arXiv subjects

Kevin McCarthy

Publications and source records attributed to Kevin McCarthy.

5 recordsLinked to original sources

arXiv2026

PIFFLE: Characterizing the Foreground Contributions from 4 Decades in Halo Mass to the FRB20230907D Dispersion Measure

We characterize the foreground environment of FRB20230907D, localized to a galaxy at $z=0.464$, which has an observed dispersion measure of ${\rm DM}_{\rm obs}=1031~{\rm pc~cm^{-3}}$. At its redshift, FRB20230907D lies above the Macquart relation, the expected relation between cosmological dispersion measure and the source redshift, indicating a substantial excess DM along this line of sight. We use Subaru/PFS and SDSS spectroscopy, published group catalogs, Rubin/LSST imaging, and eROSITA X-ray data to characterize the foreground structures that may account for this excess. A friends-of-friends search identifies a massive foreground system at $z\simeq0.09$ with $M_{200}\simeq5.2\times10^{14}~M_\odot$, while low redshift catalogs reveal an additional group at $z\simeq0.02565$. Assuming that the halo gas follows a modified-NFW halo density profile, we estimate observer frame contributions of $150^{+110}_{-70}~{\rm pc~cm^{-3}}$ and $80^{+60}_{-40}~{\rm pc~cm^{-3}}$ from these systems, respectively. Together with the Milky Way, diffuse intergalactic medium, Virgo cluster, M49 group, and host galaxy contributions, these foreground structures can account for the excess dispersion measure of FRB20230907D within uncertainties. This highlights the importance of dense foreground spectroscopy and multi-wavelength data.

astro-ph.CO↗

arXiv2026

AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology I: Literature Review

We investigate how well large language models (LLMs) can assist with literature reviews for scientific research. We perform a controlled study of eight expert-conceived research projects across the areas of physics, astrophysics, and cosmology. Each project has a defined background and goal, and human experts and AI prompters are asked to perform identical literature review tasks in parallel. We compare the relevant literature selected by humans with that selected by mid-2025 LLMs (ChatGPT-4o, ChatGPT Deep Research, and Gemini). We find the overlap between human- and AI-selected references to be small ($<$6\%), indicating that AI models do not yet reproduce a competent expert search on their own, though they have the potential to complement literature searches by humans. We then assess the reliability and completeness of AI-generated candidate references, distinguishing two types of hallucination: fabrications (references to nonexistent papers) and metadata mismatches (real papers with one or more incorrect fields). We find that while fabricated references make up 3\% of the AI-generated references, 64\% are real papers with at least one incorrect field (title, author, year, journal, DOI, or link), indicating that the mid-2025 models require systematic verification. However, the performance is significantly improved for the 2026 model ChatGPT Pro 5.5, with a single-project test showing zero fabrication or metadata mismatches.

astro-ph.IM↗

arXiv2026

AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology II: Project Planning and Proposal Evaluation

We investigate how well large language models (LLMs) can assist scientific project planning and proposal evaluation. One-page project plans were independently generated for eight expert-conceived research projects in physics, astrophysics, and cosmology by human researchers and three contemporary LLMs (ChatGPT, Claude, and DeepSeek; mid-2025 models, used with their default tool access). The resulting 32 proposals were blindly evaluated by four human reviewers and two newer frontier LLMs (Claude Opus 4.8 and ChatGPT Pro 5.5) using a four-aspect evaluation rubric. Reviewers were also asked to identify whether each proposal was written by a human or an AI. Human reviewers rated human- and AI-written proposals similarly overall, whereas both AI reviewers scored AI-written proposals about one point higher (on a five-point scale) than human-written proposals. Human reviewers correctly identified human- and AI-written proposals 72% and 79% of the time, respectively, while both AI reviewers correctly classified all 32 proposals (100%). These results suggest that current LLMs can produce project plans comparable to human-written ones in the eyes of human reviewers, but that AI reviewers show a systematic preference for AI-generated proposals. Our results suggest caution when deploying LLMs widely in proposal preparation and evaluation.

cs.CL↗

arXiv2026

The Hunt for Red Dual AGNs I: Spatially-Resolved Mid-IR Dual AGNs in the DeCam Legacy Survey

Theoretical studies predict that dual AGNs are a critical stage of galaxy merger-driven supermassive black hole growth. Systematic searches for dual AGNs typically target late-stage mergers ($\leq10$ kpc nuclear separations) and select AGNs based on optical diagnostics. Yet, simulations predict that obscuration can occur early in the merger sequence, and that a significant fraction of dual AGNs can be found beyond $10$ kpc. Here, we report on a new sample of 157 spatially resolved mid-IR dual AGNs candidates selected based upon their mid-IR $W1-W2$ colors from the Wide-Field Infrared Survey Explorer and optically classified as galaxy merger candidates using imaging from the Dark Energy Camera Legacy Survey. Spectroscopic results are presented for approximately 2/3 of the sample. 76 candidates have been confirmed to reside in galaxy mergers; among these, 13 have been confirmed as bona fide mid-IR dual AGNs, while 63 represent strong dual AGN candidates that require further examination. 46 candidates have been rejected as non-merger contaminants (foreground-background AGNs, separations inconsistent with interacting galaxies, etc.). 35 candidates still await spectroscopic coverage. The confirmed and high confidence dual AGN candidates exhibit separations of 14.5-129 kpc; $>50$% reside at separations $>50$ kpc. Confirmed and high confidence candidates also exhibit a diversity of nuclear optical BPT classes. Seyfert-Seyferts and Seyfert-HIIs dominate the overall BPT pairs sample. 31% of confirmed mid-IR dual AGNs reside in multi-mergers involving three or more galaxies. The diversity in AGN properties and environments identified in this work highlights the importance of multiwavelength selection strategies and analyses in the quest to holistically understand dual AGNs as a population.

astro-ph.GA↗

arXiv2025

Searching for Low-Redshift Hot Dust-Obscured Galaxies

Hot Dust-Obscured Galaxies (Hot DOGs), discovered by the "W1W2 dropout" selection at high redshifts ($z\sim$ 2-4), are a rare population of hyper-luminous obscured quasars. Their number density is comparable to similarly luminous type 1 quasars in the same redshift range, potentially representing a short, yet critical stage in galaxy evolution. The evolution in their number density towards low redshift, however, remains unclear as their selection function is heavily biased against objects at $z\lesssim2$. We combine data from the WISE and Herschel archives to search for Hot DOGs at $z<0.5$ based on their unique spectral energy distributions. We find 68 candidates, and spectroscopic observations confirm that 3 of them are at $z<0.5$. For those 3 we find their black hole accretion is close to the Eddington limit, with lower bolometric luminosities and black hole masses than those of higher-$z$ Hot DOGs. Compared to high-$z$ systems, these low-$z$ systems are closer to the local relation between host galaxy stellar mass and black hole mass but still lie above it, and we discuss several possible scenarios for it. Finally, we also find the surface number density of $z<$0.5 Hot DOGs is $\rm 2.4 \times 10^{-3}$ deg$^{-2}$, about an order of magnitude lower than high-$z$ Hot DOGs but comparable to hyper-luminous unobscured quasars in the same redshift range. These results further support the idea that Hot DOGs may be a transitional phase of galaxy evolution.

astro-ph.GA↗