Constraining the GENIE model of neutrino-induced single pion production using reanalyzed bubble chamber data

The longstanding discrepancy between bubble chamber measurements of $\nu_\mu$-induced single pion production channels has led to large uncertainties in pion production cross section parameters for many years. We extend the reanalysis of pion production data in deuterium bubble chambers where this discrepancy is solved (Wilkinson et al., PRD 90 (2014) 112017) to include the $\nu_{\mu}n\rightarrow \mu^{-}p\pi^{0}$ and $\nu_{\mu}n\rightarrow \mu^{-}n\pi^{+}$ channels, and use the resulting data to fit the parameters of the GENIE (Rein-Sehgal) pion production model. We find a set of parameters that can describe the bubble chamber data better than the GENIE default parameters, and provide updated central values and reduced uncertainties for use in neutrino oscillation and cross section analyses which use the GENIE model. We find that GENIE's non-resonant background prediction has to be significantly reduced to fit the data, which may help to explain the recent discrepancies between simulation and data observed by the MINERvA coherent pion and NOvA oscillation analyses.Comment: v3: Updated to match published versio

Why social scientists should engage with natural scientists

It has become part of the mantra of contemporary science policy that the resolution of besetting problems calls for the active engagement of a wide range of sciences. The paper reviews some of the key challenges for those striving for a more impactful social science by engaging strategically with natural scientists. It argues that effective engagement depends upon overcoming basic assumptions that have structured past interactions: particularly, the casting of social science in an end-of-pipe role in relation to scientific and technological developments. These structurings arise from epistemological assumptions about the underlying permanence of the natural world and the role of science in uncovering its fundamental order and properties. While the impermanence of the social world has always put the social sciences on shakier foundations, twenty-first century concerns about the instability of the natural world pose different epistemological assumptions that summon a more equal, immediate and intense interaction between field and intervention oriented social and natural scientists. The paper examines a major research programme that has exemplified these alternative epistemological assumptions. Drawing on a survey of researchers and other sources it seeks to draw out the lessons for social/natural science cross-disciplinary engagement

Life just got complicated

The fossil record of ancient life is, in general, poor. Certainly, fossils are abundant in many rock successions and may reveal remarkable details about evolution and environmental change, but they typically consist of disarticulated or broken skeletal material, such as shells, bones and teeth. Even worse, the record of entirely (or largely) soft-bodied organisms, such as jellyfish and worms, is extremely scant, despite the fact that such animals dominate modern marine environments and presumably did so in the past. The reason is obvious — such organisms are highly susceptible to post-mortem decay and typically decompose more rapidly than the ‘normal’ processes of fossilisation operate. This significantly blurs our view of ancient life, with obvious consequences for those interested in understanding evolution and past ecosystems

ABC for Climate: Dealing with Expensive Simulators

A single molecule or molecule complex detection method is disclosed in certain aspects, comprising nano- or micro-fluidic channels.U

Spatial Interaction Models in a Big Data Grocery Retailing Environment

Grocery expenditure is responsible for around 10% of total household spend in the UK, making the grocery retail market worth over £200bn a year in 2021. The size of this market and the nature of retailing competition makes it important for retailers to make the right decisions. One such decision is the location of their stores for which there have been a number of changes in the location, format and channel of consumer interaction along with the methods that have been employed to determine new store location. In recent years it has been suggested that the spatial interaction model is the most appropriate method for estimating new store revenue and hence location. However, previous attempts to explore the performance of the spatial interaction model in grocery retailing have been limited by access to loyalty card data. In this thesis we show that these models are unable to account for the heterogeneity in store conditions and consumer behaviour to model total store revenue. Notably, we find that at the regional scale the size of the errors are such that these models are unlikely to be used consistently in practice for estimating store revenue or locating new stores. Furthermore, that the performance achieved in previous applications are unlikely to be consistently replicated. Thus our results demonstrate that the spatial interaction model in its current form is no longer appropriate for modelling grocery store revenue. It is anticipated that these results may become a starting point for the development and application of alternative forms of models and methods for predicting grocery retailing store revenue. Notably, such new methods must be able to account for recent changes in consumer behaviour such as convenience store shopping, multi-purpose trips and the growing influence of e-commerce, alongside changes in retailers interaction strategies