Afonso Vale | Searcharxiv

arXiv2026

Accurate spectroscopic redshift estimation using non-negative matrix factorization: application to MUSE spectra

Accurate and automated galaxy redshift determination is essential for maximizing the scientific return of spectroscopic surveys. In this paper, we propose a data-driven method to address this challenge. The method first learns a rest-frame representation of galaxy spectra using Non-negative Matrix Factorization (NMF). The method then reconstructs new spectra using this representation at different trial redshifts, and identifies the correct redshift by selecting the one that minimizes the reconstruction error. We apply our method to galaxy spectra from the Multi Unit Spectroscopic Explorer (MUSE), covering redshifts from 0 to 6.7. Our method achieves an overall success rate of 93.7%. We further demonstrate two applications: (i) the separation between true and false sources, and (ii) the detection of blended sources from one-dimensional spectra. Our results demonstrate that NMF-based representations provide a powerful and physically motivated framework for redshift estimation in current and future large spectroscopic surveys.

astro-ph.IM↗

arXiv2025

Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy

Quadcopter attitude control involves two tasks: smooth attitude tracking and aggressive stabilization from arbitrary states. Although both can be formulated as tracking problems, their distinct state spaces and control strategies complicate a unified reward function. We propose a multitask deep reinforcement learning framework that leverages parallel simulation with IsaacGym and a Graph Convolutional Network (GCN) policy to address both tasks effectively. Our multitask Soft Actor-Critic (SAC) approach achieves faster, more reliable learning and higher sample efficiency than single-task methods. We validate its real-world applicability by deploying the learned policy - a compact two-layer network with 24 neurons per layer - on a Pixhawk flight controller, achieving 400 Hz control without extra computational resources. We provide our code at https://github.com/robot-perception-group/GraphMTSAC\_UAV/.

cs.RO↗