arXiv subjects

Mark Daly

Publications and source records attributed to Mark Daly.

5 recordsLinked to original sources

arXiv2025

Type II and Type III Solar Radio Burst Classification Using Transfer Learning

The Sun periodically emits intense bursts of radio emission known as solar radio bursts (SRBs). These bursts can disrupt radio communications and be indicative of large solar events that can disrupt technological infrastructure on Earth and in space. The risks posed by these events highlight the need for automated SRB classification, providing the potential to improve event detection and real-time monitoring. This would advance the techniques used to study space weather and related phenomena. A dataset containing images of radio spectra was created using data recorded by the Compound Astronomical Low frequency Low cost Instrument for Spectroscopy and Transportable Observatory (e-Callisto) network. This dataset comprises three categories: empty spectrograms; spectrograms containing Type II SRBs; and spectrograms containing Type III SRBs. These images were used to fine-tune several popular pre-trained deep learning models for classifying Type II and Type III SRBs. The evaluated models included VGGnet-19, MobileNet, ResNet-152, DenseNet-201, and YOLOv8. Testing the models on the test set produced F1 scores ranging from 87\% to 92\%. YOLOv8 emerged as the best-performing model among them, demonstrating that using pre-trained models for event classification can provide an automated solution for SRB classification. This approach provides a practical solution to the limited number of data samples available for Type II SRBs.

astro-ph.SR↗

arXiv2023

Improved Type III solar radio burst detection using congruent deep learning models

Solar flares are energetic events in the solar atmosphere that are often linked with solar radio bursts (SRBs). SRBs are observed at metric to decametric wavelengths and are classified into five spectral classes (Type I--V) based on their signature in dynamic spectra. The automatic detection and classification of SRBs is a challenge due to their heterogeneous form. Near-realtime detection and classification of SRBs has become a necessity in recent years due to large data rates generated by advanced radio telescopes such as the LOw Frequency ARray (LOFAR). In this study, we implement congruent deep learning models to automatically detect and classify Type III SRBs. We generated simulated Type III SRBs, which were comparable to Type IIIs seen in real observations, using a deep learning method known as Generative Adversarial Network (GAN). This simulated data was combined with observations from LOFAR to produce a training set that was used to train an object detection model known as YOLOv2 (You Only Look Once). Using this congruent deep learning model system, we can accurately detect Type III SRBs at a mean Average Precision (mAP) value of 77.71%.

astro-ph.SR↗

arXiv2021

Type III solar radio burst detection and classification: A deep learning approach

Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have five major spectral classes, labelled Type I to Type V depending on their shape and extent in frequency and time. Due to their complex characterisation, a challenge in solar radio physics is the automatic detection and classification of such radio bursts. Classification of SRBs has become fundamental in recent years due to large data rates generated by advanced radio telescopes such as the LOw-Frequency ARray, (LOFAR). Current state-of-the-art algorithms implement the Hough or Radon transform as a means of detecting predefined parametric shapes in images. These algorithms achieve up to 84% accuracy, depending on the Type of radio burst being classified. Other techniques include procedures that rely on Constant-FalseAlarm-Rate detection, which is essentially detection of radio bursts using a de-noising and adaptive threshold in dynamic spectra. It works well for a variety of different Types of radio bursts and achieves an accuracy of up to 70%. In this research, we are introducing a methodology named You Only Look Once v2 (YOLOv2) for solar radio burst classification. By using Type III simulation methods we can train the algorithm to classify real Type III solar radio bursts in real-time at an accu

astro-ph.SR↗

arXiv2016

Evanescent field trapping of nanoparticles using nanostructured ultrathin optical fibers

While conventional optical trapping techniques can trap objects with submicron dimensions, the underlying limits imposed by the diffraction of light generally restrict their use to larger or higher refractive index particles. As the index and diameter decrease, the trapping difficulty rapidly increases; hence, the power requirements for stable trapping become so large as to quickly denature the trapped objects in such diffraction-limited systems. Here, we present an evanescent field based device capable of confining low index nanoscale particles using modest optical powers as low as 1.2 mW, with additional applications in the field of cold atom trapping. Our experiment uses a nanostructured optical micro-nanofiber to trap 200 nm, low index contrast, fluorescent particles within the structured region, thereby overcoming diffraction limitations. We analyze the trapping potential of this device both experimentally and theoretically, and show how strong optical traps are achieved with low input powers.

physics.optics↗

arXiv2014

Nanostructured optical nanofibres for atom trapping

We propose an optical dipole trap for cold neutral atoms based on the electric field produced from the evanescent fields in a hollow rectangular slot cut through an optical nanofibre. In particular, we discuss the trap performance in relation to laser-cooled rubidium atoms and show that a far off-resonance, blue-detuned field combined with the attractive surface-atom interaction potential from the dielectric material forms a stable trapping configuration. With the addition of a red-detuned field, we demonstrate how three dimensional confinement of the atoms at a distance of 140 - 200 nm from the fibre surface within the slot can be accomplished. This scheme facilitates optical coupling between the atoms and the nanofibre that could be exploited for quantum communication schemes using ensembles of laser-cooled atoms.

physics.atom-ph↗