T. Heikkila

发表

Observational constraints on the optical and near-infrared emission from the neutron star–black hole binary merger candidate S190814bv

M. L. Pumo, D. A. Kann, R. Kotak, 2020, Astronomy & Astrophysics.

The Gravitational-wave Optical Transient Observer (GOTO): Prototype performance and prospects for transient science

K. Wiersema, A. Levan, J. Maund, 2021, Monthly Notices of the Royal Astronomical Society.

Searching for electromagnetic counterparts to gravitational-wave merger events with the prototype Gravitational-Wave Optical Transient Observer (GOTO-4)

R. Kotak, K. Wiersema, A. Levan, 2020, Monthly Notices of the Royal Astronomical Society.

Searching for Fermi GRB optical counterparts with the prototype Gravitational-wave Optical Transient Observer (GOTO)

R. Kotak, K. Wiersema, A. Levan, 2021, Monthly Notices of the Royal Astronomical Society.

Transient-optimized real-bogus classification with Bayesian convolutional neural networks – sifting the GOTO candidate stream

R. Kotak, K. Wiersema, A. Levan, 2021, Monthly Notices of the Royal Astronomical Society.

Machine learning for transient recognition in difference imaging with minimum sampling effort

Gavin Ramsay, Evert Rol, Paul O'Brien, 2020, 2008.10178.

Light-curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

R. Kotak, K. Wiersema, A. Levan, 2021, Monthly Notices of the Royal Astronomical Society.

Progenitor constraints for core-collapse supernovae from Chandra X-ray observations

S. Mattila, S. Tsygankov, J. Poutanen, 2016, 1601.00871.

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

R. Kotak, K. Wiersema, A. Levan, 2022, Monthly Notices of the Royal Astronomical Society.

Machine learning for transient recognition in difference imaging with minimum sampling effort

R. Kotak, K. Wiersema, A. Levan, 2020, Monthly Notices of the Royal Astronomical Society.