Search

We investigate the effects of non-thermally produced dark matter on large-scale structure formation, focusing on the gravitino dark matter. Our analysis shows that large-scale structure measurements from the SDSS LRG data offer a promising approach to probing non-thermally produced gravitinos. Specifically, if gravitinos resulting from neutralino decay constitute a fraction $F_\text{dec}=0.35~(0.1)$ of the dark matter component, the data exclude bino-like neutralino with a mass $m_{\chi_1^0}\lesssim25~(11)\text{ GeV}$. Furthermore, this constraint appears to be largely independent of the gravitino mass., Comment: 7 pages, 3 figures

In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers.

Neural Code Intelligence -- leveraging deep learning to understand, generate, and optimize code -- holds immense potential for transformative impacts on the whole society. Bridging the gap between Natural Language and Programming Language, this domain has drawn significant attention from researchers in both research communities over the past few years. This survey presents a systematic and chronological review of the advancements in code intelligence, encompassing over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works. We follow the historical progression to trace the paradigm shifts across different research phases (e.g., from modeling code with recurrent neural networks to the era of Large Language Models). Concurrently, we highlight the major technical transitions in models, tasks, and evaluations spanning through different stages. For applications, we also observe a co-evolving shift. It spans from initial endeavors to tackling specific scenarios, through exploring a diverse array of tasks during its rapid expansion, to currently focusing on tackling increasingly complex and varied real-world challenges. Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains. Finally, we delve into both the opportunities and challenges associated with this field, alongside elucidating our insights on the most promising research directions. An ongoing, dynamically updated project and resources associated with this survey have been released at https://github.com/QiushiSun/NCISurvey., Comment: 64 pages, 6 figures, 10 tables, 692 references

We present a minimal framework that realises successful Dirac Leptogenesis through the Affleck-Dine mechanism. A single right-handed neutrino and a neutrinophillic Higgs doublet are introduced to the Standard Model, which couple via a Yukawa interaction. The inflationary setting is induced by a combination of the two Higgs doublets, with their global symmetry violating interactions leading to a net charge generation via the Affleck-Dine mechanism. This simple Standard Model extension exhibits a unique and connected set of phenomenological implications including the resultant baryon asymmetry, inflationary predictions, cosmological implications, relic right-handed neutrinos, and its low energy phenomenology, while also being able to be embedded in various neutrino mass generating mechanisms., Comment: 6 pages, 2 figures

Self-correction has achieved impressive results in enhancing the style and security of the generated output from large language models (LLMs). However, recent studies suggest that self-correction might be limited or even counterproductive in reasoning tasks due to LLMs' difficulties in identifying logical mistakes. In this paper, we aim to enhance the self-checking capabilities of LLMs by constructing training data for checking tasks. Specifically, we apply the Chain of Thought (CoT) methodology to self-checking tasks, utilizing fine-grained step-level analyses and explanations to assess the correctness of reasoning paths. We propose a specialized checking format called "Step CoT Check". Following this format, we construct a checking-correction dataset that includes detailed step-by-step analysis and checking. Then we fine-tune LLMs to enhance their error detection and correction abilities. Our experiments demonstrate that fine-tuning with the "Step CoT Check" format significantly improves the self-checking and self-correction abilities of LLMs across multiple benchmarks. This approach outperforms other formats, especially in locating the incorrect position, with greater benefits observed in larger models. For reproducibility, all the datasets and code are provided in https://github.com/bammt/Learn-to-check.

Autonomous interaction with the computer has been a longstanding challenge with great potential, and the recent proliferation of large language models (LLMs) has markedly accelerated progress in building digital agents. However, most of these agents are designed to interact with a narrow domain, such as a specific software or website. This narrow focus constrains their applicability for general computer tasks. To this end, we introduce OS-Copilot, a framework to build generalist agents capable of interfacing with comprehensive elements in an operating system (OS), including the web, code terminals, files, multimedia, and various third-party applications. We use OS-Copilot to create FRIDAY, a self-improving embodied agent for automating general computer tasks. On GAIA, a general AI assistants benchmark, FRIDAY outperforms previous methods by 35%, showcasing strong generalization to unseen applications via accumulated skills from previous tasks. We also present numerical and quantitative evidence that FRIDAY learns to control and self-improve on Excel and Powerpoint with minimal supervision. Our OS-Copilot framework and empirical findings provide infrastructure and insights for future research toward more capable and general-purpose computer agents., Comment: Project page: https://os-copilot.github.io

Type-II seesaw leptogenesis is a model that integrates inflation, baryon number asymmetry, and neutrino mass simultaneously. It employs the Affleck-Dine mechanism to generate lepton asymmetry, with the Higgs bosons serving as the inflaton. Previous studies assumed inflation to occur in a valley of the potential, employing the single-field approximation. In this work, we explore an alternative scenario for the type-II seesaw leptogenesis, where the inflation takes place along a ridge of the potential. Firstly, we conduct a comprehensive numerical calculation in the canonical scenario, where inflation occurs in a valley, confirming the effectiveness of the single-field approximation. Then, we introduce a novel scenario wherein inflation initiates along the potential's ridge and transitions to the valley in the late stages. In this case, the single-field inflation approximation is no longer valid, yet leptogenesis is still successfully achieved. We find that this scenario can generate a significant non-Gaussianity signature, offering testable predictions for future experiments.

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors., Comment: Accepted to EMNLP 2023

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of multimodalities into different low-dimensional spaces and cannot be applied to the sequential input data. To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer. We first use two encoders to separately map multimodal inputs into different low-dimensional spaces. Then we employ two decoders to regularize the embeddings and pull them into the same space. The two decoders capture the correlations between texts and images with the image captioning task and the text-to-image generation task, respectively. Further, based on the regularized embeddings, we present CrossTransformer, which uses two Transformer encoders with shared parameters as the backbone model to exchange knowledge between multimodalities. Specifically, CrossTransformer first learns the global contextual information of the inputs in the shallow layers. After that, it performs inter-modal exchange by selecting a proportion of tokens in one modality and replacing their embeddings with the average of embeddings in the other modality. We conduct extensive experiments to evaluate the performance of MuSE on the Multimodal Named Entity Recognition task and the Multimodal Sentiment Analysis task. Our results show the superiority of MuSE against other competitors. Our code and data are provided at https://github.com/RecklessRonan/MuSE.

We explore a scenario of high scale supersymmetry where all supersymmetric particles except gauginos stay at a high energy scale $M_{\rm SUSY}$ which is much larger than the reheating temperature $T_\text{RH}$. The dark matter is dominated by bino component with mass around the electroweak scale and the observed relic abundance is mainly generated by the freeze-in process during the early universe. Considering the various constraints, we identify two available scenarios in which the supersymmetric sector at an energy scale below $T_\text{RH}$ consists of: a) bino; b) bino and wino. Typically, for a bino mass around 0.1-1 TeV and a wino mass around 2 TeV, we find that $M_{\rm SUSY}$ should be around $10^{12-14}$ GeV with $T_\text{RH}$ around $10^{4-6}$ GeV., Comment: 23 pages, 4 figures, revised version accepted by Physical Review D for publication

The self-interacting dark matter (SIDM) paradigm offers a potential solution to small-scale structure problems faced by the collision-less cold dark matter. This framework incorporates self-interactions among dark matter particles, typically mediated by a particle with a MeV-scale mass. Recent evidences of nano-Hertz gravitational waves from pulsar timing arrays (PTAs) such as NANOGrav, CPTA, EPTA, and PPTA suggest the occurrence of a first-order phase transition (FOPT) at a MeV-scale temperature. Considering the close proximity between these two scales, we propose that the mediator mass in the SIDM model originates from the spontaneous breaking of a $U(1)'$ symmetry, which is driven by the FOPT indicated by PTA data. Consequently, the alignment of these two scales is believed to be deeply connected by the same underlying physics. By extensively exploring the parameter space, remarkably, we find that the parameter space favored by SIDM just provides an explanation for the PTA data., Comment: 5 pages, 2 figures, more references added

Large pre-trained language models (PLMs) have garnered significant attention for their versatility and potential for solving a wide spectrum of natural language processing (NLP) tasks. However, the cost of running these PLMs may be prohibitive. Furthermore, PLMs may not be open-sourced due to commercial considerations and potential risks of misuse, such as GPT-3. The parameters and gradients of PLMs are unavailable in this scenario. To solve the issue, black-box tuning has been proposed, which utilizes derivative-free optimization (DFO), instead of gradient descent, for training task-specific continuous prompts. However, these gradient-free methods still exhibit a significant gap compared to gradient-based methods. In this paper, we introduce gradient descent into black-box tuning scenario through knowledge distillation. Furthermore, we propose a novel method GDFO, which integrates gradient descent and derivative-free optimization to optimize task-specific continuous prompts in a harmonized manner. Experimental results show that GDFO can achieve significant performance gains over previous state-of-the-art methods.

Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks. However, tuning these models for downstream tasks usually needs exorbitant costs or is unavailable due to commercial considerations. Recently, black-box tuning has been proposed to address this problem by optimizing task-specific prompts without accessing the gradients and hidden representations. However, most existing works have yet fully exploited the potential of gradient-free optimization under the scenario of few-shot learning. In this paper, we describe BBT-RGB, a suite of straightforward and complementary techniques for enhancing the efficiency and performance of black-box optimization. Specifically, our method includes three plug-and-play components: (1) Two-stage derivative-free optimization strategy that facilitates fast convergence and mitigates overfitting; (2) Automatic verbalizer construction with its novel usage under few-shot settings; (3) Better prompt initialization policy based on instruction search and auto-selected demonstration. Extensive experiments across various tasks on natural language understanding and inference demonstrate the effectiveness of our method. Our codes are publicly available at https://github.com/QiushiSun/BBT-RGB., Comment: Accepted by LREC-COLING 2024

Type II seesaw leptogenesis simultaneously explains the origin of neutrino masses, the baryon asymmetry of our universe, and the inflation. The Large Hadron Collider(LHC) provides an opportunity to directly test type II seesaw leptogenesis by looking for the predicted triplet Higgs. In this paper, we perform an analysis of the detection prospect for the triplet Higgs at the LHC through the multi-electron channels. We find that due to the contribution of $pp\to H^{\pm \pm }H^{\mp }$ process, the sensitivity of multi-electron channels searching for the doubly-charged Higgs pair production can be improved. We also investigate the $3e+ {E}^{\rm miss}_{\rm T}$ signals to probe the $pp\to H^{\pm \pm }H^{\mp }$ production and find that the future high luminosity LHC could probe a triplet Higgs around 1.2 TeV at $2\sigma$ level., Comment: 13 pages,5 figures

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10~TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work., Comment: 118 pages, 103 figures

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet.

Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO). Despite the success of PROTO, there still exist three main problems: (1) ignore the randomness of the sampled support sets when computing prototype vectors; (2) disregard the importance of labeled samples; (3) construct meta-tasks in a purely random manner. In this paper, we propose a Meta-Learning Siamese Network, namely, Meta-SN, to address these issues. Specifically, instead of computing prototype vectors from the sampled support sets, Meta-SN utilizes external knowledge (e.g. class names and descriptive texts) for class labels, which is encoded as the low-dimensional embeddings of prototype vectors. In addition, Meta-SN presents a novel sampling strategy for constructing meta-tasks, which gives higher sampling probabilities to hard-to-classify samples. Extensive experiments are conducted on six benchmark datasets to show the clear superiority of Meta-SN over other state-of-the-art models. For reproducibility, all the datasets and codes are provided at https://github.com/hccngu/Meta-SN.

There is increasing evidence suggesting a discrepancy between the cosmic dipole observed in the number count of distant galaxies and the one derived from the cosmic microwave background (CMB). In this study, we investigate the possibility that the cosmic dipole problem can be addressed by considering the QCD axion, a hypothetical particle that arises from the spontaneous breaking of the Peccei-Quinn symmetry and is postulated to constitute the dark matter in our universe., Comment: 6 pages, 3 figures, version accepted by PRD

Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, which ignores the information of entity boundaries, and inevitably the performance is affected by the massive non-entity tokens. To this end, we propose a seminal span-based prototypical network (SpanProto) that tackles few-shot NER via a two-stage approach, including span extraction and mention classification. In the span extraction stage, we transform the sequential tags into a global boundary matrix, enabling the model to focus on the explicit boundary information. For mention classification, we leverage prototypical learning to capture the semantic representations for each labeled span and make the model better adapt to novel-class entities. To further improve the model performance, we split out the false positives generated by the span extractor but not labeled in the current episode set, and then present a margin-based loss to separate them from each prototype region. Experiments over multiple benchmarks demonstrate that our model outperforms strong baselines by a large margin.

Recently it has been found that introducing a triplet Higgs to the standard model could provide a feasible leptogenesis to generate the baryon asymmetry of our universe, providing that the inflation is driven by the mixing state of the triplet Higgs and SM Higgs. In this work, we survey the viable parameter space satisfying the vacuum stability and perturbativity in this model. We find that the introduction of the triplet Higgs would also ameliorate the problem of the Higgs vacuum instability. We present two representative parameter regions where the origin of neutrino masses, baryon asymmetry of the universe as well as inflation can be explained while keeping consistent with the condition of vacuum stability and perturbativity.

It is known that a light scalar field obtains fluctuations in the de Sitter inflationary background. Such fluctuations could provide an initial condition for baryogenesis through the Affleck-Dine mechanism, where an approximate $U(1)_B$ symmetry is usually assumed. However, an interpretation of the baryon number generation in this way is strongly related to the correlation length of the angular mode. In this work, we calculate the correlation length of the angular mode for a model exhibiting an approximate $U(1)$ symmetry. We find that for a massive nearly non-interacting field, the correlation length of the angular mode is determined by the mass parameter of the model and it is similar to $H^{-1} \exp(H^2/m^2)$. Applying this result to baryogenesis via the Affleck-Dine mechanism with a stochastic origin, we find that only for $m \ll \mathcal O(0.1) H$(assuming $N_*=60$) can the correlation length of the baryon number density be much larger than our current horizon size, such that we live in the baryon-rich region. If this is not true, in the early time our universe would consist of numerous patches of baryon-rich and anti-baryon-rich regions with the average baryon number being nearly zero., Comment: 15 pages, 2 figures, version accepted by Physics Letter B

The Type II Seesaw Mechanism provides a minimal framework to explain the neutrino masses involving the introduction of a single triplet Higgs to the Standard Model. However, this simple extension was believed to be unable to successfully explain the observed baryon asymmetry of the universe through Leptogenesis. In our previous work (Phys. Rev. Lett. 128, 141801), we demonstrated that the triplet Higgs of the Type II Seesaw Mechanism alone can simultaneously generate the observed baryon asymmetry of the universe and the neutrino masses while playing a role in setting up Inflation. This is achievable with a triplet Higgs mass as low as 1 TeV, and predicts that the neutral component obtains a small vacuum expectation value $v_\Delta < 10 $ keV. We find that our model has very rich phenomenology and can be tested by various terrestrial experiments as well as by astronomical observations. Particularly, we show that the successful parameter region may be probed at a future 100 TeV collider, upcoming lepton flavor violation experiments such as Mu3e, and neutrinoless double beta decay experiments. Additionally, the tensor-to-scalar ratio from the inflationary scenario will be probed by the LiteBIRD telescope, and observable isocurvature perturbations may be produced for some parameter choices. In this article, we present all the technical details of our calculations and further discussion of its phenomenological implications., Comment: 36 pages, 7 figures

Motivated by the Xenon1T excess in electron-recoil measurements, we investigate the prospects of probing axion-like particles (ALP) in lepton flavor violation experiments. In particular, we identify such ALP as a pseudo-Goldstone from the spontaneous breaking of the flavor symmetries that explain the mixing structure of the Standard Model leptons. We present the case of the flavor symmetries being a non-Abelian U(2) and the ALP originating from its U(1) subgroup, which is anomaly-free with the Standard Model group. We build two explicit realistic examples that reproduce leptonic masses and mixings and show that the ALP which is consistent with Xenon1T anomaly could be probed by the proposed LFV experiments., Comment: 18 pages, 1 figure

In the path towards a muon collider with center of mass energy of 10 TeV or more, a stage at 3 TeV emerges as an appealing option. Reviewing the physics potential of such muon collider is the main purpose of this document. In order to outline the progression of the physics performances across the stages, a few sensitivity projections for higher energy are also presented. There are many opportunities for probing new physics at a 3 TeV muon collider. Some of them are in common with the extensively documented physics case of the CLIC 3 TeV energy stage, and include measuring the Higgs trilinear coupling and testing the possible composite nature of the Higgs boson and of the top quark at the 20 TeV scale. Other opportunities are unique of a 3 TeV muon collider, and stem from the fact that muons are collided rather than electrons. This is exemplified by studying the potential to explore the microscopic origin of the current $g$-2 and $B$-physics anomalies, which are both related with muons., Comment: 73 pages, 28 figures; Contribution to Snowmass 2021

In this paper, we study knowledge tracing in the domain of programming education and make two important contributions. First, we harvest and publish so far the most comprehensive dataset, namely BePKT, which covers various online behaviors in an OJ system, including programming text problems, knowledge annotations, user-submitted code and system-logged events. Second, we propose a new model PDKT to exploit the enriched context for accurate student behavior prediction. More specifically, we construct a bipartite graph for programming problem embedding, and design an improved pre-training model PLCodeBERT for code embedding, as well as a double-sequence RNN model with exponential decay attention for effective feature fusion. Experimental results on the new dataset BePKT show that our proposed model establishes state-of-the-art performance in programming knowledge tracing. In addition, we verify that our code embedding strategy based on PLCodeBERT is complementary to existing knowledge tracing models to further enhance their accuracy. As a side product, PLCodeBERT also results in better performance in other programming-related tasks such as code clone detection.

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. In this paper, we propose a novel meta-learning framework integrated with an adversarial domain adaptation network, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, the accuracy of 1-shot and 5-shot classification on the dataset of 20 Newsgroups is boosted from 52.1% to 59.6%, and from 68.3% to 77.8%, respectively.

We find that the triplet Higgs of the Type II seesaw mechanism can simultaneously generate the neutrino masses and observed baryon asymmetry, while playing a role in inflation. We survey the allowed parameter space and determine that this is possible for triplet masses as low as a TeV, with a preference for a small vacuum expectation value for the triplet $v_\Delta < 10 $ keV. This requires that the triplet Higgs must decay dominantly into the leptonic channel. Additionally, this model will be probed at the future 100 TeV collider, upcoming lepton flavor violation experiments such as Mu3e, and neutrinoless double beta decay experiments. Thus, this simple framework provides a unified solution to the three major unknowns of modern physics - inflation, the neutrino masses, and the observed baryon asymmetry - while simultaneously providing unique phenomenological predictions that will be probed terrestrially at upcoming experiments., Comment: 7 pages + Supplemental Material, version accepted by Phys. Rev. Lett

We study the constraints of the CP violation in the muon $g-2$ preferred region of the minimal supersymmetric standard model assuming a universal slepton masses within first two generations. We present two particular scenarios where the $g-2$ anomaly is predicted within 2 $\sigma$ level mainly through the chargino loop or the bino loop. We found that for both cases the electron EDM experiment already highly constrained the CP phase of the parameters: either the Arg[$\mu M_1]$ or Arg[$\mu M_2]$ should be smaller than $\mathcal O$(2-3)$\times10^{-5}$. If the muon $g-2$ anomaly is explained by the MSSM, a particular SUSY breaking mechanism is needed to guarantee the small CP phase of SUSY parameters. Otherwise, a tuning of $\mathcal O(10^{-5})$ is needed to cancel the phase in a general CP violated SUSY model., Comment: 6 Pages, 2 figures

We demonstrate that the recent measurement of the anomalous magnetic moment of the muon and dark matter can be simultaneously explained within the Minimal Supersymmetric Standard Model. Dark matter is a mostly-bino state, with the relic abundance obtained via co-annihilations with either the sleptons or wino. The most interesting regions of parameter space will be tested by the next generation of dark matter direct detection experiments., Comment: 5 pages, 3 figures; v2: Updated results include 1-loop corrections to slepton masses. Journal version

The tau lepton plays important role in the correlation between the low-energy neutrino oscillation data and the lepton flavor structure in heavy neutrino decay. We investigate the lepton flavor signatures with tau lepton at hadron collider through lepton number violating (LNV) processes. In the Type I Seesaw with U$(1)_{\rm B-L}$ extension, we study the pair production of heavy neutrinos via a $Z'$ resonance. We present a detailed assessment of the search sensitivity to the channels with tau lepton in the subsequent decay of heavy neutrinos. For the benchmark model with $Z'$ only coupled to the third generation fermions, we find that the future circular collider (FCC-hh) can discover the LNV signal with tau lepton for $M_{Z'}$ up to 2.2 (3) TeV with the gauge coupling $g'=0.6$ and the integrated luminosity of 3 (30) ab$^{-1}$. The test on the flavor combinations of SM charged leptons would reveal the specific nature of different heavy neutrinos., Comment: 17 pages, 5 figures, 1 table. version accepted by PRD

The spontaneous muonium-to-antimuonium conversion is one of the interesting charged lepton flavor violation processes. MACE is the next generation experiment to probe such a phenomenon. In models with a triplet Higgs to generate neutrino masses, such as Type-II seesaw and its variant, this process can be induced by the doubly-charged Higgs contained in it. In this article, we study the prospect of MACE to probe these models via the muonium-to-antimuonium transitions. After considering the limits from $\mu^+ \rightarrow e^+ \gamma $ and $\mu^+ \rightarrow e^+ e^- e^+$, we find that MACE could probe a parameter space for the doubly-charged Higgs which is beyond the reach of LHC and other flavor experiments., Comment: 12 pages, 2 figures

We consider a scenario where light bino is the next-to-lightest supersymmetric particle (NLSP) and gravitino/axino is the lightest superysmmetric particle (LSP). For a bino mass less than or around hundred GeV, it can be pair produced at the future lepton colliders through $t-$channel slepton exchange, subsequently decaying into a gravitino/axino plus a photon. We study the prospects to look for such binos at the future colliders and find that a bino mass around 100 GeV can be probed at the $2\sigma$ ($5\sigma$) level for a slepton below 2 TeV (1.5 TeV) with a luminosity 3 $ab^{-1}$. For a bino mass around 10 GeV, a slepton mass less than 4 TeV (3 TeV) can be probed at the $2\sigma$ ($5\sigma$) level, which is much beyond the reach of the LHC for direct slepton searches., Comment: 12 pages, 7 figures

We consider a supersymmetric type-I seesaw framework with non-universal scalar masses at the GUT scale to explain the long-standing discrepancy of the anomalous magnetic moment of the muon. We find that it is difficult to accommodate the muon g-2 while keeping charged-lepton flavor violating processes under control for the conventional SO(10)-based relation between the up sector and neutrino sector. However, such tension can be relaxed by adding a Georgi-Jarlskog factor for the Yukawa matrices, which requires a non-trivial GUT-based model. In this model, we find that both observables are compatible for small mixings, CKM-like, in the neutrino Dirac Yukawa matrix., Comment: 27 pages, 9 figures

We present a supersymmetric solution to the strong CP problem based on spontaneous CP violation which simultaneously addresses the affects coming from supersymmetry breaking. The generated CP violating phase is communicated to the quark sector by interacting with a heavy quark a la Nelson-Barr. The Majorana mass of the right handed neutrinos is generated by interactions with the CP violating sector and so does not conserve CP. This gives the neutrino sector a non-trivial CP violating phase which can then generate the baryon asymmetry of the universe through leptogeneis. The problematic phase in the supersymmetry breaking parameters are suppressed by appealing to a particular gauge mediation model which naturally suppresses the phases of the tree-level gluino mass. This suppression plus the fact that in gauge mediation all loop generated flavor and CP violation is of the minimal flavor violation variety allows for a complete and consistent solution to the strong CP problem., Comment: 6 pages, 1 figure, 1 table

We investigate a $U(1)_{B-L}$ gauge extension of the Standard Model (SM) where the gauge boson mass is generated by the Stueckelberg mechanism. Three right-handed neutrinos are added to cancel the gauge anomaly and hence the neutrino masses can be explained. A new Dirac fermion could be a WIMP dark matter whose interaction with the SM sector is mediated by the new gauge boson. Assuming the perturbativity of the gauge coupling up to the Planck scale, we find that only the resonance region is feasible for the dark matter abundance. After applying the $\Delta N_{eff}$ constraints from the current Planck experiment, the collider search constraints as well as the dark matter direct detection limits, we observe that the $B-L$ charge of dark matter satisfies $|Q_{\chi}|>0.11$. Such a scenario might be probed conclusively by the projected CMB-S4 experiment, assuming the right-handed neutrinos are thermalized with the SM sector in the early universe., Comment: 12 pages, 3 figures

We consider a scalar field $\phi$ whose coupling to the kinetic term of a non-abelian gauge field is set at an UV scale $M$. Then the confinement of the gauge sector will induce a $\phi$-dependent vacuum energy which generates a dimensionful potential for the scalar. It provides a good example of dynamical generation of a new physics scale below $M$ through the vacuum expectation value $\langle \phi \rangle$. This mechanism may shed light on the origin of dark matter, or spontaneous symmetry breaking applicable to the electroweak symmetry., Comment: 4 pages, 3 figures