ORCID Profile
0000-0002-2457-3431
Current Organisation
Imperial College London
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: American Astronomical Society
Date: 12-2021
Abstract: In the first two papers of this series, we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line ratios directly from the spectra observed by the SITELLE instrument located at the Canada–France–Hawai’i Telescope. In this third installment, we develop a framework using a convolutional neural network trained on synthetic spectra to determine the number of line-of-sight components present in the SN3 filter (656–683 nm) spectral range of SITELLE. We compare this methodology to standard practice using Bayesian inference. Our results demonstrate that a neural network approach returns more accurate results and uses fewer computational resources over a range of spectral resolutions. Furthermore, we apply the network to SITELLE observations of the merging galaxy system NGC 2207/IC 2163. We find that the closest interacting sector and the central regions of the galaxies are best characterized by two line-of-sight components while the outskirts and spiral arms are well-constrained by a single component. Determining the number of resolvable components is crucial in disentangling different galactic components in merging systems and properly extracting their respective kinematics.
Publisher: Society for Neuroscience
Date: 08-03-2019
DOI: 10.1523/JNEUROSCI.1934-18.2019
Abstract: Cognitive decision-making is known to be sensitive to the values of potential options, which are the probability and size of rewards associated with different choices. Here, we examine whether rapid motor responses to perturbations of visual feedback about movement, which mediate low-level and involuntary feedback control loops, reflect computations associated with high-level value-based decision-making. In three experiments involving human participants, we varied the value associated with different potential targets for reaching movements by controlling the distributions of rewards across the targets (Experiment 1), the probability with which each target could be specified (Experiment 2), or both (Experiment 3). We found that the size of rapid and involuntary feedback responses to movement perturbations was strongly influenced by the relative value between targets. A statistical model of relative value that includes a term for risk sensitivity provided the best fit to the visuomotor response data, illustrating that feedback control policies are biased to favor more frequent task success at the expense of the overall extrinsic reward accumulated through movement. Importantly however, the regulation of rapid feedback responses was associated with successful pursuit of high-value task outcomes. This implies that when we move, the brain specifies a set of feedback control gains that enable low-level motor areas not only to generate efficient and accurate movement, but also to rapidly and adaptively respond to evolving sensory information in a manner consistent with value-based decision-making. SIGNIFICANCE STATEMENT Current theories of sensorimotor control suggest that, rather than selecting and planning the details of movements in advance, the role of the brain is to set time-varying feedback gains that continuously transform sensory information into motor commands by feedback control. Here, we examine whether the fastest motor responses to perturbations of movement, which mediate low-level and involuntary feedback control loops (i.e., reflexes), reflect computations associated with high-level, value-based decision-making. We find that rapid feedback responses during reaching reflect the relative probabilities and rewards associated with target options. This suggests that low-order components of the sensorimotor control hierarchy, which generate rapid and automatic responses, can continuously evaluate evolving sensory evidence and initiate responses according to the prospect of reward.
Publisher: Public Library of Science (PLoS)
Date: 21-02-2013
Publisher: American Astronomical Society
Date: 04-2021
Abstract: In the first paper of this series, we demonstrated that neural networks can robustly and efficiently estimate kinematic parameters for optical emission-line spectra taken by SITELLE at the Canada–France–Hawaii Telescope. This paper expands upon this notion by developing an artificial neural network to estimate the line ratios of strong emission lines present in the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000 synthetic spectra using line ratios taken from the Mexican Million Model database replicating H ii regions. Residual analysis of the network on the test set reveals the network’s ability to apply tight constraints to the line ratios. We verified the network’s efficacy by constructing an activation map, checking the [ N ii ] doublet fixed ratio, and applying a standard k-fold cross-correlation. Additionally, we apply the network to SITELLE observations of M33 the residuals between the algorithm’s estimates and values calculated using standard fitting methods show general agreement. Moreover, the neural network reduces the computational costs by two orders of magnitude. Although standard fitting routines do consistently well depending on the signal-to-noise ratio of the spectral features, the neural network can also excels at predictions in the low signal-to-noise regime within the controlled environment of the training set as well as on observed data when the source spectral properties are well constrained by models. These results reinforce the power of machine learning in spectral analysis.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Myriam Prasow-Émond.