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PHYSTAT-Gamma 2022

Sep 27 – 30, 2022
Europe/Zurich timezone

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Contribution List

35 / 35
19. Statistics 101: A very fast introduction I
Glen Cowan
9/27/22, 2:00 PM

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
20. Statistics 101: A very fast introduction II
Glen Cowan
9/27/22, 2:50 PM

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
21. Selected applications of 'Statistics 101' in HE/VHE gamma-ray astronomy
Ullrich Schwanke
9/27/22, 3:55 PM

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)
22. Introduction to Machine Learning in astroparticle physics
Tim Ruhe
9/27/22, 4:30 PM

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...
13. Workshop Introduction
Ullrich Schwanke
9/28/22, 2:00 PM
1. Introduction: Making the sum greater than its parts
Joshua Speagle (University of Toronto)
9/28/22, 2:10 PM

Without a proper accounting of known and unknown systematics and uncertainties, combining information across multiple surveys, wavelengths, and detectors may be risky. Realizing the true potential of multi-messenger and panchromatic astrophysics requires getting data integration right.
33. Discussion
9/28/22, 2:30 PM
2. Statistical methods for VHE gamma-ray astronomy: Gammapy
Axel Donath , Regis Terrier
9/28/22, 2:35 PM
23. Discussion
9/28/22, 3:05 PM
3. Statistical methods for X-ray astronomy
Johannes Buchner
9/28/22, 3:35 PM
24. Discussion
9/28/22, 4:05 PM
4. Statistical methods for the combination of data: ThreeML
J Michael Burgees
9/28/22, 4:20 PM

As the multi-messenger era is now fully active, it is crucial that the community has a framework within which to analyze data from multiple messengers, wavelengths, and instruments in a statistically robust, common way. 3ML (https://threeml.readthedocs.io) provides an abstract, plugin-based data interface for instruments to combine analysis through each instrument's own unique likelihood....
25. Discussion
9/28/22, 4:50 PM
34. Selected problems from HAWC analyses
James Linnemann
9/28/22, 5:00 PM
35. Discussion
9/28/22, 5:30 PM

Minutes contain the references which were discussed during the session.
14. Closing remarks
Ullrich Schwanke
9/28/22, 5:55 PM
15. Session introduction
Ullrich Schwanke
9/29/22, 2:00 PM
9. Astrostatistics: Overview and highlights
Eric Feigelson
9/29/22, 2:05 PM
10. Chi-square, K-S, and bootstrap: Fitting astrophysical models to data
Jogesh Babu
9/29/22, 2:45 PM

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...
26. Discussion
9/29/22, 3:25 PM
11. Overview of Bayesian methods for multiwavelength gamma-ray astronomy
Tom Loredo
9/29/22, 4:00 PM

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...
27. Discussion
9/29/22, 4:40 PM
12. Time Series Analysis In the Dynamic Universe
Jeff Scargle
9/29/22, 4:55 PM
28. Discussion
9/29/22, 5:35 PM
16. Closing remarks
Ullrich Schwanke
9/29/22, 5:55 PM
17. Session introduction
Ullrich Schwanke
9/30/22, 2:30 PM
5. Counterpart identification: Overview
Tamas Budavari
9/30/22, 2:35 PM
29. Discussion
9/30/22, 3:05 PM
6. VHE gamma-ray surveys with CTA
Jean-Philippe Lenain , Quentin Remy
9/30/22, 3:20 PM

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...
30. Discussion
9/30/22, 3:50 PM
7. Identifying correct counterparts to high-energy sources by "multiwavelength educated guesses" imbibed in a Bayesian statistic environment
Mara Salvato
9/30/22, 4:20 PM

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...
31. Discussion
9/30/22, 4:50 PM
8. Radio surveys
Beatriz Mingo
9/30/22, 5:05 PM
32. Discussion
9/30/22, 5:35 PM
18. Closing remarks
Ullrich Schwanke
9/30/22, 5:50 PM
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