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Black Hole

Lensing of space time around a black hole. At Oxford we study black holes observationally and theoretically on all size and time scales - it is some of our core work.

Credit: ALAIN RIAZUELO, IAP/UPMC/CNRS. CLICK HERE TO VIEW MORE IMAGES.

Prof Chris Lintott

Professor of Astrophysics and Citizen Science Lead

Research theme

  • Astronomy and astrophysics

Sub department

  • Astrophysics

Research groups

  • Zooniverse
  • Beecroft Institute for Particle Astrophysics and Cosmology
  • Rubin-LSST
chris.lintott@physics.ox.ac.uk
Telephone: 01865 (2)73638
Denys Wilkinson Building, room 532C
www.zooniverse.org
orcid.org/0000-0001-5578-359X
  • About
  • Citizen science
  • Group alumni
  • Publications

Zooniverse labs

Zooniverse lab
Build your own Zooniverse project

The Zooniverse lab lets anyone build their own citizen science project

Zooniverse Lab

The Galactic Interstellar Object Population: A Framework for Prediction and Inference

The Astronomical Journal American Astronomical Society 166:6 (2023) 241

Authors:

Matthew J Hopkins, Chris Lintott, Michele T Bannister, J Ted Mackereth, John C Forbes
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Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 526:3 (2023) 4768-4786

Authors:

Mike Walmsley, Tobias Géron, Sandor Kruk, Anna MM Scaife, Chris Lintott, Karen L Masters, James M Dawson, Hugh Dickinson, Lucy Fortson, Izzy L Garland, Kameswara Mantha, David O’Ryan, Jürgen Popp, Brooke Simmons, Elisabeth M Baeten, Christine Macmillan
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Signatures of feedback in the spectacular extended emission region of NGC 5972

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 526:3 (2023) 4174-4191

Authors:

Thomas Harvey, W Peter Maksym, William Keel, Michael Koss, Vardha N Bennert, S Drew Chojnowski, Ezequiel Treister, Carolina Finlez, Chris J Lintott, Alexei Moiseev, Brooke D Simmons, Lia F Sartori, Megan Urry
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The Galactic Interstellar Object Population: A Framework for Prediction and Inference

(2023)

Authors:

Matthew J Hopkins, Chris Lintott, Michele T Bannister, J Ted Mackereth, John C Forbes
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Bursts from Space: MeerKAT – the first citizen science project dedicated to commensal radio transients

Monthly Notices of the Royal Astronomical Society Oxford University Press 523:2 (2023) 2219-2235

Authors:

Alex Andersson, chris Lintott, rob Fender, joe Bright, francesco Carotenuto, ian Heywood, Lauren Rhodes, Sara Motta, David Williams

Abstract:

The newest generation of radio telescopes is able to survey large areas with high sensitivity and cadence, producing data volumes that require new methods to better understand the transient sky. Here, we describe the results from the first citizen science project dedicated to commensal radio transients, using data from the MeerKAT telescope with weekly cadence. Bursts from Space: MeerKAT was launched late in 2021 and received ∼89 000 classifications from over 1000 volunteers in 3 months. Our volunteers discovered 142 new variable sources which, along with the known transients in our fields, allowed us to estimate that at least 2.1 per cent of radio sources are varying at 1.28 GHz at the sampled cadence and sensitivity, in line with previous work. We provide the full catalogue of these sources, the largest of candidate radio variables to date. Transient sources found with archival counterparts include a pulsar (B1845-01) and an OH maser star (OH 30.1–0.7), in addition to the recovery of known stellar flares and X-ray binary jets in our observations. Data from the MeerLICHT optical telescope, along with estimates of long time-scale variability induced by scintillation, imply that the majority of the new variables are active galactic nuclei. This tells us that citizen scientists can discover phenomena varying on time-scales from weeks to several years. The success both in terms of volunteer engagement and scientific merit warrants the continued development of the project, while we use the classifications from volunteers to develop machine learning techniques for finding transients.
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