Will Handley
researcher, ORCID id # 0000-0002-5866-0445
Facts
11 properties- Instance of
- human
- Country of citizenship
- United Kingdom[2]
- Occupation
- astrophysicist
- Educated at
- University of Cambridge, University of Cambridge
- Doctoral advisor
- Anthony Lasenby, Michael Paul Hobson[4][5]
- Doctoral student
- Lukas Hergt[7]
🏷️ Also known as
en W. Handley
en Will Handley
🔖 Identifiers · 10
- ORCID iD
- 0000-0002-5866-0445
- ArXiv author ID
- handley_w_1
- AstroGen ID
- 40375
- Dimensions author ID
- 012542377721.50
- GitHub account
- williamjameshandley
- Google Scholar author ID
- 9Ow4mn0AAAAJ
- LinkedIn personal profile ID
- will-handley-81b28382
- MR Author ID
- 1220241
- Mathematics Genealogy Project ID
- 294495
- ResearcherID
- S-9134-2018
🔗 Connections
Author of 19
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications,
A general Bayesian framework to account for foreground map errors in global 21-cm experiments,
Bayesian approach to radio frequency interference mitigation,
Bayesian evidence-driven diagnosis of instrumental systematics for sky-averaged 21-cm cosmology experiments,
Bayesian evidence-driven likelihood selection for sky-averaged 21-cm signal extraction,
Bayesian noise wave calibration for 21-cm global experiments,
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning,
Informing antenna design for sky-averaged 21-cm experiments using a simulated Bayesian data analysis pipeline
+11 more
← Authored by 19
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications,
A general Bayesian framework to account for foreground map errors in global 21-cm experiments,
Bayesian approach to radio frequency interference mitigation,
Bayesian evidence-driven diagnosis of instrumental systematics for sky-averaged 21-cm cosmology experiments,
Bayesian evidence-driven likelihood selection for sky-averaged 21-cm signal extraction,
Bayesian noise wave calibration for 21-cm global experiments,
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning,
Informing antenna design for sky-averaged 21-cm experiments using a simulated Bayesian data analysis pipeline
+11 more
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