PHYSTAT-Gamma 2022

Europe/Zurich
Gerrit Spengler , Louis Lyons (Imperial College (GB)) , Manuel Meyer (University of Hamburg) , Olaf Behnke (Deutsches Elektronen-Synchrotron (DE)) , Thomas Lohse (Humboldt University of Berlin (DE)) , Ullrich Schwanke (Humboldt University Berlin)
Description

Zoom link

Meeting ID: 622 3933 3803; Passcode: 496169

This link is for the pre-school on statistical methods (Tue, 27 Sep 2022) and PHYSTAT-Gamma (28-30 Sep 2022)

 

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This compact workshop deals with statistical issues in the analyses of data from gamma-ray experiments, and is meant to establish tighter connections to observatories in other wavebands (e.g. x-rays, radio).

 

The workshop is complemented by an afternoon of introductory lectures on Tuesday, 27.9.22. Note that the ml.astro workshop on machine learning in astroparticle physics will take place on Monday, 26.9.22. This workshop is independent of PHYSTAT but will highlight complementary aspects.

 

The summary of the program for the week 26.9.22-30.9.22 is:

Mon, 26.9.22: ml.astro (Machine Learning in astroparticle physics)

Tue, 27.9.22: Introductory talks on Statistics and Machine learning

Wed-Fri (28.9.22.-30.9.22): PHYSTAT-gamma: Statistical Methods in gamma-ray astronomy

 

The homepage of PHYSTAT with a list of all workshops and seminars is at https://espace.cern.ch/phystat. 

Registration
Registration
Participants
  • Aayush Gautam
  • Abhijit Roy
  • Abhradeep Roy
  • Aditya Narendra
  • Adrien Laviron
  • Agustín Sánchez Losa
  • Ajay Sharma
  • Akash Parmar
  • Alan Litke
  • Alberto Carramiñana
  • Alejandra Aguirre-Santaella
  • Alejandro Lara
  • Alejo Cifuentes
  • Alena Khokhriakova
  • Alessandra Azzollini
  • Alexander Held
  • Alexis Coleiro
  • Ali Arda Gençali
  • Alicja Wierzcholska
  • Alina Donea
  • Aman Desai
  • Ambra Di Piano
  • Aminabi Thekkoth
  • Amy Furniss
  • Ana Babic
  • Andrea Bulgarelli
  • Andres Delgado
  • Angel Fernando Campoverde Quezada
  • Anilkumar Tolamatti
  • Ansh Chopra
  • ANSHU CHATTERJEE
  • Antonio Stamerra
  • Armand Fiasson
  • Armelle Jardin-Blicq
  • Arnab Sarker
  • Arnau Aguasca-Cabot
  • Atreya Acharyya
  • Atreyee Sinha
  • Atul Pathania
  • Axel Donath
  • Bakhtiyar Iskakov
  • başak ekinci
  • Beatriz Mingo
  • Benedetta Bruno
  • Benedetto Di Ruzza
  • Benyamen Nathanael
  • Bhanu Pant
  • Bhuvaneshwari Kashi
  • BISWARAJ PALIT
  • Brandon Khan Cantlay
  • Bruno KHÉLIFI
  • Carlos Mañá
  • Chad Schafer
  • Chaitanya Priyadarshi
  • Christopher Eckner
  • Ciro Bigongiari
  • Clara Escanuela
  • Claudio Gasbarra
  • Colin Adams
  • Connor Mooney
  • Constantin Steppa
  • Cosimo Nigro
  • Csaba Balazs
  • Cynthia Ventura
  • Daariimaa Battulga
  • Daniel Kerszberg
  • Davide Cerasole
  • Davide Miceli
  • Deirdre Horan
  • Denis Bernard
  • Derlei Silva
  • Despina Karavola
  • Dimakatso Jeannett Maheso
  • Douglas Carlos
  • Edoardo Maria Tronci
  • Eduardo Moreno
  • Egemen Tunca
  • Ekrem Oguzhan Angüner
  • Elena Pinetti
  • Eleonora Barbano
  • Eliana Palazzi
  • Elif Köksal
  • Elisa Pueschel
  • Emma de Ona Wilhelmi
  • Erbil Can Artun
  • Eric Feigelson
  • Ettore Bronzini
  • Fakhira Afzal
  • fangsheng Min
  • Fayez Bajjali
  • Federica Bradascio
  • Florian Leitgeb
  • Floriane Cangemi
  • Franca Cassol
  • Francesca Romana Pantaleo
  • Francesco Visconti
  • Francisco Fenias Macucule
  • Francisco Salesa Greus
  • Francois BRUN
  • Gabriel Emery
  • Gabriele Panebianco
  • Gaetano Di Marco
  • Gaia Verna
  • Galin Jones
  • Garima Rajguru
  • Gaëtan Fichet de Clairfontaine
  • Georgios Vasilopoulos
  • Gernot Maier
  • Gerrit Roellinghoff
  • Gerrit Spengler
  • Giacomo D'Amico
  • Giada Peron
  • Giovanni Cozzolongo
  • Gonzalo Rodriguez Fernandez
  • Gowri A
  • Guillaume Grolleron
  • Guillem Martí-Devesa
  • Habib Ahammad Mondal
  • Hao Zhou
  • Heiko Salzmann
  • Helena Ren
  • Hemanth Bommireddy
  • Hiiko Katjaita
  • Hitesh Arora
  • Hubert Siejkowski
  • Hugo Ayala
  • Hüsne Dereli-Bégué
  • Ibrahim Torres
  • Ioana Ciuca
  • Ioulia Florou
  • Irene Burelli
  • Isabelle Grenier
  • J. Michael Burgess
  • Jakub Jurysek
  • Jann Aschersleben
  • Jason Fan
  • Jaume Zuriaga
  • Jean Damascene Mbarubucyeye
  • Jean-Philippe Lenain
  • Jeff Grube
  • Jeff Scargle
  • Jenni Jormanainen
  • Jim Linnemann
  • JImmy Shapopi
  • Jogesh Babu Gutti
  • Johannes Buchner
  • Johannes Schaefer
  • Jooyun Woo
  • Jorge Cotzomi Paleta
  • Jorge Otero-Santos
  • Joseph Omojola
  • Joshua Speagle
  • José Fernando Mandeur Díaz
  • José Luis Carrasco Huillca
  • Juan Bernete Medrano
  • Julian Kuhlmann
  • Julien Bolmont
  • Kafayat Bakarr
  • Kalyani Mehta
  • Kathrin Egberts
  • Kazuma Ishio
  • Keerthana Rajan L
  • Ken Kreul
  • Konstancja Satalecka
  • Laksha Pradip Das
  • Lars Mohrmann
  • Lenz Oswald
  • Leonard Pfeiffer
  • Leonardo Baroncelli
  • Leonardo Di Venere
  • Lorenzo Ducci
  • Louis Lyons
  • Luana Reis
  • Luca Giunti
  • Lucia Haerer
  • Luigi Tibaldo
  • Luiz Augusto Stuani Pereira
  • Lutendo Nyadzani
  • mahmoud emad
  • Manoneeta Chakraborty
  • Manuel Artero
  • Manuel Meyer
  • Mar Carretero-Castrillo
  • Mara Salvato
  • Marc Klinger
  • Marcos López-Moya
  • Margherita De Toma
  • Maria Carolina Kherlakian
  • Maria Martinez-Chicharro
  • Maria Myrto Pegioudi
  • Marie-Sophie Carrasco
  • Mario Riquelme
  • Markus Holler
  • Marta Felcini
  • María Láinez
  • Matt Roth
  • Matthias Fuessling
  • Matthieu CARRERE
  • Matías Sotomayor
  • Md Alam
  • Mfuphi Ntshatsha
  • Michele Doro
  • Michele Mastropietro
  • Miguel Araya
  • Miguel Sánchez-Conde
  • Mikael Kuusela
  • Miltiadis Michailidis
  • Monica Barnard
  • Muhammad Waqas
  • Namasiku Santy Simasiku
  • Naomi Tsuji
  • Nataly Ospina
  • Nhan Nguyen
  • Nicole Araneda
  • Nicolo' Parmiggiani
  • Nikita Khatiya
  • Noah Biederbeck
  • Olaf Behnke
  • Olaf Reimer
  • Oleg Kalekin
  • Olivier Tourmente
  • Orel Gueta
  • Pablo Peñil
  • Panebianco Laura
  • Paolo Cristarella Orestano
  • PARTHA SARATHI PAL
  • Parul Sharma
  • Patricia Rebello Teles
  • Patrik Milan Veres
  • Patrizia Romano
  • Pauline Chambery
  • Paweł Gliwny
  • Pedro Batista
  • Pietro Vischia
  • Pooja BHATTACHARJEE
  • Pooja Bhattacharjee
  • Pooja Sharma
  • Pouya M. Kouch
  • Pradeep Chandra
  • Pranjali Sharma
  • Pratik Majumdar
  • Priyadarshini Bangale
  • Priyatam Kumar Mahto
  • Qi Feng
  • Qixin Yu
  • Quentin Remy
  • Radha Gharapurkar
  • Rajeev Singh
  • Riccardo Di Tria
  • Rishank Diwan
  • Rishi Babu
  • Rishita Ray
  • Rita de Cassia Dos Anjos
  • Rizwan UL Hassan
  • Rowan Batzofin
  • Ruo-Yu Shang
  • Rupali H
  • Régis Terrier
  • Sajan Kumar
  • Salvatore De Gaetano
  • Samantha Wong
  • Sanchez David
  • Sara Algeri
  • Sara Buson
  • Sarah Recchia
  • Saverio Lombardi
  • Scott Joffre
  • Sebastian Panny
  • Serena Loporchio
  • Sheridan Lloyd
  • Shubham Bangalia
  • SHUBHAM BHARDWAJ
  • Shuichi Gunji
  • Shunsaku Nagasawa
  • Sigrid Shilunga
  • Silvia Crestan
  • Silvia Manconi
  • Simon Steinmassl
  • Simone Garrappa
  • Sivasish Paul
  • Solomon Appekey
  • Sonal Ramesh Patel
  • Sotiris Loucatos
  • Sriyasriti Acharya
  • Sruthiranjani Ravikularaman
  • Stamatios Ilias Stathopoulos
  • Stefan Wagner
  • Stefano Ciprini
  • Stefano Vercellone
  • Sunder Sahayanathan
  • Sushmita Agarwal
  • Susrestha Paul
  • T.S.Sachin Venkatesh
  • Tamador Aldowma
  • Tamador Khalil Mansoor Aldowma
  • Tamas Budavari
  • Thara Caba
  • Thierry Stolarczyk
  • Thomas Lohse
  • Thomas Vuillaume
  • Tim Lukas Holch
  • Tim Ruhe
  • Tim Unbehaun
  • Tina Wach
  • Tom Junk
  • Tomás Capistrán
  • Toni Saric
  • Tuğçe Kocabıyık
  • Tülün Ergin Gürcan
  • Ullrich Schwanke
  • Vardan Baghmanyan
  • Vasundhara Shaw
  • Victor Barbosa Martins
  • Vincent Marandon
  • Vincenzo Antonuccio-Delogu
  • Vir Dhar
  • Vo Hong Minh Phan
  • Walter Max-Moerbeck
  • Xueying Zheng
  • Yixiong Zhou
  • Yong Sheng
  • Yvonne Becherini
  • Ömer Faruk ÇOBAN
    • Pre-School on Statistical methods
      • 1
        Statistics 101: A very fast introduction I

        Probability and Bayes theorem, Frequentist and Bayesian statistics, likelihood
        function, parameter estimation and properties of estimators, maximum likelihood
        estimators (MLE), information inequality, asymptotic properties of MLE,
        variance of MLE

        Speaker: Glen Cowan
      • 2
        Statistics 101: A very fast introduction II

        Frequentist hypothesis tests, significance level and power of a test, Neyman-Pearson lemma/likelihood ratio, goodness of fit, p values and significances, confidence interval from a test, coverage, confidence intervals and selected problems (e.g. limits near the boundary of the parameter space), Wilk's theorem and confidence regions

        Speaker: Glen Cowan
      • 3:40 PM
        Coffee break
      • 3
        Selected applications of 'Statistics 101' in HE/VHE gamma-ray astronomy

        Error propagation, combination of stat+syst errors, profile likelihood, inter-experiment combination of likelihoods, trial factors, binned likelihood and applications in gamma-ray astronomy (Poisson Maximum Likelihood Estimation, On-Off Likelihood statistics)

        Speaker: Ullrich Schwanke
      • 4
        Introduction to Machine Learning in astroparticle physics

        This very short introduction will summarize basic machine learning concepts and introduce and discuss a few feature selection and learning algorithms. The selected algorithms include: Naive Bayes, Nearest Neighbour Methods, Decicison Trees, Ensemble Methods and Neural Networks. Furthermore, the talk will address the selection of appropriate input variables as well as possibilities to exclude badly simulated observables.

        Speaker: Tim Ruhe
    • Statistics Session
      • 17
        Session introduction
        Speaker: Ullrich Schwanke
      • 18
        Astrostatistics: Overview and highlights
        Speaker: Eric Feigelson
      • 19
        Chi-square, K-S, and bootstrap: Fitting astrophysical models to data

        Complicated models from astrophysical theory are often fit to observational data. There are several issues with the classical procedures used in astronomy literature. First, `chi-square minimization' is commonly used for fitting functions often disregard mathematical assumptions. Second, the Kolmogorov-Smirnov (K-S) test for goodness-of-fit is misused in astronomy when the model parameters are estimated from the dataset under study. Third, the KS is inefficient at detecting deviations between the data and model at the tails of the distribution. Fourth, the K-S test cannot justifiably be applied to multivariate data as KS is no longer distribution-free. Recent developments of bootstrap resampling method, a simple Monte Carlo procedure on data, will be described, to address these issues.

        Speaker: Jogesh Babu
      • 20
        Discussion
      • 3:40 PM
        Coffee break
      • 21
        Overview of Bayesian methods for multiwavelength gamma-ray astronomy

        Bayesian data analysis (BDA) gets its name from Bayes's theorem, stating that posterior probabilities for hypotheses are proportional to the product of their prior probabilities and likelihoods (predictive probabilities for the observed data based on each hypothesis). It's tempting to view the Bayesian approach as merely using priors to "modulate" the familiar frequentist maximum likelihood approach. But BDA uses all of probability theory, not just Bayes's theorem. In particular, many Bayesian calculations use the law of total probability to compute probabilities for composite hypotheses (e.g., hypotheses with uncertain parameters). These computations average ("marginalize") the likelihood function, rather than maximize it. Many of the key capabilities of Bayesian methods follow from this key distinction—performing
        computations that integrate rather than optimize over parameter space. I will highlight the role of marginalization in a variety of BDA methods relevant to multiwavelength gamma-ray astronomy: counterpart searches (cross-identification), accounting for systematics such as uncertain background rates, period searches using time-tagged event data, and population modeling accounting for measurement errors and selection effects in a hierarchical Bayesian framework.

        Speaker: Tom Loredo
      • 22
        Discussion
      • 23
        Time Series Analysis In the Dynamic Universe
        Speaker: Jeff Scargle
      • 24
        Discussion
      • 25
        Closing remarks
        Speaker: Ullrich Schwanke
    • Survey Session: Statistical methods for MWL counterpart identification
      • 26
        Session introduction
        Speaker: Ullrich Schwanke
      • 27
        Counterpart identification: Overview
        Speaker: Tamas Budavari
      • 28
        Discussion
      • 29
        VHE gamma-ray surveys with CTA

        The Cherenkov Telescope Array (CTA) will be the first astronomical observatory fully covering the gamma-ray sky in an energy range from 20 GeV up to 300 TeV. The observatory will be composed of two arrays of tens of telescopes located in La Palma, Spain, and Paranal, Chile.

        Among the Key Science Projects proposed by the CTA Consortium, Galactic and extragalactic surveys will be conducted during the first years of operation. With an unprecedented sensitivity and improved angular resolution, CTA surveys promise the discovery of several hundred of new gamma-ray sources, but the challenges coming along with the analyses of these data will also scale up. We will focus on the challenges of source variability, extended sources modeling, source confusion, source association with multi-wavelength catalogues, classification in source populations, and sources contamination due to the systematic errors in the modeling of instrumental and astrophysical backgrounds.

        Speakers: Jean-Philippe Lenain , Quentin Remy
      • 30
        Discussion
      • 4:05 PM
        Coffee break
      • 31
        Identifying correct counterparts to high-energy sources by "multiwavelength educated guesses" imbibed in a Bayesian statistic environment

        The identification of the counterparts to sources detected by
        instruments with large positional uncertainties can not be done using match in coordinates, due to the very high number density of the ancillary source catalogs.
        In addition, given that now the entire sky is literally covered by a plethora of multiwavelength surveys, the search for the counterparts by using a single band at a time is outdated. Instead, the entire SED for every single source in the sky can be created and used for discriminating the actual emitter from the field population.
        Finally, at least with respect to X-ray observations, we have more than 20 years of XMM and Chandra detection with a secure counterpart that can be used for creating a training sample to educate our guess.
        This is the basis of NWAY, a cross-matching code based Bayesian statistics that works with arbitrarily many catalogs, can handle varying positional errors, can incorporate additional prior information (the educated guesses and works accurately and fast in small areas and all-sky catalogues. In my talk, I will present how NWAY is now routinely used in the determination of the counterparts to Xray sources detected by e.g, ROSAT, XMMSlew, NUSTAR, and eROSITA. In particular, I will show how the prior (based on photometry, colors, parallax, and SNR of the detection) was built for eROSITA using Random Forest and tested on a validation sample providing 96% completeness and purity. The final goal is to discuss with the audience how a similar approach could be built for CTA.

        Speaker: Mara Salvato
      • 32
        Discussion
      • 33
        Radio surveys
        Speaker: Beatriz Mingo
      • 34
        Discussion
      • 35
        Closing remarks
        Speaker: Ullrich Schwanke