arXiv subjects

M. Ho

Publications and source records attributed to M. Ho.

6 recordsLinked to original sources

arXiv2026

Let There Be Claws: An Early Social Network Analysis of AI Agents on Moltbook

Within twelve days of launch, an AI-native social platform exhibits extreme attention concentration, hierarchical role separation, and one-way attention flow, consistent with the hypothesis that stratification in agent ecosystems can emerge rapidly rather than gradually. We analyse publicly observable traces from a 12-day window of Moltbook (28 January -- 8 February 2026), comprising 20,040 posts and 192,410 comments from 15,083 accounts across 759 submolts. We construct co-participation and directed-comment graphs and report reciprocity, community structure, and centrality, alongside descriptive content themes. Under a commenter--post-author tie definition, interaction is strongly asymmetric (reciprocity ~1%), and HITS centrality separates cleanly into hub and authority roles, consistent with broadcast-style attention rather than mutual exchange. Engagement is highly unequal: attention is far more concentrated than production (upvote Gini = 0.992 vs. posting Gini = 0.601), and early-arriving accounts accumulate substantially higher cumulative upvotes prior to exposure-time correction, suggesting rich-get-richer dynamics. Participation is brief and bursty (median observed lifespan 2.48 minutes; 54.8% of posts occur within six peak UTC hours). Embedding-based topic modelling identifies diverse thematic clusters, including technical discussion of memory and identity, onboarding messages, and formulaic token-minting content. These results provide an early structural baseline for large-scale agent--agent social interaction and suggest that familiar forms of hierarchy, amplification, and role differentiation can arise on compressed timescales in agent-facing platforms.

physics.soc-ph↗

arXiv2025

Construction of the Damped Ly$\alpha$ Absorber Catalog for DESI DR2 Ly$\alpha$ BAO

We present the Damped Ly$\alpha$ Toolkit for automated detection and characterization of Damped Ly$\alpha$ absorbers (DLA) in quasar spectra. Our method uses quasar spectral templates with and without absorption from intervening DLAs to reconstruct observed quasar forest regions. The best-fitting model determines whether a DLA is present while estimating the redshift and \texttt{HI} column density. With an optimized quality cut on detection significance ($\Delta \chi_{r}^2>0.03$), the technique achieves an estimated 80\% purity and 79\% completeness when evaluated on simulated spectra with S/N~$>2$ that are free of broad absorption lines (BAL). We provide a catalog containing candidate DLAs from the DLA Toolkit detected in DESI DR1 quasar spectra, of which 21,719 were found in S/N~$>2$ spectra with predicted $\log_{10} (N_\texttt{HI}) > 20.3$ and detection significance $\Delta \chi_{r}^2 >0.03$. We compare the Damped Ly$\alpha$ Toolkit to two alternative DLA finders based on a convolutional neural network (CNN) and Gaussian process (GP) models. We present a strategy for combining these three techniques to produce a high-fidelity DLA catalog from DESI DR2 for the Ly$\alpha$ forest baryon acoustic oscillation measurement. The combined catalog contains 41,152 candidate DLAs with $\log_{10} (N_\texttt{HI}) > 20.3$ from quasar spectra with S/N~$>2$. We estimate this sample to be approximately 85\% pure and 79\% complete when BAL quasars are excluded.

astro-ph.CO↗

arXiv2025

DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cosmological Constraints

We present baryon acoustic oscillation (BAO) measurements from more than 14 million galaxies and quasars drawn from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2), based on three years of operation. For cosmology inference, these galaxy measurements are combined with DESI Lyman-$\alpha$ forest BAO results presented in a companion paper. The DR2 BAO results are consistent with DESI DR1 and SDSS, and their distance-redshift relationship matches those from recent compilations of supernovae (SNe) over the same redshift range. The results are well described by a flat $\Lambda$CDM model, but the parameters preferred by BAO are in mild, $2.3\sigma$ tension with those determined from the cosmic microwave background (CMB), although the DESI results are consistent with the acoustic angular scale $\theta_*$ that is well-measured by Planck. This tension is alleviated by dark energy with a time-evolving equation of state parametrized by $w_0$ and $w_a$, which provides a better fit to the data, with a favored solution in the quadrant with $w_0>-1$ and $w_a<0$. This solution is preferred over $\Lambda$CDM at $3.1\sigma$ for the combination of DESI BAO and CMB data. When also including SNe, the preference for a dynamical dark energy model over $\Lambda$CDM ranges from $2.8-4.2\sigma$ depending on which SNe sample is used. We present evidence from other data combinations which also favor the same behavior at high significance. From the combination of DESI and CMB we derive 95% upper limits on the sum of neutrino masses, finding $\sum m_\nu<0.064$ eV assuming $\Lambda$CDM and $\sum m_\nu<0.16$ eV in the $w_0w_a$ model. Unless there is an unknown systematic error associated with one or more datasets, it is clear that $\Lambda$CDM is being challenged by the combination of DESI BAO with other measurements and that dynamical dark energy offers a possible solution.

astro-ph.CO↗

arXiv2023

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.

astro-ph.IM↗

arXiv2021

CLMM: a LSST-DESC Cluster weak Lensing Mass Modeling library for cosmology

We present the v1.0 release of CLMM, an open source Python library for the estimation of the weak lensing masses of clusters of galaxies. CLMM is designed as a standalone toolkit of building blocks to enable end-to-end analysis pipeline validation for upcoming cluster cosmology analyses such as the ones that will be performed by the LSST-DESC. Its purpose is to serve as a flexible, easy-to-install and easy-to-use interface for both weak lensing simulators and observers and can be applied to real and mock data to study the systematics affecting weak lensing mass reconstruction. At the core of CLMM are routines to model the weak lensing shear signal given the underlying mass distribution of galaxy clusters and a set of data operations to prepare the corresponding data vectors. The theoretical predictions rely on existing software, used as backends in the code, that have been thoroughly tested and cross-checked. Combined, theoretical predictions and data can be used to constrain the mass distribution of galaxy clusters as demonstrated in a suite of example Jupyter Notebooks shipped with the software and also available in the extensive online documentation.

astro-ph.CO↗

arXiv2020

Noisy pre-processing facilitating a photonic realisation of device-independent quantum key distribution

Device-independent quantum key distribution provides security even when the equipment used to communicate over the quantum channel is largely uncharacterized. An experimental demonstration of device-independent quantum key distribution is however challenging. A central obstacle in photonic implementations is that the global detection efficiency, i.e., the probability that the signals sent over the quantum channel are successfully received, must be above a certain threshold. We here propose a method to significantly relax this threshold, while maintaining provable device-independent security. This is achieved with a protocol that adds artificial noise, which cannot be known or controlled by an adversary, to the initial measurement data (the raw key). Focusing on a realistic photonic setup using a source based on spontaneous parametric down conversion, we give explicit bounds on the minimal required global detection efficiency.

quant-ph↗