More Like this

1. On the sensitivity reach of $$\textrm{LQ}$$ production with preferential couplings to third generation fermions at the LHC

   https://doi.org/10.1140/epjc/s10052-023-12177-4  

   Flórez, A. ; Jones-Pérez, J. ; Gurrola, A. ; Rodriguez, C. ; Peñuela-Parra, J. ( November 2023 , The European Physical Journal C)

   Leptoquarks ($$\textrm{LQ}$$$\text{LQ}$s) are hypothetical particles that appear in various extensions of the Standard Model (SM), that can explain observed differences between SM theory predictions and experimental results. The production of these particles has been widely studied at various experiments, most recently at the Large Hadron Collider (LHC), and stringent bounds have been placed on their masses and couplings, assuming the simplest beyond-SM (BSM) hypotheses. However, the limits are significantly weaker for$$\textrm{LQ}$$$\text{LQ}$models with family non-universal couplings containing enhanced couplings to third-generation fermions. We present a new study on the production of a$$\textrm{LQ}$$$\text{LQ}$at the LHC, with preferential couplings to third-generation fermions, considering proton-proton collisions at$$\sqrt{s} = 13 \, \textrm{TeV}$$$\sqrt{s}=13\text{TeV}$and$$\sqrt{s} = 13.6 \, \textrm{TeV}$$$\sqrt{s}=13.6\text{TeV}$. Such a hypothesis is well motivated theoretically and it can explain the recent anomalies in the precision measurements of$$\textrm{B}$$$\text{B}$-meson decay rates, specifically the$$R_{D^{(*)}}$$$R_{D^{(\ast )}}$ratios. Under a simplified model where the$$\textrm{LQ}$$$\text{LQ}$masses and couplings are free parameters, we focus on cases where the$$\textrm{LQ}$$$\text{LQ}$decays to a$$\tau $$$\tau$lepton and a$$\textrm{b}$$$\text{b}$quark, and study how the results are affected by different assumptions about chiral currents and interference effects with other BSM processes with the same final states, such as diagrams with a heavy vector boson,$$\textrm{Z}^{\prime }$$${\text{Z}}^{\prime }$. The analysis is performed using machine learning techniques, resulting in an increased discovery reach at the LHC, allowing us to probe new physics phase space which addresses the$$\textrm{B}$$$\text{B}$-meson anomalies, for$$\textrm{LQ}$$$\text{LQ}$masses up to$$5.00\, \textrm{TeV}$$$5.00\text{TeV}$, for the high luminosity LHC scenario.

    

   more » « less
2. Amortized simulation-based frequentist inference for tractable and intractable likelihoods

   https://doi.org/10.1088/2632-2153/ad218e  

   Al Kadhim, Ali ; Prosper, Harrison B. ; Prosper, Olivia F. ( February 2024 , Machine Learning: Science and Technology)

   High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. If the likelihood is known, inference can proceed using standard techniques. However, when the likelihood is intractable or unknown, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations when coupled with machine learning. We introduce an extension of the recently proposed likelihood-free frequentist inference (LF2I) approach that makes it possible to construct confidence sets with thep-value function and to use the same function to check the coverage explicitly at any given parameter point. LikeLF2I, this extension yields provably valid confidence sets in parameter inference problems for which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology³3

   Code to reproduce all of our results is available onhttps://github.com/AliAlkadhim/ALFFI.

   .

    

   more » « less
3. Probing a $$\textrm{Z}^{\prime }$$ with non-universal fermion couplings through top quark fusion, decays to bottom quarks, and machine learning techniques

   https://doi.org/10.1140/epjc/s10052-023-11506-x  

   Barbosa, Diego ; Díaz, Felipe ; Quintero, Liliana ; Flórez, Andrés ; Sanchez, Manuel ; Gurrola, Alfredo ; Sheridan, Elijah ; Romeo, Francesco ( May 2023 , The European Physical Journal C)

   Abstract The production of heavy neutral mass resonances, $$\text {Z}^{\prime }$$ Z ′ , has been widely studied theoretically and experimentally. Although the nature, mass, couplings, and associated quantum numbers of this hypothetical particle are yet to be determined, current LHC experimental results have set strong constraints assuming the simplest beyond Standard Model (SM) hypotheses. We present a new feasibility study on the production of a $$\text {Z}^{\prime }$$ Z ′ boson at the LHC, with family non-universal couplings, considering proton–proton collisions at $$\sqrt{s} = 13$$ s = 13 and 14 TeV. Such a hypothesis is well motivated theoretically and it can explain observed differences between SM predictions and experimental results, as well as being a useful tool to further probe recent results in searches for new physics considering non-universal fermion couplings. We work under two simplified phenomenological frameworks where the $$\textrm{Z}^{\prime }$$ Z ′ masses and couplings to the SM particles are free parameters, and consider final states of the $$\text {Z}^{\prime }$$ Z ′ decaying to a pair of $$\textrm{b}$$ b quarks. The analysis is performed using machine learning techniques to maximize the sensitivity. Despite being a well motivated physics case in its own merit, such scenarios have not been fully considered in ongoing searches at the LHC. We note the proposed search methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constrains. In addition, the proposed search is complementary to existing strategies. 

   more » « less
4. Free Energy Wells and Overlap Gap Property in Sparse PCA

   Ben Arous, Gerard ; Wein, Alexander S ; Zadik, Ilias ( July 2020 , Proceedings of Thirty Third Conference on Learning Theory)

   Abernethy, Jacob ; Agarwal, Shivani (Ed.)

   We study a variant of the sparse PCA (principal component analysis) problem in the “hard” regime, where the inference task is possible yet no polynomial-time algorithm is known to exist. Prior work, based on the low-degree likelihood ratio, has conjectured a precise expression for the best possible (sub-exponential) runtime throughout the hard regime. Following instead a statistical physics inspired point of view, we show bounds on the depth of free energy wells for various Gibbs measures naturally associated to the problem. These free energy wells imply hitting time lower bounds that corroborate the low-degree conjecture: we show that a class of natural MCMC (Markov chain Monte Carlo) methods (with worst-case initialization) cannot solve sparse PCA with less than the conjectured runtime. These lower bounds apply to a wide range of values for two tuning parameters: temperature and sparsity misparametrization. Finally, we prove that the Overlap Gap Property (OGP), a structural property that implies failure of certain local search algorithms, holds in a significant part of the hard regime. 

   more » « less
5. Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

   https://doi.org/10.1093/sysbio/syad074  

   Thompson, Ammon ; Liebeskind, Benjamin J. ; Scully, Erik J. ; Landis, Michael J. ; Perez, ed., Manolo ( January 2024 , Systematic Biology)

   Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

    

   more » « less