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**What is this?**

*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).*

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**This is a textual notebook**

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![Time Domain Astrophysics](Pics/TimeDomainBanner.jpg)

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# Introduction

***

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## Contacts

***

![Stefano](Pics/Stefano.png)

- Stefano Covino

- INAF / Brera Astronomical Observatory

- +39 02 72320475

- +39 3316748534 (if urgent…)

- Emails: [stefano.covino@inaf.it](mailto:stefano.covino@inaf.it)  - [stefano.covino@uninsubria.it](mailto:stefano.covino@uninsubria.it)

- Web: https://sites.google.com/a/inaf.it/stefano-s-site/

![Banner](Pics/Banner.png)

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## Main Goal of the course: Have fun!

***

|![data](Pics/data.jpg)|![regression](Pics/regression.jpg)|

|----------------------|----------------------------------|

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## Program (for 6 or 7 courses, roughly…)

***

1. Introduction to time series

2. Time (and spatial) variability in astrophysics

3. Fourier analysis and noise characterization

4. Case study: stellar variability

5. Case study: exo-planet transits

6. Case study: pulsars

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.

![CO_2 vs Temp](Pics/CO2T.png)

![CO_2](Pics/CO2.png)

![Neptune](Pics/Neptune.png)

- As a matter of fact, a time-series does not need to have "time" as index!

![PAMELA](Pics/PAMELA.png)

![Satellite](Pics/satellite.png)

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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 - part I

8. 20/3 - Irregularly sampled time series - part II

9. 26/3 - Science Cases - Variable Stars - AGN and blazars

10. 27/3 - Time domain analysis - part I

11. 2/4 - Time domain analysis - part II

12. 3/4 - Guest lecture - Spectral analysis in Cosmology

13. 9/4 - Guest lecture - X-ray pulsators

14. 10/4 - Time domain analysis - ARIMA models

15. 16/4 - Time domain analysis - Advanced  tools

16. 30/4 - Wavelet analysis

17. 7/5 - Guest lecture - Exoplanets

18. 8/5 - Time of arrival analysis

19. 14/5 - Non-parametric methods

20. 15/5 - Gaussian processes

21. 21/5 - Science case: GRBs

22. 22/5 - 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.

![Frontal Lectures](Pics/FrontalLectures.jpg)

### 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.

![Paperino astronomo](Pics/Paperino.jpg)

### (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.

![Author et al.](Pics/Papersetal.jpg)

### 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.

![Questions](Pics/Questions.gif)

### 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.

![Nilus](Pics/Nilus.jpg)

### Language

- According to university guidelines, lectures will be delivered in English. Of course, a fair evaluation of the context might ask some flexibility.

![Language](Pics/language.jpg)

### 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.

![Statistics](Pics/Bayesians.png)

### 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.

|![python](Pics/python.png)|![julia](Pics/julia.png)|

|--------------------------|------------------------|

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- 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.

![Physicists and astrophysicists](Pics/astrophysics.jpg)

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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.

|![gitlab](Pics/gitlab.jpg)|![course](Pics/gitlabcourse.png)|

|-------------------------|-------------------------------|

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## Relaxing time(-series...)

![Relax](Pics/relaxing.png)

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## Reference & Material

- 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)

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## Course Flow

<table>

  <tr>

    <td>Previous lecture</td>

    <td>Next lecture</td>

  </tr>

  <tr>

    <td><a href="../../Course.ipynb">Course Summary</a></td>

    <td><a href="../Lecture%20-%20Statistics%20Reminder/Lecture-StatisticsReminder.ipynb">Statistics Reminder</a></td>

  </tr>

 </table>

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**Copyright**

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*.

"""

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md"""

- A remarkable introducti0n to the `julia` language for a scientist is available online [here](https://juliadatascience.io/). 

"""

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md"""

## Warning! The course is not only for astrophysicists!

# ╟─1ef75ad8-5f7f-4146-af0f-66982f2e7d62

# ╟─d3443a4d-62b5-4ca9-84dc-66e19c284ac2

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# ╟─3c960134-e9d9-4b66-891f-6b3522c691c4

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# ╟─c5257f84-999b-4655-9ade-2bcbc7eb324c

    "    <td>Next lecture</td>\n",

    "  </tr>\n",

    "  <tr>\n",

    "    <td><a href=\"../Lecture%20-%20Spectral%20Analysis/Lecture-SpectralAnalysis.ipynb\">Lecture about spectral analysis</a></td>\n",

    "    <td><a href=\"../Science%20Case%20-%20X-Ray%20Binaries/Lecture-X-RayBinaries.ipynb\">Science case about X-ray binaries</a></td>\n",

    "    <td><a href=\"../Science%20Case%20-%20Variable%20Stars/Lecture-VariableStars.ipynb\">Science case about variable stars</a></td>\n",

    "  </tr>\n",

    " </table>\n",