Probing Soft Interactions in Collider Experiments: From the Standard Model to Beyond

In modern experimental particle physics with colliders, the most common particle collisions of interest contain hard interactions to probe physics at high energy scales, while the soft interactions are considered backgrounds. However, the soft interactions are related to many fundamental aspects of quantum field theory, which lead to rich experimental phenomena. Due to the non-perturbative nature of these interactions and difficulties in first-principle predictions, the theoretical description relies heavily on experimental inputs, which motivated the three analyses of experimental data in this thesis. Inelastic proton-proton (pp) collisions are predominantly governed by quantum chromodynamics (QCD) interactions with low energy transfer, referred to as soft QCD interactions. The event-shapes of these collisions encode key interaction properties through the kinematics of outgoing particles. The first analysis in this dissertation measures event shapes at a center-of-mass energy of 13TeV using the pp collision data taken by the CMS detector in 2018, demonstrating the need for further theoretical developments to improve event shape predictions. The second analysis examines the intrinsic kT models in event generators, alongside the initial-state radiations in parton showers, to describe transverse recoils in the hard-scattering process. The tuning of intrinsic kT parameters to experimental data at center-of-mass energies from 38.8 GeV to 13 TeV taken by either fixed-target or collider experiments, along with their scaling behavior concerning colliding energy and hard-scattering scales, provides insights into soft emissions not accounted for in parton showers. The third analysis searches for new physics within hidden valley models, which predict exotic soft particle emissions through QCD-like extensions of the Standard Model. Sphericity, an event shape observable, along with multiplicity, is used to distinguish these soft unclustered energy patterns from the QCD multijet background. By leveraging the scouting stream of CMS Run 2 data (2016-2018) at 13 TeV, this search achieves high sensitivity across a broad range of signal hypotheses, placing constraints on signal parameters and excluding the incompatible models

Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.Comment: 14 pages, 9 figures, 2 tables. This paper has been accepted for publication by the IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessibl

Beam Performance Optimization of Multibeam Imaging Sonar Based on the Hybrid Algorithm of Binary Particle Swarm Optimization and Convex Optimization

It should be noted that the peak sidelobe level (PSLL) significantly influences the performance of the multibeam imaging sonar. Although a great amount of work has been done to suppress the PSLL of the array, one can verify that these methods do not provide optimal results when applied to the case of multiple patterns. In order to suppress the PSLL for multibeam imaging sonar array, a hybrid algorithm of binary particle swarm optimization (BPSO) and convex optimization is proposed in this paper. In this algorithm, the PSLL of multiple patterns is taken as the optimization objective. BPSO is considered as a global optimization algorithm to determine best common elements’ positions and convex optimization is considered as a local optimization algorithm to optimize elements’ weights, which guarantees the complete match of the two factors. At last, simulations are carried out to illustrate the effectiveness of the proposed algorithm in this paper. Results show that, for a sparse semicircular array with multiple patterns, the hybrid algorithm can obtain a lower PSLL compared with existing methods and it consumes less calculation time in comparison with other hybrid algorithms

Extracting Graphs Properties with Semantic Joins

This paper proposes an approach to querying a relational database D and a graph G taken together in SQL. We introduce a semantic extension of joins across D and G such that if a tuple t in D and a vertex v in G refer to the same real-world entity, then we join t and v to correlate their information and complement tuple t with additional properties of vertex v from the graph. Moreover, we extract hidden relationships between t and other entities by exploring paths from v. To support the semantic joins, we develop an extraction scheme based on LSTM, path clustering and ranking, to fetch important properties from graphs, and incrementally maintain the extracted data in response to updates. We also provide methods for implementing static joins when t is a tuple in D, dynamic joins when t comes from the intermediate result of a sub-query, and heuristic joins to strike a balance between the complexity and accuracy. Using reallife data and queries, we experimentally verify the effectiveness,scalability and efficiency of the methods

Effects of 12-week gait retraining on plantar flexion torque, architecture, and behavior of the medial gastrocnemius in vivo

Objective:This study aims to explore the effects of 12-week gait retraining (GR) on plantar flexion torque, architecture, and behavior of the medial gastrocnemius (MG) during maximal voluntary isometric contraction (MVIC).Methods:Thirty healthy male rearfoot strikers were randomly assigned to the GR group (n = 15) and the control (CON) group (n = 15). The GR group was instructed to wear minimalist shoes and run with a forefoot strike pattern for the 12-week GR (3 times per week), whereas the CON group wore their own running shoes and ran with their original foot strike pattern. Participants were required to share screenshots of running tracks each time to ensure training supervision. The architecture and behavior of MG, as well as ankle torque data, were collected before and after the intervention. The architecture of MG, including fascicle length (FL), pennation angle, and muscle thickness, was obtained by measuring muscle morphology at rest using an ultrasound device. Ankle torque data during plantar flexion MVIC were obtained using a dynamometer, from which peak torque and early rate of torque development (RTD50) were calculated. The fascicle behavior of MG was simultaneously captured using an ultrasound device to calculate fascicle shortening, fascicle rotation, and maximal fascicle shortening velocity (Vmax).Results:After 12-week GR, 1) the RTD50 increased significantly in the GR group (p = 0.038), 2) normalized FL increased significantly in the GR group (p = 0.003), and 3) Vmax increased significantly in the GR group (p = 0.018).Conclusion:Compared to running training, GR significantly enhanced the rapid strength development capacity and contraction velocity of the MG. This indicates the potential of GR as a strategy to improve muscle function and mechanical efficiency, particularly in enhancing the ability of MG to generate and transmit force as well as the rapid contraction capability. Further research is necessary to explore the effects of GR on MG behavior during running in vivo

Missile-Borne SAR Raw Signal Simulation for Maneuvering Target

SAR raw signal simulation under the case of maneuver and high-speed has been a challenging and urgent work recently. In this paper, a new method based on one-dimensional fast Fourier transform (1DFFT) algorithm is presented for raw signal simulation of maneuvering target for missile-borne SAR. Firstly, SAR time-domain raw signal model is given and an effective Range Frequency Azimuth Time (RFAT) algorithm based on 1DFFT is derived. In this algorithm, the “Stop and Go” (SaG) model is adopted and the wide radar scattering characteristic of target is taken into account. Furthermore, the “Inner Pulse Motion” (IPM) model is employed to deal with high-speed case. This new RFAT method can handle the maneuvering cases, high-speed cases, and bistatic radar cases, which are all possible in the missile-borne SAR. Besides, this raw signal simulation adopts the electromagnetic scattering calculation so that we do not need a scattering rate distribution map as the simulation input. Thus, the multiple electromagnetic reflections can be considered. Simulation examples prove the effectiveness of our method

Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance

Cartilage Repair and Subchondral Bone Migration Using 3D Printing Osteochondral Composites: A One-Year-Period Study in Rabbit Trochlea

Increasing evidences show that subchondral bone may play a significant role in the repair or progression of cartilage damagein situ. However, the exact change of subchondral bone during osteochondral repair is still poorly understood. In this paper, biphasic osteochondral composite scaffolds were fabricated by 3D printing technology using PEG hydrogel andβ-TCP ceramic and then implanted in rabbit trochlea within a critical size defect model. Animals were euthanized at 1, 2, 4, 8, 16, 24, and 52 weeks after implantation. Histological results showed that hyaline-like cartilage formed along with white smooth surface and invisible margin at 24 weeks postoperatively, typical tidemark formation at 52 weeks. The repaired subchondral bone formed from 16 to 52 weeks in a “flow like” manner from surrounding bone to the defect center gradually. Statistical analysis illustrated that both subchondral bone volume and migration area percentage were highly correlated with the gross appearance Wayne score of repaired cartilage. Therefore, subchondral bone migration is related to cartilage repair for critical size osteochondral defects. Furthermore, the subchondral bone remodeling proceeds in a “flow like” manner and repaired cartilage with tidemark implies that the biphasic PEG/β-TCP composites fabricated by 3D printing provides a feasible strategy for osteochondral tissue engineering application.</jats:p

A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection

The generalized Radon-Fourier transform (GRFT) has been proposed to detect radar weak maneuvering targets by realizing coherent integration via jointly searching in motion parameter space. Two main drawbacks of GRFT are the heavy computational burden and the blind speed side lobes (BSSL) which will cause serious false alarms. The BSSL learning-based particle swarm optimization (BPSO) has been proposed before to reduce the computational burden of GRFT and solve the BSSL problem simultaneously. However, the BPSO suffers from an apparent loss in detection performance compared with GRFT. In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO) is proposed. In the BMWDO, the BSSL learning procedure is also used to deal with the BSSL phenomenon. Besides, the MWDO adjusts the coefficients in WDO with Levy distribution and uniform distribution, and it outperforms PSO in a noisy environment. Compared with BPSO, the proposed method can achieve better detection performance with a similar computational cost. Several numerical experiments are also provided to demonstrate the effectiveness of the proposed method