arXiv subjects

Ricco C. Venterea

Publications and source records attributed to Ricco C. Venterea.

2 recordsLinked to original sources

arXiv2026

The Atacama Cosmology Telescope: Release of A databaSe of millimeTeR ObservatioNs of Asteroids Using acT (ASTRONAUT)

We present A databaSe of millimeTeR ObservatioNs of Asteroids Using acT (ASTRONAUT) hosted on Amazon Web Services, Inc. (AWS) in the form of a public Amazon Simple Storage Service (S3) bucket. This bucket is an Amazon cloud storage database containing flux measurements for a group of asteroids at millimeter (mm) wavelengths. These measurements were collected by the Atacama Cosmology Telescope (ACT) from 2017 to 2021 in frequency bands centered near 90, 150, and 220 GHz. The ASTRONAUT database contains observation times, normalized flux values, and associated error bars for 170 asteroids above a signal-to-noise ratio of 5 for a single frequency band over the stacked co-added maps. We provide an example in generating light curves with this database. We also present a Jupyter notebook to serve as a reference guide when using the S3 bucket. The container and notebook are publicly available in a GitHub repository.

astro-ph.EP↗

arXiv2025

A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes.

gr-qc↗