Global asteroseismology of 19,000 red giants in the TESS Continuous Viewing Zones
arXiv:2604.00498v1 Announce Type: cross Abstract: TESS (Transiting Exoplanet Survey Satellite) has produced long-term photometry for millions of stars across the sky. In this work, we present an asteroseismic catalogue of 19,151 red giants in the TESS Continuous Viewing Zones using sectors 1--87 (Years 1--7). We visually assessed the power spectra for oscillations, and then applied the computationally efficient nuSYD method to confirm reliability. We identified an increase of 80% in the number of previously known oscillating red giants at a TESS magnitude $>$ 8. We determined the frequency of maximum power ($\rm \nu_{max}$) and the large frequency separation ($\rm \Delta \nu$) using the pySYD pipeline, achieving typical precisions of 1.5% and 1%, respectively. We classified the stars into
View PDF
Abstract:TESS (Transiting Exoplanet Survey Satellite) has produced long-term photometry for millions of stars across the sky. In this work, we present an asteroseismic catalogue of 19,151 red giants in the TESS Continuous Viewing Zones using sectors 1--87 (Years 1--7). We visually assessed the power spectra for oscillations, and then applied the computationally efficient nuSYD method to confirm reliability. We identified an increase of 80% in the number of previously known oscillating red giants at a TESS magnitude $>$ 8. We determined the frequency of maximum power ($\rm \nu_{max}$) and the large frequency separation ($\rm \Delta \nu$) using the pySYD pipeline, achieving typical precisions of 1.5% and 1%, respectively. We classified the stars into Red Giant Branch (RGB) and Core Helium Burning (CHeB) classes using a Convolutional Neural Network. Using spectroscopic data for 10,298 stars with reliable asteroseismic measurements, we have been able to measure stellar mass and radii with precisions of 7.5% and 2.8%, which is comparable to that from 4-yr $Kepler$ data. A comparison of the seismic radii with Gaia radii shows excellent agreement. With three years of TESS data, the asteroseismic parameters are precise enough to identify the RGB bump and delineate the Zero Age Helium Burning edge. Combined with astrometric data, these parameters reveal established trends across the Galactic plane, providing a valuable set of uniformly determined asteroseismic parameters for Galactic Archaeology.
Comments: Accepted for publication in MNRAS. The gif version of figure 14 is available at this this https URL
Subjects:
Solar and Stellar Astrophysics (astro-ph.SR); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:2604.00498 [astro-ph.SR]
(or arXiv:2604.00498v1 [astro-ph.SR] for this version)
https://doi.org/10.48550/arXiv.2604.00498
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sreenivasan K R [view email] [v1] Wed, 1 Apr 2026 05:30:56 UTC (10,113 KB)
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
neural networkannouncemillion
Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration
arXiv:2604.02869v1 Announce Type: new Abstract: Training tool-calling agents with reinforcement learning on multi-turn tasks remains challenging due to sparse outcome rewards and difficult credit assignment across conversation turns. We present the first application of MT-GRPO (Multi-Turn Group Relative Policy Optimization) combined with GTPO (Generalized Token-level Policy Optimization) for training a tool-calling agent on realistic customer service tasks with an LLM-based user simulator. Through systematic analysis of training rollouts, we discover that naively designed dense per-turn rewards degrade performance by up to 14 percentage points due to misalignment between reward discriminativeness and advantage direction. We introduce Iterative Reward Calibration, a methodology for designin
EMS: Multi-Agent Voting via Efficient Majority-then-Stopping
arXiv:2604.02863v1 Announce Type: new Abstract: Majority voting is the standard for aggregating multi-agent responses into a final decision. However, traditional methods typically require all agents to complete their reasoning before aggregation begins, leading to significant computational overhead, as many responses become redundant once a majority consensus is achieved. In this work, we formulate the multi-agent voting as a reliability-aware agent scheduling problem, and propose an Efficient Majority-then-Stopping (EMS) to improve reasoning efficiency. EMS prioritizes agents based on task-aware reliability and terminates the reasoning pipeline the moment a majority is achieved from the following three critical components. Specifically, we introduce Agent Confidence Modeling (ACM) to esti
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!