arXiv Machine Learning June 15, 2026 By Bruno Santos Meneses Barreto, Marcio Eisencraft

Multi-Variable Stellar Parameter Estimation Using Residual Multitask Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2606. 13868v1 Announce Type: cross Abstract: We present an end-to-end pipeline for estimating stellar parameters from Sloan Digital Sky Survey Data Release 12 spectra using a fully connected multitask neural network with residual blocks, whose hyperparameters are tuned via Bayesian optimization.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 2

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.

By Siddharth Chaini, Federica B. Bianco, Ashish Mahabal

benchmarks

arXiv Machine Learning

Jun 24

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.

By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga

safety

arXiv Machine Learning

Jun 18

Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection

arXiv:2606. 18464v1 Announce Type: cross Abstract: Detecting the tiny Doppler shifts induced by Earth-mass planets in stellar radial-velocity measurements remains extremely challenging due to stellar activity.

By Isidro G\'omez-Vargas, Xavier Dumusque, Yinan Zhao, Khaled Al Moulla, Michael Cretignier

arXiv Machine Learning

Jun 18

ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets

arXiv:2606. 18338v1 Announce Type: new Abstract: The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets.

By Edward T. Stevenson, Mei Ting Mak, Eric Wolf, Denis E. Sergeev, Tobi Hammond, N. J. Mayne, Miles Cranmer

benchmarks

arXiv Machine Learning

Jun 24

Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission

arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.

By Muallim Yakubu, Vwavware Oruaode Jude

llms diffusion benchmarks safety

arXiv Machine Learning

Jun 17

Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows

arXiv:2606. 17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget.

By Alejandro Calle-Saldarriaga, Felix Jimenez, Jack Grosskreuz, Jiazheng Wang, Jonathan Hobbs, Matthias Katzfuss

Related stories

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection

ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets

Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission

Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows