关注
Michelle Lochner
Michelle Lochner
在 uwc.ac.za 的电子邮件经过验证
标题
引用次数
引用次数
年份
Photometric supernova classification with machine learning
M Lochner, JD McEwen, HV Peiris, O Lahav, MK Winter
The Astrophysical Journal Supplement Series 225 (2), 31, 2016
2302016
The MeerKAT Galaxy Cluster Legacy Survey-I. Survey Overview and Highlights
K Knowles, WD Cotton, L Rudnick, F Camilo, S Goedhart, R Deane, ...
Astronomy & Astrophysics 657, A56, 2022
942022
Machine learning cosmological structure formation
L Lucie-Smith, HV Peiris, A Pontzen, M Lochner
Monthly Notices of the Royal Astronomical Society 479 (3), 3405-3414, 2018
742018
Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: a pioneering process of community-focused experimental design
FB Bianco, Ž Ivezić, RL Jones, ML Graham, P Marshall, A Saha, ...
The Astrophysical Journal Supplement Series 258 (1), 1, 2021
732021
Science-driven optimization of the LSST observing strategy
P Marshall, T Anguita, FB Bianco, EC Bellm, N Brandt, W Clarkson, ...
arXiv preprint arXiv:1708.04058, 2017
692017
ASTRONOMALY: Personalised active anomaly detection in astronomical data
M Lochner, BA Bassett
Astronomy and Computing 36, 100481, 2021
682021
The role of machine learning in the next decade of cosmology
M Ntampaka, C Avestruz, S Boada, J Caldeira, J Cisewski-Kehe, ...
arXiv preprint arXiv:1902.10159, 2019
672019
DeepSource: point source detection using deep learning
A Vafaei Sadr, EE Vos, BA Bassett, Z Hosenie, N Oozeer, M Lochner
Monthly Notices of the Royal Astronomical Society 484 (2), 2793-2806, 2019
492019
Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream
G Narayan, T Zaidi, MD Soraisam, Z Wang, M Lochner, T Matheson, ...
The Astrophysical Journal Supplement Series 236 (1), 9, 2018
442018
MeerKLASS: MeerKAT large area synoptic survey
MG Santos, M Cluver, M Hilton, M Jarvis, GIG Jozsa, L Leeuw, O Smirnov, ...
arXiv preprint arXiv:1709.06099, 2017
432017
Practical galaxy morphology tools from deep supervised representation learning
M Walmsley, AMM Scaife, C Lintott, M Lochner, V Etsebeth, T Géron, ...
Monthly Notices of the Royal Astronomical Society 513 (2), 1581-1599, 2022
392022
Unsupervised machine learning for transient discovery in deeper, wider, faster light curves
S Webb, M Lochner, D Muthukrishna, J Cooke, C Flynn, A Mahabal, ...
Monthly Notices of the Royal Astronomical Society 498 (3), 3077-3094, 2020
362020
Bayesian inference for radio observations
M Lochner, I Natarajan, JTL Zwart, O Smirnov, BA Bassett, N Oozeer, ...
Monthly Notices of the Royal Astronomical Society 450 (2), 1308-1319, 2015
332015
The photometric lsst astronomical time-series classification challenge plasticc: Selection of a performance metric for classification probabilities balancing diverse science goals
AI Malz, R Hložek, T Allam, A Bahmanyar, R Biswas, M Dai, L Galbany, ...
The Astronomical Journal 158 (5), 171, 2019
302019
Wide-field multi-object spectroscopy to enhance dark energy science from LSST
R Mandelbaum, J Blazek, NE Chisari, T Collett, L Galbany, E Gawiser, ...
arXiv preprint arXiv:1903.09323, 2019
262019
The impact of observing strategy on cosmological constraints with LSST
M Lochner, D Scolnic, H Almoubayyed, T Anguita, H Awan, E Gawiser, ...
The Astrophysical Journal Supplement Series 259 (2), 58, 2022
242022
Bayesian anomaly detection and classification
E Roberts, BA Bassett, M Lochner
arXiv preprint arXiv:1902.08627, 2019
212019
zBEAMS: a unified solution for supernova cosmology with redshift uncertainties
E Roberts, M Lochner, J Fonseca, BA Bassett, PY Lablanche, S Agarwal
Journal of Cosmology and Astroparticle Physics 2017 (10), 036, 2017
212017
Extending BEAMS to incorporate correlated systematic uncertainties
M Knights, BA Bassett, M Varughese, R Hlozek, M Kunz, M Smith, ...
Journal of Cosmology and Astroparticle Physics 2013 (01), 039, 2013
212013
LADUMA: looking at the distant universe with the MeerKAT array
SL Blyth, AJ Baker, BW Holwerda, BA Bassett, MA Bershady, A Bouchard, ...
Proceedings of Science, 2016
202016
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