5 papers
Machine Learning Closure Audits for LSST Photometric Supernova Cosmology
Ayan Mitra
Modern and next generation supernova cosmology analyses rely on end to end simulations to train photometric classifiers, characterise selection effects, validate light curve models…
A Fully Photometric Approach to Type Ia Supernova Cosmology in the LSST Era: Host Galaxy Redshifts and Supernova Classification
Ayan Mitra, Richard Kessler, Rebecca C. Chen +9
The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to discover nearly a million Type Ia supernovae (SNeIa), offering an unprecedented oppor…
FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology
Benjamin M. Boyd, Kaisey S. Mandel, Matthew Grayling +10
We present FlowSN, a statistical framework using simulation-based inference (SBI) with normalising flows to account for selection effects in observational astronomy. Failure to acc…
Probing Physics Beyond the Standard Model through Combined Analyses of Next-Generation Type Ia Supernova, CMB, and BAO Surveys
Srinivasan Raghunathan, Ayan Mitra, Nikolina Å arÄeviÄ +11
Observations of Type Ia supernovae (\sne), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe th…
Lens Model Accuracy in the Expected LSST Lensed AGN Sample
Padmavathi Venkatraman, Sydney Erickson, Phil Marshall +13
Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST…