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Chris Richardson

Publications and source records attributed to Chris Richardson.

5 recordsLinked to original sources

Constructing X-ray Spectral Models of Galaxies: Varying Contributions from X-ray Binary Populations with Host Galaxy Properties

Recent work has shown that the emission from X-ray binary (XRB) populations in galaxies varies with stellar mass ($M_\star$), star formation rate (SFR), and metallicity ($Z$). Such scaling relations are widely used to predict the XRB contributions to galaxy-integrated X-ray luminosities including studies focused on dwarf active galactic nuclei (AGN) and the X-ray radiation field during the epoch of heating in the early ($z \geq 8$) universe. However, as galaxies approach low SFR and low $Z$, the relatively shallow slope of the XRB luminosity function (XLF) can yield very large stochastic variations in the total X-ray luminosity expected from the XRB population, for fixed values of $M_\star$, SFR, and $Z$. We have created a procedure to statistically sample any XLF and model total X-ray spectra for XRB populations and their stochastic uncertainties. We demonstrate the accuracy of this procedure using data for galaxies ranging from high to low $M_\star$, SFR, and $Z$ and generating X-ray spectral models consistent with Chandra observations. For galaxies that lie on the galactic main-sequence, we can relate SFR and $Z$ to $M_\star$ using established $M_\star$-SFR and $M_\star$-$Z$ relations. Applying these relations, we construct main-sequence (MS) XRB spectral models, which provide typical XRB spectral shapes, normalizations, and uncertainties as a function of $M_\star$. The spectral model library associated with this work is available at https://doi.org/10.5281/zenodo.20126734.

astro-ph.GA

The Goldilocks Molecule: H$_{2}$ Emission Lines Can Identify Elusive Dwarf AGN

We propose using H$_{2}$ emission lines as a novel diagnostic to identify dwarf AGN by running photoionization models incorporating active intermediate-mass black holes and using an observed WISE dwarf AGN as a template. Though many dwarf AGN would be incorrectly classified as star-forming if the H$_{2}$~2.12~$\mu$m/Br$\gamma$ ratio is used, ratios between H$_{2}$ emission lines can reveal active IMBHs in cases where log $n_{\text{H}} \gtrsim 4.0$. This includes the case where $M_{\text{BH}} = 10^3~M_{\odot}$, even though the optical strong lines and NIR coronal lines would likely go undetected. We conclude that H$_{2}$ emission lines show promise in detecting the most elusive IMBHs.

astro-ph.GA

Trends in AI and Human-AI Interaction in Clinical Trials -- A Hybrid Human-AI Exploration

This paper examines records retrieved from the ClinicalTrials.gov registry to characterize temporal trends in AI terminology and the geographical distribution of AI trials. The work also reports on an exploratory hybrid human-AI approach to analyzing human-AI interaction trends in registered clinical trials. The hybrid workflow comprised a frontier generative AI model (GPT-5.5) and human review to screen and categorize records returned by an AI-focused search. The findings indicate a marked increase in AI-related trials over time, with recent growth in references to machine learning, deep learning, chatbots, GPTs, and large language models. Geographically, China and the United States accounted for the largest numbers of AI-related trials, with notable recent increases in several other countries including Italy, France, Spain, the UK and Turkey (Türkiye). In a random sample of 100 records, human and AI classifiers showed good agreement in identifying studies not substantively using AI, but lower agreement in classifying human-AI interaction, particularly where health professional interaction was ambiguous or insufficiently described. Overall, the results suggest that hybrid human-AI screening of clinical trial records is potentially viable, but clearer trial reporting and more precise interaction definitions will benefit the process.

cs.AI

Exploring the mysterious high-ionization source powering [Ne V] in high-z analog SBS0335-052 E with JWST/MIRI

Nearby blue compact dwarf galaxies (BCDs) share similar properties with objects from the Epoch of Reionization revealed by JWST, in terms of low stellar mass, low metallicity and high specific star-formation rate. Thus, they represent ideal local laboratories for detailed multi-wavelength studies to understand their properties and the mechanisms shaping them. We report the first JWST MIRI/MRS observations of the BCD SBS 0335-052 E, analyzing MIR emission lines tracing different levels of ionization (e.g., [NeII], [SIV], [NeIII], [OIV], [NeV]) of the ionized gas. SBS 0335-052 E MIR emission is characterized by a bright point source, located in one of the youngest and most embedded stellar clusters ($t\sim3$ Myr, $A_V\sim15$), and underlying extended high-ionization emission (i.e., [OIV], [NeV]) from the surroundings of the older and less dusty stellar clusters ($t< 20 $ Myr, $A_V\sim8$). From the comparison with state-of-the-art models, we can exclude shocks, X-ray binaries, and old stellar populations as the main sources of the high ionization. Interestingly, a 4-16% contribution of a $\sim10^5$ M$_\odot$ intermediate massive black hole (IMBH) is needed to justify the strong [NeV]/[NeII] and would be consistent with optical/UV line ratios from previous studies. However, even IMBH models cannot explain the strongest [OIV]/[NeIII]. Also, star-forming models (regardless of including X-ray binaries) struggle to reproduce even the lower ionization line ratios (e.g., [SIV]/[NeII]) typically observed in BCDs. Overall, while current models suggest the need to account for an accreting IMBH in this high-$z$ analog, limitations still exist in predicting high-ionization emission lines (I.P. $>54$ eV) when modeling these low-metallicity environments, thus other sources of ionization cannot be fully ruled out.

astro-ph.GA

Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models

Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model's output, a straightforward approach is to incorporate past user data into the language model prompt, but this approach can result in lengthy inputs exceeding limitations on input length and incurring latency and cost issues. Existing approaches tackle such challenges by selectively extracting relevant user data (i.e. selective retrieval) to construct a prompt for downstream tasks. However, retrieval-based methods are limited by potential information loss, lack of more profound user understanding, and cold-start challenges. To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs. The summaries can be generated and stored offline, enabling real-world systems with runtime constraints like voice assistants to leverage the power of LLMs. Experiments show our method with 75% less of retrieved user data is on-par or outperforms retrieval augmentation on most tasks in the LaMP personalization benchmark. We demonstrate that offline summarization via LLMs and runtime retrieval enables better performance for personalization on a range of tasks under practical constraints.

cs.CL