3 research outputs found
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Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests
Abstract
Identifying merging galaxies is an important—but difficult—step in galaxy evolution studies. We present random forest (RF) classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We describe (a) constructing the simulated images from IllustrisTNG and the Santa Cruz SAM and modifying them to mimic future CEERS observations and nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the RFs using the merger history information for the simulated galaxies available from IllustrisTNG. The RFs correctly classify ∼60% of non-merging and merging galaxies across 0.5 < z < 4.0. Rest-frame asymmetry parameters appear more important for lower-redshift merger classifications, while rest-frame bulge and clump parameters appear more important for higher-redshift classifications. Adjusting the classification probability threshold does not improve the performance of the forests. Finally, the shape and slope of the resulting merger fraction and merger rate derived from the RF classifications match with theoretical Illustris predictions but are underestimated by a factor of ∼0.5.</jats:p
Recommended from our members
Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests
Abstract
Identifying merging galaxies is an important—but difficult—step in galaxy evolution studies. We present random forest (RF) classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We describe (a) constructing the simulated images from IllustrisTNG and the Santa Cruz SAM and modifying them to mimic future CEERS observations and nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the RFs using the merger history information for the simulated galaxies available from IllustrisTNG. The RFs correctly classify ∼60% of non-merging and merging galaxies across 0.5 < z < 4.0. Rest-frame asymmetry parameters appear more important for lower-redshift merger classifications, while rest-frame bulge and clump parameters appear more important for higher-redshift classifications. Adjusting the classification probability threshold does not improve the performance of the forests. Finally, the shape and slope of the resulting merger fraction and merger rate derived from the RF classifications match with theoretical Illustris predictions but are underestimated by a factor of ∼0.5.</jats:p
Effects of high-frequency transcranial magnetic stimulation on functional performance in individuals with incomplete spinal cord injury: study protocol for a randomized controlled trial
Background:
Repetitive transcranial magnetic stimulation (rTMS) has been investigated as a new tool in neurological rehabilitation of individuals with spinal cord injury (SCI). However, due to the inconsistent results regarding the effects of rTMS in people with SCI, a randomized controlled double-blind crossover trial is needed to clarify the clinical utility and to assess the effect size of rTMS intervention in this population. Therefore, this paper describes a study protocol designed to investigate whether the use of rTMS can improve the motor and sensory function, as well as reduce spasticity in patients with incomplete SCI.
Methods:
A double-blind randomized sham-controlled crossover trial will be performed by enrolling 20 individuals with incomplete SCI. Patients who are at least six months post incomplete SCI (aged 18–60 years) will be recruited through referral by medical practitioners or therapists. Individuals will be randomly assigned to either group 1 or group 2 in a 1:1 ratio, with ten individuals in each group. The rTMS protocol will include ten sessions of high-frequency rTMS (5 Hz) over the bilateral lower-limb motor area positioned at the vertex (Cz). Clinical evaluations will be performed at baseline and after rTMS active and sham.
Discussion:
rTMS has produced positive results in treating individuals with physical impairments; thus, it might be promising in the SCI population. The results of this study may provide new insights to motor rehabilitation thereby contributing towards the better usage of rTMS in the SCI population.
Trial registration:
ClinicalTrials.gov,
NCT02899637
. Registered on 25 August 2016.Medicine, Faculty ofNon UBCReviewedFacult