PHYSTAT-Anomalies

Europe/Zurich
Olaf Behnke (olaf.behnke@cern.ch) (Deutsches Elektronen-Synchrotron (DE)), Louis Lyons (l.lyons@physics.ox.ac.uk) (Imperial College (GB)), Ben Nachman (Lawrence Berkeley National Lab. (US)), Gregor Kasieczka (Hamburg University (DE)), Mikael Kuusela (Carnegie Mellon University (US))
Description

A workshop on model independent searches, bringing together physicists and statisticians

With higher accelerator energies and beam intensities, searches for New Physics (NP) have been a very active area. While there is motivation for NP, so far model-driven searches have successfully excluded increasing volumes of parameter space, but not yielded evidence for new particles. This has led, over the last few years, to the development of model-independent searches. By now, they have become a useful complement to traditional approaches targeting some specific form of NP. In many of the new searches, Machine Learning has played an important role. The aim of this meeting is to compare and contrast the assumptions and performance on these approaches, and to see what can be learned from Goodness of Fit methodology. This meeting will bring together physicists who are active in this field, those who want to be involved in the future, and Statisticians, to discuss the relevant issues.

The meeting is on 24th and 25th May 2022, and will be remote. The PHYSTAT Seminar on 27th April by Mikael Kuusela (CMU) on "Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests"  is also part of the PHYSTAT-Anomalies meeting. See  https://indico.cern.ch/event/1148820/

 

The PHYSTAT series of Workshops started in 2000. They were the first meetings devoted solely to the statistical issues that occur in analyses in Particle Physics and neighbouring fields.The homepage of PHYSTAT with a list of all workshops and seminars is at https://espace.cern.ch/phystat . 

 

 

 

Participants
  • aaron mankel
  • Abdelhamid Haddad
  • Abdi Usman
  • Abdul-Hafiz Alabi
  • Abdullah Alsubie
  • Abhijith Gandrakota
  • Abhishek Jha
  • Adinda De Wit
  • Adrian Alan Pol
  • Adrian Rubio Jimenez
  • Adrien Matta
  • Afnan Shatat
  • Ahsan Ullah
  • Ajay Kumar
  • Alan Litke
  • Alessandro Calandri
  • Alessandro Morandini
  • Alexander Himmel
  • Alibordi Muhammad
  • Amal Saif
  • Aman Mukesh Desai
  • Amandeep Kaur
  • Amitav Mitra
  • Amitkumar Pandey
  • Ana Andrade
  • Andre Sznajder
  • Andrea Bulla
  • Andrea Wulzer
  • Andrei Oskin
  • Andrew Fowlie
  • Andrew Norman
  • Anirudhan A M
  • Ann Lee
  • Anna Ivina
  • Annapaola De Cosa
  • Annika Stein
  • Antonin Sulc
  • Antonio D'Avanzo
  • Antonio Giannini
  • Antonis Agapitos
  • Aravind Thachayath Sugunan
  • Arnab Laha
  • Arnab Purohit
  • Arnd Meyer
  • Asar Ahmed
  • Avnish .
  • Ayse Bat
  • Bahman Dehnadi
  • Balint Radics
  • Basit Balogun
  • Ben Nachman
  • Benedict Westhenry
  • Berkan Kaynak
  • Bhumika Mehta
  • Biao Wang
  • Bill Murray
  • Bisnupriya Sahu
  • Bob Cousins
  • Bogdan Malaescu
  • Boping Chen
  • Bruna Pascual Dias
  • Cecilia Uribe Estrada
  • Cenk Turkoglu
  • Cesare Tiziano Cazzaniga
  • Changzheng Yuan
  • Christophe Bronner
  • Daniel Britzger
  • Daniel Durnford
  • Daniel Felea
  • Daniel Mayer
  • Daniela Katherinne Paredes Hernandez
  • Daniele Dal Santo
  • David Bailey
  • David Shih
  • David van Dyk
  • Debajyoti Sengupta
  • Debarun Paul
  • Dimitri Bourilkov
  • Dror Berechya
  • Duong Nguyen
  • Elham Khazaie
  • Eli Piasetzky
  • Ema Puljak
  • Enzo Canonero
  • Eugene Proskurins
  • Evan Koenig
  • Fahad Ghenaim
  • Faisal Khamis
  • Federica Cecilia Colombina
  • Federico De Matteis
  • Feifei Huang
  • Fernando Torales Acosta
  • Florentina Manolescu
  • Florian Eble
  • Francesco Conventi
  • Francesco Dettori
  • Francisco Alonso
  • Francisco Matorras
  • Francisco Sili
  • Franco Simonetto
  • Franz Glessgen
  • Gabriel Facini
  • Gabriele Sirri
  • Gagandeep Kaur
  • Gaia Grosso
  • Galin Jones
  • Geliang Liu
  • Gemai Chen
  • Giacomo Boldrini
  • Gilles Quéméner
  • Gilson Correia Silva
  • Giovanni Battista Marozzo
  • Giulia Lavizzari
  • Gobinda Majumder
  • Gonza Orellana
  • Gourab Saha
  • Grace Cummings
  • Gregor Kasieczka
  • Haesung Park
  • Haris Painesis
  • Hiroyuki Sekiya
  • Huijing Li
  • Humberto Reyes-González
  • Hyejin Kwon
  • Igor Volobouev
  • Ilias Tsaklidis
  • Ilona Zubrytska
  • Imene OUADAH
  • Ines Ochoa
  • Ivan Oleksiyuk
  • Jack Harrison
  • Jahnavi reddy
  • Jake McKean
  • Jamie Riggs
  • Javier Mauricio Duarte
  • Jayashri Padmanaban
  • Jeffrey Picka
  • Jeremi Niedziela
  • Jeremiah Juevesano
  • Jieun Yoo
  • Jing-Ge Shiu
  • John Plows
  • Johnny Raine
  • Jonas Eschle
  • Joon-Bin Lee
  • Joosep Pata
  • Joseph Reichert
  • José Fernando Mandeur Díaz
  • João Pedro de Arruda Gonçalves
  • Judita Mamuzic
  • Jyotirmoi Borah
  • Ka Wa Ho
  • Kacper Bilko
  • Kamal Lamichhane
  • Karim El Morabit
  • Karolos Potamianos
  • Kartik Sharma
  • Kinga Anna Wozniak
  • Larry Wasserman
  • Lennart Kämmle
  • Lennart Uecker
  • Lev Dudko
  • Licheng ZHANG
  • Liliana Teodorescu
  • Lorenzo Moneta
  • Louis Lyons
  • Louis Moureaux
  • Lucas Kang
  • Luisa Lovisetti
  • Lukas Berns
  • M Naimuddin
  • Maeve Madigan
  • Mahak Garg
  • Manuel Sommerhalder
  • Marcello Rotondo
  • Marcin Poblocki
  • Marco Letizia
  • Marco Monteno
  • Marco Riggirello
  • Marco Zanetti
  • Marek Biros
  • Maria Belobrova
  • Maria Moreno Llacer
  • Marie Hein
  • Marina Chadeeva
  • Marjolein Verhulst
  • Markus Klute
  • Marta Felcini
  • Maryam Ranjbar
  • Masahiko Saito
  • Matin Torkian
  • Matteo Tenti
  • Maurizio Pierini
  • Mauro Donega
  • Melissa Kathryn Quinnan
  • Michael Finger
  • Michael Krämer
  • Michael Schmelling
  • Michael Soughton
  • Micol Olocco
  • Mikael Kuusela
  • Miroslav Finger
  • Miroslav Kubu
  • Mohamad Alsebeei
  • Nathan Aviles
  • Nelson Hartunian
  • Nicholas Kyriacou
  • Nikolaos Manthos
  • Nimmitha Karunarathna
  • Nishita Desai
  • Noel Alberto Cruz Venegas
  • Ofer Aviv
  • Olaf Behnke
  • Olga Petrova
  • Ons Oueslati
  • Onur Potok
  • Orhan Cakir
  • Patrick de Perio
  • Patrick Dougan
  • Patrick Odagiu
  • Paul Feichtinger
  • Pedrame Bargassa
  • Peicho Petkov
  • Peilian Li
  • Peter McKeown
  • Philipp Windischhofer
  • Philippe Debie
  • Pierre Paul Louis Mayencourt
  • Pietro Vischia
  • Piljun Gwak
  • Piotr Zalewski
  • Pratik Jawahar
  • Priyanka Barik
  • Pueh Leng Tan
  • Purvasha Chakravarti
  • racha cheaib
  • Rachik Soualah
  • Raghav Kansal
  • Rahul Balasubramanian
  • Rahul Shah
  • Rajeev Singh
  • Ram Krishna Dewanjee
  • Rashmi Dhamija
  • Rauf Ahmad
  • Raul Ramos Pollan
  • Renato Campanini
  • Richard Lockhart
  • Rituparna Maji
  • Robert Hatcher
  • Roberto Ruiz
  • Roberto Salerno
  • Robin Erbacher
  • Rogelio Almanza
  • Roger Huang
  • Roger Wolf
  • Rohan Shenoy
  • Roy Lemmon
  • Rui Zhang
  • Sabine Kraml
  • Samuel English
  • Sanae Ezzarqtouni
  • Sang Eon Park
  • Sanu Varghese
  • Sara Motalebi
  • Sascha Caron
  • Saumya Saumya
  • Saurabh Shukla
  • Saurabh Shukla
  • Sebastian Schmitt
  • Selaiman Ridouani
  • Sergey Korpachev
  • Sergey Magedov
  • Shalini Epari
  • Shivam Raj
  • Shivani Sanjay Lomte
  • Shuheng Zhou
  • Shuhui Huang
  • Si Hyun Jeon
  • Siavash Neshatpour
  • Siddharth Chaini
  • Sima Bashiri
  • Simone Amoroso
  • Siqi Yuan
  • Sophia Farrell
  • Souparna Dhar
  • Sourav Pal
  • Stephane Zsoldos
  • Steven Clark
  • Steven Schramm
  • Sudeshna Banerjee
  • Suman Das Gupta
  • Suvankar Roy Chowdhury
  • Suzanne Rosenzweig
  • Sven Bollweg
  • Sweta Baradia
  • Thea Aarrestad
  • Thomas Calvet
  • Thomas Junk
  • Thomas Sievert
  • Thorben Finke
  • Tim Kolar
  • Tobias Quadfasel
  • Todd Adams
  • Tom Loredo
  • Tommy Martinov
  • Tongguang Cheng
  • TRIPURARI SRIVASTAVA
  • Ubi Wichoski
  • V. PONNI
  • Vasilis Belis
  • Vilius Cepaitis
  • Vinicius Massami Mikuni
  • Vitaliano Ciulli
  • Vladimir Cherepanov
  • Vladimir Samoylenko
  • Volker Andreas Austrup
  • Waleed Syed Ahmed
  • Wasikul Islam
  • Wolfgang Rolke
  • Yacine Haddad
  • Yang Chen
  • Yassine El Ghazali
  • Yingjie Wei
  • Yohan Lee
  • Yongbin Feng
  • Yoxara Sánchez Villamizar
  • Yuping Zhang
  • Yuxin Fan
  • Zafer Acar
Videoconference
Statistics
Zoom Meeting ID
68793225561
Host
Olaf Behnke
Alternative host
Nicholas Wardle
Useful links
Join via phone
Zoom URL
Surveys
PHYSTAT-Anomalies Workshop Survey
    • 17:00 19:10
      Session 1a. Chair: Mikael Kuusela
      • 17:00
        Introduction 10m
        Speakers: Louis Lyons (Imperial College (GB)), Olaf Behnke (Deutsches Elektronen-Synchrotron (DE))
      • 17:10
        Landscape of model independent searches 30m

        ABSTRACT:
        This talk will briefly define and motivate anomaly detection at the Large Hadron Collider and will then give an overview of various method classifications based on the underlying physics goals and assumptions (and how these translate to statistical concepts). No one method will be able to cover all possibilities and it is essential to have a spectrum of techniques to achieve broad and deep model-independent sensitivity to physics beyond the Standard Model.

        Speaker: Ben Nachman (Lawrence Berkeley National Lab. (US))
      • 17:40
        Questions 15m
      • 17:55
        Learning New Physics from a machine 30m

        ABSTRACT:
        Strategies to detect data departures from a given reference model, with no prior bias on the nature of the new physical model responsible for the discrepancy might play a vital role in experimental programs where, like at the LHC, increasingly rich experimental data are accompanied by an increasingly blurred theoretical guidance in their interpretation. I will describe one such strategy that employs neural networks, leveraging their virtues as flexible function approximants, but builds its foundations directly on the canonical likelihood-ratio approach to hypothesis testing. The algorithm compares observations with an auxiliary set of reference-distributed events, possibly obtained with a Monte Carlo event generator. It returns a p-value, which measures the compatibility of the reference model with the data. It also identifies the most discrepant phase-space region of the dataset, to be selected for further investigation. Imperfections due to mismodelling in the reference dataset can be taken into account straightforwardly as nuisance parameters. After illustrating the methodology, I will demonstrate its applicability to problems at a similar scale of complexity of realistic LHC analyses.

        Speaker: Andrea Wulzer (Padova)
      • 18:25
        Questions 15m
      • 18:40
        Discussion 15m
      • 18:55
        Break 15m
    • 19:10 20:40
      Session 1b. Chair Tom Junk
      • 19:10
        Relation of model independent searches with Goodness of Fit, 2-sample tests, etc. 30m

        ABSTRACT:
        I will review goodness of fit testing and two sample testing in the
        context of trying to test for a new signal. My goal is to point to
        results in the statistics literature that might be unfamiliar in the
        physics community. Topics will include: optimal tests,
        classifier-based tests, reproducing kernel Hilbert space tests, level set tests, bump tests and robustness.

        Speakers: Larry Wasserman (Carnegie Mellon University), Larry Wasserman (Carnegie Mellon University)
      • 19:40
        Questions 15m
      • 19:55
        Discussion 30m
        Speakers: Robert Cousins Jr (University of California Los Angeles (US)), Robert Cousins Jr (University of California Los Angeles (US))
    • 17:00 19:00
      Session 2a: Chair: Ann Lee
      • 17:00
        Challenges of anomaly detection with LHC data 30m

        ABSTRACT:
        In recent years, there have been many proposed methodologies for machine learning anomaly detection at the LHC, such as those reported in the LHC Olympics and Dark Machines community reports.  The first search using machine-learning anomaly detection was performed by ATLAS in the dijet final state, a fully data-driven analysis that uses the Classification Without Labels method and is complementary to the existing dedicated resonance searches. 
        In this talk, I will use the experience gained with the ATLAS weakly supervised dijet analysis to discuss the general
        challenges of doing anomaly detection with LHC data and methodologies to address them.

        Speaker: Ines Ochoa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Particulas (PT))
      • 17:30
        Questions 15m
      • 17:45
        LHC Olympics 30m

        ABSTRACT:
        We are at the beginning of a new era of data-driven, model-agnostic new physics searches at colliders that combine recent breakthroughs in anomaly detection and machine learning. This contribution will report on the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. We will review this challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

        Speaker: Gregor Kasieczka (Hamburg University)
      • 18:15
        Questions 15m
      • 18:30
        Discussion 15m
      • 18:45
        Break 15m
    • 19:00 20:30
      Session 2b. Chair: David van Dyk
      • 19:00
        Supervised, unsupervised and data-derived signal regions 30m

        ABSTRACT:
        Data-driven methods are becoming increasingly popular and could give us new insights when searching for signals from new physics. On the other hand, theoretical models and supervised learning approaches should not be neglected.
        In this talk we present and compare different ways of defining "signal regions" at the Large Hadron Collider that are of interest for a "goodness-of-fit" test. We compare the performance of 3 of these approaches and discuss a new way to define "signal regions".

        Speaker: Sascha Caron (Nikhef National institute for subatomic physics (NL))
      • 19:30
        Questions 15m
      • 19:45
        Discussion 30m
        Speaker: Richard Lockhart (SFU)
      • 20:15
        Closing remarks 5m
        Speaker: Olaf Behnke (Deutsches Elektronen-Synchrotron (DE))