Bronze Medal Solution ( 113th) for \"NeurIPS - Ariel Data Challenge 2024: Derive Exoplanet Signals from Ariel's Optical Instruments\". The goal of the competition is to predict the spectra of exoplanets using data from the Ariel telescope.
\nhttps://www.kaggle.com/competitions/ariel-data-challenge-2024/
I would like to express my deepest gratitude to Kaggle and the competition Hosts University College London(UCL) for organizing this very exciting competition. Congratulations to all the prize and medal winners in this competition. I am glad to win the Bronze medal with a rank of 113th.
\nThis Competition generated very interesting public solutions and discussions posted by active participants in this competition. I am excited to participate in a competition involving cutting edge space instrumentation and datasets.
\nFor my solution for the NeurIPS Ariel Data Challenge Competition, I started with public notebooks and refined them. The solution is implemented leverages libraries such as pandas, NumPy, scikit-learn, pqdm and Astropy. My code is structured into several classes and functions that perform specific tasks. Let me explain my solution step-by-step:
\nSignal Preprocessing:
\n●Calibration: The Calibrator class handles the calibration of the raw sensor data. It performs several steps to correct for sensor-specific artifacts:
\n○ Dark-frame subtraction to remove background noise.
\n○ Dead pixel correction to eliminate faulty pixel readings.
\n○ Flat-fielding to normalize pixel responses.
\n○ Linear correction using polynomial fitting to adjust for nonlinearities in sensor response.
\n● Preprocessing: The Preprocessor class further processes the calibrated signal:
\n○ Spatial cropping to focus on the relevant region of the sensor data.
\n○ Signal differencing and binning to reduce noise and improve the signal-to-noise ratio.
\n● Parallelization: The preprocessing steps are parallelized using the pqdm library to speed up the computation.
Modelization:
\nPhase Detection: The phase_detector function identifies the start and end points of the planet transit event (phase1 and phase2). This is achieved by analyzing the gradient of the preprocessed signal and locating points of significant change.
\nSpectrum Prediction: The predict_spectra function determines a scaling factor (s) that is applied to the planet transit portion of the signal. This scaling factor aims to minimize the error between the observed signal and a polynomial model fit to the data. The optimization is performed using the Nelder-Mead method from the scipy.optimize library.
A Priori Scales and Rounding:
\n●A Priori Scales Calculation: My code utilizes precomputed a priori scales to adjust the predicted spectra. These scales are derived from the training data and represent the average ratio between the true spectra and the predicted spectra.
\n○The predicted spectra are repeated for each wavelength to create a 2D array.
\n○Constant sigma values (representing uncertainties) are generated and concatenated with the predicted spectra.
\n○The a priori scales are applied to both the predicted spectra and the sigma values to further refine the predictions.
\n●My solution heavily relies on careful signal processing and calibration to extract meaningful information from the raw sensor data.
\n●The phase detection step is crucial for isolating the planet transit event and reducing the influence of noise on the spectral predictions.
\n●The use of a priori scales derived from the training data demonstrates a data-driven approach to improve the prediction accuracy.
\n●The parallelization using pqdm of preprocessing steps significantly enhances the computational efficiency of the solution.
\n-I also rounded the predicted values to 6 decimals and saw some improvement in scores.
Final scores:
\nPrivate-LB 0.5743219
\nPublic-LB: 0.5543416
Areas for improvement:
\nRobustness to Missing or Corrupted Calibration Files
\n●Adaptive Extreme Value Threshold
\n○Dynamically determine the extreme value threshold based on the data distribution.
\n●Exploration of Alternative Phase Detection Methods
\n○Wavelet analysis: Wavelets can effectively detect abrupt changes in signals, making them suitable for identifying phase transitions.
\n●Automated Hyperparameter Tuning: Exploring the inclusion of additional features derived from the raw sensor data or other external sources could potentially enhance the predictive power of the model.
\n●Uncertainty Quantification
References:
\nBronze Medal Solution ( 113th) for \"NeurIPS - Ariel Data Challenge 2024: Derive Exoplanet Signals from Ariel's Optical Instruments\". The goal of the competition is to predict the spectra of exoplanets using data from the Ariel telescope.
\nhttps://www.kaggle.com/competitions/ariel-data-challenge-2024/
I would like to express my deepest gratitude to Kaggle and the competition Hosts University College London(UCL) for organizing this very exciting competition. Congratulations to all the prize and medal winners in this competition. I am glad to win the Bronze medal with a rank of 113th.
\nThis Competition generated very interesting public solutions and discussions posted by active participants in this competition. I am excited to participate in a competition involving cutting edge space instrumentation and datasets.
\nFor my solution for the NeurIPS Ariel Data Challenge Competition, I started with public notebooks and refined them. The solution is implemented leverages libraries such as pandas, NumPy, scikit-learn, pqdm and Astropy. My code is structured into several classes and functions that perform specific tasks. Let me explain my solution step-by-step:
\nSignal Preprocessing:
\n●Calibration: The Calibrator class handles the calibration of the raw sensor data. It performs several steps to correct for sensor-specific artifacts:
\n○ Dark-frame subtraction to remove background noise.
\n○ Dead pixel correction to eliminate faulty pixel readings.
\n○ Flat-fielding to normalize pixel responses.
\n○ Linear correction using polynomial fitting to adjust for nonlinearities in sensor response.
\n● Preprocessing: The Preprocessor class further processes the calibrated signal:
\n○ Spatial cropping to focus on the relevant region of the sensor data.
\n○ Signal differencing and binning to reduce noise and improve the signal-to-noise ratio.
\n● Parallelization: The preprocessing steps are parallelized using the pqdm library to speed up the computation.
Modelization:
\nPhase Detection: The phase_detector function identifies the start and end points of the planet transit event (phase1 and phase2). This is achieved by analyzing the gradient of the preprocessed signal and locating points of significant change.
\nSpectrum Prediction: The predict_spectra function determines a scaling factor (s) that is applied to the planet transit portion of the signal. This scaling factor aims to minimize the error between the observed signal and a polynomial model fit to the data. The optimization is performed using the Nelder-Mead method from the scipy.optimize library.
A Priori Scales and Rounding:
\n●A Priori Scales Calculation: My code utilizes precomputed a priori scales to adjust the predicted spectra. These scales are derived from the training data and represent the average ratio between the true spectra and the predicted spectra.
\n○The predicted spectra are repeated for each wavelength to create a 2D array.
\n○Constant sigma values (representing uncertainties) are generated and concatenated with the predicted spectra.
\n○The a priori scales are applied to both the predicted spectra and the sigma values to further refine the predictions.
\n●My solution heavily relies on careful signal processing and calibration to extract meaningful information from the raw sensor data.
\n●The phase detection step is crucial for isolating the planet transit event and reducing the influence of noise on the spectral predictions.
\n●The use of a priori scales derived from the training data demonstrates a data-driven approach to improve the prediction accuracy.
\n●The parallelization using pqdm of preprocessing steps significantly enhances the computational efficiency of the solution.
\n-I also rounded the predicted values to 6 decimals and saw some improvement in scores.
Final scores:
\nPrivate-LB 0.5743219
\nPublic-LB: 0.5543416
Areas for improvement:
\nRobustness to Missing or Corrupted Calibration Files
\n●Adaptive Extreme Value Threshold
\n○Dynamically determine the extreme value threshold based on the data distribution.
\n●Exploration of Alternative Phase Detection Methods
\n○Wavelet analysis: Wavelets can effectively detect abrupt changes in signals, making them suitable for identifying phase transitions.
\n●Automated Hyperparameter Tuning: Exploring the inclusion of additional features derived from the raw sensor data or other external sources could potentially enhance the predictive power of the model.
\n●Uncertainty Quantification
References:
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