SPHEREx

SPHEREx is a IR space telescope with a linear variable filter. Every pixel corresponds to a linearly varying spectral sample across the detectors.

Every time it images an asteroid or comet it will be sampling a different part of the spectrum of that object.

Here is a small worked example of identifying where every known object lands in all of the public data that has been released so far for this telescope.

import kete
import matplotlib.pyplot as plt
import numpy as np

Fields of View and States

First we download all of the fields of view that have been released so far for SPHEREx.

# Download all of the fields of view from IRSA.
# This does not download the images! This only downloads the metadata
# which describes the position on sky of every frame.
# The SPICE kernel for SPHEREx is not publicly available, as a result of this
# This uses a custom SPICE kernel, built from publicly available data.
# THIS IS NOT AN OFFICIAL KERNEL! However it matches the positions available
# on JPL Horizons to within about 30km.
fovs = kete.spherex.fetch_fovs()

# Load the list of all known objects from the MPC.
# This contains a complete list of all asteroids.
table = kete.mpc.fetch_known_orbit_data()
states = kete.conversion.table_to_states(table)

# About a dozen of these objects have hit the earth, we need to remove them
states = kete.propagate_n_body(states, fovs[0].jd, suppress_impact_errors=True)
states = [s for s in states if s.is_finite]

What is in frame!

Perform N-body propagation for every object in every frame, this result typically matches horizons to within a few km per decade.

Note

This takes about 5 minutes to run on a laptop, as it is performing orbit propagation using full N-Body mechanics of all planets with corrections for general relativity. This is propagating the entire MPC catalog.

# Compute what object lands in every frame.
visible = kete.fov_state_check(states, fovs)

The Busiest Frame

Plotting the frame with the most objects in it.

indices = np.argsort([-len(v) for v in visible])[:10]
idx = indices[1]

plt.figure(dpi=300)

fov = visible[idx].fov
frame = kete.spherex.fetch_frame(fov)
wcs = kete.plot.plot_fits_image(frame, percentiles=None)

for idx, vec in enumerate(visible[idx].obs_vecs):
    kete.plot.annotate_plot(wcs, vec, style='o', px_gap=1, lw=0.5)
plt.xlim(0, frame.data.shape[0])
plt.ylim(0, frame.data.shape[1])
plt.title(f"SPHEREx Frame")
plt.savefig("data/spherex.png")
plt.close()
SPHEREx labelling of all objects.