Search Results for author: Maurizio Pierini

Found 54 papers, 29 papers with code

Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC

no code implementations2 Feb 2024 Patrick Odagiu, Zhiqiang Que, Javier Duarte, Johannes Haller, Gregor Kasieczka, Artur Lobanov, Vladimir Loncar, Wayne Luk, Jennifer Ngadiuba, Maurizio Pierini, Philipp Rincke, Arpita Seksaria, Sioni Summers, Andre Sznajder, Alexander Tapper, Thea K. Aarrestad

We study various machine learning based algorithms for performing accurate jet flavor classification on field-programmable gate arrays and demonstrate how latency and resource consumption scale with the input size and choice of algorithm.


Triggering Dark Showers with Conditional Dual Auto-Encoders

no code implementations22 Jun 2023 Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, Maurizio Pierini

We present a search formulated as an anomaly detection (AD) problem, using an AE to define a criterion to decide about the physics nature of an event.

Anomaly Detection

Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC

no code implementations7 Jun 2023 Rohan Shenoy, Javier Duarte, Christian Herwig, James Hirschauer, Daniel Noonan, Maurizio Pierini, Nhan Tran, Cristina Mantilla Suarez

In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations.

Data Compression

Goodness of fit by Neyman-Pearson testing

1 code implementation23 May 2023 Gaia Grosso, Marco Letizia, Maurizio Pierini, Andrea Wulzer

The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family of models, while controlling the impact of statistical fluctuations.

Symbolic Regression on FPGAs for Fast Machine Learning Inference

no code implementations6 May 2023 Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles Cranmer, Sridhara Dasu, Peter Elmer, Philip Harris, Isobel Ojalvo, Maurizio Pierini

The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints.

Neural Architecture Search regression +1

Progress towards an improved particle flow algorithm at CMS with machine learning

no code implementations30 Mar 2023 Farouk Mokhtar, Joosep Pata, Javier Duarte, Eric Wulff, Maurizio Pierini, Jean-Roch Vlimant

The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity.

Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier

no code implementations9 Mar 2023 Sonja Grönroos, Maurizio Pierini, Nadezda Chernyavskaya

More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN.

Anomaly Detection

Unravelling physics beyond the standard model with classical and quantum anomaly detection

no code implementations25 Jan 2023 Julian Schuhmacher, Laura Boggia, Vasilis Belis, Ema Puljak, Michele Grossi, Maurizio Pierini, Sofia Vallecorsa, Francesco Tacchino, Panagiotis Barkoutsos, Ivano Tavernelli

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).

Anomaly Detection

Quantum anomaly detection in the latent space of proton collision events at the LHC

1 code implementation25 Jan 2023 Kinga Anna Woźniak, Vasilis Belis, Ema Puljak, Panagiotis Barkoutsos, Günther Dissertori, Michele Grossi, Maurizio Pierini, Florentin Reiter, Ivano Tavernelli, Sofia Vallecorsa

The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder.

Anomaly Detection Quantum Machine Learning

Evaluating generative models in high energy physics

2 code implementations18 Nov 2022 Raghav Kansal, Anni Li, Javier Duarte, Nadezda Chernyavskaya, Maurizio Pierini, Breno Orzari, Thiago Tomei

There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP).

Generative Adversarial Network Vocal Bursts Intensity Prediction

LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics

1 code implementation28 Sep 2022 Zhiqiang Que, Hongxiang Fan, Marcus Loo, He Li, Michaela Blott, Maurizio Pierini, Alexander Tapper, Wayne Luk

This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performance.

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

no code implementations16 May 2022 Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip Harris

In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving.

Autonomous Driving Quantization +1

End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

no code implementations4 Apr 2022 Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz

We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector.

Machine Learning for Particle Flow Reconstruction at CMS

no code implementations1 Mar 2022 Joosep Pata, Javier Duarte, Farouk Mokhtar, Eric Wulff, Jieun Yoo, Jean-Roch Vlimant, Maurizio Pierini, Maria Girone

The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy.

BIG-bench Machine Learning

Autoencoders for Semivisible Jet Detection

no code implementations6 Dec 2021 Florencia Canelli, Annapaola De Cosa, Luc Le Pottier, Jeremi Niedziela, Kevin Pedro, Maurizio Pierini

The experimental signature is characterised by the presence of reconstructed missing momentum collinear with the visible components of the jets.

Anomaly Detection

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

1 code implementation24 Nov 2021 Steven Tsan, Raghav Kansal, Anthony Aportela, Daniel Diaz, Javier Duarte, Sukanya Krishna, Farouk Mokhtar, Jean-Roch Vlimant, Maurizio Pierini

We explore the use of graph-based autoencoders, which operate on jets in their "particle cloud" representations and can leverage the interdependencies among the particles within a jet, for such tasks.

Anomaly Detection

Explaining machine-learned particle-flow reconstruction

2 code implementations24 Nov 2021 Farouk Mokhtar, Raghav Kansal, Daniel Diaz, Javier Duarte, Joosep Pata, Maurizio Pierini, Jean-Roch Vlimant

The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors.

Decision Making

Applications and Techniques for Fast Machine Learning in Science

no code implementations25 Oct 2021 Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.

BIG-bench Machine Learning

Particle Cloud Generation with Message Passing Generative Adversarial Networks

2 code implementations NeurIPS 2021 Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos

We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models.

A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC

no code implementations4 May 2021 Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig, Manuel Blanco Valentin, Javier Duarte, Cristian Gingu, Philip Harris, James Hirschauer, Martin Kwok, Vladimir Loncar, Yingyi Luo, Llovizna Miranda, Jennifer Ngadiuba, Daniel Noonan, Seda Ogrenci-Memik, Maurizio Pierini, Sioni Summers, Nhan Tran

We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile.

Data Compression Quantization

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

1 code implementation21 Jan 2021 Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria Spiropulu

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum.

Anomaly Detection With Conditional Variational Autoencoders

no code implementations12 Oct 2020 Adrian Alan Pol, Victor Berger, Gianluca Cerminara, Cecile Germain, Maurizio Pierini

Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question.

Anomaly Detection

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

no code implementations5 Oct 2020 Cheng Chen, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini

We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets.

Data Augmentation

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

4 code implementations15 Jun 2020 Claudionor N. Coelho Jr., Aki Kuusela, Shan Li, Hao Zhuang, Thea Aarrestad, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Adrian Alan Pol, Sioni Summers

Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption.


Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

1 code implementation4 May 2020 Oliver Knapp, Guenther Dissertori, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider.

Anomaly Detection

Fast inference of Boosted Decision Trees in FPGAs for particle physics

3 code implementations5 Feb 2020 Sioni Summers, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan Rankin, Nhan Tran, Zhenbin Wu

We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process.


Learning Multivariate New Physics

1 code implementation27 Dec 2019 Raffaele Tito D'Agnolo, Gaia Grosso, Maurizio Pierini, Andrea Wulzer, Marco Zanetti

It is designed to be sensitive to small discrepancies that arise in datasets dominated by the reference model.

High Energy Physics - Phenomenology High Energy Physics - Experiment

The DNNLikelihood: enhancing likelihood distribution with Deep Learning

1 code implementation8 Nov 2019 Andrea Coccaro, Maurizio Pierini, Luca Silvestrini, Riccardo Torre

We introduce the DNNLikelihood, a novel framework to easily encode, through Deep Neural Networks (DNN), the full experimental information contained in complicated likelihood functions (LFs).

High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics High Energy Physics - Experiment Data Analysis, Statistics and Probability

$\texttt{HEPfit}$: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models

1 code implementation30 Oct 2019 Jorge de Blas, Debtosh Chowdhury, Marco Ciuchini, Antonio M. Coutinho, Otto Eberhardt, Marco Fedele, Enrico Franco, Giovanni Grilli di Cortona, Victor Miralles, Satoshi Mishima, Ayan Paul, Ana Penuelas, Maurizio Pierini, Laura Reina, Luca Silvestrini, Mauro Valli, Ryoutaro Watanabe, Norimi Yokozaki

$\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to $\textit{i)}$ fit the model parameters to a given set of experimental observables; $\textit{ii)}$ obtain predictions for observables.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Interaction networks for the identification of boosted $H\to b\overline{b}$ decays

3 code implementations26 Sep 2019 Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B. Newman, Avikar Periwal, Maria Spiropulu, Javier M. Duarte, Maurizio Pierini

We develop a jet identification algorithm based on an interaction network, designed to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs, distinguish them from ordinary jets originating from the hadronization of quarks and gluons.

High Energy Physics - Experiment High Energy Physics - Phenomenology

JEDI-net: a jet identification algorithm based on interaction networks

2 code implementations14 Aug 2019 Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, Jean-Roch Vlimant

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons.

High Energy Physics - Experiment High Energy Physics - Phenomenology

FPGA-accelerated machine learning inference as a service for particle physics computing

1 code implementation18 Apr 2019 Javier Duarte, Philip Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Suffian Khan, Benjamin Kreis, Brian Lee, Mia Liu, Vladimir Lončar, Jennifer Ngadiuba, Kevin Pedro, Brandon Perez, Maurizio Pierini, Dylan Rankin, Nhan Tran, Matthew Trahms, Aristeidis Tsaris, Colin Versteeg, Ted W. Way, Dustin Werran, Zhenbin Wu

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains.

Data Analysis, Statistics and Probability High Energy Physics - Experiment Computational Physics Instrumentation and Detectors

LHC analysis-specific datasets with Generative Adversarial Networks

1 code implementation16 Jan 2019 Bobak Hashemi, Nick Amin, Kaustuv Datta, Dominick Olivito, Maurizio Pierini

Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost.

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

1 code implementation26 Nov 2018 Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events.

Two-sample testing

Pileup mitigation at the Large Hadron Collider with Graph Neural Networks

no code implementations18 Oct 2018 Jesus Arjona Martinez, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

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.

Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider

1 code implementation27 Jul 2018 Adrian Alan Pol, Gianluca Cerminara, Cecile Germain, Maurizio Pierini, Agrima Seth

Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment.

Anomaly Detection

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

3 code implementations29 Jun 2018 Thong Q. Nguyen, Daniel Weitekamp III, Dustin Anderson, Roberto Castello, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

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.

General Classification

Fast inference of deep neural networks in FPGAs for particle physics

2 code implementations16 Apr 2018 Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan Rivera, Nhan Tran, Zhenbin Wu

For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

BIG-bench Machine Learning

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