Search for: All records

Abstract Unfolding is an ill-posed inverse problem in particle physics aiming to infer a true particle-level spectrum from smeared detector-level data. For computational and practical reasons, these spaces are typically discretized using histograms, and the smearing is modeled through a response matrix corresponding to a discretized smearing kernel of the particle detector. This response matrix depends on the unknown shape of the true spectrum, leading to a fundamental systematic uncertainty in the unfolding problem. To handle the ill-posed nature of the problem, common approaches regularize the problem either directly via methods such as Tikhonov regularization, or implicitly by using wide-bins in the true space that match the resolution of the detector. Unfortunately, both of these methods lead to a non-trivial bias in the unfolded estimator, thereby hampering frequentist coverage guarantees for confidence intervals constructed from these methods. We propose two new approaches to addressing the bias in the wide-bin setting through methods called One-at-a-time Strict Bounds (OSB) and Prior-Optimized (PO) intervals. The OSB intervals are a bin-wise modification of an existing guaranteed-coverage procedure, while the PO intervals are based on a decision-theoretic view of the problem. Importantly, both approaches provide well-calibrated frequentist confidence intervals even in constrained and rank-deficient settings. These methods are built upon a more general answer to the wide-bin bias problem, involving unfolding with fine bins first, followed by constructing confidence intervals for linear functionals of the fine-bin counts. We test and compare these methods to other available methodologies in a wide-bin deconvolution example and a realistic particle physics simulation of unfolding a steeply falling particle spectrum. 

Abstract Atmospheric aerosols influence the Earth’s climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining the parameters of expensive climate models by comparing model outputs with observations. Using the C3.AI Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with 2 weeks’ worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every 3 hr and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods. 

Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

 

Abstract. The Earth climate system is out of energy balance, and heat hasaccumulated continuously over the past decades, warming the ocean, the land,the cryosphere, and the atmosphere. According to the Sixth Assessment Reportby Working Group I of the Intergovernmental Panel on Climate Change,this planetary warming over multiple decades is human-driven and results inunprecedented and committed changes to the Earth system, with adverseimpacts for ecosystems and human systems. The Earth heat inventory providesa measure of the Earth energy imbalance (EEI) and allows for quantifyinghow much heat has accumulated in the Earth system, as well as where the heat isstored. Here we show that the Earth system has continued to accumulateheat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to aheating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority,about 89 %, of this heat is stored in the ocean, followed by about 6 %on land, 1 % in the atmosphere, and about 4 % available for meltingthe cryosphere. Over the most recent period (2006–2020), the EEI amounts to0.76±0.2 W m−2. The Earth energy imbalance is the mostfundamental global climate indicator that the scientific community and thepublic can use as the measure of how well the world is doing in the task ofbringing anthropogenic climate change under control. Moreover, thisindicator is highly complementary to other established ones like global meansurface temperature as it represents a robust measure of the rate of climatechange and its future commitment. We call for an implementation of theEarth energy imbalance into the Paris Agreement's Global Stocktake based onbest available science. The Earth heat inventory in this study, updated fromvon Schuckmann et al. (2020), is underpinned by worldwide multidisciplinarycollaboration and demonstrates the critical importance of concertedinternational efforts for climate change monitoring and community-basedrecommendations and we also call for urgently needed actions for enablingcontinuity, archiving, rescuing, and calibrating efforts to assure improvedand long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4.