My calculated HI density fraction from a Galform sub-volume is orders of magnitude too small. What am I missing?

by

Summary

The issue at hand is that the calculated HI density fraction from a Galform sub-volume is orders of magnitude too small. The expected value of average Ω_HI is around 3-5e-4 for z = 0, but the current calculation yields a value of approximately 3e-7. The code provided seems to conserve mass during the gridding process, indicating that the issue may lie elsewhere.

Root Cause

The root cause of this issue can be attributed to several potential factors, including:

  • Incorrect box dimensions: The box dimensions used in the calculation may not accurately represent the sub-volume of the P-Millennium simulation.
  • Insufficient grid resolution: The number of pixels in the grid may be too low, leading to an inaccurate representation of the HI density field.
  • Unit conversions: Although the code appears to handle units correctly, there may be an issue with the conversion of units that is not immediately apparent.
  • Data extraction: The data extraction process from the HDF5 file may be incomplete or incorrect, leading to an inaccurate representation of the galaxy data.

Why This Happens in Real Systems

This issue can occur in real systems due to:

  • Complexity of astronomical data: Astronomical data, such as that from the Galform model, can be complex and difficult to work with, leading to errors in calculation or data extraction.
  • Limited understanding of underlying physics: The underlying physics of the Galform model may not be fully understood, leading to incorrect assumptions or calculations.
  • Numerical errors: Numerical errors can occur due to the limitations of computational methods or the accuracy of the data.

Real-World Impact

The real-world impact of this issue can be significant, as it can lead to:

  • Inaccurate predictions: Inaccurate calculations of the HI density fraction can lead to incorrect predictions about the formation and evolution of galaxies.
  • Misinterpretation of data: Incorrect data extraction or calculation can lead to misinterpretation of the results, which can have significant implications for our understanding of the universe.
  • Waste of computational resources: Incorrect calculations can lead to a waste of computational resources, as incorrect results may need to be recalculated.

Example or Code

import numpy as np
import h5py

# Open the file with h5py
with h5py.File("file.hdf5", "r") as gals:
    group = gals["group"]
    x = group["xgal"][:]
    y = group["ygal"][:]
    z = group["zgal"][:]
    mHI = group["mcold_atom"][:]

# Shift positions so min=0
x -= np.min(x)
y -= np.min(y)
z -= np.min(z)

# Define grid parameters
box_dim = np.array([542.16, 542.16, 542.16])
N_grids = np.array([150, 150, 150])

# Project HI onto grid
galaxy_pos = np.column_stack((x, y, z))
mHI_per_cell = project_particle_to_regular_grid(
    particle_pos=galaxy_pos,
    box_len=box_dim,
    box_ndim=N_grids,
    particle_mass=mHI,
    average=False
)[0]

# Calculate HI mass density per cell
V_cell = np.prod(box_dim / N_grids)
rhoHI_cell = mHI_per_cell / V_cell

# Calculate average Omega_HI
rho_crit = 2.775e11
OmegaHI_cell = rhoHI_cell / rho_crit
print("average Omega_HI =", OmegaHI_cell.mean())

How Senior Engineers Fix It

Senior engineers can fix this issue by:

  • Verifying the code: Carefully reviewing the code to ensure that it accurately represents the underlying physics and data.
  • Checking unit conversions: Verifying that unit conversions are correct and consistent throughout the code.
  • Testing with different grid resolutions: Testing the code with different grid resolutions to ensure that the results are accurate and consistent.
  • Consulting with experts: Consulting with experts in the field to ensure that the code accurately represents the underlying physics and data.

Why Juniors Miss It

Juniors may miss this issue due to:

  • Lack of experience: Limited experience working with complex astronomical data and codes.
  • Insufficient understanding of underlying physics: Limited understanding of the underlying physics of the Galform model and the HI density fraction.
  • Overreliance on code: Overreliance on the code without carefully verifying its accuracy and consistency.
  • Failure to test thoroughly: Failure to test the code thoroughly with different grid resolutions and unit conversions.

Leave a Comment