The “Chaire Georges Lemaître 2018” is taking place at
**IRMP** the week
of the 26th of November. **Prof. Roberto Trotta** will be giving a
serie of lectures from Monday to Friday on “Astrostatistics in
Action”.

The program consists in an Inaugural Lecture followed by a reception
(**Registration here**),
Monday at 16:15, CYCL01.

### If You Don’t need (Astro)Statistics, You Have Done the Wrong Experiment

*At the beginning of last century, the physicist and Nobel Prize
Winner Ernest Rutherford reportedly believed that “If your experiment
needs statistics, you ought to have done a better experiment”. If he
were alive today, he probably would not recognize the way cosmology
(the study of the Universe on its largest scale) has developed:
essentially all of the exciting discoveries in the last two decades
have relied on sophisticated statistical analyses of very large and
complex datasets. Today, advanced astrostatistical methods belong to
the toolbox of almost every cosmologist. In this talk I will give an
overview of how cosmologists have established a “cosmological
concordance model” that explains extraordinarily well very accurate
observations ranging from the relic radiation from the Big Bang to the
distribution of galaxies in the sky in the modern Universe. The
emerging picture of a cosmos remains puzzling: 95% of the Universe is
constituted of unknown components, dark matter and dark energy. Our
understanding of the Universe is – already today – limited by our
statistical and computational methods. I will discuss how
astrostatistics will meet the challenges posed by upcoming extremely
large data sets and thereby be instrumental in answering some of the
most fundamental questions about the physical reality of the cosmos.*

The 2 hours lectures start on Tuesday 10:00 am, room E-349, till
Friday. If you intend to follow one, or more, lectures, you **MUST**
register below.

### Lecture 1: How to Learn from Experience: Principle of Bayesian Inference

*The problem of inference from noisy and/or partial data is ubiquitous
in science. It is particularly acute in observational disciplines,
like astrophysics and cosmology, where we don’t have the luxury of
being able to control our experiments. I will introduce the
fundamental principles of Bayesian inference as a complete theory for
how to learn from experience, and contrast this approach with the more
traditional frequentist view of probability. I will also discuss
practical Bayesian methods and technology to determine posterior
distributions, including Markov Chain Monte Carlo and related sampling
schemes.*

### Lecture 2: Shaving Theories with Occam’s Razor: Bayesian Model Comparison

*Many of the questions in astroparticle physics, cosmology and
particle physics aim at establishing which theoretical model is the
best explanation for the available data. Classically, theories can
only be falsified by ruling them out (the Popperian view). I will
demonstrate how the Bayesian model comparison approach is more
general, enabling physicists to correctly reward models that make
precise predictions that are then verified by experiment or
observation. I will contrast this with the hypothesis testing
paradigm, and discuss consequences for our understanding of what a
“significant result” means. I will illustrate analytical and numerical
methods for the implementation of Bayesian model comparison with
examples from cosmology and particle physics.*

### Lecture 3: Bayesian Hierarchical Models and Applications to Supernova Type Ia Cosmology

*Thanks to large and accurate measurements obtained in the last 2 decades, and to
sophisticated statistical analyses, cosmologists have established a “cosmological
concordance model” that reproduces well observations ranging from the relic radiation
from the Big Bang to the distribution of galaxies in the sky in the modern Universe. I will
review the observational and theoretical underpinnings of this so-called “Lambda-CDM”
concordance model of cosmology, which strongly points to the existence of both dark
matter and dark energy.
I will then focus on recent advances in supernova type Ia cosmology. Supernovae type Ia are
a type of stellar explosion that can be used as standard candles to measure extragalactic
distances, and have been instrumental in determining the accelerated expansion of the
Universe – a smoking gun observation for the existence of dark energy. I will present recent
results and a novel, powerful (Bayesian) statistical framework for interpreting the data,
including cosmological parameter inference, selection effects and classification in the
absence of spectroscopic data. I will discuss upcoming challenges for the field as the
quantity and quality of upcoming data sets increases.*

### Lecture 4: Applications of Astrostatistics to Dark Matter Phenomenology and Beyond the Standard Model Theories

*The study of dark matter phenomenology and of underlying theoretical
models has advanced dramatically in recent years thanks to the
development of “global fits” an approach to combine all available
data in a statistically convergent fit of Beyond the Standard Model
theoretical parameters. This approach now includes sophisticated
methods to simultaneously use direct detection, indirect detection and
collider data to constrain the parameter space for dark matter, and
map this onto the underlying theory parameters, e.g. in
Supersymmetry. I will describe the “global fits” approach and its
ability to deliver quantitative inference both from a Bayesian and a
frequentist (profile likelihood) point of view. I will review current
results in the field and associated challenges.*