Searching for Majorana Neutrinos at a Same-Sign Muon Collider

Majorana properties of neutrinos have long been a focus in the pursuit of possible new physics beyond the standard model, which has motivated lots of dedicated theoretical and experimental studies. A future same-sign muon collider is an ideal platform to search for Majorana neutrinos through the Lepton Number Violation process. Specifically, this t-channel kind of process is less kinematically suppressed and has a good advantage in probing Majorana neutrinos at high mass regions up to 10 TeV. In this paper, we perform a detailed fast Monte Carlo simulation study through examining three different final states: 1) pure-leptonic state with electrons or muons, 2) semi-leptonic state, and 3) pure-hadronic state in the resolved or merged categories. Furthermore, we perform a full simulation study on the pure-leptonic final state to validate our fast simulation results.Comment: 15 pages, 8 figure

AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents

Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework \framework that can collaboratively and dynamically adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that \framework framework can effectively deploy multi-agent groups that outperform a single agent. Furthermore, we delve into the emergence of social behaviors among individual agents within a group during collaborative task accomplishment. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups. Our codes for \framework will soon be released at \url{https://github.com/OpenBMB/AgentVerse}.Comment: Work in progres

Imaging dynamics beneath turbid media via parallelized single-photon detection

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard methods aim to form images based upon optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such data to demonstrate deep-tissue imaging of decorrelation dynamics. In this work, we take advantage of a single-photon avalanche diode (SPAD) array camera, with over one thousand detectors, to simultaneously detect speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. We then apply a deep neural network to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating liquid tissue phantoms. We demonstrate the ability to record video of dynamic events occurring 5-8 mm beneath a decorrelating tissue phantom with mm-scale resolution and at a 2.5-10 Hz frame rate

Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed Classifying Rapid decorrelation Events via Parallelized single photon dEtection (CREPE)}, a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a $32\times32$ pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1-0.4s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to noninvasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe.Comment: Journal submissio

Structure and Properties of Phosphate-Based Geopolymer Synthesized with the Spent Fluid Catalytic-Cracking (SFCC) Catalyst

As a common industrial by-product, the spend fluid catalytic-cracking (SFCC) catalyst was used to prepare phosphate-based geopolymer for the first time. The structure and property of geopolymer with phosphoric acid concentration ranging from 6 to 14 mol/L was characterized by compressive strength measurements, X-ray powder diffraction (XRD), Fourier Transform Infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and 27Al and 29Si nuclear magnetic resonance (NMR). A stable binder was formed with the compressive strength in the range of 9.8 to 30.2 MPa when the acid concentration was between 6 and 12 mol/L. The higher concentration of acid can promote the dissolution of raw materials and formation of geopolymer gels. The coordination of silicon and aluminum in geopolymer gel synthesized with the SFCC catalyst and metakaolin is similar. Compared with the geopolymer with metakaolin, which forms more Si-O-Al bonds, in the networks of geopolymer with the SFCC catalyst, more Si(Al)-O-P bonds were formed. These results indicate that the SFCC catalyst can be an excellent raw material for the synthesis of phosphate-based geopolymer

Synthesis and Characterization of Fly Ash-Based Geopolymers Activated with Spent Caustic

The spent caustic with strong alkali first replaced the alkali activator to prepare the geopolymer. The influence of spent caustic to the geopolymer was characterized through compressive strength measurement, XRD, MIP analysis and NMR, and the immobilization efficiency of organic in geopolymer was evaluated through the measurement of total organic carbon (TOC). The results show that the spent caustic can partially replace the alkali activator to prepare the geopolymer, and it shows a better performance than that which was activated with pure NaOH solution when the alkalinity is between 4 mol and 14 mol. The organic matter in the spent alkali can be effectively fixed in the geopolymer, which will hinder the geopolymerization in the initial stage of the polymerization reaction but has little effect on the chemical structure and mechanical properties of the final product. With the degree of alkalinity increasing, the immobilization efficiency is improved, and the maximum can reach 84.5%. The organics in the spent caustic will hinder geopolymerization at the initial stage but has little effect on the chemical structure and mechanical property of the final product. This study proposes a new method for the recycling of spent caustic, which also reduces the preparation cost of geopolymers