arXiv subjects

Christopher B. Prior

Publications and source records attributed to Christopher B. Prior.

6 recordsLinked to original sources

arXiv2026

Multi-Thermal CME Detection with ALMANAC

Reliable identification of low-coronal CME origins remains a key limitation in space weather forecasting with coronagraphs not directly resolving low-coronal signatures. We present a re-engineered multi-thermal implementation of the ALMANAC algorithm, designed to detect eruptive signatures in EUV observations. The framework extends the method to a multi-wavelength system, improving robustness against projection effects, instrumental artifacts, and wavelength-dependent ambiguities via complementary temperature responses. A spatiotemporal clustering scheme merges detections across channels, reducing event bifurcation and improving coherence while maintaining NRT performance through parallel computing. Benchmarking against 20 halo CMEs from CDAW shows improved interpretability and operational usability, with clearer separation of eruptions and more consistent onset localisation relative to coronagraph estimates. The main benefits arise from improved event discrimination, reduced fragmentation, and more interpretable source region identification. ALMANAC shows sensitivity to precursor low-coronal activity not always captured in white-light catalogues, highlighting its advantages for early warning detection. When coupled with the ARTop framework, it enables co-analysis of coronal intensity variability and photospheric magnetic evolution. In this context, kurtosis time-series from multi-wavelength EUV data exhibit recurrent pre-eruptive spikes that frequently align with enhancements in magnetic winding and helicity injection. Across multiple regions, these signatures often precede solar activity, including potential discrimination of X-class flares, while remaining suppressed during magnetically quiet intervals. Overall, integrating coronal diagnostics with photospheric topology offers a pathway toward improved eruption forecasting and space weather prediction.

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arXiv2025

Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. II. XGBoost Model

Solar flares are a primary driver of space weather, and forecasting their occurrence remains a significant challenge. This paper presents a novel flare prediction model based on topologically derived photospheric magnetic parameters. We employ the \texttt{ARTop} framework to compute the time-dependent input rates of magnetic winding and helicity across more than $10^5$ active region (AR) observations, decomposing them into current-carrying and potential components to reduce sensitivity to optical flow methods. An \texttt{XGBoost} machine learning model is trained on these topological time series, alongside engineered features including rolling statistics, kurtosis, and flare history, to predict the probability of $\geq$M1.0-class flares within the next 24 hours. The model demonstrates strong performance on a validation set, with a True Skill Statistic (TSS) of 0.804 for once daily operational region forecasts. When applied to a fully independent holdout set, the operational forecast achieves a TSS of \tsssa. A SHapley Additive exPlanations (SHAP) analysis confirms the model's physical interpretability, identifying flare history and accumulated current-carrying winding and helicity as the most important features. The main challenges identified are false positives arising from ARs with frequent C-class flaring and systematic errors introduced by projection effects when ARs are near the limb. Excluding limb-affected data yields no improvement in the holdout set TSS (\TSSalert\ versus \tsssa), due to the overall decreased number of flares. However, our per-region analysis indicates that mitigating these projection effects is crucial for future operational deployment. This work establishes magnetic topology, particularly its current-carrying components, as a highly effective and physically meaningful set of predictors for solar flare forecasting.

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arXiv2025

Witnessing Magnetic Reconnection in Tangled Superpenumbral Fibrils Around a Sunspot

Three-dimensional magnetic reconnection is a fundamental plasma process crucial for heating the solar corona and generating the solar wind, but resolving and characterizing it on the Sun remains challenging. Using high-quality data from the Chinese New Vacuum Solar Telescope, the Solar Dynamics Observatory, and the Interface Region Imaging Spectrograph, this work presents highly suggestive direct imaging evidence of magnetic reconnection during the untangling of braided magnetic structures above a sunspot. These magnetic structures, visible as bright superpenumbral threads in extreme ultraviolet passbands, initially bridge opposite-polarity magnetic fluxes and then gradually tangle in their middle section. Magnetic extrapolation reveals the fibrils to form a small flux rope that is twisted and braided, possibly created by persistent and complex photospheric motions. During untangling, repetitive reconnection events occur inside the flux rope, accompanied by transient plasma heating, bidirectional outflowing blobs, and signatures of nanojets. Emission analysis reveals that the outflowing blobs are multi-thermal structures with temperatures well below 1 MK, undergoing rapid cooling and leaving emission imprints in H{\alpha} images. The measured reconnection angles indicate that 16%-22% of the magnetic field along each thread is anti-parallel, with the remaining field acting as a guide field. The estimated energy released during these reconnection events is comparable to nanoflares, which can be powered by up to 6% of the magnetic energy stored in the anti-parallel field. This work presents a textbook example of magnetic flux rope reconnection in the solar atmosphere, providing new insights into fine-scale energy release processes within sunspot superpenumbral fibrils.

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arXiv2025

Pattern Formation as a Resilience Mechanism in Cancer Immunotherapy

Mathematical and computational modelling in oncology has played an increasingly important role in not only understanding the impact of various approaches to treatment on tumour growth, but in optimizing dosing regimens and aiding the development of treatment strategies. However, as with all modelling, only an approximation is made in the description of the biological and physical system. Here we show that tissue-scale spatial structure can have a profound impact on the resilience of tumours to immunotherapy using a classical model incorporating IL-2 compounds and effector cells as treatment parameters. Using linear stability analysis, numerical continuation, and direct simulations, we show that diffusing cancer cell populations can undergo pattern-forming (Turing) instabilities, leading to spatially-structured states that persist far into treatment regimes where the corresponding spatially homogeneous systems would uniformly predict a cancer-free state. These spatially-patterned states persist in a wide range of parameters, as well as under time-dependent treatment regimes. Incorporating treatment via domain boundaries can increase this resistance to treatment in the interior of the domain, further highlighting the importance of spatial modelling when designing treatment protocols informed by mathematical models. Counter-intuitively, this mechanism shows that increased effector cell mobility can increase the resilience of tumours to treatment. We conclude by discussing practical and theoretical considerations for understanding this kind of spatial resilience in other models of cancer treatment, in particular those incorporating more realistic spatial transport.

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arXiv2024

Investigating the Efficacy of Topologically Derived Time-Series for Flare Forecasting. I. Dataset Preparation

The accurate forecasting of solar flares is considered a key goal within the solar physics and space weather communities. There is significant potential for flare prediction to be improved by incorporating topological fluxes of magnetogram datasets, without the need to invoke three-dimensional magnetic field extrapolations. Topological quantities such as magnetic helicity and magnetic winding have shown significant potential towards this aim, and provide spatio-temporal information about the complexity of active region magnetic fields. This study develops time-series that are derived from the spatial fluxes of helicity and winding that show significant potential for solar flare prediction. It is demonstrated that time-series signals, which correlate with flare onset times, also exhibit clear spatial correlations with eruptive activity; establishing a potential causal relationship. A significant database of helicity and winding fluxes and associated time series across 144 active regions is generated using SHARP data processed with the ARTop code that forms the basis of the time-series and spatial investigations conducted here. We find that a number of time-series in this dataset often exhibit extremal signals that occur 1-8 hours before a flare. This, publicly available, living dataset will allow users to incorporate these data into their own flare prediction algorithms.

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arXiv2023

Deciphering Pre-solar-storm Features Of September-2017 Storm From Global And Local Dynamics

We investigate whether global toroid patterns and the local magnetic field topology of solar active region AR12673 together can hindcast occurrence of the biggest X-flare of solar cycle (SC)-24. Magnetic toroid patterns (narrow latitude-belts warped in longitude, in which active regions are tightly bound) derived from surface distributions of active regions, prior/during AR12673 emergence, reveal that the portions of the South-toroid containing AR12673 was not tipped-away from its north-toroid counterpart at that longitude, unlike the 2003 Halloween storms scenario. During the minimum-phase there were too few emergences to determine multi-mode longitudinal toroid patterns. A new emergence within AR12673 produced a complex/non-potential structure, which led to rapid build-up of helicity/winding that triggered the biggest X-flare of SC-24, suggesting that this minimum-phase storm can be anticipated several hours before its occurrence. However, global patterns and local dynamics for a peak-phase storm, such as that from AR11263, behaved like 2003 Halloween storms, producing the third biggest X-flare of SC-24. AR11263 was present at the longitude where the North/South toroids tipped-away from each other. While global toroid patterns indicate that pre-storm features can be forecast with a lead-time of a few months, its application on observational data can be complicated by complex interactions with turbulent flows. Complex/non-potential field structure development hours before the storm are necessary for short term prediction. We infer that minimum-phase storms cannot be forecast accurately more than a few hours ahead, while flare-prone active regions in peak-phase may be anticipated much earlier, possibly months ahead from global toroid patterns.

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