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Manfred Paulini

Publications and source records attributed to Manfred Paulini.

At least 19 recordsLinked to original sources
arXiv2024

Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring

A real-time autoencoder-based anomaly detection system using semi-supervised machine learning has been developed for the online Data Quality Monitoring system of the electromagnetic calorimeter of the CMS detector at the CERN LHC. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. Additionally, the first results from deploying the autoencoder-based system in the CMS online Data Quality Monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.

physics.ins-det↗
arXiv2023

Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter

The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hinder physics-quality data taking. Although the existing ECAL DQM system has been continuously updated to respond to new problems, it remains one step behind newer and unforeseen issues. Using unsupervised deep learning, a real-time autoencoder-based anomaly detection system is developed that is able to detect ECAL anomalies unseen in past data. After accounting for spatial variations in the response of the ECAL and the temporal evolution of anomalies, the new system is able to efficiently detect anomalies while maintaining an estimated false discovery rate between $10^{-2}$ to $10^{-4}$, beating existing benchmarks by about two orders of magnitude. The real-world performance of the system is validated using anomalies found in 2018 and 2022 LHC collision data. Additionally, first results from deploying the autoencoder-based system in the CMS online DQM workflow for the ECAL barrel during Run 3 of the LHC are presented, showing its promising performance in detecting obscure issues that could have been missed in the existing DQM system.

physics.ins-det↗
arXiv2022

Searching for Intelligent Life in Gravitational Wave Signals Part I: Present Capabilities and Future Horizons

We show that the Laser Interferometer Gravitational Wave Observatory (LIGO) is a powerful instrument in the Search for Extraterrestrial Intelligence (SETI). LIGO's ability to detect gravitational waves (GWs) from astrophysical sources, such as binary black hole mergers, also provides the potential to detect extraterrestrial mega-technology, such as Rapid and/or Massive Accelerating spacecraft (RAMAcraft). We show that LIGO is sensitive to RAMAcraft of 1 Jupiter mass accelerating to a fraction of the speed of light (e.g. 30\%) from $10 - 100\,$kpc or a Moon mass from $1-10\,$pc. While existing SETI searches can probe on the order of ten-thousand stars for human-scale technology (e.g. radio waves), LIGO can probe all 10$^{11}$ stars in the Milky Way for RAMAcraft. Moreover, thanks to the $f^{-1}$ scaling of RAMAcraft signals, our sensitivity to these objects will increase as low-frequency detectors are developed and improved, allowing for the detection of smaller masses further from Earth. In particular, we find that DECIGO and the Big Bang Observer (BBO) will be about 100 times more sensitive than LIGO, increasing the search volume by 10$^{6}$, while LISA and Pulsar Timing Arrays (PTAs) may improve sensitivities to objects with long acceleration periods. In this paper, we calculate the waveforms for linearly-accelerating RAMAcraft in a form suitable for LIGO, Virgo, and KAGRA searches and provide the range for a variety of masses and accelerations. We expect that the current and upcoming GW detectors will soon become an excellent complement to the existing SETI efforts.

astro-ph.IM↗
arXiv2022

DeepSNR: A deep learning foundation for offline gravitational wave detection

All scientific claims of gravitational wave discovery to date rely on the offline statistical analysis of candidate observations in order to quantify significance relative to background processes. The current foundation in such offline detection pipelines in experiments at LIGO is the matched-filter algorithm, which produces a signal-to-noise-ratio-based statistic for ranking candidate observations. Existing deep-learning-based attempts to detect gravitational waves, which have shown promise in both signal sensitivity and computational efficiency, output probability scores. However, probability scores are not easily integrated into discovery workflows, limiting the use of deep learning thus far to non-discovery-oriented applications. In this paper, the Deep Learning Signal-to-Noise Ratio (DeepSNR) detection pipeline, which uses a novel method for generating a signal-to-noise ratio ranking statistic from deep learning classifiers, is introduced, providing the first foundation for the use of deep learning algorithms in discovery-oriented pipelines. The performance of DeepSNR is demonstrated by identifying binary black hole merger candidates versus noise sources in open LIGO data from the first observation run. High-fidelity simulations of the LIGO detector responses are used to present the first sensitivity estimates of deep learning models in terms of physical observables. The robustness of DeepSNR under various experimental considerations is also investigated. The results pave the way for DeepSNR to be used in the scientific discovery of gravitational waves and rare signals in broader contexts, potentially enabling the detection of fainter signals and never-before-observed phenomena.

gr-qc↗
arXiv2022

End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use low-level detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an AUC score of 0.975$\pm$0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to 0.9824$\pm$0.0013, serving as the first performance benchmark for these CMS Open Data samples. We additionally provide a timing performance comparison of different processor unit architectures for training the network.

physics.data-an↗
arXiv2020

End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC

This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN. To better understand what end-to-end classifiers are capable of learning from the data and to address a number of associated challenges, we distinguish the decay of the standard model Higgs boson into two photons from its leading background sources using high-fidelity simulated CMS Open Data. We demonstrate the ability of end-to-end classifiers to learn from the angular distribution of the photons recorded as electromagnetic showers, their intrinsic shapes, and the energy of their constituent hits, even when the underlying particles are not fully resolved, delivering a clear advantage in such cases over purely kinematics-based classifiers.

physics.data-an↗
arXiv2020

End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data

We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise spatial information and demonstrate competitive performance to existing state-of-the-art jet classifiers. We further generalize the end-to-end approach to event-level classification of quark vs. gluon di-jet QCD events. We compare the fully end-to-end approach to using hand-engineered features and demonstrate that the end-to-end algorithm is robust against the effects of underlying event and pile-up.

hep-ex↗
arXiv2019

End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data

From particle identification to the discovery of the Higgs boson, deep learning algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider (LHC). We present an innovative end-to-end deep learning approach for jet identification at the Compact Muon Solenoid (CMS) experiment at the LHC. The method combines deep neural networks with low-level detector information, such as calorimeter energy deposits and tracking information, to build a discriminator to identify different particle species. Using two physics examples as references: electron vs. photon discrimination and quark vs. gluon discrimination, we demonstrate the performance of the end-to-end approach on simulated events with full detector geometry as available in the CMS Open Data. We also offer insights into the importance of the information extracted from various sub-detectors and describe how end-to-end techniques can be extended to event-level classification using information from the whole CMS detector.

hep-ex↗
arXiv2019

Machine Learning in High Energy Physics Community White Paper

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

physics.comp-ph↗
arXiv2015

Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum

This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches. A prioritized, compact set of benchmark models is proposed, accompanied by studies of the parameter space of these models and a repository of generator implementations. This report also addresses how to apply the Effective Field Theory formalism for collider searches and present the results of such interpretations.

hep-ex↗
arXiv2009

Physics of B0s Mesons and Bottom Baryons

We discuss the physics of B0s mesons focusing on CP violation in B0s -> J/Psi Phi decays at the Tevatron. We summarize measurements of the properties of bottom baryons at the Tevatron including the Sigma_b states and the Xi_b baryon. We also discuss the discovery of the Omega_b baryon.

hep-ex↗
arXiv2009

Properties of Heavy B Hadrons

We review recent measurements of heavy B hadron states including masses and lifetimes of the Bc meson as well as excited B states (B**, Bs**). We discuss properties of the Bs meson such as lifetime, lifetime difference delta_Gamma/Gamma and CP violation in Bs -> J/Psi Phi decays. We also summarize new measurements of the masses and lifetimes of bottom baryons including the Lambda_b baryon, the Sigma_b baryon states as well as the Xi_b and Omega_b baryons.

hep-ex↗
arXiv2009

B States at the Tevatron

The CDF and D0 experiments have produced a wealth of heavy flavour physics results since the beginning of Run II of the Fermilab Tevatron. We review recent measurements of B hadron states including excited B states (B^{**}, B_s^{**}) and the Bc+ meson. We also summarize the discoveries of the Sigma_b baryon states and the Xi_b baryon.

hep-ex↗
arXiv2007

Spectroscopy and Decay of B Hadrons at the Tevatron

This paper reviews the spectroscopy and decay of B hadrons at the Tevatron. We discuss the spectroscopy of excited B states (B**, Bs**) and the observation of the Sigma_b baryon at the Tevatron. The second part of this review discusses the decays of B hadrons and measurements of branching fractions. We focus on charmless two-body decays of B->hh.

hep-ex↗
arXiv2004

B Physics at the Tevatron

After a five year upgrade period, the Fermilab experiments CDF and DO are taking high quality data in Run II of the Tevatron Collider. We report on the start-up of both detectors and present a selection of first B physics results from the Tevatron. We also compare different B hadron producers such as the Y(4S) with the hadron collider environment and discuss general features of B physics at a hadron collider.

hep-ex↗
arXiv2003

Future of B Physics at CDF and DO

In this contribution to the panel discussion on ``The Future of Hadron B Experiments'' held at the 8th International Conference on B Physics at Hadron Machines (Beauty 2002) at Santiago de Compostela, Spain, we explore the physics potential for B physics at CDF and DO in five years and beyond. After a brief introduction to precision flavour physics, we concentrate our discussion on the future of CP violation by evaluating the prospects for measuring the CKM angles beta, gamma and alpha at the Tevatron Collider experiments CDF and DO by the end of Run II.

hep-ex↗