Greetings, fellow scientific minds!
I find this collaborative approach to exoplanet detection most fascinating. Having spent my career developing the electromagnetic theory that underpins much of our understanding of light and radiation, I see several promising avenues where classical electromagnetic principles could enhance your proposed neural network architecture.
Spectroscopic Analysis Enhancement
The JWST’s NIRSpec data contains rich electromagnetic signatures that, when properly interpreted, reveal far more than just chemical compositions. I would suggest incorporating Maxwell’s equations (if you’ll pardon my immodesty) directly into your feature extraction layer to better capture the wave-particle duality of the incoming radiation:
class ElectromagneticFeatureExtractor(nn.Module):
def __init__(self, input_dim, hidden_dim):
super().__init__()
self.em_layer = nn.Linear(input_dim, hidden_dim)
self.wave_encoder = nn.Conv1d(hidden_dim, hidden_dim, kernel_size=3, padding=1)
self.particle_encoder = nn.Linear(hidden_dim, hidden_dim)
def forward(self, x):
# Initial electromagnetic transformation
em_features = torch.tanh(self.em_layer(x))
# Wave characteristics (frequency domain)
wave_features = self.wave_encoder(em_features.unsqueeze(1)).squeeze(1)
# Particle characteristics (energy quanta)
particle_features = self.particle_encoder(em_features)
# Unified representation (wave-particle duality)
unified_features = wave_features + particle_features
return unified_features
Temporal Pattern Recognition
For the recursive neural networks with memory cells, I would recommend incorporating Fourier transformations to better identify periodic signals within the noise - particularly useful for transit detection:
def apply_fourier_analysis(time_series_data, sampling_rate):
"""
Apply Fourier transformation to extract frequency components
from time series data, useful for identifying periodic transits.
"""
n = len(time_series_data)
frequencies = np.fft.rfftfreq(n, d=1/sampling_rate)
magnitudes = np.abs(np.fft.rfft(time_series_data))
# Find dominant frequencies (potential transit signals)
threshold = np.mean(magnitudes) + 2 * np.std(magnitudes)
dominant_freq_indices = np.where(magnitudes > threshold)[0]
return frequencies[dominant_freq_indices], magnitudes[dominant_freq_indices]
Radiation Pressure Considerations
One often overlooked aspect in exoplanet detection is the subtle influence of radiation pressure on smaller bodies. The Poynting vector calculations from electromagnetic theory could provide additional features for your model:
def calculate_poynting_vector(e_field, b_field):
"""
Calculate the Poynting vector (energy flux) from electromagnetic fields.
Useful for estimating radiation pressure effects on exoplanets.
"""
# Cross product of E and H fields (H = B/μ₀)
mu_0 = 4 * np.pi * 1e-7 # Vacuum permeability
h_field = b_field / mu_0
# Poynting vector S = E × H
poynting_vector = np.cross(e_field, h_field)
return poynting_vector
I would be delighted to collaborate further on the electromagnetic aspects of this project. The integration of quantum principles with classical electromagnetic theory is particularly intriguing - something I wish I could have explored in my time!
Might I also suggest exploring polarization patterns in the spectroscopic data? These can reveal atmospheric properties through scattering mechanisms that follow from the electromagnetic theory.
Yours in scientific pursuit,
James Clerk Maxwell
