*This jupyter notebook is part of a collection of notebooks on various topics discussed during the Time Domain Astrophysics course delivered by Stefano Covino at the [Università dell'Insubria](https://www.uninsubria.eu/) in Como (Italy). Please direct questions and suggestions to [stefano.covino@inaf.it](mailto:stefano.covino@inaf.it).*
7. Time-domain analysis and auto-regressive processe
8. Irregular sampling, Lomb-Scargle periodograms
9. Case studies: AGN variability
10. Advanced topics: non-parametric analysis
11. Matching filters
12. Case study: LIGO/Virgo gravitational wave signals
13. Data exploration
14. Case study: SETI data analysis
15. Big-data, machine learning and “intelligent” systems for time-series analysis
16. Case studies: spatial variability (CMB, large scale structure)
17. Final topics: forecasting
> In reality these are just topics that can be covered. We can stress different aspects depending on the interests of the *students*.
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## Time-Series are ubiquitous
***
- Anytime we have a measurement repetated multiple times we have a time-series.
- As a matter of fact, a time-series does not need to have "time" as index!
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## Temptative schedule (don’t trust it too much…)
***
1. 26/2 - Introduction
2. 27/2 - Statistics reminder - part I
3. 5/3 - Statistics reminder - part II
4. 6/3 - Spectral analysis - part I
5. 12/3 - Spectral analysis - part II
6. 13/3 - Science cases: Sunspots Number - X-ray Binaries
7. 19/3 - Irregularly sampled time series
8. 20/3 - Science Cases - Variable Stars
9. 2/4 - Time domain analysis - part I
10. 9/4 - Guest lecture - X-ray pulsators
11. 10/4 - Time domain analysis - part II
12. 16/4 - Science Cases - AGN and blazars
13.30/4 - Wavelet analysis
14.7/5 - Guest lecture - Exoplanets
15.8/4 - Time of arrival analysis
16.14/5 - Non-parametric methods
17. 15/5 - Gaussian processes
18.21/5 - Science case: GRBs
19. 22/5 - Astrostatistics final considerations
13.17/4 - Wavelet analysis
14.30/4 - Time of arrival analysis
15.7/5 - Guest lecture - Exoplanets
16.8/4 - Non-parametric methods
17. 14/5 - Gaussian processes
18. 15/5 - Science case: GRBs
19. 21/5 - Astrostatistics final considerations
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## How is the course managed?
***
### Frontal lectures
- These are the traditional university lectures.
- Although this increases the organizational complexity substantially, I am availbale to stream and record my lectures, if needed.
- There are contraindications. As a matter of fact, this is one of few cases where a remote access is not even close as effective as being in presence.
### Real research life examples…
- Scientists working in the field will deliver "didactic lectures", allowing one to see most of ideas deveooped during the course applied in a real research environment.
### (Optional) papers to deepen our knowledge…
- Most of the topics discussd during the course can be investigated thoroughly and papers from astrophysical (mainly) literature are presented for particularly concerned readers.
### Question time
- The course is divided in several main sections. At the end of each of them, some time will be devoted to open discussions and questions.
### Lectures from specialists in the field
- Together with regular lectures, a few specialists in the field, i.e. scientist carrying out researches by time-domain tools and techniques, are invited to describe their works.
### Language
- According to university guidelines, lectures will be delivered in English. Of course, a fair evaluation of the context might ask some flexibility.
### Statistical framework
- During this course we are going to work in a Bayesian framework.
- Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events.
- Nevertheless, we are not dogmatic and mentions or applications based on familiar "frequentist" approaches are preseneted and discussed, when we deem it opportune.
### Programming languages
- Most of the examples we are going to analyze during the course are based on some sort of computer analysis.
-`Python` is *de-facto* the standard language in data science.
- Yet, while this language is definitely truly amazing, well designed and worth mastering, for the specific needs of scientific computing there are alternatives of growing popularity.
- We threfore provide examples mainly with `Julia`, and encourage the students to get some confidence with this programming language too.
- Notebooks are written by the [markdown language](https://www.markdownguide.org/basic-syntax/), a simple language integrating features of the HTML and latex languages.
- A remarkable introducti0n to the `julia` language for a scientist is available online [here](https://juliadatascience.io/).
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## Warning! The course is not only for astrophysicists!
- It is indeed part of the set of courses for future astrophysicists. Nevertheles, almost nothing we are going to discuss is truly only for astrophysics. In reality, several applications and ideas are taken from other fields, i.e. economics, social sciences, climatology, etc.
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## Final assessment
- The final examination is an oral one.
-*Students* must interact with the teacher in advance of the examination and a science case obtained by the modern literature will be selected.
- The *student* will be asked to properly describe the main formal aspects of the study and discuss critically the reliability and limits of the presented results.
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## Gitlab repository
- Slides, notebooks, papers, etc. are available on [gitlab](https://www.ict.inaf.it/gitlab/stefano.covino/TimeDomainAstrophysics.git)
- Check the repository frequently since is (rather often) updated during the course.
- The course is based on published scientific papers distributed by the teacher before any main topic is addressed.
- Science cases are based on actual scientific papers as well.
- Slides prepared by the teacher will also be distributed.
- A general introductory text to time series analysis as: [“Introduction to Time Series and Forecasting”, by P.J. Brockwell and R.A Davis](https://link.springer.com/book/10.1007/978-3-319-29854-2) might be useful. However, any other analogous text easily obtainable by the student will be fine as well.
- Two textbooks more strictly related to the topics discussed during the course mainly, but not only, for astrophysical applications are:
-[“Modern Statistical Methods for Astronomy”, by E.D. Feigelson and G.J. Babu](https://www.cambridge.org/core/books/modern-statistical-methods-for-astronomy/941AE392A553D68DD7B02491BB66DDEC)
-[“Statistics, data Mining and Machine Learning in Astronomy”, by Ivezić et al.](https://press.princeton.edu/books/hardcover/9780691198309/statistics-data-mining-and-machine-learning-in-astronomy)
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## Further Material
Papers for examining more closely some of the discussed topics.
-[Voughan et al. (2013) - "Random Time Series in Astronomy"](https://royalsocietypublishing.org/doi/10.1098/rsta.2011.0549)
-[Storopoli et al. (2021) - "Julia Data Science"](https://juliadatascience.io/)
This notebook is provided as [Open Educational Resource](https://en.wikipedia.org/wiki/Open_educational_resources). Feel free to use the notebook for your own purposes. The text is licensed under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), the code of the examples, unless obtained from other properly quoted sources, under the [MIT license](https://opensource.org/licenses/MIT). Please attribute the work as follows: *Stefano Covino, Time Domain Astrophysics - Lecture notes featuring computational examples, 2025*.
This is a repository with material (notebooks, papers, etc.) for the **Time Domain Astrophysics** course delivered at the *Università dell'Insubria* by Stefano Covino.
