Suman Rakshit

A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely η _ν and V _ν , and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.

A novel clustering framework is proposed to identify playing styles in invasion sports, particularly Australian football, based on match transactional data. These data are segmented at a granular level, representing an intrinsic partitioning of the entire match period into short-time interval event data. Clustering based on such granular time-event data provides opportunities to identify team playing styles at different periods within a match. Identifying effective team playing styles over short time intervals can be crucial for improving overall match strategies. Due to the complexity involved in forming interpretable clusters from vast amounts of data, this research has not been attempted before. As a key component of the clustering framework, this paper introduces a composite clustering assessment index, enabling the selection of the optimal clustering algorithm and the appropriate number of clusters. The composite index integrates subject matter expert knowledge, ensuring that the resulting clusters are not only data-driven but also aligned with the goals of the analysis. The framework is applied to identify offensive playing styles within Australian football by clustering possession chain data of offensive phases from 414 matches during the 2021 and 2022 Australian Football League (AFL) seasons. A total of 27 features from 112,530 offensive possession chains were clustered, identifying five distinct offensive team playing styles: (i) ‘Ineffective Ball Movers’, (ii) ‘Uncontested Ball Movers’, (iii) ‘Direct Ball Movers’, (iv) ‘Handball Ball Movers’, and (v) ‘Kick Ball Movers’.

Fungicide resistance in foliar fungal pathogens is an increasing challenge to crop production. Yield impacts due to loss of fungicide efficacy may be reduced through effective surveillance and appropriate management intervention. For stubble-borne pathogens, off-season crop residues may be used to monitor fungicide resistance to inform pre-planting decisions; however, appropriate sampling strategies and support sizes for crop residues have not previously been considered. Here, we used Pyrenophora teres f. teres ( Ptt) with resistance to demethylase inhibitor fungicides as a model system to assess spatial dependency and to compare the effects of different sampling strategies and support sizes on pathogen density ( Ptt DNA concentration) and the frequency of fungicide resistance mutation. The results showed that sampling strategies (hand-picked versus raked) did not affect estimates of pathogen density or fungicide resistance frequency; however, sample variances were lower from raked samples. The effects of differing sample support size, as the size of the collection area (1.2, 8.6, or 60 m²), on fungicide resistance frequency were not evident ( P > 0.05). However, measures of pathogen density increased with area size ( P < 0.05); the 60 m² area yielded the highest Ptt DNA concentration and produced the lowest number of pathogen-absent samples. Sample variances for pathogen density and fungicide resistance frequency were generally homogeneous between area sizes. The pattern of pathogen density was spatially independent; however, spatial dependency was identified for fungicide resistance frequency, with a range of 110 m, in one of the two fields surveyed. Collectively, the results inform designs for monitoring of fungicide resistance in stubble-borne pathogens.

Summary: Motivated by the analysis of a comprehensive database of road traffic accidents, we investigate methods of variable selection for spatial point process models on a linear network. The original data may include explanatory spatial covariates, such as road curvature, and ‘mark’ variables attributed to individual accidents, such as accident severity. The treatment of mark variables is new. Variable selection is applied to the canonical covariates, which may include spatial covariate effects, mark effects and mark‐covariate interactions. We approximate the likelihood of the point process model by that of a generalised linear model, in such a way that spatial covariates and marks are both associated with canonical covariates. We impose a convex penalty on the log likelihood, principally the elastic‐net penalty, and maximise the penalised loglikelihood by cyclic coordinate ascent. A simulation study compares the performances of the lasso, ridge regression and elastic‐net methods of variable selection on their ability to select variables correctly, and on their bias and standard error. Standard techniques for selecting the regularisation parameterγoften yielded unsatisfactory results. We propose two new rules for selectingγwhich are designed to have better performance. The methods are tested on a small dataset on crimes in a Chicago neighbourhood, and applied to a large dataset of road traffic accidents in Western Australia.

On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment; show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects; and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.