion factor and found to be positively correlated with the total stellar mass derived from the WISE $3.4 μ$m band data. The fraction of the total molecular gas mass to the total stellar mass in galaxies does not depend on their Hubble types nor the existence of a galactic bar, although when galaxies in individual morphological types are investigated separately, the fraction seems to decrease with the total stellar mass in early-type galaxies and vice versa in late-type galaxies. No differences in the distribution of the total molecular gas mass, stellar mass, and the total molecular gas to stellar mass ratio was observed between barred and non-barred galaxies, which is likely the result of our sample selection criteria, in that we prioritized observing FIR bright (and thus molecular gas-rich) galaxies.

astro-ph.GA↗