Courses

Contents

Courses#

Course 1: Stellar Astrophysics#

Alessandro Bressan (SISSA, Italy), 5 lectures#

Lecture 1) Stars and stellar populations.#

  • Magnitudes; Colors; Distances; Proper Motions;

  • Spectral classification; The Hertzsprung-Russell diagram; Star clusters;

  • Stellar ages and element abundances: archaeology of the Milky Way.

Lecture 2) The equations of stellar structure.#

  • Mass and momentum conservation. The Virial theorem.

  • Energy transport and energy conservation.

  • Numerical Methods: solution of the system of stellar structure equations.

Lecture 3) From the Pre Main Sequence to the Main Sequence.#

  • Cloud collapse, fragmentation and proto-star contraction.

  • Opacity of stellar matter.

  • Low temperature nuclear reactions.

  • Proton-Proton cycle and CNO cycle;

  • Mixing of elements.

  • Evolution in the HRD

Lecture 4) Post Main Sequence evolution of Low and Intermediate mass stars.#

  • Equation of state with electron degeneracy.

  • The Red Giant Branch. The Helium Flash. The Horizontal Branch.

  • Cepheids.

  • The Asymptotic Giant Branch.

  • White Dwarfs.

Lecture 5) Massive stars.#

  • The HRD of massive stars.

  • Stellar winds and stellar evolution with mass-loss.

  • Stellar Rotation.

  • Wolf-Rayet stars.

  • Advanced evolutionary phases, neutrino losses and pre-supernova nucleosynthesis.

  • Supernovae: electron-capture and core collapse SN; pair-instability SN; compact remnants.

  • Type Ia Supernovae

Course 2: Compact Objects#

Jorge A. Rueda Hernández (ICRA, Italy), 6 lectures#

Abstract:#

In the present era of multi-messenger astrophysics, the information on most extreme astrophysical sources arrives from several carriers at different energies/frequencies: photons in the electromagnetic spectrum (from the microwaves to the high-energy gamma-rays ~TeV), massive charged particles (cosmic rays up to ultra-high energies ~millions of PeV), neutrinos (from the MeV to PeV), gravitational waves (in the millihertz region from space-based instruments and the kHz region from ground-based ones). We can also include dark matter particles via their gravitational interaction with astrophysical systems. All this information allows us to understand the most extreme astrophysical sources in the universe with unprecedented accuracy and detail. The explanation of the nature of the most energetic and powerful astrophysical sources (both quiescent and transient), such as binary X-ray sources, pulsars, novae, kilonovae, supernovae, hypernovae, quasars, active galactic nuclei and gamma-ray bursts, involve the presence of compact objects: white dwarfs, neutron stars and black holes in different flavors; e.g. in a variety of masses, rotation rates and present as single systems or in binaries. The increasing quality and amount of data allow us to test our knowledge and put stringent constraints on the nature of the sources and the structure, matter content, and properties of their associated exterior spacetime. The successful and helpful extraction of the above information on compact objects from data requires that we know, with sufficient accuracy, how different physical ingredients change the properties of the objects. I focus here on compact objects’ physical and astrophysical aspects, emphasizing their role in multi-messenger astrophysics. Basic knowledge of atomic physics, nuclear physics, electromagnetism, statistical mechanics, classical field theory, Newtonian gravity, and General Relativity are needed to accomplish this task.

Lectures on Physical Aspects of Compact Objects:#

Lecture 1) White dwarfs (WDs): Equation of State (EOS) and Structure#

  • EOS at high densities and temperatures: basic concepts, zero-temperature, finite temperatures, and electromagnetic effects

  • Equations of equilibrium: Newtonian gravity and General Relativity

  • WD structure: equilibrium configurations (mass-density and mass-radius relation)

  • Gravitational collapse: the concept of critical mass

  • Rotating equilibrium configurations

  • Observational constraints on the WD mass-radius relation

Lecture 2) Neutron stars (NSs): EOS and Structure#

  • The Oppenheimer and Volkoff model

  • NS structure: equilibrium configurations (mass-density and mass-radius relation)

  • Strong interactions and modern models of an NS

  • NS critical mass

  • Rotating equilibrium configurations

  • Observational constraints on the NS mass-radius relation

Lecture 3) Black holes#

  • Classic black hole solutions

  • Particle motion around black holes

  • The black hole mass-energy formula

  • The relevance of black hole electrodynamics

Lectures on Astrophysical Aspects of Compact objects#

  • Electromagnetic radiation: pulsar mechanism, unipolar inductor

  • Accretion disks (including neutrino-cooling)

  • Gravitational waves: deformed rotating stars and binaries

  • Compact objects and dark matter

  • Binary mergers: NS-NS, NS-WD, WD-WD and NS-BH mergers

  • Gamma-ray bursts: observations, theory, the association with supernovae, and the role of compact objects

Course 3: Galaxies#

Itziar Aretxaga (INAOE, México), 5 lectures#

An overview of galaxies in the nearby and distant universe, their constituents, scaling laws and evolution through cosmic time.

Lecture 1) Introduction to the galaxy zoo#

  • Galaxy types and classification schemes

  • General properties and structure

  • Standard stellar population indicators

  • Dark matter in galaxies

Lecture 2) Scaling relations#

  • Tully-Fisher

  • Dn-sigma

  • Fundamental planes

  • Main sequence of star formation and Starbursts

Lecture 3) Active Galactic Nuclei#

  • Classification of AGN

  • Multifrequency detection of nuclear activity

  • Energetics

  • Unification

  • Basic concepts of the standard model of AGN

  • Demographics of QSOs and BHs

  • Feedback

Lecture 4) Galaxies through cosmic time#

  • Surveys

  • Star formation history

  • Gas depletion history

  • Simple models of galaxy formation and evolution

Lecture 5) LSS and Galaxy clusters#

  • Local group and nearby structures

  • Search for clusters

  • Galaxies in clusters

  • Cluster mass estimates

  • Cosmological probes of clusters

Bibliography:#

  • Extragalactic Astronomy and Cosmology (2015), Peter Schneider, Springer.

  • Mo, van den Bosch & White (2010). Galaxy Formation and Evolution, CUP.

  • Galaxies in the Universe, Sparke & Gallagger, (2007), Cambridge University Press

  • “An Introduction to Active Galactic Nuclei”, B.P.Peterson, 1997, CUP

  • “The Physics and Evolution of AGN”, H. Netzer, 2013, CUP

The papers can be found in ADS or arXiv lists

Course 4: Cosmology#

David Fonseca Mota (Univ. Oslo, Norway), 5 lectures#

This course explores key aspects of cosmology, including the development of cosmological models, the nature of the universe’s constituents, and its thermal history. It also examines observational evidence, theoretical foundations, and research frontiers in cosmology.

Lecture 1) The Observational Context#

  • Large-scale structure distribution in the universe

  • The Cosmological Principle

  • Hubble expansion and observational evidence

  • Dark matter and its observational signatures

  • Dark energy and its role in cosmic evolution

Lecture 2) The Cosmic Microwave Background Radiation (CMB)#

  • Discovery and significance of the CMB

  • Physical properties: temperature, isotropy, and anisotropy

  • Origin during recombination and its relation to the early universe

  • Angular power spectrum and its cosmological implications

  • CMB as a probe of:

    Density fluctuations in the early universe

    Parameters of the standard cosmological model

    Evidence for inflation and the Lambda-CDM model

Lecture 3) The Theory of Gravitation and Inflationary Cosmology#

  • Fundamental assumptions of General Relativity and the Einstein field equations

  • The Robertson-Walker metric: measuring distances, luminosities, and angular sizes

  • The Friedmann models of classical cosmology

  • Key puzzles: expansion, flatness, and the horizon problem

  • The inflationary scenario: solving cosmological puzzles

  • Emergence of the fluctuation spectrum from the inflationary epoch

Lecture 4) Particle Physics and Big-Bang Nucleosynthesis#

  • The Standard Model of Particle Physics and its relevance to cosmology

  • Thermal history of the universe

  • Synthesis of light elements during Big-Bang nucleosynthesis

  • Observational measurements of primordial light element abundances

Lecture 5) Research Topics in Cosmology#

  • Galaxies and clusters of galaxies as tools to probe:

    Dark energy and its properties

    Dark matter distribution and behavior

    Gravity beyond General Relativity

Course 5: Radio Astronomy#

Helga Dénes (Yachay Tech, Ecuador), 5 practicals#

This course will cover the basics of how radio telescopes work, how we process radio data to obtain continuum images and spectral line data cubes, and which sources produce radio emission. In addition, the course will contain practical sessions on how to analyse radio continuum and spectral line data with python tools.

Lecture 1) Radio astronomy fundamentals#

  • Radiation fundamentals

  • Detectors

  • Radio telescopes

Lecture 2)⁠ ⁠Data processing - from raw observations to products#

  • Software packages for radio astronomy

  • Calibration of radio data

  • Imaging of radio data

Lecture 3)⁠ ⁠Radio sources#

  • Interstellar medium

  • Galaxies

  • Pulsars

  • Black holes

Lecture 4) ⁠Radio continuum analysis with python#

  • Plotting an image with radio sources

  • Identifying source in a radio image

  • Measuring the flux of a radio source

  • Calculating the spectral index for radio sources

Lecture 5)⁠ ⁠Spectral line analysis of 3D radio data with python#

  • Handling 3D data cubes

  • Making moment maps

  • Plotting HI spectra

  • Calculating HI mass

Bibliography:#

Course 6: Python for Astrophysics#

Wladimir Banda-Barragán (Yachay Tech), 5 practicals#

Lecture 1) Python essentials for computational astrophysics#

  • Python notebooks and kernels

  • Pandas, Astropy and pyRAF

  • 1D data analysis

  • Astronomical image processing

  • Multi-dimensional data analysis

Lecture 2) Simulation data formats and visualisation#

  • Astrophysical gas simulations

  • VTK and HDF5 data formats

  • 2D and 3D visualisation

Lecture 3) Analysis of 3D meshed data of ISM simulations#

  • Interstellar medium simulations

  • 3D simulation data analysis

  • Loops and animations

Lecture 4) Shock finding algorithms and the py4shocks module#

  • Hydrodynamic shock theory

  • Magnetohydrodynamic shock theory

  • Velocity-jump methods for shock finding

Lecture 5) Tutorial on using py4shocks for characterising ISM shocks#

  • Python modules to find and characterise shock waves

  • Research applications of shock finding

Bibliography:#

  • Landau, Rubin, Computational physics : problem solving with python, 2015

  • Kong, Qingkai; Siauw, Timmy; Bayen, Alexandre, Python Programming And Numerical Methods: A Guide For Engineers And Scientists, 2020

Course 7: Optical Observational Astronomy#

Karín Menéndez-Delmestre (Valongo Obs., Brazil), 6 lectures#

In this course we aim to familiarize students with basic concepts related to astronomical instrumentation, observational strategies and the processing of astronomical data. After a brief review of fundamental concepts including celestial coordinate systems, the impact of the Earth’s atmosphere on ground-based observations and the different sources of noise, we will combine lectures and hands-on activities to delve into the following topics: planning astronomical observations, remote observing (pending formal arrangements with ISYA), reduction of imaging data (with brief discussion on how to handle spectroscopic data), signal characterization and basic photometric analysis of astronomical data. These topics will be addressed primarily in the context of optical observations.

Lecture 1) Basic Concepts in Observational Astronomy#

  • Telescopes

  • Coordinate systems

  • Image quality (point spread function)

  • Atmosphere transmission

  • Airmass

  • Seeing

Lecture 2) Signal and Sources of Noise#

  • Detectors

  • Poisson statistics

  • Shot noise

  • Sky

  • Read noise

  • Dark current

Lecture 3) Observing Strategies & Planning your observing night (Hands-on)#

Lecture 4) Basics of Data Reduction#

  • Bias, Flats, Darks

  • What, Why, When, How long and How many

Lecture 5) Data Reduction (Hands-on)#

  • Simple arithmetics!

  • Bringing in the computer tools

  • Using basic IRAF routines or Python

Lecture 6: Basic Aperture Photometry (Hands-on)#

Course 8: Virtual Observatory and the use of online databases#

Thiago S. Goncalves (Valongo Obs., Brazil), 5 lectures#

This is a hands-on class on the use of publicly available data to perform astronomical research. The course will handle data at different stages from different repositories, ranging from telescope science archives to value added catalogues, finishing with the use of theoretical data from simulations. Each lecture will comprise an individual science goal to be accomplished.

Lecture 1) Introduction to data acquisition and processing#

  • Basics of astronomical observations

  • Fundamentals of data reduction: bias, flats, sky subtraction, wavelength calibration

  • From data to physics: the production of value added catalogues

  • Navigating the universe: basic search tools and data exploration

  • Universe in a box: simulated data

Lecture 2) Imaging data — ESO Science Portal#

  • Navigating the ESO Science Portal

  • Raw data vs Processed data

  • Data access and manipulation

  • Example: Photometry

Lecture 3) Spectroscopic data — SDSS MaNGA Survey#

  • Navigating the spectroscopic dataset

  • Example: Measuring spectroscopic indices

Lecture 4) Value added catalogues — MaNGA Pipe3D#

  • Using physical measurements for science — pros and cons

  • Example: Galaxy evolution as seen by MaNGA

Lecture 5) Simulations and theoretical data — IllustrisTNG and Flathub#

  • How do simulations work?

  • Fundamentals of radiative transfer codes and the production of synthetic observational data

  • Types of theoretical data and comparison with the real universe

Bibliography:#

Astronomy Methods: A Physical Approach to Astronomical Observations (2003), Hale Bradt, Cambridge University Press.

Course 9: Interstellar Medium#

Laurence Sabin (UNAM, México), 5 lectures#

Lecture 1) Components of the ISM (Observations)#

  • Physical conditions (density, temperature, and pressure) - Hot gas

  • Warm gas (ionized and neutral)

  • Cold gas (atomic and molecular)

  • Dust

  • Magnetic fields and cosmic rays

Lecture 2) Interstellar Dust#

  • Composition (silicates, graphite, PAHs)

  • Physical properties (sizes, heating, cooling, and charge)

  • Formation and destruction

Lecture 3) Heating and Cooling Processes#

  • Molecular gas

  • Neutral gas (HI)

  • Photodissociation regions - Photoionized regions

  • Hot ionized regions

Lecture 4) Observational Diagnostics#

  • Emission and absorption lines

  • Radio observations of molecular lines

  • 21 cm HI line

  • Optical and UV absorption lines in neutral gas

  • Photoionized regions:

    • Recombination lines and collisional excitation lines

    • Free-free continuum in radio

    • Density and temperature diagnostics (optical and radio)

  • Hot gas:

    • UV

    • X-rays

  • Dust:

    • Extinction

    • Emission

  • Masers

  • Chemical composition

  • Kinematics

  • Photoionized regions (HII regions and planetary nebulae)

    • Strömgren sphere

    • Ionization structure

    • Energy balance

Lecture 5) Dynamics#

  • Shock waves

  • Jets and outflows - Disks

Requirements : Projector for classes Basic Bibliography :#

  • George B. Rybicki & Alan P. Lightman. Radiative processes in Astrophysics. John Wiley & Sons, 1985

  • J. E. Dyson & D. A. The Physics of the Interstellar Medium. Williams John Wiley & Sons, 1997.

  • Lyman Spitzer, Jr. Physical Processes in the Interstellar Medium. JohnWiley & Sons, 1998.

  • Donald E. Osterbrock & Gary J. Ferland. Astrophysics of Gaseous Nebulae and Active Galactic Nuclei. University Science Books, 2005.

  • Frank Shu, The Physics of Astrophysics, Vols. 1 y 2. University Science Books, 1991.

  • Bruce T.Draine. Physics of the Interstellar and Intergalactic Medium. Princeton Series in Astrophysics, 2010.

  • Michael A. Dopitay, Ralph S. Sutherland. Astrophysics of the Diffuse Universe. Astronomy and Astrophysics Library, 2003.

  • J. Lequeux. Interstellar Medium. Springer Berlin Heidelberg, 2005.

Course 10: Machine Learning for Astronomy#

Juan Rafael Martínez Galarza (AstroAI, CfA, Harvard & Smithsonian)#

This course will provide the students with a starting toolkit for the use of machine learning in astronomy, with an emphasis on state-of-the-art approaches of self-supervised learning, sequential analysis , and statistical inference. The goal is twofold: provide a general overview of traditional tools in machine learning in astronomy (e.g. convolutional neural networks for supervised image classification), while at the same time introducing the students to the latest developments in the field (e.g. self-supervised learning, transformers, simulation based inference). The course will include practical exercises in the form of Jupyter notebooks.

Lecture 1) Introduction to Machine Learning#

  • Types of machine learning: supervised, unsupervised, self supervised.

  • Introduction to neural networks: fully connected neural networks.

  • Convolutional neural networks.

  • Supervised learning for classification and regression

  • Lab: Classification of galaxy images with CNNs.

Lecture 2) Evaluation Metrics in Machine Learning#

  • Validation, cross-validation.

  • Metrics: accuracy, precision, recall

  • The ROC curve.

  • Data pre-processing: imbalanced datasets, missing data

  • Fine-tuning hyperparameters

  • Lab: Evaluating the galaxy image classifier.

Lecture 3) Unsupervised machine learning#

  • Clustering methods.

  • Dimensionality reduction.

  • Principal component analysis.

  • The t-SNE and UMAP methods.

  • The autoencoders as non-linear PCA

  • Lab: Clustering galaxy morphology/anomaly detection

Lecture 4) Machine learning for time-domain astrophysics#

  • Introduction to time domain astronomy

  • Recurrent neural networks.

  • Transformers.

  • Self-supervised learning

  • Lab: Light curve forecasting

Lecture 5) Statistical Inference#

  • Bayesian analysis of data.

  • Variational inference.

  • MCMC versus variational inference.

  • Approximate Bayesian Computation

  • Normalizing flows.

  • Lab: Simulation Based Inference

Bibliography#

  • Machine Learning techniques for Physics and Astronomy, Princeton University Press, Acquaviva, ISBN: 9780691203928

  • Statistics, Data Mining & Machine Learning in Astronomy, Princeton University Press, Ivezić, Connolly, VanderPlas, & Gray, ISBN: 9780691198309

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media, Géron, ISBN: 978109812

Course 11: Cultural astronomy#

Nicolás Vásquez (Escuela Politécnica Nacional, Ecuador)#

Lecture 1) Astronomy and society#

  • The sky in human history

  • Astronomy for development

  • Are Theories Just Cultural Constructs?

Lecture 2) Native ontologies in the Andes (J. Vásquez, Universidad San Francisco de Quito, Ecuador)#

  • Ontologies

  • Solar dancers

  • Should Asteroids Be Considered Heritage?

Contents