Tootfinder

Search for a $\mu^ \mu^-$ resonance in four-muon final states at Belle II
Collaboration, Adachi, Adamczyk, Aggarwal, Ahmed, Aihara, Akopov, Aloisio, Ky, Asner, Atmacan, Aushev, Aversano, Ayad, Babu, Bae, Bahinipati, Bambade, Banerjee, Bansal, Barrett, Baudot, Baur, Beaubien, Becherer, Becker, Bennett, Bernlochner, Bertacchi, Bertemes, Bertholet, Bessner, Bettarini, Bhuyan, Bianchi, Bilka, Bilokin, Biswas, Bobrov, Bodrov, Bolz, Bozek, Bra\v{c}ko, Branchini, Browder, Budano, Bussino, Campajola, Cao, Casarosa, Cecchi, Cerasoli, Chang, Chang, Cheaib, Cheema, Cheon, Chilikin, Chirapatpimol, Cho, Cho, Cho, Choi, Choudhury, Corona, Cremaldi, Das, Dattola, De La Cruz-Burelo, De La Motte, De Nardo, De Nuccio, De Pietro, de Sangro, Destefanis, Dhamija, Di Canto, Di Capua, Dingfelder, Dole\v{z}al, Dong, Dorigo, Dort, Dreyer, Dubey, Dujany, Ecker, Eliachevitch, Epifanov, Feichtinger, Ferber, Ferlewicz, Fillinger, Finck, Finocchiaro, Fodor, Forti, Frey, Fulsom, Gabrielli, Ganiev, Garcia-Hernandez, Garg, Gaudino, Gaur, Gaz, Gellrich, Ghevondyan, Ghosh, Ghumaryan, Giakoustidis, Giordano, Giri, Glazov, Gobbo, Godang, Gogota, Goldenzweig, Gradl, Grammatico, Graziani, Greenwald, Gruberov\'a, Gu, Gudkova, Halder, Han, Hara, Hayashii, Hazra, Hearty, Hedges, Heidelbach, de la Cruz, Villanueva, Higuchi, Hoek, Hohmann, Horak, Hsu, Humair, Iijima, Inguglia, Ipsita, Ishikawa, Itoh, Iwasaki, Jackson, Jacobs, Jang, Ji, Jia, Jin, Joo, Junkerkalefeld, Kalita, Kandra, Kang, Karyan, Kawasaki, Keil, Kiesling, Kim, Kim, Kim, Kim, Kindo, Kinoshita, Kody\v{s}, Koga, Kohani, Kojima, Korobov, Korpar, Kovalenko, Kowalewski, Kraetzschmar, Kri\v{z}an, Krokovny, Kuhr, Kumar, Kumar, Kumar, Kumara, Kunigo, Kuzmin, Kwon, Lacaprara, Lai, Lam, Lanceri, Lange, Laurenza, Lautenbach, Leboucher, Le Diberder, Lee, Levit, Lewis, Li, Li, Li, Li, Libby, Liu, Liu, Liu, Liventsev, Longo, Lueck, Lyu, Ma, Maggiora, Maharana, Maiti, Maity, Mancinelli, Manfredi, Manoni, Mantovano, Marcantonio, Marcello, Marinas, Martel, Martellini, Martini, Martinov, Massaccesi, Masuda, Matsuoka, Matvienko, Maurya, McKenna, Mehta, Meier, Merola, Metzner, Milesi, Miller, Mirra, Miyabayashi, Miyake, Mizuk, Mohanty, Molina-Gonzalez, Mondal, Moneta, Moser, Mrvar, Mussa, Nakamura, Nakao, Nakazawa, Charan, Naruki, Narwal, Natkaniec, Natochii, Nayak, Nayak, Nazaryan, Niebuhr, Nishida, Ogawa, Onishchuk, Ono, Onuki, Oskin, Otani, Pakhlova, Panta, Pardi, Parham, Park, Park, Paschen, Passeri, Patra, Paul, Pedlar, Peschke, Pestotnik, Piccolo, Piilonen, Angioni, Podesta-Lerma, Podobnik, Pokharel, Praz, Prell, Prencipe, Prim, Purwar, Rados, Raeuber, Raiz, Rauls, Reif, Reiter, Remnev, Ripp-Baudot, Rizzo, Robertson, Roehrken, Roney, Rostomyan, Rout, Russo, Sanders, Sandilya, Santelj, Sato, Savinov, Scavino, Schmitt, Schwanda, Schwickardi, Seino, Selce, Senyo, Serrano, Sevior, Sfienti, Shan, Shen, Shi, Shillington, Shimasaki, Shiu, Shtol, Sibidanov, Simon, Singh, Skorupa, Sobie, Sobotzik, Soffer, Sokolov, Solovieva, Spataro, Spruck, Stari\v{c}, Stavroulakis, Stefkova, Stroili, Sumihama, Sumisawa, Sutcliffe, Svidras, Takizawa, Tamponi, Tanaka, Tanida, Tenchini, Tittel, Tiwary, Tonelli, Torassa, Trabelsi, Tsaklidis, Uchida, Ueda, Unger, Unno, Uno, Uno, Urquijo, Ushiroda, Vahsen, van Tonder, Varvell, Veronesi, Vinokurova, Vismaya, Vitale, Vobbilisetti, Volpe, Wach, Wakai, Wallner, Wang, Wang, Wang, Wang, Warburton, Watanuki, Wessel, Won, Xu, Yabsley, Yamada, Yan, Yang, Yelton, Yin, Yoshihara, Yuan, Yusa, Zani, Zhang, Zhilich, Zhou, Zhou, Zhukova
https://arxiv.org/abs/2403.02841 https://arxiv.org/pdf/2403.02841
arXiv:2403.02841v1 Announce Type: new
Abstract: We report on a search for a resonance $X$ decaying to a pair of muons in $e^{ }e^{-}\rightarrow \mu^ \mu^- X$ events in the 0.212-9.000 GeV/$c^{2}$ mass range, using 178 fb$^{-1}$ of data collected by the BelleII experiment at the SuperKEKB collider at a center of mass energy of 10.58 GeV. The analysis probes two different models of $X$ beyond the standard model: a $Z^{\prime}$ vector boson in the $L_{\mu}-L_{\tau}$ model and a muonphilic scalar. We observe no evidence for a signal and set exclusion limits at the 90\% confidence level on the products of cross section and branching fraction for these processes, ranging from 0.046 fb to 0.97 fb for the $L_{\mu}-L_{\tau}$ model and from 0.055 fb to 1.3 fb for the muonphilic scalar model. For masses below 6 GeV/$c^{2}$, the corresponding constraints on the couplings of these processes to the standard model range from 0.0008 to 0.039 for the $L_{\mu}-L_{\tau}$ model and from 0.0018 to 0.040 for the muonphilic scalar model. These are the first constraints on the muonphilic scalar from a dedicated search.

This https://arxiv.org/abs/1709.05504 has been replaced.
link: https://scholar.google.com/scholar?q=a

A priori bounds for geodesic diameter. Part III. A Sobolev-Poincaré inequality and applications to a variety of geometric variational problems
Based on a novel type of Sobolev-Poincaré inequality (for generalised weakly differentiable functions on varifolds), we establish a finite upper bound of the geodesic diameter of generalised compact connected surfaces-with-boundary of arbitrary dimension in Euclidean space in terms of the mean curvatures of the surface and its boundary. Our varifold setting includes smooth immersions, surfaces with finite Willmore energy, two-convex hypersurfaces in level-set mean curvature flow, integral curr…

On Independent Samples Along the Langevin Diffusion and the Unadjusted Langevin Algorithm
Jiaming Liang, Siddharth Mitra, Andre Wibisono
https://arxiv.org/abs/2402.17067

On Independent Samples Along the Langevin Diffusion and the Unadjusted Langevin Algorithm
We study the rate at which the initial and current random variables become independent along a Markov chain, focusing on the Langevin diffusion in continuous time and the Unadjusted Langevin Algorithm (ULA) in discrete time. We measure the dependence between random variables via their mutual information. For the Langevin diffusion, we show the mutual information converges to $0$ exponentially fast when the target is strongly log-concave, and at a polynomial rate when the target is weakly log-co…

This https://arxiv.org/abs/2312.17008 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Thermodynamic formalism and anomalous transport in 1D semiclassical Bose-Hubbard chain
We analyze the time-dependent free energy functionals of the semiclassical one-dimensional Bose-Hubbard chain. We first review the weakly chaotic dynamics and the consequent early-time anomalous diffusion in the system. The anomalous diffusion is robust, appears with strictly quantized coefficients, and persists even for very long chains (more than hundred sites), crossing over to normal diffusion at late times. We identify fast (angle) and slow (action) variables and thus consider annealed and…

This https://arxiv.org/abs/2304.05323 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A nonparametric framework for treatment effect modifier discovery in high dimensions
Heterogeneous treatment effects are driven by treatment effect modifiers, pre-treatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncovering these variables are limited to low-dimensional data, data with weakly correlated covariates, or data generated according to parametric processes. We resolve these issues by developing a framework for defining model-agnostic treatment effect modifier variable importance parameters applicable to high-dimensional d…

Convergence of stochastic integrals with applications to transport equations and conservation laws with noise
Kenneth H. Karlsen, Peter H. C. Pang
https://arxiv.org/abs/2404.16157

Convergence of stochastic integrals with applications to transport equations and conservation laws with noise
Convergence of stochastic integrals driven by Wiener processes $W_n$, with $W_n \to W$ almost surely in $C_t$, is crucial in analyzing SPDEs. Our focus is on the convergence of the form $\int_0^T V_n\, \mathrm{d} W_n \to \int_0^T V\, \mathrm{d} W$, where $\{V_n\}$ is bounded in $L^p(Ω\times [0,T];X)$ for a Banach space $X$ and some finite $p > 2$. This is challenging when $V_n$ converges to $V$ weakly in the temporal variable. We supply convergence results to handle stochastic integral limits …

A modified debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization
Youpeng Su, Siqi Xu, Yilei Ma, Ping Yin, Wing Kam Fung, Peng Wang
https://arxiv.org/abs/2402.15086

A modified debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization
Mendelian randomization uses genetic variants as instrumental variables to make causal inferences about the effects of modifiable risk factors on diseases from observational data. One of the major challenges in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the risk factor of interest, a setting known as many weak instruments. Many existing methods, such as the popular inverse-variance weighted (IVW) method, could be biased when the instru…

This https://arxiv.org/abs/2403.16828 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Asymptotics of predictive distributions driven by sample means and variances
Let $α_n(\cdot)=P\bigl(X_{n+1}\in\cdot\mid X_1,\ldots,X_n\bigr)$ be the predictive distributions of a sequence $(X_1,X_2,\ldots)$ of $p$-variate random variables. Suppose $$α_n=\mathcal{N}_p(M_n,Q_n)$$ where $M_n=\frac{1}{n}\sum_{i=1}^nX_i$ and $Q_n=\frac{1}{n}\sum_{i=1}^n(X_i-M_n)(X_i-M_n)^t$. Then, there is a random probability measure $α$ on $\mathbb{R}^p$ such that $α_n\rightarrowα$ weakly a.s. If $p\in\{1,2\}$, one also obtains $\lVertα_n-α\rVert\overset{a.s.}\longrightarrow 0$ wher…

This https://arxiv.org/abs/2402.00307 has been replaced.
link: https://scholar.google.com/scholar?q=a

Debiased Multivariable Mendelian Randomization
Multivariable Mendelian randomization (MVMR) uses genetic variants as instrumental variables to infer the direct effect of multiple exposures on an outcome. Compared to univariable Mendelian randomization, MVMR is less prone to horizontal pleiotropy and enables estimation of the direct effect of each exposure on the outcome. However, MVMR faces greater challenges with weak instruments -- genetic variants that are weakly associated with some exposures conditional on the other exposures. This art…

Asymptotics of predictive distributions driven by sample means and variances
Samuele Garelli, Fabrizio Leisen, Luca Pratelli, Pietro Rigo
https://arxiv.org/abs/2403.16828

Asymptotics of predictive distributions driven by sample means and variances
Let $α_n(\cdot)=P\bigl(X_{n+1}\in\cdot\mid X_1,\ldots,X_n\bigr)$ be the predictive distributions of a sequence $(X_1,X_2,\ldots)$ of $p$-variate random variables. Suppose $$α_n=\mathcal{N}_p(M_n,Q_n)$$ where $M_n=\frac{1}{n}\sum_{i=1}^nX_i$ and $Q_n=\frac{1}{n}\sum_{i=1}^n(X_i-M_n)(X_i-M_n)^t$. Then, there is a random probability measure $α$ on $\mathbb{R}^p$ such that $α_n\rightarrowα$ weakly a.s. If $p\in\{1,2\}$, one also obtains $\lVertα_n-α\rVert\overset{a.s.}\longrightarrow 0$ wher…