Research
I am the Professor of Astrostatistics and Data Science at the University of Cambridge. I hold this interdisciplinary faculty position jointly at the Institute of Astronomy and at the Statistical Laboratory of the Department of Pure Mathematics and Mathematical Statistics. My astronomy office is located in the Kavli Institute for Cosmology. As of 2024, I am the Past Chair of the Astrostatistics Interest Group of the American Statistical Association, a member of the Council of the International Astrostatistics Association, and Turing Fellow Alumnus of The Alan Turing Institute. I am a founder and co-organiser of the Cambridge Astro Data Science Discussion Group.
My research interests lie at the intersections of astrophysics, cosmology, statistics, and machine learning, and include:
- Supernova cosmology
- Astrostatistics, astronomical machine learning, astroinformatics
- Time-domain and transient astronomy
- Bayesian modeling and inference
- Statistical computation
My research group is a partner in the Young Supernova Experiment time-domain survey using the Pan-STARRS telescopes. We are also members of:
- LSST:UK (UK participation in the Legacy Survey of Space and Time)
- Time-Domain Extragalactic Survey (TiDES) with 4MOST
- International CHASC Astro-Statistics Collaboration
- MSCA-RISE ASTROSTAT-II Collaboration Network
We are funded by a European Research Council Consolidator Grant.
Selected papers
[A more comprehensive list via the Astrophysics Data System]
Karchev, K. Grayling, M., Boyd, B., Trotta, R., Mandel, K., Weniger, C. 2024. SIDE-real: Truncated marginal neural ratio estimation for Supernova Ia Dust Extinction with real data. MNRAS, accepted. [ads]
Thorp, S., Mandel, K., Jones, D.O., Kirshner, R.P., Challis, P.M. 2024. Using Rest-Frame Optical and NIR Data from the RAISIN Survey to Explore the Redshift Evolution of Dust Laws in SN Ia Host Galaxies. MNRAS, accepted. [ads]
Grayling, M., Thorp, S., Mandel, K., Dhawan, S., Uzsoy, A.S., Boyd, B., Hayes, E., Ward, S. 2024. Scalable hierarchical BayeSN inference: Investigating dependence of SN Ia host galaxy dust properties on stellar mass and redshift. submitted. [ads]
O'Callaghan, M., Gilmore, G., Mandel, K. 2023. Quantifying Interstellar Extinction at High Galactic Latitudes. submitted. [ads]
Hayes, E., Thorp, S., Mandel, K., Arendse, N., Grayling, M., Dhawan, S. 2023. GausSN: Bayesian Time-Delay Estimation for Strongly Lensed Supernovae. MNRAS, accepted. [ads]
Ward, S.M., Dhawan, S., Mandel, K.S., Grayling, M., Thorp, S. 2023. Bird-Snack: Bayesian Inference of dust law RV Distributions using SN Ia Apparent Colours at peaK. MNRAS, 526, 5715. [ads]
Ward, S.M., Thorp, S., Mandel, K., Dhawan, S. et al. (The Young Supernova Experiment). 2023. Relative intrinsic scatter in hierarchical Type Ia supernova siblings analyses: Application to SNe 2021hpr, 1997bq & 2008fv in NGC 3147. ApJ, 956, 111. [ads]
Finalist, 2023 Astrostatistics Student Paper Competition
Dhawan, S., Thorp, S., Mandel, K., Ward, S.M., Narayan, G., Jha, S.W., Chant, T. 2023. A BayeSN Distance Ladder: H0 from a consistent modelling of Type Ia supernovae from the optical to the near infrared. MNRAS, 524, 235. [ads]
Kelly, P. et al. 2023. Constraints on the Hubble constant from supernova Refsdal's reappearance. Science, 380, 6649. [ads]
Kelly, P. et al. 2023. The Magnificent Five Images of Supernova Refsdal: Time Delay and Magnification Measurements. ApJ, 948, 93. [ads]
Patel, E. & Mandel, K.S. 2023. Evidence for a Massive Andromeda Galaxy Using Satellite Galaxy Proper Motions. ApJ, 948, 104. [ads]
Thorp, S. & Mandel, K. 2022. Constraining the SN Ia Host Galaxy Dust Law Distribution and Mass Step: Hierarchical BayeSN Analysis of Optical and Near-Infrared Light Curves. MNRAS, 517, 2360. [ads]
Jones, D.O., Mandel, K.S., Kirshner, R.P., Thorp, S., Challis, P., Avelino A. et al. 2022. Cosmological Results from the RAISIN Survey: Using Type Ia Supernovae in the Near Infrared as a Novel Path to Measure the Dark Energy Equation of State. ApJ, 933, 172. [ads][arXiv]
Roberts, C., Shorttle, O., Mandel, K., Jones, M., Ijzermans, R., Hirst, B. & Jonathan, P. 2022. Enhanced monitoring of atmospheric methane from space with hierarchical Bayesian inference. Environmental Research Letters, 17, 06437. [ads][arXiv]
Winner, 2022 CSAR PhD Student Award
Muthukrishna, D., Mandel, K., Lochner, M., Webb, S. & Narayan, G. 2022. Real-time detection of anomalies in large-scale transient surveys. MNRAS, 517, 393. [ads]
Thorp, S., Mandel, K., Jones, D.O., Ward, S.M. & Narayan, G. 2021. Testing the Consistency of Dust Laws in SN Ia Host Galaxies: A BayeSN Examination of Foundation DR1. MNRAS, 508, 4310. [ads]
Finalist, 2022 Astrostatistics Student Paper Competition
Mandel, K., Thorp, S., Narayan, G., Friedman, A. & Avelino, A. 2021. A Hierarchical Bayesian SED Model for Type Ia Supernovae in the Optical to Near-Infrared. MNRAS, accepted & published. [ads]
Avelino, A., Friedman, A., Mandel, K., Jones. D.O., Challis, P. & Kirshner, R.P. 2019. Type Ia Supernovae are Excellent Standard Candles in the Near-Infrared. ApJ, 887, 106. [ads]
Muthukrishna, D., Narayan, G., Mandel, K., Biswas, R. & Hlozek, R. 2019. RAPID: Early Classification of Explosive Transients using Deep Learning. PASP, 131, 118002. [ads]
Winner, 2020 Institute of Astronomy Murdin Prize
Finalist, 2019 Astrostatistics Student Paper Competition
Patel, E., Besla, G., Mandel, K. & Sohn, S.T. 2018. Estimating the Mass of the Milky Way Using the Ensemble of Classical Satellite Galaxies. ApJ, 857, 78. [ads]
Mandel, K. Scolnic, D., Shariff, H., Foley, R., Kirshner, R.P. 2017. The Type Ia Supernova Color-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model. ApJ, 842, 93. [ads][arXiv]
Czekala, I., Mandel, K., Andrews, S., Dittman, J., Ghosh, S., Montet, B., Newton, E. 2017. Disentangling Time Series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis. ApJ, 840, 49. [ads]
Patel, E., Besla, G. & Mandel, K. 2017. Orbits of Massive Satellite Galaxies II: Bayesian Estimates of the Milky Way and Andromeda Masses using high-precision astrometry and cosmological simulations. MNRAS, 468, 3428. [ads][arxiv]
Tak, Hyungsuk, Mandel, K., van Dyk, D., Kashyap, V., Meng, Xiao-Li, Siemiginowska, A. 2017. Bayesian Estimates of Astronomical Time Delays between Gravitationally Lensed Stochastic Light Curves. The Annals of Applied Statistics, 11, 1309. [ads][arXiv]
Czekala, I., Andrews, S., Mandel, K., Hogg, D., Green, G. 2015. Constructing a Flexible Likelihood Function for Spectroscopic Inference. ApJ, 812, 128. [ads]
Mandel, K., Foley, R.J. & Kirshner, R.P. 2014. Type Ia Supernova Colors and Ejecta Velocities: Hierarchical Bayesian Regression with Non-Gaussian Distributions. ApJ, 797, 75. [ads][arXiv]
Foley, R.J. & Mandel, K. 2013. Classifying Supernovae using only galaxy data. ApJ, 778, 167. [ads][arXiv]
Foster, J., Mandel, K., Pineda, J., Covey, K., Arce, H., Goodman, A. 2013. Evidence for grain growth in molecular clouds: A Bayesian examination of the extinction law in Perseus. MNRAS, 428, 1606. [ads][arXiv]
Mandel, K., Narayan, G., & Kirshner, R.P. 2011. Type Ia Supernova Light Curve Inference: Hierarchical Models in the Optical and Near Infrared. ApJ, 731, 120. [ads][arXiv]
Blondin, S., Mandel, K., & Kirshner, R.P. 2011. Do spectra improve distance measurements of Type Ia supernovae? Astronomy & Astrophysics, 526: A81. [ads]
Mandel, K., Wood-Vasey, W.M., Friedman, A., & Kirshner, R.P. 2009. Type Ia Supernova Light Curve Inference: Hierarchical Bayesian Analysis in the Near Infrared. ApJ, 704: 629-651. [ads]
Mandel, K. and Zaldarriaga, M. 2006. Weak Gravitational Lensing of High-Redshift 21 cm Power Spectra. ApJ, 647: 719-736. [ads]
Mandel, K. and Agol, E. 2002. Analytic Light Curves for Planetary Transit Searches. The Astrophysical Journal, 580: L171-L175. [ads]
Awards and Prizes
2020 - European Research Council Consolidator Grant
Teaching
Astrostatistics (Part III Maths/Astrophysics), Lent 2018-2022
