arXiv subjects

Mark Larson

Publications and source records attributed to Mark Larson.

3 recordsLinked to original sources

arXiv2025

Enabling searches for long-lived particles at a future 10 TeV Muon Collider

Muon Colliders offer fantastic opportunities to explore new phenomena at the energy frontier. However, beam-induced-backgrounds from muon decays pose significant challenges for detector design, readout, and reconstruction. Previous detector studies have employed stringent hit-timing requirements to reduce occupancy to manageable levels with negligible efficiency loss for prompt Standard Model particles. In the spirit of maximizing discovery potential, we investigate the capability of detecting meta-stable charged long-lived particles at a 10 TeV Muon Collider. As a benchmark, we consider a Gauge Mediated Supersymmetry Breaking (GMSB) model in which the stau is long-lived and can be identified as a high momentum, slowly moving track. We find that nominal hit-timing selections are too restrictive, and investigate the impact of looser requirements. We demonstrate that it is possible to recover sensitivity to particles with masses close to $\sqrt{s}/2$ by expanding time acceptance, and provide recommendations to further improve tracker design and track reconstruction.

hep-ex↗

arXiv2025

MAIA: A new detector concept for a 10 TeV muon collider

Muon colliders offer a compelling opportunity to explore the TeV scale and conduct precision tests of the Standard Model, all within a relatively compact geographical footprint. This paper introduces a new detector concept, MAIA (Muon Accelerator Instrumented Apparatus), optimized for $\sqrt{s}=10$ TeV $\mu^+ \mu^-$ collisions. The detector features an all-silicon tracker immersed in a 5T solenoid field. High-granularity silicon-tungsten and iron-scintillator calorimeters surrounding the solenoid capture high-energy electronic and hadronic showers, respectively, and support particle-flow reconstruction. The outermost subsystem comprises an air-gap muon spectrometer, which contributes to muon identification. The performance of the MAIA detector is evaluated in terms of differential particle reconstruction efficiencies and resolutions. Beam-induced background and incoherent pair production simulations are overlaid to single particle gun samples to assess detector reconstruction capabilities under realistic experimental conditions. Even in the presence of backgrounds, reconstruction efficiencies exceed approximately 95\% for energetic tracks, photons, and charged pions in the central region of the detector. This paper outlines promising avenues for future work, including forward region optimization, opportunities for enhanced flavor tagging and boosted object reconstruction, and technological developments needed to achieve the desired detector performance.

physics.ins-det↗

arXiv2023

The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons

The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking. Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here. In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations.

cs.CV↗