Search for rare b to open-charm two-body decays of baryons at LHCb

A search for the rare two-body decays Λb → D°Λ and Ξb° → D°Λ is performed with proton-proton collision data, collected by the LHCb experiment at a center-of-mass energy of 13 TeV. The decay Λb → D°Λ is seen with a statistical significance of 5.5 standard deviations and constitutes the discovery for this decay. An excess of Ξb° → D°Λ candidates w.r.t. the background is observed with a statistical significance of 1.8 standard deviations.In dieser Arbeit wird eine Suche nach den seltenen Zweikörper-Zerfällen Λb → D°Λ und Ξb° → D°Λ mit Proton-Proton Kollisionen präsentiert. Der analysierte Datensatz wurde durch das LHCb Experiment bei einer Schwerpunktsenergie von 13 TeV aufgezeichnet. Der Zerfall Λb → D°Λ wird mit einer statistischen Signifikanz von 5,5 Standardabweichungen beobachtet und ist somit als Neuentdeckung einzustufen. Eine Anhäufung von Ξb° → D°Λ Kandidaten gegenüber dem Untergrund wird mit einer statistischen Signifikanz von 1,8 Standardabweichungen beobachtet

PyTorch and automatic differentiation

I will explain automatic differentiation for the gifted amateur and how to use it in the PyTorch framework. You don't have to know the PyTorch framework but some experience with NumPy could help to follow the tutorial session. I would like to keep the session interactive but you will not need to run code on your laptops. Instead, I will present code snippets, discuss them and thus introduce some common pitfalls when using PyTorch. In the end I hope that you understand better what happens behind the curtain of PyTorch (and other autodiff libraries), helping you to more effectively debug your every day code

The Unreasonable Effectiveness of Deep Evidential Regression

There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, notably with the capabilities to disentangle aleatoric and epistemic uncertainties. Despite some empirical success of Deep Evidential Regression (DER), there are important gaps in the mathematical foundation that raise the question of why the proposed technique seemingly works. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a heuristic rather than an exact uncertainty quantification. We go on to propose corrections and redefinitions of how aleatoric and epistemic uncertainties should be extracted from NNs.Comment: 11 pages, 25 figure

Hunter Power Plant, Emery County, Utah [1632]

Scan of transparency of sunflowers in foreground of Hunter plan

Inverted CERN School of Computing 2020

From a syntactical point of view, the Lambda expression of C++ is nothing but syntactic sugar of a struct with an appropriate call operator overload. On the other hand, this simple syntax is shockingly flexible and allows powerful abstractions in a functional way, while providing elegant and easy to read code in a language that is notoriously famous for being unnecessary clunky and verbose. I will give an overview about the basic syntax and best practices. I will then talk about stateful Lambdas, Lambda inheritance and their real-world applications

Search for Rare bb to Open-Charm Two-Body Decays of Baryons at LHCb

A search for the rare two-body decays $\Lambda_b\rightarrow D^0 \Lambda$ and $\Xi^0_b\rightarrow D^0 \Lambda$ is performed with proton-proton collision data, corresponding to an integrated luminosity of 6$fb^{-1}$, collected by the LHCb experiment at a center-of-mass energy of 13 TeV. The decay $\Lambda_b\rightarrow D^0 \Lambda$ is seen with a statistical significance of 5.5 standard deviations, and constitutes the discovery for this decay. The branching fraction, measured using the $\Lambda_b\rightarrow D^0p\pi^-$ decay for normalization, is \begin{equation*} B(\Lambda_b\rightarrow D^0 \Lambda) = (9.9 \pm 2.3 \pm 1.6 \pm 1.1) \times 10^{-6} \,, \end{equation*} where the uncertainties are statistical, systematic, and external, respectively. An excess of $\Xi^0_b\rightarrow D^0 \Lambda$ candidates w.r.t. the background is observed with a statistical significance of 1.8 standard deviations and is used to estimate the upper limit \begin{equation*} \frac{f_{\Xi^0_b}}{f_{\Lambda_b}} \times \frac{B{(\Xi^0_c \rightarrow D^0\Lambda)}}{B{(\Lambda_b\rightarrow D^0\Lambda})} < 0.5 \quad (\text{CL}\,=\,95\,\%) \,, \end{equation*} where $f_{\Xi^0_b} / f_{\Lambda^b}$ is the ratio of the fragmentation fractions of $b$-quarks into $\Xi^0_b$ and $\Lambda_b$ baryons

Calculating Lower Bounds within the PyTorch Framework

Lower estimation bounds are an important tool in the development of parametric estimators, which form a basis for a large number of navigation and position solutions. The well-known Cramér-Rao bound (CRB) is such a bound and provides the optimal mean squared error performance of locally unbiased estimators based on a signal model. If the model depends on a random variable, the bound depends on the realization of this variable. We consider the R-Mode navigation system as a case study in this paper. In this case, the signal is influenced by a modulated signal where, in general, the transmitted bit sequence is unknown. Therefore, it becomes difficult to derive and evaluate the performance bound as the complexity of the computation increases. To overcome the aforementioned challenge, we suggest utilizing PyTorch and its automatic differentiation framework to calculate the bound for each realization, thus leveraging fast calculation for each given scenario

PYROCAST: a Machine Learning Pipeline to ForecastPyrocumulonimbus (PyroCb) Clouds

5 pages, 2 figures, Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022Pyrocumulonimbus (pyroCb) clouds are storm clouds generated by extreme wildfires. PyroCbs are associated with unpredictable, and therefore dangerous, wildfire spread. They can also inject smoke particles and trace gases into the upper troposphere and lower stratosphere, affecting the Earth's climate. As global temperatures increase, these previously rare events are becoming more common. Being able to predict which fires are likely to generate pyroCb is therefore key to climate adaptation in wildfire-prone areas. This paper introduces PYROCAST, a pipeline for pyroCb analysis and forecasting. The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented. The database brings together geostationary imagery and environmental data for over 148 pyroCb events across North America, Australia, and Russia between 2018 and 2022. Random Forests, Convolutional Neural Networks (CNNs), and CNNs pretrained with Auto-Encoders were tested to predict the generation of pyroCb for a given fire six hours in advance. The best model predicted pyroCb with an AUC of 0.90 ± 0.04