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Abstract Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variableYgiven complex inputsX. Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for allx, and when needed, to reshape the densities ofytoward ‘instance-wise’ calibration. This paper introduces the local amortized diagnostics and reshaping of conditional densities (LADaR) framework and proposes a new computationally efficient algorithm (Cal-PIT) that produces interpretable local diagnostics and provides a mechanism for adjusting conditional density estimates (CDEs).Cal-PITlearns a single interpretable local probability–probability map from calibration data that identifies where and how the initial model is miscalibrated across feature space, which can be used to morph CDEs such that they are well-calibrated. We illustrate the LADaR framework on synthetic examples, including probabilistic forecasting from image sequences, akin to predicting storm wind speed from satellite imagery. Our main science application involves estimating the probability density functions of galaxy distances given photometric data, whereCal-PITachieves better instance-wise calibration than all 11 other literature methods in a benchmark data challenge, demonstrating its utility for next-generation cosmological analyzes⁹9Code available as a Python package here:https://github.com/lee-group-cmu/Cal-PIT.. 

Abstract Poststarburst galaxies (PSBs) are young quiescent galaxies that have recently experienced a rapid decrease in star formation, allowing us to probe the fast-quenching period of galaxy evolution. In this work, we obtained Hubble Space Telescope (HST)/WFC3 F110W imaging to measure the sizes of 171 massive ( $\log (M_{*}/M_{\odot })\sim 11)$spectroscopically identified PSBs at 1 <z1.3 selected from the DESI Survey Validation luminous red galaxy sample. This statistical sample constitutes an order of magnitude increase from the ∼20 PSBs with space-based imaging and deep spectroscopy. We perform structural fitting of the target galaxies withpysersicand compare them to quiescent and star-forming galaxies in the 3D-HST survey. We find that these PSBs are more compact than the general population of quiescent galaxies, lying systematically ∼0.1 dex below the established size–mass relation. However, their central surface mass densities are similar to those of their quiescent counterparts ( $\log \left({\mathrm{\Sigma }}_{1\mathrm{kpc}}/\right(M_{\odot }{\mathrm{kpc}}^{-2}\left)\right)\sim 10.1$). These findings are easily reconciled by later ex situ growth via minor mergers or a slight progenitor bias. These PSBs are round in projection (b/a_median∼ 0.8), suggesting that they are primarily spheroids, not disks, in 3D. We find no correlation between the time since quenching and light-weighted PSB sizes or central densities. This disfavors apparent structural growth due to the fading of centralized starbursts in this galaxy population. Instead, we posit that the fast quenching of massive galaxies at this epoch occurs preferentially in galaxies with preexisting compact structures.