STARTastro Hack Session

What is the STARTastro Hack Session?

Opportunity to work collaboratively on a small-scale project using your new python skillz

Involves:

• A 1-slide pitch @ 12:30pm
• Some work
• A 1-slide presentation @ 4pm

Requires:

• Working in teams for 2-3 - no solo projects
• Do your best! Even a small step forward can open up new opportunities & ideas

Some ideas

Learn how to run MESA following tutorials

Try out the scikit-learn machine learning tutorials

Work through some of the astropy tutorials

Use astroquery to access datasets relevant to your project

Try accessing data from JWST, Euclid, DESI, LSST, TESS, etc…

Try reproducing some plots from publications with your matplotlib skillz

Make an animation of multi-star systems orbiting each other

Work on a project your advisor[s] have suggested

Questions

PITCH SLIDES

[EXAMPLE] Adam & Kate's Pitch

We're going to see if we can combine AI, SIMBAD, and astroquery to write a code that takes any astronomical object and generates a complete review of the literature about that star.

Possible steps:

• Feed an object name into astroquery/simbad to get the references for a SIMBAD object
• Feed these references (how?) into an AI (which one?) and ask for a literature review (API for this?)
• See what results we get for some objects we know well

Opening and plotting FITS files - Evan / Marilyn

• FITS files are the standard way of storing Astronomical Data!

Sky Region Processor - Jack & Julian

We want to create a program that will query data from GAIA of an INPUT cone region of the sky (given ra, dec, and circle radius) and generate/OUTPUT:

• A color-coded image/plot of all stars in the region
• An HR diagram of all stars in the region
• A list of statistics of the stars in that region (e.g. # of stars per spectral type)
• MAYBE a rotation curve of the stars in that region (time permitting)

Possible Steps:

• Take user input of ra, dec, and circle radius
• Query data from GAIA
• Each of the four tasks will be their own python files
• Save all processed information (images and plaintext files) into a folder

Using Machine Learning to Classify Galaxies

-Tyler Donahue, Ricong Huang

We are going to try machine learning by feeding it galaxies and their classifications then feed it a mass of galaxy image files from a database and have it return a classification

• Learn how to use scikit to train galaxy classification
• Feed scikit image files to classify
• Check if scikit has properly classified the galaxies

The KAM Project

Accessing data and determining whether it’s an exoplanet using Jupyter notebook.

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Possible steps:

• Importing data from google drive
• Analyzing the dips
• Light curve analysis
• Determines exoplanet status
• Calculating orbital period
• Use for loop to check all fit files

Steven Tyler’s Pitch

Utilize both the Gaia and TESS archives to cross reference stellar systems. Checking for both partner stars as well as potential exoplanet. After will be checking for spectroscopy of the system. WIth both sets, Determine masses, radii and composition.

Possible steps:

• Search TESS data catalog examining light curves for exoplanet/binary star dips
• Cross reference the TESS data with Gaia data for spectroscopy
• Calculate the system masses, radii, and composition using python

Hayley & Natalie’s Pitch

Possible steps:

• Astroquery + SIMBAD: to retrieve metadata and references for a given object
• AI: to summarize relevant research articles
• Jupyter Notebook or Web App: for user interface/testing
• ​

Overall Goal: Create a simulated orbit of Jupiter’s Moons (Callisto, Io, Lysithea), using astroquery to gather their data.

• ​

Unity could be used for 3D animation!

Victor and Edwin’s pitch

We will be attempting to install and use MESA in order to simulate rapidly accreting protostars with varying solar masses and possibly varying accretion rates

Possible Steps

-Installing mesa

-Editing starting conditions in files in order to simulate a wide range of stars at different masses and possibly varying rotation and metallicity rates

-Observe the results

Building COMPAS on Windows - Suoi-Nguon Pham

PRESENTATION SLIDES

Plotting Fits!

• Evan, Marylin, Julia

Sky Region Processor - Jack & Julian

The KAM Project-Results

• Exoplanet: TOI-101
• Used BoxLeastSquares method to detect exoplanets and analyse dips
• Importing BLS to see the dip of the exoplanet and plot the clean light curve

Steven Tyler’s presentation

• We started out by looking through the TESS data catalogue and plugging the TIC number into a fast lightcurve inspector.
• After finding the candidate stars, we analyzed the Eclipse duration, and period in an attempt to uncover the systems radii and size

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Hayley & Natalie’s presentation

Progress:

:

Final Animation:

-Data-

IO:period> 1.76 days radius> 1

CALLISTO:period> 3.55 days radius> 4.5

LYSITHEA:period> 7.16 days radius> 10

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Karin & Adam - First SPHEREx data!

30 Doradus (in the LMC)

Paschen-alpha 1.87 µm

Victor and Edwin Conclusions

WE HAVE PICTURES

Ricong Tyler’s presentation

• Use the skscikit-learn’s classification library to help classify which galaxies in which category

Ricong Tyler’s presentation conti.

COMPAS (SN)

Collaboration w/:

Saina Kadni

Esther Park

Ana Lam

Floor Broekgaarden