L. Rinaldi | Searcharxiv

arXiv2025

Performance of the High-Angle Time Projection Chambers in the Upgraded T2K Off-Axis Near Detector

The off-axis magnetic near detector of the T2K experiment has undergone a significant upgrade, including the construction and installation of two new Time Projection Chambers featuring innovative resistive Micromegas technology and a field cage composed of thin composite walls. This paper provides a detailed description of the new components of the chambers, including the gas system, gas monitoring chambers, and data acquisition system. Additionally, it reports the results of extensive testing using both neutrino beams and cosmic rays, with comparisons between data and Monte Carlo simulations. The new detectors achieve improved spatial resolution and enhanced particle identification capabilities which are crucial for the precision goals of the T2K experiment.

physics.ins-det↗

arXiv2021

Automatic Cell Counting in Flourescent Microscopy Using Deep Learning

Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to arbitrariness due to the operator's interpretation of the borderline cases. We propose a Machine Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest. Counts are then retrieved as the number of detected items. Specifically, we adopt a UNet-like architecture leveraging residual units and an extended bottleneck for enlarging the field-of-view. In addition, we make use of weighted maps that penalize the errors on cells boundaries increasingly with overcrowding. These changes provide more context and force the model to focus on relevant features during pixel-wise classification. As a result, the model performance is enhanced, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior assessment of the results with domain experts confirms that the model detects cells of interest correctly. The model demonstrates a human-level ability inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. This qualitative assessment is also corroborated by quantitative metrics as an ${F_1}$ score of 0.87. Despite some difficulties in interpretation, results are also satisfactory with respect to the counting task, as testified by mean and median absolute error of, respectively, 0.8 and 1.

cs.CV↗