# Where can I learn these Astrophysical techniques? [Read below]

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Whenever I read any Astro papers in http://arxiv.org/ they usually talk about Data fitting or modelling or sentences like 'we fitted the spectral region by analytic functions i.e. by multi-component Gaussian model plus continuum'.

Is there any websites, online books or maybe YouTube videos which teaches these techniques in detail?

Any info will be much appreciated!

If you are going to do data analysis, you need to understand how the fitting procedure works. This means a lot of statistics, and new terminology and so on, which is hard and takes much time.

If you want to start to fit, e.g., a spectrum, you should definitely read the XSPEC guide. You can find a pdf online as well.

In short, fitting is to take a model and measure how well this model fits to your data. To quantify this "how well", usually the chi-squared distribution is considered:

$$chi^2 = sumlimits_{i=1}^n(frac{X_i - mu_i}{sigma_i})^2$$

where $$X$$ is your data (observed value), $$mu$$ is your expected value (which, in this case, corresponds with the prediction of the model), and $$sigma$$ is the variance on your data point (the error).

This is the most general information that you need. You can read more here, and especially on the Numerical Recipes (don't be scared by the huge format of the last document, the pages you need are only few around Chapter 15).

For fun, you can just try to play with XSPEC, to take confidence, and see at least how things change when you change your model or your data (especially if you know which model is the best-fit for your data). To understand everything will take years, but if you never start, you'll never arrive ;)

## ASTROPHYSICAL TECHNIQUES KITCHIN PDF

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## Module learning outcomes

Subject content

• describe the emission processes which give rise to high energy electromagnetic radiation
• describe and apply the physical principles underlying radio detectors
• discuss the imaging and interferometry techniques used by radio astronomers
• compare and contrast the physical processes by which X-ray and gamma-ray emission occur and describe the astrophysical sites where this arises
• appreciate why satellite observations are required for X-ray and gamma-ray astronomy and be able to describe some typical satellite parameters
• compare the various types of detectors used to detect radio waves, X-ray, gamma-ray radiation and explain how their operations vary
• apply equations to determine the attenuation of high energy radiation through a shield and the likelihood of its interaction with a target
• formulate designs for detector systems based on flux and energy of incident radiation
• evaluate the physical basis resulting in the &ldquosolar neutrino problem&rdquo and how it was ultimately resolved
• explain how neutrinos interact with matter and describe how this can be used to detect neutrinos through the weak interaction
• identify some of the astrophysical sites where neutrino emission can occur
• Describe and compare current theories of solar system formation and evolution, from molecular cloud through to its current incarnation, including planetary migration.
• Use classical mechanics to derive the motion of planets, satellites and tidal effects, setting limits on the locations of stable orbits.
• Differentiate between and account for the wide variety of objects and planetary environments within the solar system, with focus on planetary interiors, atmospheres and magnetospheres.
• Investigate and apply methods of exoplanet detection. Make distinctions between the various types of exoplanet. Use knowledge of detection methods to predict sensitivity to different populations and calculate planetary characteristics.
• Demonstrate an understanding of the chemical, physical, atmospheric and biological conditions which existed such that life developed on Earth. Evaluate the basic astronomical and astrophysical principles and techniques used in the search for bio-signatures on worlds in our own solar system and beyond.
• Apply scientific rigour to discussions pertaining to life beyond Earth. Critically analyse the Drake equation and its astronomical context.
• Contextualise current research and scientific debates within planetary, exoplanetary and astrobiological sciences.
• investigate either independently and/or in groups, the solution to an open-ended astrophysics problem and communicate the outcomes succinctly
• summarise a specific topic from the astrophysics programme such that material produced is complete, consistent and supported by theory

• absorb, organise and synthesise lots of information from many different fields
• synthesise information to write coherent essays on general questions in astrobiology, supporting arguments with relevant information/facts/ideas
• encourage critical skills when applied to open-ended questions
• appreciate the differences between fact, theory, and speculation of various degrees
• construct coherent arguments and discussions of broad questions in astrobiology supported by facts, theories, and speculation
• communicate information and ideas to an appropriate standard and in such a way as to enable understanding and engagement by academic and non-specialist audiences
• select and adapt the appropriate style to convey accurate clear information, attitudes and ideas in an appropriate written format in a way which enables use and facilitates auditing
• identify, select, synthesise and evaluate information/data to enable the achievement of a desired outcome making effective use of multiple databases and sources of information
• reflect on and critically evaluate strengths, limitations, personal and contextual factors which have an impact on performance and establish ways to improve
• demonstrate the independent learning ability needed to continue to develop at an advanced level
• create and implement a plans to achieve key career objectives
• identify ways to make professional use of others to achieve aims and desired outcomes
• respond appropriately to peer expectations

Our graduate programs offer advanced training in physics and astronomy and provide opportunities spanning a wide variety of research and professional areas. Students in the MS Physics program can choose between either a research (i.e., thesis) option or a “professional” option. Students in the Astrophysical Sciences & Technology programs engage with RIT’s world-class research centers offering cutting-edge opportunities in gravitational waves, new advanced sensor technologies, and multi-wavelength astrophysics.

An astrophysics degree that explores the depths of the universe through multidisciplinary research. Dive into an area that most interests you, whether it's general relativity, theoretical astrophysics, observational or instrumentation development, or another area related to astrophysics.

An astrophysics Ph.D. centered on phenomena beyond the Earth and on the development of the technologies that will enable the next major strides in the field.

RIT's physics master's solidifies your understanding on the core aspects of physics in both research and technical skill as you study areas of physics that support your career interests.

## Study Options

 Year 1 48 units ASTR6007 Stars 6 units Complementary Science course list 6 units ASTR course list 6 units ANU elective 6 units ASTR6002 Galaxies and Cosmology 6 units ASTR6013 Astrophysical Processes 6 units Science & Society course list 6 units ANU elective 6 units Year 2 48 units ASTR course list 6 units ASTR course list 6 units ASTR course list 6 units ANU elective 6 units ASTR8001 Astronomy and Astrophysics Research Project 6 to 24 units ASTR8001 Astronomy and Astrophysics Research Project 6 to 24 units Science & Society course list 6 units ANU elective 6 units

## Study Options

 Year 1 48 units ASTR6002 Galaxies and Cosmology 6 units Complementary Science Course list 8000-level ASTR course list 8000-level ASTR course list ASTR6007 Stars 6 units ASTR6013 Astrophysical Processes 6 units ASTR8010 Astrophysics Research Project 6 to 24 units ASTR8010 Astrophysics Research Project 6 to 24 units Year 2 48 units 8000-level ASTR course list ASTR8010 Astrophysics Research Project 6 to 24 units ASTR8010 Astrophysics Research Project 6 to 24 units ASTR8010 Astrophysics Research Project 6 to 24 units 8000-level ASTR course list ASTR8010 Astrophysics Research Project 6 to 24 units ASTR8010 Astrophysics Research Project 6 to 24 units ASTR8010 Astrophysics Research Project 6 to 24 units

## Focus on Tools and Techniques for Time-domain Astronomy

Astronomy has gone from being a static science to a dynamic one through a combination of large area surveys, fast cadence, real-time processing and rapid follow-up. At optical wavelengths, this has led to discoveries of hundreds of thousands of well characterized variable stars, the characterization of hundreds of thousands of active galactic nuclei and quasars, not to mention the thousands of planets that have been found and the era of localization of gravitational wave counterparts. Combining this with other areas of astronomy, like the far better understood world of gamma-ray bursts, the emerging world of fast radio bursts, and the nascent neutrino astronomy, one can see the breadth and variety that has become possible by these advancements. The observations that have made such discoveries possible include, on the one hand, time baselines from individual surveys that are well over 10 years (e.g., CRTS), and, on the other, surveys of smaller regions with very high cadence (e.g., Kepler). In addition, there are surveys like PTF/iPTF/ZTF (explosive transients), Pan-STARRS (deeper, multicolor), and those at NIR (e.g., ALLWISE), radio (SKA pathfinders), etc. Novel algorithms applied to this variety of surveys have made searches for both short-period and long-period objects achievable.

The recent explosion of techniques involving GPUs and deep learning methods are making it possible to perform searches that could only be dreamt of just a few years ago. For stochastically varying sources also a variety of statistical models are being applied. Many processing pipelines make use of a given survey, and a small fraction of data from other surveys as needed. Advances in database technology allow us to go far beyond, connecting datasets through APIs, and fast and reliable cross-matching. The partial release from Gaia (DR2) led to a flurry of activity with tens of papers being submitted in a matter of days. Most of these articles merely scratch the data science possibilities inherent in the live and archival datasets. As we prepare for even larger surveys and larger telescopes, it is important to collect tools and techniques that help combine and explore the complex multiwavelength data collected through these new capabilities. This focus issue on the Tools and Techniques for Time-Domain Astronomy seeks to provide a resource which describes current techniques, methods, and their associated results derived from the broad spectrum of astrophysical problems in time-domain astronomical research with an eye on the future.

• Using time-domain data from single well contained surveys
• Combining archival data with current datasets
• Combining X-Ray, IR, radio, etc. with optical data
• Real-time applications of time-domain data, e.g., to identify counterparts to transients
• Searches for asteroids and planets
• Searches for lenses, light-echoes, etc.
• Searches for periodic sources
• Techniques like deep learning applied in different scenarios

The articles listed below are the first accepted contributions to the collection and further additions will appear on an ongoing basis.

### Papers

Andrej Prša et al. 2019 PASP 131 068001

Light curves of astrophysical objects frequently contain strictly periodic signals. In such cases, we can use that property to aid the detrending algorithm to fully disentangle an unknown periodic signal and an unknown baseline signal with no power at that period. The periodic signal is modeled as a discrete probability distribution function (pdf), while the baseline signal is modeled as a residual timeseries. Those two components are disentangled by minimizing the total variation (length) of the residual timeseries with regard to the per-bin pdf fluxes. We demonstrate the use of the algorithm on a synthetic case, on the eclipsing binary KIC 3953981 and on the eccentric ellipsoidal variable KIC 3547874. We further discuss the parameters and the limitations of the algorithm and speculate on the two most common use cases: detrending the periodic signal of interest and measuring the dependence of instrumental response on controlled instrumental variables. A more sophisticated version of the algorithm is released as open source on github and available via pip.

Federica B. Bianco et al. 2019 PASP 131 068002

We identify minimal observing cadence requirements that enable photometric astronomical surveys to detect and recognize fast and explosive transients and fast transient features. Observations in two different filters within a short time window (e.g., g-and- i, or r-and- z, within <0.5 hr) and a repeat of one of those filters with a longer time window (e.g., >1.5 hr) are desirable for this purpose. Such an observing strategy delivers both the color and light curve evolution of transients on the same night. This allows the identification and initial characterization of fast transient&mdashor fast features of longer timescale transients&mdashsuch as rapidly declining supernovae, kilonovae, and the signatures of SN ejecta interacting with binary companion stars or circumstellar material. Some of these extragalactic transients are intrinsically rare and generally all hard to find, thus upcoming surveys like the Large Synoptic Survey Telescope (LSST) could dramatically improve our understanding of their origin and properties. We colloquially refer to such a strategy implementation for the LSST as the Presto-Color strategy (rapid-color). This cadence&rsquos minimal requirements allow for overall optimization of a survey for other science goals.

Igor Andreoni et al. 2019 PASP 131 068004

We present a cadence optimization strategy to unveil a large population of kilonovae using optical imaging alone. These transients are generated during binary neutron star and potentially neutron star&ndashblack hole mergers and are electromagnetic counterparts to gravitational-wave signals detectable in nearby events with Advanced LIGO, Advanced Virgo, and other interferometers that will be online in the near future. Discovering a large population of kilonovae will allow us to determine how heavy-element production varies with the intrinsic parameters of the merger and across cosmic time. The rate of binary neutron star mergers is still uncertain, but only few (󗒗) events with associated kilonovae may be detectable per year within the horizon of next-generation ground-based interferometers. The rapid evolution (&simdays) at optical/infrared wavelengths, relatively low luminosity, and the low volumetric rate of kilonovae makes their discovery difficult, especially during blind surveys of the sky. We propose future large surveys to adopt a rolling cadence in which g- i observations are taken nightly for blocks of 10 consecutive nights. With the current baseline2018a cadence designed for the Large Synoptic Survey Telescope (LSST), 𕡻.5 poorly sampled kilonovae are expected to be detected in both the Wide Fast Deep (WFD) and Deep Drilling Fields (DDF) surveys per year, under optimistic assumptions on their rate, duration, and luminosity. We estimate the proposed strategy to return up to &sim272 GW170817-like kilonovae throughout the LSST WFD survey, discovered independently from gravitational-wave triggers.

Laurent Eyer et al. 2019 PASP 131 088001

In astronomy, we are witnessing an enormous increase in the number of source detections, precision, and diversity of measurements. Additionally, multi-epoch data is becoming the norm, making time-series analyses an important aspect of current astronomy. The Gaia mission is an outstanding example of a multi-epoch survey that provides measurements in a large diversity of domains, with its broad-band photometry spectrophotometry in blue and red (used to derive astrophysical parameters) spectroscopy (employed to infer radial velocities, , and other astrophysical parameters) and its extremely precise astrometry. Most of all that information is provided for sources covering the entire sky. Here, we present several properties related to the Gaia time series, such as the time sampling the different types of measurements the Gaia G, G BP and G RP-band photometry and Gaia-inspired studies using the CORrelation-RAdial-VELocities data to assess the potential of the information on the radial velocity, the FWHM, and the contrast of the cross-correlation function. We also present techniques (which are used or are under development) that optimize the extraction of astrophysical information from the different instruments of Gaia, such as the principal component analysis and the multi-response regression. The detailed understanding of the behavior of the observed phenomena in the various measurement domains can lead to richer and more precise characterization of the Gaia data, including the definition of more informative attributes that serve as input to (our) machine-learning algorithms.

Renée Hložek 2019 PASP 131 118001

Data challenges are emerging as powerful tools with which to answer fundamental astronomical questions. Time-domain astronomy lends itself to data challenges, particularly in the era of classification and anomaly detection. With improved sensitivity of wide-field surveys in optical and radio wavelengths from surveys like the Large Synoptic Survey Telescope (LSST) and the Canadian Hydrogen Intensity Mapping Experiment, we are entering the large-volume era of transient astronomy. We highlight some recent time-domain challenges, with particular focus on the Photometric LSST Astronomical Time series Classification Challenge, and describe metrics used to evaluate the performance of those entering data challenges.

Daniel Muthukrishna et al. 2019 PASP 131 118002

We present Real-time Automated Photometric IDentification ( RAPID), a novel time series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with gated recurrent units (GRUs), we present the first method specifically designed to provide early classifications of astronomical timeseries data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPID well suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of observed transients from the ZTF data stream. We have made RAPID available as an open-source software package 8 for machine-learning-based alert brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds.

## The Astronomy Major

If you are thinking about majoring in Astronomy, you should go to the departmental office, Merrill 214, and ask to talk to Professor Follette. In the meantime, this page provides an outline of the requirements for a major. (The catalog is the official word on these matters, so read it, too.)

The Astronomy major is designed to introduce students to the computational techniques, statistical tools, instrumentation, and physical principles that underlie modern Astronomy. Computational and statistical techniques are introduced in the first course in the major sequence, ASTR 200 (Practical Astronomy), and further honed in ASTR 228 (Introductory Astrophysics) and ASTR 352 (Advanced Astrophysics). ASTR 228 and 352 also draw on physical principles introduced in the three course required physics sequence (PHYS 123, 124 and 225).

A joint Five College Astronomy Department offers courses beyond those offered at Amherst. All required courses are taught at Amherst, but students are also encouraged to take elective courses at the four other institutions, Hampshire, Mount Holyoke and Smith Colleges and the University of Massachusetts (http://www.astro.umass.edu/about/fcad/). As a result of this five college partnership, students can enjoy the benefits of a first-rate liberal arts education while maintaining association with a research department of international stature. Students may pursue independent theoretical and observational work in association with any member of the Five College Astronomy Department, either during the academic year or the summer term. The facilities of all five institutions are available to departmental majors. Students may search for Astronomy courses through the Five College online catalog.

Once you have decided to declare the major, you will need to obtain the appropriate form from the Registrar's page, complete it, and have the current Physics and Astronomy Department chair, as well as your current advisor, sign it, before returning it to the Registrar.

### Major Requirements:

Students who wish to major in Astronomy are required to complete the following coursework: (Astronomy major checklist is available on page 8 of the Physics & Astronomy Student Handbook)

#### Required Courses in Mathematics and Physics

• Mathematics 111: Introduction to the Calculus
• Mathematics 121: Intermediate Calculus
• Physics 123: The Newtonian Synthesis: Dynamics of Particles and Systems, Waves (or Physics 116)
• Physics 124: The Maxwellian Synthesis: Dynamics of Charges and Fields, Optics (or Physics 117)
• Physics 225: Modern Physics

#### Required Astronomy Courses

• Astronomy 200: Practical Astronomy
• Astronomy 228 (FC28): Introductory Astrophysics: Stars and the Interstellar Medium
• Astronomy 352 (FC52): Advanced Astrophysics: Galaxies and Cosmology

Students who have placed out of calculus or introductory physics are excused from taking those courses. Astronomy majors may place out of up to two courses without having to replace those courses. Students placing out of more than two courses must replace all but two of those courses with additional Astronomy courses numbered 200 or higher, approved Physics courses numbered 200 or higher, or other courses approved by the Department to complete the major.

The Comprehensive Evaluation for the Astronomy major will consist of an oral presentation of a published scientific paper (selected in consultation with Amherst faculty), and will take place in the second semester of a student’s senior year.

All students majoring in Astronomy must also attend at least nine public astronomy lectures during the senior year.

#### Electives

Along with the 8 required courses, a major must complete 3 elective courses according to the following specifications:

1. At least one elective course in Astronomy to satisfy a depth requirement in the major.
2. At least two additional electives, one of which must be at the 300-level or higher, e.g., a 300-level Astronomy course, one selected from the list below, or one approved by the department.

Depending on background, Astronomy majors may place out of several of these courses. Students who have placed out of calculus or introductory physics are excused from taking those courses. Astronomy majors may place out of up to two courses without having to replace those courses. Students placing out of more than two courses must replace all but two of those courses with additional Astronomy courses numbered 200 or higher, approved Physics courses numbered 200 or higher, or other courses approved by the Department to complete the major.

For courses taken in Spring 2020, Fall 2020, January 2021, or Spring 2021, the department will accept a grade of P for any major requirement. Before electing to convert a grade to P, we strongly encourage you to discuss the decision with your advisor. It may have repercussions when applying for jobs or graduate and professional schools.

#### Approved electives

Astronomy is a data-driven, interdisciplinary science. Any of the following courses may be used to fulfill the elective requirements.

• Astronomy 220 (FC20): [Topical Courses, e.g., Black Holes, Astrobiology, etc.]
• Astronomy 223 (FC23): Planetary Science
• Astronomy 224 (FC24): Stellar Astronomy
• Astronomy 225 (FC25): Galaxies and Dark Matter
• Astronomy 226 (FC26): Cosmology
• Astronomy 301 (FC) : Writing about Astronomy
• Astronomy 330: (FC30): [Topical Courses, e.g. Exoplanet Atmospheres, High Energy Astrophysics]
• Astronomy 335 (FC35) : Astrophysics II: Stellar and Planetary Structure
• Astronomy 337 (FC37): Observational Techniques I
• Astronomy 339 (FC39) : Astronomy in a Global Context
• Astronomy 341 (FC41): Observational Techniques II
• Astronomy 444 (FC44): Radiative Processes
• Astronomy 445 (FC45): Astrophysical Dynamics
• Physics 226: Signals and Noise Laboratory
• Physics 227: Methods of Theoretical Physics
• Physics 230: Statistical Mechanics and Thermodynamics
• Physics 343: Dynamics
• Physics 347: Electromagnetic Theory I
• Physics 348: Quantum Mechanics I
• Physics 490: [Special Topics]
• Chemistry 351: Quantum Chemistry and Spectroscopy
• Chemistry 361: Physical Chemistry
• Geology 331: Paleoclimatology
• Geology 341: Environmental and Solid Earth Geophysics
• Geology 431: Geochemistry
• Geology 450: Seminar in Biogeochemistry
• Mathematics 230: Intermediate Statistics
• Mathematics 260: Differential Equations
• Mathematics 272: Linear Algebra with Applications
• Mathematics 284: Numerical Analysis
• Mathematics 320: Wavelet and Fourier Analysis
• Mathematics 335: Time Series Analysis and Applications
• Math/Stats 360: Probability
• Mathematics 365: Stochastic Processes
• Math/Stats 370: Theoretical Statistics
• Statistics 220: Bayesian Modeling and Inference
• Statistics 225: Nonparametric Statisitcs
• Statistics 230: Intermediate Statistics
• Statistics 240: Multivariate Data Analysis
• Statistics 495: Advanced Data Analysis
• Computer Science 201: Data Structures and Algorithms I
• Computer Science 247: Machine Learning
• Computer Science 301: Data Structures and Algorithms II

To gain approval for an alternate elective, students must file a petition for the Department to consider. To submit a petition, email the Chair of the Department with relevant information about the course to be considered, for example, a syllabus from a recent semester of the course or a link to the course description.

### Preparation for Graduate School in Astronomy

Students wishing to pursue graduate work in Astronomy should consider a double major in physics and should endeavor to complete as many of the following additional courses as possible: Physics 230, Physics 343, Physics 347, Physics 348, Math 211, Math 260, and Math 271 or 272. In addition, a solid foundation in Computer Science and Statistics are highly recommended.

### Comprehensive Evaluation

The Comprehensive Evaluation for the Astronomy major will consist of an oral presentation of a published scientific paper (selected in consultation with Amherst faculty), and will take place in the second semester of a student’s senior year.

All students majoring in Astronomy must also attend at least nine public astronomy lectures during the senior year. Colloquium schedules are online here:

## Learn About Astronomy Education in a New Ebook

The American Astronomical Society recently launched a new partnership with IOP to produce a series of ebooks about astronomy and astrophysics. One of the newest books in this line, Astronomy Education, Volume 1: Evidence-based instruction for introductory courses, is edited by University of Arizona professors Chris Impey and Sanlyn Buxner, and it’s now available for download with an institutional IOP ebook subscription.

#### Why Should We Care About Astronomy Education?

Any astronomer can (and should!) argue the importance of sharing our understanding of the universe with students. But astronomy education has a particularly unique role in undergraduate education: it’s one of the most popular subjects for non-science majors, and it often represents the last formal exposure to science for these students.

It stands to reason, then, that a well-taught introductory astronomy course can be enormously impactful. But what does it mean to teach an intro astronomy class well?

#### Where Astronomy Education Research Comes In

Cover of the new AAS/IOP ebook edited by Drs. Chris Impey and Sanlyn Buxner, Astronomy Education, Volume 1.

To address questions about student learning, we turn to the field of education research. In this field, scientists methodically explore different teaching techniques and conduct studies to determine what strategies are most effective when trying to achieve specific outcomes — like improving test scores, increasing retention, or maximizing student engagement.

This education research has established many evidence-based instruction methods and practices that can be used to improve undergraduate education — and these strategies, specifically as applied to undergraduate introductory astronomy courses, are clearly outlined in the sections of Astronomy Education, Volume 1.

#### What Can You Learn from Astronomy Education, Volume 1?

Central to the strategies discussed in this ebook is the idea of learner-centered teaching — an alternative to a lecture-based instruction format that instead encourages students to be active participants in their education. The authors of Astronomy Education, Volume 1 provide insight into many different aspects of learner-centered teaching, like how to create student buy-in, how to develop appropriate course materials, and how to measure the impact your teaching strategies are having.

Dr. Impey and Dr. Buxner’s informative book provides information and resources for those who are teaching intro astronomy for the first time, as well as for those who want to add to their toolkits and improve their students’ learning. Chapters in the book include:

• Learner-Centered Teaching in Astronomy
• Effective Course Design
• Lecture-Tutorials in Introductory Astronomy
• Technology and Engagement in the University Classroom
• Using Simulations Interactively in the Introductory Astronomy Classroom
• Practical Considerations for Using a Planetarium for Astronomy Instruction
• Authentic Research Experiences in Astronomy to Teach the Process of Science
• Citizen Science in Astronomy Education
• WorldWide Telescope in Education
• Measuring Students’ Understanding in Astronomy with Research-based Assessment Tools
• Everyone’s Universe: Teaching Astronomy in Community Colleges
• Making Your Astronomy Class More Inclusive

If you plan to be at AAS 235 in Honolulu, HI, come by to celebrate the publication of Astronomy Education, Volume 1 and to meet Dr. Impey, Dr. Buxner, and many of the book’s contributors in person! We’ll be at the AAS booth (#423) in the exhibit hall on Sunday, 5 January at 5:30 p.m. during the poster session.

Keep an eye out in 2020 for Astronomy Education, Volume 2: Best Practices for Online Learning Environments, edited by Chris Impey and Matthew Wenger.

#### Citation

Chris Impey and Sanlyn Buxner 2019. Astronomy Education, Volume 1: Evidence based instruction for introductory courses. doi:10.1088/2514-3433/ab2b42

## Textbook¶

The astroML project was started in 2012 to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy, by Željko Ivezić, Andrew Connolly, Jacob Vanderplas, and Alex Gray, published by Princeton University Press. The table of contents is available here(pdf) , or you can preview or purchase the book on Amazon.

A second edition is published in December 2019. This updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.

Did you find a mistake or typo in the book? We maintain an up-to-date listing of errata in the text which you can view on GitHub. If you find a mistake which is not yet noted on that page, please let us know via email or GitHub pull request!

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