Integrating Biologging and Behavioral State Modeling to Identify Cryptic Behaviors and Post-capture Recovery Processes: New Insights From a Threatened Marine Apex Predator

Multisensor biologging provides a powerful tool for ecological research, enabling fine-scale observation of animals to directly link physiology and movement to behavior across ecological contexts. However, applied research into behavioral disturbance and recovery following human interventions (e.g., capture and translocation) has mostly relied on coarse location-based tracking or unidimensional approaches (e.g., dive profiles and activity/energetic metrics) that may not resolve behaviors and recovery processes. Biologging can improve insights into both disturbed and natural behavior, which is critical for management and conservation initiatives, although challenges remain in objectively identifying distinct behavioral modes from complex multisensor datasets. Using white sharks (Carcharodon carcharias) released from a non-lethal catch-and-release shark bite mitigation program, we explored how combining multisensor biologging (video, depth, accelerometers, gyroscopes, and magnetometers), track reconstruction and behavioral state modeling using hidden Markov models (HMMs) can improve our understanding of behavioral processes and recovery. Biologging tags were deployed on eight white sharks, recording their continuous behaviors, movements, and environmental context (habitat, interactions with other organisms/objects) for periods of 10–87 h post-release. Dive profiles and tailbeat analysis (as a standard, activity-based method for assessing recovery) indicated an immediate “disturbed” period of offshore movement, displaying rapid tailbeats and an average tailbeat-derived recovery period of 9.7 h, with evidence of smaller individuals having longer recoveries. However, further integrating magnetometer-derived headings, track reconstruction and HMM modeling revealed a cryptic shift to diurnal clockwise-counterclockwise circling behavior, which we argue represents compelling new evidence for hypothesized unihemispheric sleep amongst elasmobranchs. By simultaneously providing critical information toward conservation-focused shark management and understudied aspects of shark behavior, our study highlights how integrating multisensor information through HMMs can improve our understanding of both post-release and natural behavior, especially in species that are difficult to observe directly.

Development, construction and tests of the Mu2e electromagnetic calorimeter mechanical structures

The “muon-to-electron conversion” (Mu2e) experiment at Fermilab will search for the charged lepton flavour violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of the existence of physics beyond the standard model. Mu2e detectors comprise a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. In particular, the calorimeter provides excellent electron identification, a fast calorimetric online trigger, and complementary information to aid pattern recognition and track reconstruction. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1348 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named “disks”) positioned at the relative distance of 70 cm downstream the aluminum target along the muon beamline satisfies the Mu2e physics requirements. The hostile Mu2e operational conditions, in terms of radiation levels (total expected ionizing dose of 12 krad and a neutron fluence of 5 × 1010 n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10−4 Torr) have posed tight constraints on scintillating materials, sensors, electronics and on the design of the detector mechanical structures and material choice. The support structure of each 674 crystal matrix is composed of an aluminum hollow ring and parts made of open-cell vacuum-compatible carbon fiber. The photosensors and front-end electronics for the readout of each crystal are inserted in a machined copper holder and make a unique mechanical unit. The resulting 674 mechanical units are supported by a machined plate of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal contact with the copper holders to constitute a low resistance thermal bridge. The data acquisition electronics are hosted in aluminum custom crates positioned on the external lateral surface of the disks. The crates also integrate the electronics cooling system as lines running in parallel to the front-end system. In this paper we report on the calorimeter mechanical structure design, the mechanical and thermal simulations that have determined the design technological choices, and the status of component production, quality assurance tests and plans for assembly at Fermilab.

Hybrid quantum classical graph neural networks for particle track reconstruction

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as “hit”. The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classical graph neural network that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit-based hybrid quantum-classical graph neural networks.

Charged particle tracking with quantum annealing optimization

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.

Silicon Micro-Strip Detectors

Silicon micro-strip detectors are fundamental tools for the high energy physics. Each detector is formed by a large set of parallel narrow strips of special surface treatments (diode junctions) on a slab of very high quality silicon crystals. Their development and use required a large amount of work and research. A very synthetic view is given of these important components and of their applications. Some details are devoted to the basic subject of the track reconstruction in silicon strip trackers. Recent demonstrations substantially modified the usual understanding of this argument.

Implementation of ACTS into sPHENIX Track Reconstruction

AbstractsPHENIX is a high energy nuclear physics experiment under construction at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory (BNL). The primary physics goals of sPHENIX are to study the quark-gluon-plasma, as well as the partonic structure of protons and nuclei, by measuring jets, their substructure, and heavy flavor hadrons in $$p$$ p $$+$$ + $$p$$ p , p + Au, and Au + Au collisions. sPHENIX will collect approximately 300 PB of data over three run periods, to be analyzed using available computing resources at BNL; thus, performing track reconstruction in a timely manner is a challenge due to the high occupancy of heavy ion collision events. The sPHENIX experiment has recently implemented the A Common Tracking Software (ACTS) track reconstruction toolkit with the goal of reconstructing tracks with high efficiency and within a computational budget of 5 s per minimum bias event. This paper reports the performance status of ACTS as the default track fitting tool within sPHENIX, including discussion of the first implementation of a time projection chamber geometry within ACTS.

Full Detector Simulation with Unprecedented Background Occupancy at a Muon Collider

In recent years, a Muon collider has attracted a lot of interest in the high-energy physics community, thanks to its ability of achieving clean interaction signatures at multi-TeV collision energies in the most cost-effective way. Estimation of the physics potential of such an experiment must take into account the impact of beam-induced background on the detector performance, which has to be carefully evaluated using full detector simulation. Tracing of all the background particles entering the detector region in a single bunch crossing is out of reach for any realistic computing facility due to the unprecedented number of such particles. To make it feasible a number of optimisations have been applied to the detector simulation workflow. This contribution presents an overview of the main characteristics of the beam-induced background at a Muon collider, the detector technologies considered for the experiment and how they are taken into account to strongly reduce the number of irrelevant computations performed during the detector simulation. Special attention is dedicated to the optimisation of track reconstruction with the conformal tracking algorithm in this high-occupancy environment, which is the most computationally demanding part of event reconstruction.

A GPU-Based Kalman Filter for Track Fitting

Computing centres, including those used to process High-Energy Physics data and simulations, are increasingly providing significant fractions of their computing resources through hardware architectures other than x86 CPUs, with GPUs being a common alternative. GPUs can provide excellent computational performance at a good price point for tasks that can be suitably parallelized. Charged particle (track) reconstruction is a computationally expensive component of HEP data reconstruction, and thus needs to use available resources in an efficient way. In this paper, an implementation of Kalman filter-based track fitting using CUDA and running on GPUs is presented. This utilizes the ACTS (A Common Tracking Software) toolkit; an open source and experiment-independent toolkit for track reconstruction. The implementation details and parallelization approach are described, along with the specific challenges for such an implementation. Detailed performance benchmarking results are discussed, which show encouraging performance gains over a CPU-based implementation for representative configurations. Finally, a perspective on the challenges and future directions for these studies is outlined. These include more complex and realistic scenarios which can be studied, and anticipated developments to software frameworks and standards which may open up possibilities for greater flexibility and improved performance.

VHF Omnidirectional Range (VOR) Experimental Positioning for Stratospheric Vehicles

The usage of aeronautical radio-frequency navigational aids can support the future stratospheric aviation as back-up positioning systems. Although GNSS has been extensively redundant in the last years of space operations, radio NavAids can still be supportive of navigation and tracking for novel mission profiles. As an example, in 2016, VHF Omnidirectional Range (VOR) has been proven to work well above its standard service volume limit on a stratospheric balloon flight with the STRATONAV experiment. While VOR provides the “radial” measurement, i.e., the angle between the Magnetic North and the line between the receiver and the transmitting ground station, the intersection of two or more radials at a time allows to perform ground track reconstruction for the vehicle to be tracked. This paper reports the results from the data re-processing from STRATONAV: the acquired radials have been intersected in order to achieve positioning. The radials interfacing method, the position calculation methodology, and the data acquisition strategies from STRATONAV are reported together with the data analysis results.