Pietro Vischia



Centre for Cosmology, Particle Physics and Phenomenology - CP3
Université catholique de Louvain
2, Chemin du Cyclotron - Box L7.01.05
B-1348 Louvain-la-Neuve

+32 10 473205


Personal homepage

UCL member card
- LPHYS2233 (2020/2021) v1 (chargé de cours), statistics and clustering algorithms
- LPHYS1201 (2020/2021) v2, programming course (python)
- LPHY2233 (2019/2020), medical simulation with GEANT4
- LPHYS1201 (2019/2020) v2, programming course (python)
- LPHYS1201 (2018/2019) v2, programming course (C++)
Research statement
My main research interest is in statistics and machine learning; I am currently working on developing algorithms that employ resampling techniques to the problem of anomaly detection, and on the problem of expressing the expected statistical significance in an approximate way in searches for new signals.

I am a founding member, as well as member of the Steering Board, of the MODE Collaboration, where we aim at building a differentiable pipeline to do machine-learning informed optimization of detector and experiment design.

I am also a member of the CMS Collaboration, where I work as a statistics advisor (CMS Statistics Committee) and to experimental measurements in top-Higgs physics (ttH observation and top-Higgs coupling constraints in an EFT framework) and in Standard Model precision measurements (WZ, also constraining the triboson coupling in an EFT framework).
Research directions:
Data analysis in HEP and GW experiments
Detector commissioning, operation and data processing

Experiments and collaborations:

Active projects
Advanced Multi-Variate Analysis for New Physics Searches at the LHC
Agni Bethani, Florian Bury, Christophe Delaere, Andrea Giammanco, Vincent Lemaitre, Fabio Maltoni, Pietro Vischia

With the 2012 discovery of the Higgs boson at the Large Hadron Collider, LHC, the Standard Model of particle physics has been completed, emerging as a most successful description of matter at the smallest distance scales. But as is always the case, the observation of this particle has also heralded the dawn of a new era in the field: particle physics is now turning to the mysteries posed by the presence of dark matter in the universe, as well as the very existence of the Higgs. The upcoming run of the LHC at 13 TeV will probe possible answers to both issues, providing detailed measurements of the properties of the Higgs and extending significantly the sensitivity to new phenomena.

Since the LHC is the only accelerator currently exploring the energy frontier, it is imperative that the analyses of the collected data use the most powerful possible techniques. In recent years several analyses have utilized multi-variate analysis techniques, obtaining higher sensitivity; yet there is ample room for further improvement. With our program we will import and specialize the most powerful advanced statistical learning techniques to data analyses at the LHC, with the objective of maximizing the chance of new physics discoveries.

We have been part of AMVA4NewPhysics, a network of European institutions whose goal is to foster the development and exploitation of Advanced Multi-Variate Analysis for New Physics searches. The network offered between 2015 and 2019 extensive training in both physics and advanced analysis techniques to graduate students, focusing on providing them with the know-how and the experience to boost their career prospects in and outside academia. The network develops ties with non-academic partners for the creation of interdisciplinary software tools, allowing a successful knowledge transfer in both directions. The network studies innovative techniques and identifies their suitability to problems encountered in searches for new physics at the LHC and detailed studies of the Higgs boson sector.

External collaborators: University of Oxford, INFN, University of Padova, Université Blaise Pascal, LIP, IASA, CERN, UCI, EPFL, B12 Consulting, SDG Consulting, Yandex, MathWorks.
Machine-learning Optimized Design of Experiments
Christophe Delaere, Andrea Giammanco, Pietro Vischia

We are among the founders of MODE (Machine-learning Optimized Design of Experiments,, a multi-disciplinary consortium of European and American physicists and computer scientists who target the use of differentiable programming in design optimization of detectors for particle physics applications, extending from fundamental research at accelerators, in space, and in nuclear physics and neutrino facilities, to industrial applications employing the technology of radiation detection.

We aim to develop a modular, customizable, and scalable, fully differentiable pipeline for the end-to-end optimization of articulated objective functions that model in full the true goals of experimental particle physics endeavours, to ensure optimal detector performance, analysis potential, and cost-effectiveness.
The main goal of our activities is to develop an architecture that can be adapted to the above use cases but will also be customizable to any other experimental endeavour employing particle detection at its core. We welcome suggestions, as well as interest in joining our effort, by researchers focusing on use cases for which this technology can be of benefit.

Two CP3 members currently serve as members of the MODE Supervisory Board.

External collaborators: University of Padova, INFN, Université Clermont Auvergne, Higher School of Economics of Moscow, CERN, University of Oxford, New York University, ULiege.
Publications in CP3
All my publications on Inspire

Number of publications as CP3 member: 10 Download BibTeX

Last 5 publications


CP3-21-17: Measurement of the pp→WZ inclusive and differential cross sections, polarization angles and search for anomalous gauge couplings at s√=13 TeV
Sirunyan, Albert M and others

[Full text]
To be submitted to JHEP
Public experimental note. June 16.
CP3-21-14: Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
Anna Stakia, Tommaso Dorigo, Giovanni Banelli, Daniela Bortoletto, Alessandro Casa, Pablo de Castro, Christophe Delaere, Julien Donini, Livio Finos, Michele Gallinaro, Andrea Giammanco, Alexander Held, Fabricio Jiménez Morales, Grzegorz Kotkowski, Seng Pei Liew, Fabio Maltoni, Giovanna Menardi, Ioanna Papavergou, Alessia Saggio, Bruno Scarpa, Giles C. Strong, Cecilia Tosciri, João Varela, Pietro Vischia, Andreas Weiler

[Abstract] [PDF]
Submitted to Elsevier journal
Refereed paper. May 18.
CP3-21-10: Toward Machine Learning Optimization of Experimental Design
MODE collaboration

[Full text]
Nuclear Physics News, 31:1 (2021), 25-28
March 31.


CP3-20-53: Search for a charged Higgs boson decaying into top and bottom quarks in events with electrons or muons in proton-proton collisions at $ \sqrt{\mathrm{s}} $ = 13 TeV
Sirunyan, Albert M and others

[Abstract] [PDF] [Journal] [Dial]
Refereed paper. November 22.
CP3-20-52: Measurements of the pp $\to$ WZ inclusive and differential production cross section and constraints on charged anomalous triple gauge couplings at $\sqrt{s} =$ 13 TeV
Sirunyan, Albert M and others

[Abstract] [PDF] [Journal] [Dial]
Refereed paper. November 22.

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