Denis Derkach

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase.

The B+→ Jψη′ K+ decay is observed for the first time using proton-proton collision data collected by the LHCb experiment at centre-of-mass energies of 7, 8, and 13 TeV, corresponding to a total integrated luminosity of 9 fb− 1. The branching fraction of this decay is measured relative to the known branching fraction of the B+→ ψ (2S) K+ decay and found to be

Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. These models learn to classify supernova types using their light curves as inputs. Preprocessing these curves is a crucial step that significantly affects the final quality. In this talk, we study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve. We use these approximations as inputs for supernovae classification models and demonstrate that the proposed methods outperform the state-of-the-art based on Gaussian processes applying to the Zwicky Transient Facility Bright Transient Survey light curves. MLP demonstrates similar quality as Gaussian …

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, “experience-driven” layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the …

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase.

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.

Magnetic fields are responsible for a multitude of solar phenomena, including potentially destructive events such as solar flares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Sun. The field of quantitative inference of magnetic field vectors and related solar atmospheric parameters from such observations has been investigated for a long time. In recent years, very sophisticated codes for spectropolarimetric observations have been developed. Over the past two decades, neural networks have been shown to be a fast and accurate alternative to classic inversion methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, degeneracies, and uncertainties of each parameter …

Searches for CP violation in the decays and are performed using pp collision data corresponding to 6 fb− 1 of integrated luminosity collected by the LHCb experiment. The calibration channels are used to remove production and detection asymmetries. The resulting CP-violating asymmetries are

Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.

The abundance of data arriving in the new runs of the Large Hadron Collider creates tough requirements for the amount of necessary simulated events and thus for the speed of generating such events. Current approaches can suffer from long generation time and lack of important storage resources to preserve the simulated datasets. The development of the new fast generation techniques is thus crucial for the proper functioning of experiments. We present a novel approach to simulate LHCb detector events using generative machine learning algorithms and other statistical tools. The approaches combine the speed and flexibility of neural networks and encapsulates knowledge about the detector in the form of statistical patterns. Whenever possible, the algorithms are trained using real data, which enhances their robustness against differences between real data and simulation. We discuss particularities of neural …

There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation via variational autoencoder (VAE). We suggest using a VAE to generate mathematical expressions, and our training strategy forces generated formulas to fit a given dataset. Our framework allows encoding apriori knowledge of the formulas into fast-check predicates that speed up the optimization process. We compare our method to modern symbolic regression benchmarks and show that our method outperforms the competitors under noisy conditions. The recovery rate of SEGVAE is 65% on the Ngyuen dataset with a noise level of 10%, which is better than the previously reported SOTA by 20%. We demonstrate that this value depends on the dataset and can be even higher.

Resonant structures in the dipion mass spectrum from χ c 1 (3872)→ π+ π− J/ψ decays, produced via B+→ K+ χ c 1 (3872) decays, are analyzed using proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb− 1. A sizeable contribution from the isospin conserving χ c 1 (3872)→ ω J/ψ decay is established for the first time,(21.4±2.3±2.0)%, with a significance of more than 7.1 σ. The amplitude of isospin violating decay, χ c 1 (3872)→ ρ 0 J/ψ, relative to isospin conserving decay, χ c 1 (3872)→ ω J/ψ, is properly determined, and it is a factor of 6 larger than expected for a pure charmonium state.

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, aGaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase.

Flavour mixing and CP violation as measured in weak decays and mixing of neutral mesons are a fundamental tool to test the Standard Model and to search for new physics. New analyses performed at the LHC experiment open an unprecedented insight into the Cabibbo–Kobayashi–Maskawa metrology and new evidence for rare decays. Important progress has also been achieved in theoretical calculations of several hadronic quantities with a remarkable reduction of the uncertainties. This improvement is essential since previous studies of the Unitarity Triangle did show that possible contributions from new physics, if any, must be tiny and could easily be hidden by theoretical and experimental errors. Thanks to the experimental and theoretical advances, the Cabibbo–Kobayashi–Maskawa picture provides very precise Standard Model predictions through global analyses. We present here the results of the latest …

This table presents estimated peaks for the Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS) objects, which were downloaded at 23: 29 09/22/2021 (only objects with at least ten observations per each {g} and {r} passbands). The total number of selected light curves is 1870. The peak position in approximated light curve was estimated as the timestamp with maximum total flux value (sum of fluxes in {r} and {g} passbands). The timestamps of the peak in the light curve observations are measured in Modified Julian Date (MJD). The magnitudes are calculated in ZTF BTS units. The fluxes are in mJy. In column explanations, we use following abbreviations for the names of approximation models: BNN for Bayesian Neural Networks, MLP (sklearn) for Multilayer Perceptron implemented using scikit-learn, MLP (pytorch) for Multilayer Perceptron implemented using pytorch, NF for Normalizing Flows, GP for …

Context Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and most of these objects have not been subject to such studies.Aims Flux time series are actively used as an affordable alternative for photometric classification and characterization, for instance, peak identifications and luminosity decline estimations. However, the collected time series are multidimensional and irregularly sampled, while also containing outliers and without any well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each …

The structure of Yukawa couplings of the Standard Model (SM) implies a rich phenomenology, characterized in the quark sector by the appearance of Flavour Changing Neutral Currents (FCNC) only at the loop level, and further suppressed due to the Glashow-Iliopoulos-Maiani (GIM) mechanism [1], rooted in the approximate U (2) 3 symmetry of the first two generations. In the SM transitions with units of flavour violation|∆ F| 0 as well as CP-violating observables can be studied by means of the notion of six quark masses–mu, d, s, c, b, t–and four mixing parameters [2]–λ, A, ρ, η–required to describe the unitary Cabibbo-Kobayashi-Maskawa (CKM) matrix [3, 4]–Vij–with i= u, c, t and j= d, s, b.The hierarchical structure of the CKM and the fact that the η parameter is the only source of CP violation in weak interactions, make processes like|∆ F|= 2 transitions very sensitive probes of New Physics (NP). Indeed, an active interplay of all three generations is required in order to be sensitive to CP-violating effects in the SM, strengthening the important role of loop-induced processes like FCNCs in the phenomenology of weak interactions. For these reasons, accurate theoretical estimate and measurement of CP-even and CP-odd observables from neutral meson oscillations is of particular interest for the analysis of the so-called Unitarity Triangle (UT), characterized by the determination of: Vud V∗ ub+ Vcd V∗ cb+ Vtd V∗ tb= 0. Being λ and A parameters well-constrained by leptonic and semileptonic meson decays, the UT analysis boils down to the investigation of all possible constraints in the plane (ρ, η)[5]. The sensitivity of the CKM metrology is then driven …

The volume of data processed by the Large Hadron Collider experiments demands sophisticated selection rules typically based on machine learning algorithms. One of the shortcomings of these approaches is their profound sensitivity to the biases in training samples. In the case of particle identification (PID), this might lead to degradation of the efficiency for some decays not present in the training dataset due to differences in input kinematic distributions. In this talk, we propose a method based on the Common Specific Decomposition that takes into account individual decays and possible misshapes in the training data by disentangling common and decay specific components of the input feature set. We show that the proposed approach reduces the rate of efficiency degradation for the PID algorithms for the decays reconstructed in the LHCb detector.

The coming of age of differentiable programming makes possible today to create complete computer models of experimental apparatus that include the stochastic data-generation processes, the full modeling of the reconstruction and inference procedures, and a suitably defined objective function, along with the cost of any given detector configuration, geometry and materials. This enables the end-to-end optimization of the instruments, by using techniques developed within computer science that are currently vastly exploited in fields such as fluid dynamics. The MODE Collaboration has started to consider the problem in its generality, to provide software architectures that may be useful for the optimization of experimental design. These models may be useful in a ”human in the middle” system as they provide information on the relative merit of different configurations as a continuous function of the design choices. In this short contribution we summarize the plan of studies that has been laid out, and its potential in the long term for the future of experimental studies in fundamental physics.

Abstract During Run 2 of the Large Hadron Collider at CERN, the LHCb experiment has spent more than 80% of the pledged CPU time to produce simulated data samples. The upgraded LHCb detector, being commissioned now, will be able to collect much larger data samples, requiring many more simulated events to analyze the collected data. Simulation is a key necessity of analysis to interpret signal, reject background and measure efficiencies. The needed simulation will exceed the pledged resources, requiring an evolution in technologies and techniques to produce these simulated samples. In this contribution, we discuss Lamarr, a Gaudi-based framework to deliver simulated samples parametrizing both the detector response and the reconstruction algorithms. Generative Models powered by several algorithms and strategies are employed to effectively parametrize the high-level response of the multiple components of the LHCb detector, encoding within neural networks the experimental errors and uncertainties introduced in the detection and reconstruction process. Where possible, models are trained directly on real data, leading to a simulation process completely independent of the detailed simulation.