arXiv subjects

Maria Spiropulu

Publications and source records attributed to Maria Spiropulu.

At least 73 records · Page 4Linked to original sources

arXiv2020

Development of Quantum InterConnects for Next-Generation Information Technologies

Just as classical information technology rests on a foundation built of interconnected information-processing systems, quantum information technology (QIT) must do the same. A critical component of such systems is the interconnect, a device or process that allows transfer of information between disparate physical media, for example, semiconductor electronics, individual atoms, light pulses in optical fiber, or microwave fields. While interconnects have been well engineered for decades in the realm of classical information technology, quantum interconnects (QuICs) present special challenges, as they must allow the transfer of fragile quantum states between different physical parts or degrees of freedom of the system. The diversity of QIT platforms (superconducting, atomic, solid-state color center, optical, etc.) that will form a quantum internet poses additional challenges. As quantum systems scale to larger size, the quantum interconnect bottleneck is imminent, and is emerging as a grand challenge for QIT. For these reasons, it is the position of the community represented by participants of the NSF workshop on Quantum Interconnects that accelerating QuIC research is crucial for sustained development of a national quantum science and technology program. Given the diversity of QIT platforms, materials used, applications, and infrastructure required, a convergent research program including partnership between academia, industry and national laboratories is required. This document is a summary from a U.S. National Science Foundation supported workshop held on 31 October - 1 November 2019 in Alexandria, VA. Attendees were charged to identify the scientific and community needs, opportunities, and significant challenges for quantum interconnects over the next 2-5 years.

quant-ph↗

arXiv2019

Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description

We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geometries. As an example of application, we show how such an architecture could be used as a generator of LHC parasitic collisions (pileup). We present two approaches to generate the events: unconditional generator and generator conditioned on missing transverse energy. We assess generation performances in a realistic LHC data-analysis environment, with a pileup mitigation algorithm applied.

hep-ex↗

arXiv2019

Processing Columnar Collider Data with GPU-Accelerated Kernels

At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query languages allow, such that custom numerical codes are used. At present, these codes mostly operate on individual event records and are parallelized in multi-step data reduction workflows using batch jobs across CPU farms. Based on a simplified top quark pair analysis with CMS Open Data, we demonstrate that it is possible to carry out significant parts of a collider analysis at a rate of around a million events per second on a single multicore server with optional GPU acceleration. This is achieved by representing HEP event data as memory-mappable sparse arrays of columns, and by expressing common analysis operations as kernels that can be used to process the event data in parallel. We find that only a small number of relatively simple functional kernels are needed for a generic HEP analysis. The approach based on columnar processing of data could speed up and simplify the cycle for delivering physics results at HEP experiments. We release the \texttt{hepaccelerate} prototype library as a demonstrator of such methods.

physics.data-an↗

arXiv2019

Perspectives on quantum transduction

Quantum transduction, the process of converting quantum signals from one form of energy to another, is an important area of quantum science and technology. The present perspective article reviews quantum transduction between microwave and optical photons, an area that has recently seen a lot of activity and progress because of its relevance for connecting superconducting quantum processors over long distances, among other applications. Our review covers the leading approaches to achieving such transduction, with an emphasis on those based on atomic ensembles, opto-electromechanics, and electro-optics. We briefly discuss relevant metrics from the point of view of different applications, as well as challenges for the future.

quant-ph↗

arXiv2019

Topology classification with deep learning to improve real-time event selection at the LHC

We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features are utilized. In the considered examples, a filter based on the classifier's score can be trained to retain ~99% of the interesting events and reduce the false-positive rate by as much as one order of magnitude for certain background processes. By operating such a filter as part of the online event selection infrastructure of the LHC experiments, one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives. The saved resources could be translated into a reduction of the detector operation cost or into an effective increase of storage and processing capabilities, which could be reinvested to extend the physics reach of the LHC experiments.

hep-ex↗

arXiv2019

Charged particle tracking with quantum annealing-inspired optimization

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework and to HL-LHC conditions. Furthermore, we develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML dataset are presented, demonstrating the successful application of a quantum annealing-inspired algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the LHC while leaving open the possibility of a quantum speedup for tracking.

quant-ph↗

arXiv2019

Pileup mitigation at the Large Hadron Collider with Graph Neural Networks

At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm, employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions.

hep-ph↗

arXiv2019

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.

hep-ex↗

arXiv2019

Identification of Long-lived Charged Particles using Time-Of-Flight Systems at the Upgraded LHC detectors

We study the impact of picosecond precision timing detection systems on the LHC experiments' long-lived particle search program during the HL-LHC era. We develop algorithms that allow us to reconstruct the mass of such charged particles and perform particle identification using the time-of-flight measurement. We investigate the reach for benchmark scenarios as a function of the timing resolution, and find sensitivity improvement of up to a factor of ten, depending on the new heavy particle mass.

hep-ex↗

arXiv2018

Projection for ZZZ Production Cross Section Measurements at the HL-LHC

Triple gauge boson (tri-boson) production is one of the ways to study the Quartic Gauge Couplings (QGC) and the anomalous QGC beyond the Standard Model of particle physics. In particular, we investigated the signal selection criteria for ZZZ production in the High Luminosity Large Hadron Collider (HL-LHC) scenario. We wrote code which selects and reconstructs 3 desired Z bosons from the produced electrons, muons and jets. We ran the code on selected Monte Carlo Simulation samples, including the ZZZ signal samples and other background (e.g. top pair production TT) samples. We made plots of event variables and looked for variables that can potentially separate the signal from the backgrounds. Then we put constraints on these variables to optimize the signal to background ratio for various types of final states of ZZZ and obtained the expected yields of the decays at the HL-LHC. We expect to see 1.42 +/- 0.25 ZZZ event with 0.82 +/- 0.60 background event in the rare fully leptonic channel. Furthermore, signal extraction in the 2 leptonic Z and 1 hadronic Z channel shows potential and is worth further investigation.

hep-ex↗

arXiv2018

Novel deep learning methods for track reconstruction

For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data.

hep-ex↗

arXiv2017

An MPI-Based Python Framework for Distributed Training with Keras

We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs. The framework is built on the popular Keras machine learning library. The Message Passing Interface (MPI) protocol is used to coordinate the training process, and the system is well suited for job submission at supercomputing sites. We detail the software's features, describe its use, and demonstrate its performance on systems of varying sizes on a benchmark problem drawn from high-energy physics research.

cs.DC↗

arXiv2016

Golden Probe of Electroweak Symmetry Breaking

The ratio of the Higgs couplings to $WW$ and $ZZ$ pairs, $λ_{WZ}$, is a fundamental parameter in electroweak symmetry breaking as well as a measure of the (approximate) custodial symmetry possessed by the gauge boson mass matrix. We show that Higgs decays to four leptons are sensitive, via tree level/1-loop interference effects, to both the magnitude and, in particular, overall sign of $λ_{WZ}$. Determining this sign requires interference effects, as it is nearly impossible to measure with rate information. Furthermore, simply determining the sign effectively establishes the custodial representation of the Higgs boson. We find that $h\to4\ell$ ($4\ell \equiv 2e2μ, 4e, 4μ$) decays have excellent prospects of directly establishing the overall sign at a high luminosity 13 TeV LHC. We also examine the ultimate LHC sensitivity in $h\to4\ell$ to the magnitude of $λ_{WZ}$. Our results are independent of other measurements of the Higgs boson couplings and, in particular, largely free of assumptions about the top quark Yukawa couplings which also enter at 1-loop. This makes $h\to4\ell$ a unique and independent probe of the electroweak symmetry breaking mechanism and custodial symmetry.

hep-ph↗

arXiv2014

8D Likelihood Effective Higgs Couplings Extraction Framework in the Golden Channel

In this paper we build a comprehensive analysis framework to perform direct extraction of all possible effective Higgs couplings to neutral electroweak gauge bosons in the decay to electrons and muons, the so called `golden channel'. Our framework is based on a maximum likelihood method constructed from analytic expressions of the fully differential cross sections for $h \rightarrow 4\ell$ and for the dominant irreducible $q\bar{q} \rightarrow 4\ell$ background, where $4\ell = 2e2μ, 4e, 4μ$. Detector effects are included by an explicit convolution of these analytic expressions with the appropriate transfer function over all center of mass variables. Using the full set of decay observables, we construct an unbinned 8-dimensional detector-level likelihood function which is continuous in the effective couplings and includes systematic uncertainties. We consider all possible $ZZ$, $Zγ$ and $γγ$ couplings, allowing for general CP odd/even admixtures and any possible phases. We describe how the convolution is performed and demonstrate the validity and power of the framework with a number of supporting checks and example fits. The framework can be used to perform a variety of multi-parameter extractions, including their correlations, to determine the Higgs couplings to neutral electroweak gauge bosons using data obtained at the LHC and other future colliders.

hep-ex↗

arXiv2014

Technical Note for 8D Likelihood Effective Higgs Couplings Extraction Framework in the Golden Channel

In this technical note we present technical details on various aspects of the framework introduced in arXiv:1401.2077 aimed at extracting effective Higgs couplings in the $h\to 4\ell$ `golden channel'. Since it is the primary feature of the framework, we focus in particular on the convolution integral which takes us from `truth' level to `detector' level and the numerical and analytic techniques used to obtain it. We also briefly discuss other aspects of the framework.

hep-ph↗

arXiv2013

Super-Razor and Searches for Sleptons and Charginos at the LHC

Direct searches for electroweak pair production of new particles at the LHC are a difficult proposition, due to the large background and low signal cross sections. We demonstrate how these searches can be improved by a combination of new razor variables and shape analysis of signal and background kinematics. We assume that the pair-produced particles decay to charged leptons and missing energy, either directly or through a W boson. In both cases the final state is a pair of opposite sign leptons plus missing transverse energy. We estimate exclusion reach in terms of sleptons and charginos as realized in minimal supersymmetry. We compare this super-razor approach in detail to analyses based on other kinematic variables, showing how the super-razor uses more of the relevant kinematic information while achieving higher selection efficiency on signals, including cases with compressed spectra.

hep-ph↗

arXiv2011

Priors for New Physics

The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian approach, requires the construction of multi-parameter priors. We propose a construction that uses elements of Bayesian reference analysis. Our idea is to initiate the chain of inference with the reference prior for a likelihood function that depends on a single parameter of interest that is a function of the parameters of the physics model. The reference posterior density of the parameter of interest induces on the parameter space of the physics model a class of posterior densities. We propose to continue the chain of inference with a particular density from this class, namely, the one for which indistinguishable models are equiprobable and use it as the prior for subsequent analysis. We illustrate our method by applying it to the constrained minimal supersymmetric Standard Model and two non-universal variants of it.

physics.data-an↗

arXiv2011

Model Inference with Reference Priors

We describe the application of model inference based on reference priors to two concrete examples in high energy physics: the determination of the CKM matrix parameters rhobar and etabar and the determination of the parameters m_0 and m_1/2 in a simplified version of the CMSSM SUSY model. We show how a 1-dimensional reference posterior can be mapped to the n-dimensional (n-D) parameter space of the given class of models, under a minimal set of conditions on the n-D function. This reference-based function can be used as a prior for the next iteration of inference, using Bayes' theorem recursively.

hep-ph↗