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2nd Pan-European Advanced School on Statistics in High Energy Physics

Europe/Berlin
SR 4a/b (DESY Hamburg)

SR 4a/b

DESY Hamburg

Description

We are looking forward to welcome you to the second Pan-European Advanced School on Statistics in High Energy Physics. The virtual school is open to master and PhD students as well as to Post-docs. Participants should already have a good knowledge of statistical methods in data analysis. The school focuses on two topics:

  1. Modeling of Data 
  2. Data Combination.

The compact school (3 days a ~4 hours) comprises lectures by renowned statisticians and particle physicists, followed by ample time for discussion.
 

Click here to join Zoom

The biennial series of Pan-European Advanced Schools on Statistics in High Energy Physics was founded in 2019 by the INSIGHTS Marie Sklodowska-Curie ITN and DESY. More information on INSIGHTS can be found here.

Participants
  • Abdelkader El Hamli
  • Ademar Paulo Junior
  • Adriana Simancas
  • Aimeric Landou
  • Akhilesh Tayade
  • Akshay Chatla
  • Alberto Prades
  • Alejandro Santiago García Viltres
  • Alessandro Guida
  • Alessandro Schwemmer
  • Alexandros Attikis
  • Alexey Elykov
  • Alicia Wongel
  • Alissa Fink
  • Aliya Nigamova
  • Alpana Alpana
  • Aman Desai
  • Amani Besma Bouasla
  • Amartya Rej
  • Ana Ventura Barroso
  • Anastasiia Kalitkina
  • Andre Sznajder
  • Andrea Piccinelli
  • Andrea Serafini
  • Andrej Lozar
  • Andrej Saibel
  • Anita Lavania
  • Anja Novosel
  • Ankita Mehta
  • Anna Hall
  • Anna Tegetmeier
  • Anton Stepennov
  • Antonio Giannini
  • Arne Christoph Reimers
  • Ata Sattari
  • Aurora Perego
  • Beatrice Cervato
  • Beatrice Jelmini
  • Ben Messerly
  • Benedict Westhenry
  • Benjamin Schwenker
  • Benno Kaech
  • Bensenani Samia
  • Bhawna Gomber
  • Bhumika Mehta
  • Bianca Sofia Pinolini
  • Blaž Leban
  • Bo Liu
  • Boping Chen
  • Boyang Yu
  • Brunella D'Anzi
  • Buddhadeb Mondal
  • Carmen Giugliano
  • Cedrine Alexandra Huegli
  • Cenk Turkoglu
  • Changzheng Yuan
  • Chaoyi Lyu
  • Chiara Lucarelli
  • Chiara Mancuso
  • Claudia Caterina Delogu
  • Conner Roberts
  • Craig John Wells
  • Daina Leyva Pernia
  • Daniel Buchin
  • Daniel Christian Hundhausen
  • Dibyajyoti Kalita
  • Dinesh Kumar Singha
  • Dipak Maity
  • Dmitry Dolzhikov
  • Donna Maria Mattern
  • Dror Berechya
  • Edoardo Franzoso
  • Eduardo Rodrigues
  • Eleanor Bishop
  • Eleonora Loiacono
  • Ellen Sandford
  • Emanuel Pfeffer
  • Enrico Wendrich
  • Enze Zhang
  • Enzo Canonero
  • Erik Bachmann
  • Eugene Jevgenijs Proskurins
  • Evan Goodman
  • Evgeniya Cheremushkina
  • Fabian Dünkel
  • Federica Cecilia Colombina
  • Federico Vazzoler
  • Felipe Silva
  • Filip Nechansky
  • Florian Lorkowski
  • Francesco De Santis
  • Frank Edzards
  • Franz Glessgen
  • Frederic Engelke
  • Gaelle Khreich
  • Gagandeep Kaur
  • Giorgia Tonani
  • Giulia Lavizzari
  • Giulia Sorrentino
  • Giulia Tuci
  • Gobinda Majumder
  • Hanane Riani
  • Hannu Siikonen
  • Hartmut Stadie
  • Hessamoddin Kaveh
  • Horacio Crotte Ledesma
  • Humberto Reyes-González
  • Igor Kakorin
  • Ilias Tsaklidis
  • Ilona Zubrytska
  • Isabell Melzer-Pellmann
  • Jacob Kempster
  • Jakub Kvapil
  • James Grundy
  • Jan Joachim Hahn
  • Jan-Eric Nitschke
  • Jean-Luc Hsu
  • Jeremiah Juevesano
  • Jianshe Zhou
  • Jieun Yoo
  • Jinchao Zheng
  • Joany Manjarres
  • John Nugent
  • Jona Motta
  • Jonas Neundorf
  • Juan Salvador Tafoya Vargas
  • Juhi Dutta
  • JULIA VAZQUEZ
  • Junli Ma
  • Jyotirmoi Borah
  • Kacper Bilko
  • Kacper Lasocha
  • Karol Adamczyk
  • Ken Kreul
  • Kevin Laudamus
  • Keziban Kandemir
  • Koushik Mandal
  • Kuldeep Pal
  • Kunlin Ran
  • Ladghami yahya
  • Lakshmi Pramod
  • Lars Sowa
  • Laura Martikainen
  • Le ZHENG
  • Lei Zhang
  • Leonardo Benjamin Rizzuto
  • Licheng Zhang
  • Ligang Xia
  • Linghua Guo
  • Liudmila Kolupaeva
  • Louis Ginabat
  • Lu Cao
  • Luisa Lovisetti
  • Luka Santelj
  • Luka Senekovic
  • Lukas Heinrich
  • Maharnab Bhattacharjee
  • Mahmoud Gadallah
  • Mangesh Sonawane
  • Manosh T. M.
  • Manuel Alejandro Del Rio Viera
  • Manuel Sommerhalder
  • Marcus Vinicius Gonzalez Rodrigues
  • Maria Mazza
  • Maria-Evanthia Tsopoulou
  • Marianna Liberatore
  • Marlon Brade
  • Martin Murin
  • Marzieh Bahmani
  • Marzieh Bahmani
  • María Moreno Llácer
  • Matteo Barbetti
  • Matteo Bonanomi
  • Matteo Defranchis
  • Matteo Greco
  • Matteo Marchegiani
  • Max Stange
  • Max Vincent Stange
  • Maximilian Caspar
  • Mengchuan Du
  • Michaela Mlynarikova
  • Mikhail Smirnov
  • Miroslav Saur
  • Mohammad Mobassir Ameen
  • Mohammed Bouta
  • Moritz Scham
  • Mostafa Mahdavikhorrami
  • Muhammad Ibrahim Abdulhamid Elsayed
  • Mykyta Shchedrolosiev
  • Nataliia Zakharchuk
  • Nathalie Eberlein
  • Nathan Simpson
  • Nelson Hartunian
  • Nicholas Haubrich
  • Nicola Fulvio Calabria
  • Nicola Serra
  • Nicolo Trevisani
  • Nikita Shadskiy
  • Nikolai Fomin
  • Nikolaus Owtscharenko
  • Niladri Sahoo
  • Nilima Akolkar
  • Nils Ernst Klaus Gillwald
  • Nisar Nellikunnummel
  • Nishat Parveen
  • Nitish Kumar K V
  • Noel Alberto Cruz Venegas
  • Olaf Behnke
  • Oleksii Lukianchuk
  • Orcun Kolay
  • PABLO GOLDENZWEIG
  • Papia Panda
  • Paras Koundal
  • Paris Gianneios
  • Patricia Rebello Teles
  • Paul Feichtinger
  • Paul Morrison
  • Peilian Li
  • Petar Bokan
  • Philip Ruehl
  • Philipp Rincke
  • Philippe Di Stefano
  • Polidamas Georgios Kosmoglou Kioseoglou
  • Priya Mishra
  • Priyanka Cheema
  • Pueh Leng Tan
  • Qiang Li
  • Qiao Li
  • Qingyuan Liu
  • Rafia Shabbir
  • Raghav Kansal
  • Rahima Doghmane
  • Rahmat Rahmat
  • Rahul Balasubramanian
  • Ralf Schmieder
  • Ram Krishna Sharma
  • Rameswar Sahu
  • Rashmi Dhamija
  • Reda Attallah
  • Ricardo Gomes
  • Rishabh Mehta
  • Roger Wolf
  • Rufa Kunnilan Muhammed Rafeek
  • Sahithi Rudrabhatla
  • Sara Ruiz Daza
  • Saray Arteaga Escatel
  • Sebastian Guido Bieringer
  • Sebastian Schmitt
  • Seema Choudhury
  • Seema Sharma
  • Selaiman Ridouani
  • Seraphim Koulosousas
  • Sergey Abovyan
  • Shantam Taneja
  • Shinichi Okamura
  • Shiqi Yu
  • Shivam Chaudhary
  • Shivam Raj
  • Shivaraj Mulleria Babu
  • Shriniketan Acharya
  • Shubhangi Krishan Maurya
  • Shuhui Huang
  • Si Hyun Jeon
  • Sima Bashiri
  • Simon Grewe
  • Simon Schnake
  • Simone Amoroso
  • Sitong An
  • Siva Prasad Kasetti
  • Slavomira Stefkova
  • Soham Bhattacharya
  • Soumya Dansana
  • Steffen Albrecht
  • Supriya Sinha
  • Susanne Raab
  • Suxian Li
  • Tadej Novak
  • Tailin Zhu
  • Tamas Gal
  • Thanh Nguyen
  • Theodosia Giamouki
  • Tianyu Qi
  • Timothee Pascal
  • Tobias Quadfasel
  • Tommy Martinov
  • Valentina Guglielmi
  • Varun Sharma
  • Vasilije Perovic
  • Veljko Maksimovic
  • Viktor Ananiev
  • Vitor Sousa
  • Walaa Elmetenawee
  • Wei Shi
  • Wenjie Wu
  • Xiaocong Ai
  • Xiaoping Qin
  • Xu Dong
  • Xunwu Zuo
  • Yajun HE
  • Yang Li
  • Yann Coadou
  • Yaroslav Kulii
  • Yassine El Ghazali
  • Yecheng Sun
  • Yewon Yang
  • Ying-Rui Hou
  • Yipu Liao
  • Yohan Lee
  • Zijun Xu
  • Zineb ALY
  • Zubair Dar
    • 2:00 PM 6:30 PM
      Modeling of data 1
      • 2:00 PM
        Introduction 10m
        Speaker: Olaf Behnke (CMS (CMS Fachgruppe TOP))
      • 2:10 PM
        Goodness-Of-Fit tests (Part 1) 45m

        A goodness-of-fit test is concerned with the question whether a
        given data set was generated by a specific probability distribution
        such as an exponential. In this seminar we will discuss a variety
        of such tests. We will consider their relative merits, and how to
        run several of them simultaneously. We will also discuss tests for
        multivariate data and a number of special cases such as binned
        data and comparing Monte Carlo to data.

        Speaker: Wolfgang Rolke (UPR )
      • 2:55 PM
        Discussion time 10m
      • 3:05 PM
        Virtual coffee break 10m
      • 3:15 PM
        Goodness-Of-Fit tests (Part 2) 45m
        Speaker: Wolfgang Rolke (UPR - Mayaguez)
      • 4:00 PM
        Discussion time 15m
      • 4:15 PM
        Virtual coffee break 30m
      • 4:45 PM
        Statistical Modeling 1h

        In this lecture I will given an overview of various aspects of
        statistical modeling. I'll start be reviewing parametric models.
        Then I will discuss methods for dealing with model
        misspecification. Next I will discuss nonparametric methods and
        then move on to universal inference which is a new method for
        handling irregular models. I'll finish with a few quick remarks on
        semiparametric inference.

        Speaker: Larry Wasserman (CMU )
      • 5:45 PM
        Discussion time 15m
    • 2:00 PM 8:15 PM
      Data combination
      • 2:00 PM
        Data combination - introduction 45m

        The lecture will address the topic of combining information
        from different sources in an analysis of Particle Physics data.
        The general formalism by which this is done in both the
        Bayesian and Frequentist approaches will first be reviewed.
        Combination of results relies fundamentally on constructing
        a likelihood that reflects all of the available data. Often this
        requires some approximations and assumptions as the
        detailed information needed to write down the full likelihood
        may not be available. An important aspect of combined (and
        individual) data analyses is the assignment of uncertainties to estimates
        to nuisance parameters. A method will be described by which
        uncertainties on the assigned uncertainties
        themselves can be incorporated, and the impact of this type of
        a model on combinations will be shown.

        Link to the python code used for the example of a+bx fit:
        https://www.pp.rhul.ac.uk/~cowan/stat/fitCombo.py
        https://www.pp.rhul.ac.uk/~cowan/stat/fitCombo.ipynb

        Speaker: Glen Cowan (RHUL)
      • 2:45 PM
        Discussion time 15m
      • 3:00 PM
        Virtual coffee break 30m
      • 3:30 PM
        Data combination - in practice 45m

        The lecture will address practical aspects and possible pitfalls
        when combining particle physics measurements or limits and will
        give pointers to methods and tools that can be used for that
        purpose. One particular focus will be the combination of single
        valued and multiple valued (differential) measurements with
        complex correlations between their nuisance parameters. The
        lecture will be accompanied by hands-on examples. A CERN
        account with the possibility to log in to lxplus is recommended, but
        all examples can also be followed without running the software.

        For use of convino code at lxplus.cern.ch:

        After login do:

        bash

        cd /afs/cern.ch/user/j/jkiesele/public/Convino/latest

        source lxplus_env.sh

        cd

        mkdir convino_tutorial

        cd convino_tutorial

        convino /afs/cern.ch/user/j/jkiesele/public/Convino/latest/examples/exampleconfig.txt

        cp -r /afs/cern.ch/user/j/jkiesele/public/Convino/tutorial/* .

        Speaker: Jan Kieseler (CERN )
      • 4:15 PM
        Discussion time 15m
      • 4:30 PM
        Virtual coffee break 30m
      • 5:00 PM
        What to publish 45m

        The statistical model is the a unique summary of a physics
        analysis from which many of the key results can be derived
        from. Preserving it allows not only the reproduction of key
        results but also its reuse in statistical combinations and
        reinterpretations. In this talk we will cover how to best
        publish statistical model data to enable these use-cases.

        A different way of making measurements available to physicists
        outside of the experimental collaboration is by reversing
        the smearing effects of the detector
        and reconstruction. Various different methods
        of unfolding allow to publish data in a way
        that is independent of a specific experimental setup.

        Speakers: Carsten Burgard (ATLAS (ATLAS Dark Matter with Higgs)), Lukas Heinrich (CERN)
      • 5:45 PM
        Discussion time 15m
    • 2:00 PM 8:25 PM
      Modeling of data 2
      • 2:00 PM
        Introduction to Optimal Transport 45m

        Optimal transport (OT) is a method for mapping one probability
        distribution into another. OT also leads to a method for defining a
        geodesic between distributions which allows us to morph one
        distribution into another. I will introduce the basics of optimal
        transport and I will explain how optimal transport maps and
        morphings can be estimated from data.

        Speaker: Larry Wasserman (CMU )
      • 2:45 PM
        Discussion time 15m
      • 3:00 PM
        Virtual coffee break 30m
      • 3:30 PM
        Gaussian Processes 45m

        In this lecture, I will provide an introduction to Gaussian
        processes (GPs), with a view toward applications in high-energy
        physics. I will start with the basic definition of a GP and explain
        how to perform inference with these models. I will then describe
        the choice and estimation of the mean and the covariance
        function and demonstrate these ideas with simple examples.
        I will close with a brief overview of applications of GPs in
        high-energy physics.

        Speaker: Mikael Kuusela (Carnegie Mellon University)
      • 4:15 PM
        Discussion time 15m
      • 4:30 PM
        Virtual coffee break 30m
      • 5:00 PM
        EFT Lagrangian Morphing 45m

        In this lecture I will discuss a method of morphing distributions
        that is useful to measure the parameters of an Effective Field
        Theory (EFT). I will introduce EFT which is a powerful
        theoretical framework that is used to systematically extend
        known physics lagrangians. I will then talk about the idea
        behind the morphing between distributions given the
        predictions at some point in the parameter space which allow
        to obtain a continuous prediction in terms of EFT parameters.
        I will finally show a couple of examples of the implementation
        of this technique as the RooLagrangianMorphFunc class
        within RooFit toolkit that is available with the ROOT software.

        Speaker: Rahul Balasubramanian (Nikhef and University of Amsterdam)
      • 5:45 PM
        Discussion time 15m
      • 6:00 PM
        Closing of School 10m
        Speaker: Olaf Behnke (CMS (CMS Fachgruppe TOP))