arXiv subjects

Michael Mooney

Publications and source records attributed to Michael Mooney.

6 recordsLinked to original sources

arXiv2022

Detector and Beamline Simulation for Next-Generation High Energy Physics Experiments

The success of high energy physics programs relies heavily on accurate detector simulations and beam interaction modeling. The increasingly complex detector geometries and beam dynamics require sophisticated techniques in order to meet the demands of current and future experiments. Common software tools used today are unable to fully utilize modern computational resources, while data-recording rates are often orders of magnitude larger than what can be produced via simulation. In this paper, we describe the state, current and future needs of high energy physics detector and beamline simulations and related challenges, and we propose a number of possible ways to address them.

hep-ex↗

arXiv2022

The Present and Future Status of Heavy Neutral Leptons

The existence of non-zero neutrino masses points to the likely existence of multiple SM neutral fermions. When such states are heavy enough that they cannot be produced in oscillations, they are referred to as Heavy Neutral Leptons (HNLs). In this white paper we discuss the present experimental status of HNLs including colliders, beta decay, accelerators, as well as astrophysical and cosmological impacts. We discuss the importance of continuing to search for HNLs, and its potential impact on our understanding on key fundamental questions, and additionally we outline the future prospects for next-generation future experiments or upcoming accelerator run scenarios.

hep-ph↗

arXiv2018

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.

cs.CV↗

arXiv2017

Convolutional Neural Networks: Ensemble Modeling, Fine-Tuning and Unsupervised Semantic Localization for Intraoperative CLE Images

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence technology undergoing assessment for applications in brain tumor surgery. Despite its promising potential, interpreting the unfamiliar gray tone images of fluorescent stains can be difficult. Many of the CLE images can be distorted by motion, extremely low or high fluorescence signal, or obscured by red blood cell accumulation, and these can be interpreted as nondiagnostic. However, just one neat CLE image might suffice for intraoperative diagnosis of the tumor. While manual examination of thousands of nondiagnostic images during surgery would be impractical, this creates an opportunity for a model to select diagnostic images for the pathologists or surgeon's review. In this study, we sought to develop a deep learning model to automatically detect the diagnostic images using a manually annotated dataset, and we employed a patient-based nested cross-validation approach to explore generalizability of the model. We explored various training regimes: deep training, shallow fine-tuning, and deep fine-tuning. Further, we investigated the effect of ensemble modeling by combining the top-5 single models crafted in the development phase. We localized histological features from diagnostic CLE images by visualization of shallow and deep neural activations. Our inter-rater experiment results confirmed that our ensemble of deeply fine-tuned models achieved higher agreement with the ground truth than the other observers. With the speed and precision of the proposed method (110 images/second; 85% on the gold standard test subset), it has potential to be integrated into the operative workflow in the brain tumor surgery.

cs.CV↗

arXiv2015

The MicroBooNE Experiment and the Impact of Space Charge Effects

MicroBooNE is an experiment designed to both probe neutrino physics phenomena and develop the LArTPC (Liquid Argon Time Projection Chamber) detector technology. The MicroBooNE experiment, which began taking data this year, is the first large LArTPC detector in the U.S. This experiment is the beginning of a path of detectors (both on the surface and underground) envisioned for the U.S. SBL (Short-BaseLine) and LBL (Long-BaseLine) programs. In order to interpret the data from the experiments on the surface, the impact of space charge effects must be simulated and calibrated. The space charge effect is the build-up of slow-moving positive ions in a detector due to, for instance, ionization from cosmic rays, leading to a distortion of the electric field within the detector. This effect leads to a displacement in the reconstructed position of signal ionization electrons in LArTPC detectors. The LArTPC utilized in the MicroBooNE experiment is expected to be modestly impacted from the space charge effect, with the electric field magnitude changing by roughly 5\% (at a drift field of 500 V/cm) in some locations within the TPC. We discuss the simulation of the space charge effect at MicroBooNE as well as calibration techniques that make use of a UV laser system and cosmic muon events. A successful calibration of the space charge effect is imperative both to the success of the MicroBooNE physics program as well as to the development of LArTPC technology for future experiments.

physics.ins-det↗

arXiv2013

Search for the SM Higgs Boson Produced in Association with a Vector Boson and Decaying to Bottom Quarks

A search for the Higgs boson produced in association with a W or Z boson and decaying to bottom quarks is presented. A sample of approximately 24/fb of data recorded by the CMS experiment at the Large Hadron Collider, operating at center-of-mass energies of 7 TeV and 8 TeV in 2011 and 2012, respectively, is used to search for events consistent with the signature of two b jets recoiling with high momentum from a W(lnu), Z(ll), or Z(nunu) decay, where l = electron or muon (or hadronically-decaying tau particle in the case of W bosons). Observed signal significance and 95% confidence level upper limits on the production cross section relative to the Standard Model prediction are presented for the 110-135 GeV Higgs mass range.

hep-ex↗