# Classification of Space Objects

*English translation by SOTA – AI Community of the Russian original. Organisers who would like this translation removed can email sota.ai.community@gmail.com.*

## 2.3. Engineering tour

### Task 1 (100 points)

**Classification of space objects**

*Topics: artificial intelligence, machine learning, data processing, classification.*

#### Statement

**Background**

Modern telescopes at observatories collect terabytes of data every day about millions of celestial bodies: their coordinates, brightness and spectra. Among them are ordinary stars as well as quasars, galaxies and even exoplanet candidates, which are especially interesting and important objects for science.

However, the volume of data is growing so fast that astronomers cannot analyse it by hand. To avoid missing valuable finds, an automated approach is needed that can classify objects quickly and reliably by their characteristics.

Your model will help to speed up scientific discoveries and make life easier for astronomers.

**What needs to be done?**

Create a model that determines the class of a celestial object from its numerical characteristics.

Classes to predict:

1. main_sequence_star — main-sequence star;
2. red_giant — red giant;
3. white_dwarf — white dwarf;
4. galaxy — galaxy;
5. quasar — quasar;
6. exoplanet_candidate — exoplanet candidate (a rare class).

**Data**

Download the data: https://disk.360.yandex.ru/d/mXy_XHfYrZtZMA.

The following files are available to participants:

1. train.csv — the training set with labels:
   features (25–35 in total): numerical and categorical; some of them are:
   - photometry: u_mag, g_mag, r_mag, i_mag, z_mag;
   - indices (differences of magnitudes): u_g, g_r, r_i, i_z;
   - geometry / motion: parallax, pm_ra, pm_dec, radial_velocity;
   - signal / noise: snr_u, snr_g, snr_r, snr_i, snr_z, obs_count, cloud_factor, background_noise;
   - "spectral hints": h_alpha_strength, oIII_strength, na_d_strength;
   - auxiliary: extinction, quality_flag (categorical);
2. test.csv — the test set (without the target label);
3. sample_submission.csv — an example file with predictions.

The data are synthetic: they contain noise, missing values, correlations and class imbalance.

**Prediction format**

The file submission.csv must contain:

- the column object_id;
- one column for each class, with probability 0 or 1.

  Example:

  *object_id,main_sequence_star,red_giant,white_dwarf,galaxy,quasar,exoplanet_candidate*\
  *123456,0,0,0,1,0,0*\
  ...

- First column: object_id.
- Then: the probabilities of belonging to each class (0 or 1).
- The number of rows equals the number of objects in test.csv.

**Metric**

This is a multiclass classification task. The quality of a solution is evaluated with the macro F1 score metric, which computes the F1 score for each class and averages the result.

**Baseline solution example**

The file baseline.ipynb contains a baseline solution example (a baseline) that can serve as a starting point for developing your own solutions.

Baseline solution example: https://disk.360.yandex.ru/d/CdN8a5oXxUgN-g.

*The collection's solution for this task is in the separate solution file.*