2024-01-18

What is AstroStatistics?

Fusion of Fields: Astrostatistics blends astronomy and statistics.

Real-World Impact: It aids in solving astronomical puzzles like estimating cosmic parameters.

Deepening Understanding: Astrostatistics pushes the boundaries of astronomical knowledge.

Data-Driven Discoveries: Astrostatistics enables discoveries driven by data analysis and statistical inference.

Why is Statistics important in Astrophysics

Data Analysis: Statistics aids in analyzing vast datasets from telescopes, extracting insights, and testing hypotheses.

Uncertainty and Error Analysis: Statistics quantifies and addresses measurement errors and uncertainties inherent in astrophysical observations.

Modeling and Inference: Statistical modeling infers underlying relationships in complex astrophysical phenomena, aiding predictions and parameter estimation.

Hubble’s Law

Hubble presented a linear relationship between distance and radial velocities of a number of galaxies. This relation, which is known as Hubble’s Law, is represented as follow:

\[ v = H_0D \] In this formula, v is the radial velocity, D is proper distance and H_0 is Hubble constant.

It states that galaxies are moving away from each other at a speed proportional to their distances. In other words, the farther a galaxy is from us, the faster it is moving away from us.

Hubble Data Set (Velocity vs Distance)

Scatter plot of distance to a galaxy and its velocity

Hubble Data Set (Redshift vs Distance)

The relation between redshift (z) of a galaxy, velocity (v) and the speed of light (c) is as follows

\[ z = \frac{v}{c} \]

Exoplanet Dataset

The following plot is made using a data set from NASA about exoplanets that have been discovered until now.

Code used for the plots (Loading Data)

The following is the code used for the plot of distance vs velocity using hubble data. It has been broken down into two slides. This slide contains the code which was used to load the data

library(ggplot2)

data <- "hubble1929.csv"
df <- read.csv(data)

Visualization Code

pastel_palette <- c("#A6B1E1", "#FFB6C1")

ggplot(df, aes(x = distance, y = velocity)) +
  geom_point(color = pastel_palette[1]) + 
  geom_smooth(method = "lm", formula = y ~ x, color = pastel_palette[2]) +  
  labs(x = 'Distance (Mpc)', y = 'Recessional Velocity (km/s)') +
  theme_dark() +  
  theme(
    plot.title = element_text(color = "#1F1F1F"),  
    axis.text = element_text(color = "#1F1F1F"), 
    panel.grid.major = element_blank(),  
    panel.grid.minor = element_blank(),  
    panel.background = element_rect(fill = "#1F1F1F", color = NA),  
    legend.text = element_text(color = "#1F1F1F")  
  )