University of Torino

Graduate Program in Physics

Course proposal (2019-2020)


Title 01-  Non perturbative aspects of classical and quantum field theory 
Prof. Igor Pesando, pesando@to.infn.it
CFU 5
Period First part: 5,6, 12,13, 19,20, 26,27 /11/2019, h 2pm-4pm, Sala Fubini
Second part: 2020, dates to be agreed

Prerequisites
Programme First part (12 h)

1) Solitons in scalar theories in 2D
2) Monopoles in gauge theories in 4D
3) Instantons in QM and various dimensions


Second part (8 h)

1) Anomalies in various dimensions
2) IR and UV aspects of anomalies
3) 't Hooft matching rule
4) The U(1)A problem
Note(s) Students who are willing to attend this course are **REQUESTED** to register by sending an email to Prof.

Pesando (ipesando@to.infn.it)

 


Title 02-Introduction to large-N limit
Prof. Marco Panero, panero@to.infn.it
CFU 5, 20 hrs
Period 17-28 February 2020, Sala Fubini
Prerequisites
Programme 1 - Introduction
2 - The large-N limit in O(N) vector models
3 - QCD with many colors: The 't Hooft limit and its phenomenological
implications
4 - The role of the large-N limit in the gauge/gravity correspondence
Note(s) Students who are willing to attend this course are **REQUESTED** to register by sending an email to Prof. Panero (panero@to.infn.it).

 

 


Title 03-Many-body techniques for nuclear theory
Prof. Andrea Beraudo, Marzia Nardi, Vittorio Soma' 
CFU 5, 20 hrs
Period April 16-29, 2020
Pre-requisites
Programme First part (10h, Beraudo, Nardi)

Introduction to the physics of quark-gluon plasma

-Symmetries and Thermodynamics of QCD
-Transport Theory
-Relativistic Hydrodynamics
-Phenomenology of heavy-ion Collisions

Second part (10h, Soma')

Low energy nuclear theory

 - Nuclear Hamiltonian
 - Effective Field Theory
 - ab initio methods
 - applications: double beta decay, neutrino-nucleus scattering, neutron stars
Note Students who are willing to attend this course are **REQUESTED** to register by sending an email to Prof. Beraudo (beraudo@to.infn.it) and Prof. Nardi (nardi@to.infn.it).

  


Title 04-Dark Matter and Neutrino physics
Prof. Carlo Giunti and Marco Taoso
CFU 5
Period First part: December 2-6, 2019, 3pm-5pm
Second part: February 3-7, 2020
Pre-requisites
Programme Programme:

Neutrino Physics (10h, C. Giunti)

- Theory of neutrino masses and mixing
- Theory of neutrino oscillations
- Overview of neutrino phenomenology
- Neutrinos in cosmology

Dark Matter (10h, M. Taoso)

- Evidences for dark matter
- Production mechanisms in the Early Universe
- Indirect detection: photons, charged cosmic-rays, neutrinos
- Direct detection
- Collider searches
- Axion DM
- Primordial black holes

Note Students who are willing to attend this course are **REQUESTED** to register by sending an email to Prof. Giunti (giunti@to.infn.it) .


Title 05-  Effective field theory techniques for New Physics searches
Prof. Martin Jung, martin.jung@unito.it
CFU 5, 20 hrs
Period April/May 2020
Prerequisites
Programme
Effective field theories (EFTs) constitute an essential technique in physics, allowing for the systematic separation of largely different scales in a problem, i.e. for exploiting hierarchies.
Such hierarchies occur everywhere in physics; in the Standard Model (SM) examples are the hierarchy between the masses of the massive gauge bosons and the top quark compared to those of all other fermions, or the mass of the bottom quark compared to typical hadronic scales. Importantly, the absence of indications for new states beyond the SM ones indicates that such states might be very heavy. As a consequence, their impact on low-energy observables can be treated in an EFT framework; precision measurements at much lower energies can then be used to constrain New Physics models strongly, even if the corresponding states cannot be produced at existing colliders.

The plan of the course is to introduce EFT techniques in general, discussing their applicability and restrictions. The developed methods are then applied to specific classes of observables relevant to the discourse of NP, in order to explicitly demonstrate how constraints on NP scenarios are obtained.


Note(s) Students who are willing to attend this course are **REQUESTED** to register by sending an email to Prof. Jung (martin.jung@unito.it)


Title 06-Calorimetry in particle physics experiments
Prof. R. Arcidiacono, arcidiacono@to.infn.it
CFU 4
Period January  2020
Prerequisites  
Goals
Programme

The physics of calorimetry
Detector response, energy resolution and position measurement
Calorimeter design principles
Front-end and trigger readout electronics
Electromagnetic calorimeters
Hadronics calorimeters
Calibration techniques
Some examples

NOTES Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher


Title 07-Experiment design in particle physics
Prof. Linda Finco, linda.finco@cern.ch
CFU 3
Period Spring 2020
Prerequisites Basic knowledge of particle detectors, interactions of particles and radiation with matter, physics at colliders
Goals The student will learn in an interactive way how particle physics experiments are designed, according to the processes they have to measure and the precision they want to achieve.
Programme
  • Select a physical process:
  • Why this physical process is of interest?
  • Previous measurements – if any – and their precision
  • Identify which type of accelerator machine is best for producing it
  • Analyze signal properties and topology of the final states
  • Identify the possible background contributions
  • Design an experimental apparatus to measure the process under study:
    • Analyze what kind of detectors are needed (vertex detectors, caloremeters, muon chambers...)
  • Choose their characteristics according to the precision we want to achieve 
  • Develop a strategy to select signal events (trigger and analysis procedure)
  • Identify the main systematic uncertainties due to detector effects
  • Determine the needed statistics
Bibliography
Notes Students who are willing to attend this course are **REQUESTED** to register by sending an email to  the teacher



Title 08-Data Analysis Techniques
Prof. Livio Bianchi
CFU 6
Period mid November 2019-mid December 2019
Prerequisites Basics on statistics and probability theory
Basic programming skills in c/c++
Goals  
Programme Reminder of basic probability theory
Monte Carlo methods (basic)
Statistical methods for:
- Parameter estimation (confidence intervals)
- Hypothesis testing (general, goodness-of-fit)
Bibliography See last year's course webpage
Notes Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher


Title 09-Hands-on Fitting and Statistical Tools for Data Analysis
Prof.

Giacomo Ortona (g.ortona@cern.ch)

CFU 4
Period February 10-March 13 2020
Prerequisites
Goals
Programme

The class will have an exercise oriented approach, with quick reminders of the statistical theory and a large fraction of time dedicated to practical examples.

Fitting Tools
Usage of the RooFit library:
Signal and background modelling, fitting and plotting
Treatment of extended Fits, Conditional Probability Density Functions, Toy Monte-Carlo generation

Statistics Tools
Usage of the RooStats library: Hypothesis testing
Determination of Upper Limits
Determination of confidence intervals in likelihood ratio and Feldman-Cousins approaches
Determination of probability intervals in Bayesian approaches
Bayesian numerical calculators vs Markov-Chains MC approach

Notes
- Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher



Title 10-  Big Data Science and Machine Learning
Prof. F. Legger, federica.legger@to.infn.it
CFU 4
Period  10 hours (theory) + 6 hours (hands-on), March 2020
Prerequisites  Basic knowledge of python





Goal

Data science is one of the fastest growing fields of information technology, with wide applications in key sectors such as research, industry, public administration. The course will cover the definition of big data and the basic techniques to store, handle and process them. Machine Learning (ML) and Deep Learning (DL) algorithms will be briefly introduced. We will focus on the technical implementation of different ML algorithms, focusing on the parallelisation aspects and the deployment on distributed resources  and different architectures (CPUs, FPGAs, GPUs). A basic introduction to the current computer architecture will be given, with a focus on parallel computing paradigms aimed at the exploitation of the full potential of parallel architectures. An overview of the fundamental OpenMP and MPI coding patterns is covered during hands-on sessions.

Programme

- Introduction to big data science
- The big data pipeline: state-of-the-art tools and technologies
- ML and DL methods: supervised and unsupervised training,   neural network models
- Introduction to computer architecture and parallel computing patterns
- Initiation to OpenMP and MPI
- Parallelisation of ML algorithms on distributed resources
- Beyond CPUs: ML applications on distributed architectures, GPUs, FPGAs


Bibliography

Chen, M., Mao, S. & Liu, Y. Mobile Netw Appl (2014) 19: 171. https://doi.org/10.1007/s11036-013-0489-0


Yao, Yuanshun & Xiao, Zhujun & Wang, Bolun & Viswanath, Bimal & Zheng, Haitao & Y. Zhao, Ben. (2017). Complexity vs. performance: empirical analysis of machine learning as a service. 384-397. 10.1145/3131365.3131372

NOTES Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher


Title 11-Introduction to Parallel Programming with MPI 
Prof. A. Mignone, andrea.mignone@to.infn.it
CFU 3, 12 hrs
Period  15 Feb - 15 Mar 2020
Prerequisites Good knowledge of Unix or Linux-based operative systems,
C or C++ programming basics (for loops, conditionals,          I/O).
Goal

The course intends to deliver some basic knowledge of the
Message-Passing-Interface (MPI) library for distributed memory parallel computations.

Programme

We will cover basic as well as intermediate-level construct such as basic send / receive communications, global reduction operations, blocking and collective calls, MPI datatypes

Bibliography


NOTES

Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher



Title 12- Statistical learning
Prof. Raffaele D'Abrusco (Center for Astrophysics, Harvard & Smithsonian)
CFU 4
Period  27.04.2020 - 08.05.2020
Prerequisites  
Goal
Programme

The class will last 16 hours and will be split in three modules covering different but not independent topics. The first section (6 hours) will discuss statistical learning basics and specific methods for the exploration of complex, high-dimensional and massive datasets. The second section (5 hours) will describe the general properties of supervised methodologies, describe three specific techniques and provide a general introduction to validation techniques needed to assess the performances of supervised methods.
The third and final section (5 hours) will be dedicated to the
discussion of the unsupervised approaches and will introduce the most used classes of clustering methods.

All lectures will include “hands-on” examples that employ the
statistics-oriented and versatile R programming language.

The program is as follow:

Basics of statistical learning
Supervised vs Unsupervised methods
Exploration
Multivariate analysis
Dimensionality reduction
Supervised methods
Neural Networks
Support Vector Machines
Decision Trees
Validation techniques
Unsupervised methods
Clustering
K-means
Agglomerative clustering

Bibliography


NOTES

Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher



Title 13- Data science applied to Astrophysical problems
Prof. Andrea Tramacere (University of Geneva) , andrea.tramacere@unige.ch
CFU 4
Period  18.05.2020 - 29.05.2020 
Prerequisites  
Programme

This class is divided three main blocks, with a duration of roughly 6 hours for each of the  first two, and 4 hours  for the last one.
A first  module  will deal with scientific coding in Python, with a focus on numerical and statistical algorithms, and I will introduce the students to the most popular and useful frameworks. A second module  will deal with some of the most popular methods in statistics and machine learning, ranging from supervised and unsupervised methods, to bayesian
statistics and Monte Carlo methods. A third module will be devoted to the application of these methods to astrophysical/cosmological problems.
The first and second module will be actually merged, since I plan to give a close connection between the description of an algorithm and its actual implementation, for this reason, most of the lessons will be integrated by jupyter notebooks with a dedicated git repository.


#)Python and frameworks for data science

*) introduction to object-oriented implementation  of numerical methods in Python
*) Cython/C wrapping and code optimization
*) numpy/scipy/Pandas
*) serialization (json/pickle)
*) scikit-learn
*) scikit-image
*) TensorFlow/keras
*) how to build and distribute a python package
*) documentation with sphinx
*) Jupyter notebooks
*) introduction go GIT and cloud technologies


#)Statistics and Machine Learning

Unsupervised learning
*) Kernel Density Estimation
*) Principal component Analysis
*) Clustering:
-) Spectral clustering
-) DBSCAN
-) DENCLUE
-) OPTICS
-) GAUSSIAN MIXTURES

Supervised learning
*) Nearest Neighbors

*) Decision Trees (classification/regression)

*) Ensemble methods (classification/regression)
-) Forests of randomized trees
-) AdaBoost
-) Gradient Tree Boosting
-) Linear and Quadratic Discriminant Analysis

Model selection and evaluation
*)Cross-validation
*)hyper-parameters tuning
*)regularization
*)validation curves

Dataset transformations
*) feature extraction
*) scaling/dim. reduction/sampling


Implementation of Machine learning pipelines with scikit-learn

MonteCarlo mehtods
*) sampling of distribution/bootstrap
*) MonteCarlo Markov Chains


#)Application to astrophysical/cosmological problems

*) Density-based clustering applied to the detection and deblending of astrophysical sources in gamma-ray data
*) Density-based clustering applied to the detection and deblending of astrophysical sources in optical images
*) Application of supervised Ensamble regression methods  to photometric redshift estimation
*) Application of supervised Ensamble classification methods  to galaxy shape identification
*) Application of MonteCarlo Markov Chains to the fit of data with numerical models

Bibliography


NOTES

Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher.

please, fill the form
https://forms.gle/GPbZ2y7HjJ54McqKA


Title 14. Introduction to relativistic theory of cosmological perturbations
Prof. Stefano Camera,
CFU 3
Period 12 hours, May 2020
Prerequisites
Programme 0. The concordance cosmological model in a nutshell.
1. Basic notions of general relativity in an expanding universe.
2. Perturbations in cosmology.
2.a. Newtonian perturbation theory.
3. Gauge transformations and gauge-invariant variables.
4. Evolution of perturbations.
5. Structure formation.
(6. The power spectrum of galaxy number counts.)
Bibliography * Tsagas, Challinor & Maartens, "Relativistic cosmology and large-scale structure", Phys. Rept. 465, 61 (2008)
* Malik & Wands, "Cosmological perturbations", Phys. Rept. 475, 1 (2009)
* Camera et al., "The theory of relativistic cosmological observables", Phys. Rept. (2011, in prep.)
Notes

Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher




Title 15-Search and characterization for extrasolar planets
Prof. Alessandro Sozzetti, sozzetti@oato.inaf.it
CFU 3
Period 12 hrs, March 2020
Prerequisites
Programme -Elements of theory: planetary formation, internal structure and atmosphere, dynamic evolution;
- Detection techniques, instrument limitations and astrophysics;
- Observation of extrasolar planetary systems: statistical, structural and environmental properties
- Observation of extrasolar planetary systems: the next 15 years.
Notes

Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher





Title 16-Chemo-dynamical evolution of the Milky Way
Prof. Alessandro Spagna( spagna@oato.inaf.it)
CFU 2
Period  8 hrs,  December 2019-January 2020
Prerequisites  Fundamentals of Astronomy and Astrophysics
Programme

Structure, kinematics, and chemical properties of the Galactic stellar populations (disks, bulge, halo)
Non axi-symmetric components: bar, spiral arms, flare, warp
The hierarchical CDM galactic formation scenario
Elements of Galactic dynamics and cosmological simulations of Milky Way-like disk galaxies
Wide field stellar surveys (Gaia, RAVE, APOGEE, GES)
Local cosmology: chemo-dynamical signatures of the Galactic formation processes

Bibliography
Binney & Merrifield, Galactic Astronomy

Notes

Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher




Title 17-  Quantum communication
Prof.  Ivo Degiovanni
i.degiovanni@inrim.it
CFU 4
Period February-March 2020
Goals  The most peculiar characteristics of quantum mechanics are the existence of indivisible quanta and entangled systems. Both of these are the roots of Quantum Communication which could very well be the first engineered application of quantum physics at the individual quantum level. In particular Quantum cryptography has great potential to become the key technology for securing confidentiality and privacy of communication in the future ICT world.
Here the fundamentals of quantum communication are introduced. Main applications with experimental implementations are presented. Experimental results and technological challenges are discussed.
Programme a)    Introduction to quantum information
The qubit concept
Qubit practical realisations
No-cloning theorem
Quantum state tomography

b) Quantum Cryptography with single photons
      Quantum key distribution
      Experimental implementations
      Von Neumann Entropy vs. Shannon Entropy
      Eavesdropping strategy and security criteria

c) Quantum entanglement
      Entangled states and their properties
      Practical realisations
      Bell’s inequality

d) Quantum Cryptography by entangled states
Protocols
Experimental implementations

e) Quantum protocols
Teleportation of qubits
Teleportation of entanglement: entanglement swapping
Quantum dense coding
Experimental implementations of Bell’s state analysis

f) Generalized evolution of quantum systems
       Quantum operations
       Tomography of quantum operations
Notes

Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher



Title 18- Introduction to Turbulence 
Prof. Filippo De Lillo
CFU 3
Period February-March 2020
Programme     The Navier-Stokes equations
    The phenomenology of fluid turbulence.
    Statistical description of turbulence
    A.N. Kolmogorov’s 1941 theory. 
    Intermittency and the  multifractal formalism.
    Numerical simulations of the Navier-Stokes equations.
Bibliography U. Frisch, “Turbulence: the legacy of A.N. Kolmogorov”, Cambridge University Press (1995)
Notes Students who are willing to attend this course are ** REQUESTED ** to register by sending an email to the teacher




Title 19- Case Studies in the History of Physics
Prof. Matteo Leone
CFU 2
Period April 2020, 14-24
Prerequisites
Programme The course covers one of the main topics in the historiography of physics: the importance of going back to the primary sources (archival documents, original papers, correspondence, instruments and so on). The topic will be assessed through the analysis of selected historical case-studies:- Macedonio Melloni and the birth of infrared physics (1830-1850)
- “Rutherford’s experiment” on alpha particle scattering (1906-1913)
 - The collections of scientific instruments of historical interest: the Museum of Physics of the University of Turin and the SMA (University of Turin Museum System)

Bibliography
Notes Students who are willing to attend this course are **REQUESTED** to register by sending an email to the teacher








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