MLS: Multiparametric Life Score Calculation & Analysis

Codes for calculating the Multiparametric Life Score (MLS) and for visual and interactive analysis of the results.

Overview

This repository contains the computational framework developed to assess planetary habitability through the Multiparametric Life Score (MLS). The MLS is a quantitative metric designed to evaluate the potential for extremophilic life on exoplanets by integrating atmospheric, geophysical, and stellar parameters against known biological limits.

The codebase includes modules for data preprocessing ("Warmup"), the core numerical calculation engine (Fortran/Python), and an interactive visualization suite for statistical analysis and data exploration.

Repository Structure

The project is organized into three main functional components:

1. Data Preprocessing (/warmup)

Scripts responsible for standardizing heterogeneous exoplanetary data inputs.

• Function: Ingests raw planetary and stellar metadata.
• Operations: Performs unit conversion, missing data imputation based on theoretical models (e.g., Mass-Radius relations), linear interpolation and filters targets based on preliminary observability criteria.
• Output: Generates clean, standardized datasets ready for the MLS algorithm.

2. Core Calculation Engine (/fortran & /python)

The backbone of the MLS computation, utilizing high-performance numerical routines.

• Physics: Calculates atmospheric scale height ($H$), surface gravity ($g$), and equilibrium temperatures ($T_{eq}$).
• Biology: Integates biological growth/death rates ($r_{growth}$, $r_{death}$) based on Arrhenius equations and radiation dose-response curves.
• Algorithm: Iterates through atmospheric column layers to compute the local $S_{i,j}$ (Habitability Score) and integrates these values to derive the global planetary MLS.

3. Visual & Interactive Analytics (Visual_interactive.py)

A Streamlit-based dashboard for exploring the results.

• Features:
  • Atmospheric Tomography: Layer-by-layer analysis of habitability potential.
  • Bio-Geophysical Coupling: Correlations between stellar type, gravity, and biological survivability.
  • Volumetric Taxonomy: Classification of planets based on Habitable Volume ($F_v$).
  • Hypothesis Testing: Statistical validation of astrobiological drivers (e.g., UV flux impact, Red Dwarf paradox).

Installation & Usage

Prerequisites

• Python 3.8+
• GFortran (for compiling legacy core modules)
• Python Libraries: numpy, pandas, scipy, plotly, streamlit, scikit-learn

Setup

1. Clone the repository:

   git clone [https://github.com/Spleen81/MLS.git](https://github.com/Spleen81/MLS.git)
   cd MLS

2. Install Python dependencies:

   pip install -r requirements.txt

3. Run the Interactive Dashboard:

   streamlit run Visual_interactive.py

Scientific Methodology

The MLS metric moves beyond the classical "Habitable Zone" binary by introducing a continuous spectrum of habitability ($0 \le MLS \le \infty$). It considers:

1. Thermodynamic suitability: Match between local $P, T$ conditions and extremophile tolerance windows.
2. Radiation forcing: Impact of UV and X-Ray flux on biological stability.
3. Atmospheric retention: Evaluation of scale height and escape velocity.

For detailed equations and parameter definitions, please refer to the internal documentation or associated publications.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Maintained by Marco Marcellino - INAF OApa marco.marcellino@inaf.it