arXiv subjects

Joseph Fisher

Publications and source records attributed to Joseph Fisher.

8 recordsLinked to original sources

arXiv2026

Future Detections of the Warm-Hot Intergalactic Medium using Bright Power Law Sources with NewAthena

Context. Hydrodynamical cosmological simulations based on the $\Lambda$-Cold Dark Matter ($\Lambda$CDM) model predict that $\sim$40% of the baryons in the local Universe are missing. These missing baryons are predicted to lie in low-density filamentary structures that trace connections between galaxies. This so-called warm-hot intergalactic medium (WHIM) is predicted to be observable in the far-ultraviolet (FUV) and soft X-ray regimes, and detectable in ion species with transitions in these bands. Aims. We investigate the capability of the X-ray Integral Field Unit (X-IFU) on NewAthena to detect this WHIM through O vii absorption lines imprinted on bright power-law sources in soft X-rays (0.3-1.0 keV) by examining the parameter space of different observing conditions. Methods. Through advanced simulations of the X-IFU detector, a multidimensional approach is taken to investigate the relationship between source parameters and their impacts on the significance of detection of a single absorption feature. By first studying the effect of line placement (redshift) for an observation of fixed length, followed by varying the observation length, the required counts for 3$\sigma$ and 5$\sigma$ detections are calculated for various photon indices and local Galactic absorptions. The rest-frame equivalent widths of the imprinted absorption features are then varied, and the same requirements are calculated for each line strength. Results. We discuss the detection and recovery of the imprinted WHIM features. Another method is used to derive count requirements for detecting these features at different levels of significance, which is then compared to the previously obtained requirements. Using publicly available data from the Swift archives, various non-transient sources are analysed for their suitability to detect WHIM features in absorption.

astro-ph.IM↗

arXiv2025

Machine learning for the early classification of broad-lined Ic supernovae

Science is currently at an age where there is more data than we know how to deal with. Machine learning (ML) is an emerging tool that is useful for drawing valuable science out of incomprehensibly large datasets and identifying complex trends in data that may otherwise be overlooked. Moreover, ML can potentially enhance the quality and quantity of scientific data as they are collected. This paper explores how a new ML method can improve the rate of classification of rare broad-lined Ic (Ic-BL) supernovae (SNe). We introduce new parameters called magnitude rates to train ML models to identify SNe Ic-BL in large datasets and apply this same methodology to a population of SN Ia to test if our ML approach is reproducible. The information we required to train each ML model included three magnitudes, three time differences, two magnitude rates, and the second derivative of these rates using the first three available photometric data points in a single filter. Our initial investigations showed that the random forest algorithm provides a strong foundation for the early classifications SNe Ic-BL and SNe Ia. Testing this model again on an unseen dataset showed that the model can identify upward of 13.6\% of the total true SN Ic-BL population, significantly improving on current methods. By implementing a dedicated observation campaign using this model, the number of SN Ic-BL classified and the quality of early-time data collected each year will see considerable growth in the near future.

astro-ph.HE↗

arXiv2025

Prospects for Time-Domain and Multi-Messenger Science with eXTP

In this new era of time-domain and multi-messenger astronomy, various new transients and new phenomena are constantly being discovered thanks to the rapid advances in observations, which provide the excellent opportunity to study the physics in the extreme environments. The enhanced X-ray Timing and Polarimetry mission (eXTP), planned to be launched in 2030, has several key advantages, including advanced polarimetry, high sensitivity & large effective area, and wide energy range coverage, which make it a groundbreaking project in high-energy astrophysics. In this article, we briefly introduce the potential time-domain and multi-messenger targets for eXTP, including gravitational-wave (GW) counterparts, gamma-ray bursts (GRBs), magnetars and fast radio bursts (FRBs), tidal disruption events (TDEs), supernovae, high energy neutrinos and TeV active galactic nucleus (AGNs), and so on. We discuss the advantages of future eXTP observations for detecting these sources, their detection capabilities, the abilities to distinguish theoretical models, and their applications in gravity and cosmology.

astro-ph.HE↗

arXiv2024

REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking

Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time. We address these limitations by proposing REXEL, a highly efficient and accurate model for the joint task of document level cIE (DocIE). REXEL performs mention detection, entity typing, entity disambiguation, coreference resolution and document-level relation classification in a single forward pass to yield facts fully linked to a reference knowledge graph. It is on average 11 times faster than competitive existing approaches in a similar setting and performs competitively both when optimised for any of the individual subtasks and a variety of combinations of different joint tasks, surpassing the baselines by an average of more than 6 F1 points. The combination of speed and accuracy makes REXEL an accurate cost-efficient system for extracting structured information at web-scale. We also release an extension of the DocRED dataset to enable benchmarking of future work on DocIE, which is available at https://github.com/amazon-science/e2e-docie.

cs.CL↗

arXiv2022

Improving Entity Disambiguation by Reasoning over a Knowledge Base

Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types. This limits the range of contexts in which entities can be disambiguated. To allow the use of all KB facts, as well as descriptions and types, we introduce an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion. Our model surpasses state-of-the-art baselines on six well-established ED datasets by 1.3 F1 on average. By allowing access to all KB information, our model is less reliant on popularity-based entity priors, and improves performance on the challenging ShadowLink dataset (which emphasises infrequent and ambiguous entities) by 12.7 F1.

cs.CL↗

arXiv2022

ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking

We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, making it more than 60 times faster than competitive existing approaches. ReFinED also surpasses state-of-the-art performance on standard entity linking datasets by an average of 3.7 F1. The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking. The combination of speed, accuracy and scale makes ReFinED an effective and cost-efficient system for extracting entities from web-scale datasets, for which the model has been successfully deployed. Our code and pre-trained models are available at https://github.com/alexa/ReFinED

cs.CL↗

arXiv2020

Measuring Social Bias in Knowledge Graph Embeddings

It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of graph embeddings. We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for measuring such bias. We conduct experiments on Wikidata and Freebase, and show that, as with word embeddings, harmful social biases related to professions are encoded in the embeddings with respect to gender, religion, ethnicity and nationality. For example, graph embeddings encode the information that men are more likely to be bankers, and women more likely to be homekeepers. As graph embeddings become increasingly utilized, we suggest that it is important the existence of such biases are understood and steps taken to mitigate their impact.

cs.CL↗

arXiv2019

Merge and Label: A novel neural network architecture for nested NER

Named entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many applications. In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entities forming nested structures, and then labels each of them independently. Unlike previous work, our merge and label approach predicts real-valued instead of discrete segmentation structures, which allow it to combine word and nested entity embeddings while maintaining differentiability. %which smoothly groups entities into single vectors across multiple levels. We evaluate our approach using the ACE 2005 Corpus, where it achieves state-of-the-art F1 of 74.6, further improved with contextual embeddings (BERT) to 82.4, an overall improvement of close to 8 F1 points over previous approaches trained on the same data. Additionally we compare it against BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating that its ability to predict nested structures does not impact performance in simpler cases.

cs.CL↗