Exoplanet Populations

Astro 497, Week 8, Day 3

TableOfContents()

Mid-semester Feedback

Thanks for the encouragement about the labs!

How to use Wednesdays

All respondents expressed interest in using at least some of the remaining Wednesdays for working on projects.
2nd most popular was talking through how to approach solving quantitative problems

Let's make a plan.

TwoColumn(
md"""
#### Potential project workdays:
  - Oct 26 (before Checkpoint 1)
  - Nov 9 (between Checkpoints 1 & 2)
  - Nov 16 (between Checkpoint 2 & Dashboard due)
  - Nov 30 (too late for dashboard itself, but could be used to work on presentations)
""",
    
md"""
#### Project deadlines
- Project Plan (due Oct 19)
- Project Step 1 (due Oct 31)
- Project Step 2 (due Nov 14)
- Project Dashboard (due Nov 28)
- Project Presentations (due Dec 2 - 9)
- Individual Report & Reflection (due Dec 9)
""")

Potential project workdays:

Oct 26 (before Checkpoint 1)
Nov 9 (between Checkpoints 1 & 2)
Nov 16 (between Checkpoint 2 & Dashboard due)
Nov 30 (too late for dashboard itself, but could be used to work on presentations)

Project deadlines

Project Plan (due Oct 19)
Project Step 1 (due Oct 31)
Project Step 2 (due Nov 14)
Project Dashboard (due Nov 28)
Project Presentations (due Dec 2 - 9)
Individual Report & Reflection (due Dec 9)

Other suggestions

Provide a full week to complete labs.

TwoColumn(
md"""
#### Currently:
- Lab 7: Start Oct 12 –- Due Oct 17
- Lab 8: Start Oct 19 –- Due Oct 24
- Lab 9: Start Nov 2  –- Due Nov 7
""",
md"""
#### Proposed Change:
- Lab 7: Start Oct 12 –- Due Oct 19 
- Lab 8: Start Oct 19  –- Due Oct 26  
- Lab 9: Start Nov 2 –- Due Nov 9
""")

Currently:

Lab 7: Start Oct 12 –- Due Oct 17
Lab 8: Start Oct 19 –- Due Oct 24
Lab 9: Start Nov 2 –- Due Nov 7

Proposed Change:

Lab 7: Start Oct 12 –- Due Oct 19
Lab 8: Start Oct 19 –- Due Oct 26
Lab 9: Start Nov 2 –- Due Nov 9

Mid-term Exam Feedback

Most respondents cited time as a significant challenge.
Some students mentioned not knowing which equations to use or not being confident that they used the right equations.

How to organize knowledge about exoplanets (or any new field)?

Start with very limited knowledge.
As discover more exoplanets, iteratively improve knowledge.
As detection methods improve, we expect that the first detections are likely to be extreme in some way.
Even after detecting thousands of exoplanets, detection biases sculpt the known population.

What we'd like to use

Physical characteristics (e.g., rocky, oceans, atmosphere)
How planets formed

What's the problem with this system?

We usually don't know their detailed physical characteristics. We will never know their formation history for certain.

Commonly used categories in practice

What do we measure first/best?

Orbital period (for transits or RVs)
- Relatively easy to transform in insolation
Size (for transits) or $m \sin i$ (for RVs)

Giant Planets

Hot-Jupiters (HJs)
Warm Giant Planets
Temperate/Cool Giant Planets (RV)
Wide-orbits Giant Planets (Direct Imaging)

Neptune-size/mass Planets

Hot-Neptunes
Warm Neptunes

Rocky Planets

Ultra short period planets (USPs)
Warm rocky planets
Habitable-zone rocky planets

What else can we measure?

Categories based on a notable property that is harder to measure, so is measured for only a subset of planets/systems:

Chains of planets in mean-motion resonances (transits/TTVs)
Eccentric giant planets (RVs, TTVs)
Misaligned hot-Jupiters (RM)
Bulk densities (combining transits and RVs or TTVs)
- Rocky planets
- Planets with H/He atmospheres
- Waterworlds
- Super-puffy planets

Categories for rare planets/systems

Ultra-short period planets
Warm Jupiters
Brown dwarf desert
Pairs of planets straddling the radius valley

More nuanced types of information about a population

Non-detections ("truncation")
Upper (or lower) limits ("censoring")

How to deal with censored & truncated data?

For simple models can derive likelihoods
Hierarchical models

Challenges of combining data from multiple surveys/methods

Reading Questions

Selection Effects

question(md"""
Is there one type of star that is more frequently found having exoplanets orbiting it, and if so, could that be due to selection effects as well?
""")

Question

Is there one type of star that is more frequently found having exoplanets orbiting it, and if so, could that be due to selection effects as well?

By number of known planets:

Most planets discovered by RVs around G & K type stars (sweat spot for RVs)
Most planets discovered by transits around G & F type stars (brighter)

By occurrence rate of planets:

Cool stars
Metal-rich stars

What biases could contribute to these apparent trends?

hint(md"""
- Cooler main sequence stars have smaller masses and radii →
  - RV amplitude is larger for given planet mass
  - Transit depth is larger for given planet size
- Metal-rich stars have larger opacity in photosphere →
  - Brighter for given mass and age

- Early indication of a preference for giant planets around metal-rich stars led some RV surveys to intentionally select metal-rich stars.
  
""")

Hint

Cooler main sequence stars have smaller masses and radii →
- RV amplitude is larger for given planet mass
- Transit depth is larger for given planet size
Metal-rich stars have larger opacity in photosphere →
- Brighter for given mass and age
Early indication of a preference for giant planets around metal-rich stars led some RV surveys to intentionally select metal-rich stars.

Setup

ChooseDisplayMode()

Full Width Mode Present Mode

begin
using PlutoUI, PlutoTeachingTools, HypertextLiteral
using DataFrames
end

question(str; invite="Question") = Markdown.MD(Markdown.Admonition("tip", invite, [str]))

question (generic function with 1 method)

Built with Julia 1.8.2 and

DataFrames 1.4.1
HypertextLiteral 0.9.4
PlutoTeachingTools 0.2.3
PlutoUI 0.7.44

To run this tutorial locally, download this file and open it with Pluto.jl.