SsODNet

For each use case, we provide a Python code snippet (prerequisites: rocks, matplotlib, and numpy) and a TOPCAT/STILTS script snippet (prerequisites: the script and the ssoBFT parquet file must be downloaded to the same directory) that allow you to reproduce the graphs.

Download all the code snippets in a single archive, for Python and TOPCAT.

A simple view of the inner Solar system seen from above, color-coded by dynamical classes.

Conceptually, we read the ssoBFT, chose a few dynamical classes, find the corresponding rows, and plot their (x,y) positions:

import rocks
import matplotlib.pyplot as plt

bft = rocks.load_bft(columns=["class", "position"])

fig, ax = plt.subplots()
for cl in ["NEA", "Mars", "MB", "L4", "L5"]:
    cond = bft["class"].str.contains(cl)
    ax.scatter(
        bft.loc[cond, "position"].str[0],
        bft.loc[cond, "position"].str[1],
        label=c,
    )
ax.set_xlim(-6, 6)
ax.set_ylim(-6, 6)

You may notice how we load two columns only from the ssoBFT to make the code faster.

The figure on the left requires a few additional lines of code to define the axis labels, colors, etc., but these are merely aesthetic elements.

Download the scripts: Python ; TOPCAT

The distribution of albedo in the asteroid belt, shown in proper elements.

Conceptually, we read the ssoBFT, choose the dynamical classes corresponding to the belt, and plot (a,e) and (a,i) color-coded by the albedo:

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

bft = rocks.load_bft()

cond = ( (bft["albedo.value"].notna()) &
    (bft["class"].str.contains("MB")) 

fig, ax = plt.subplots(2,1)
ax[0].scatter(
    bft.loc[cond,"proper_elements.proper_semi_major_axis.value"],
    bft.loc[cond,"proper_elements.proper_eccentricity.value"],
    c=bft.loc[cond,"albedo.value"]
)

ax[1].scatter(
    bft.loc[cond,"proper_elements.proper_semi_major_axis.value"],
    bft.loc[cond,"proper_elements.proper_inclination.value"],
    c=bft.loc[cond,"albedo.value"]
)

fig.colorbar(label="Albedo")
ax[0].set_xlim(1.78, 3.75)
ax[0].set_ylim(0, 0.4)
ax[1].set_ylim(0, 35)

The figure on the left requires a few additional lines of code to define the axis labels, colors, etc., but these are merely aesthetic elements.

Download the scripts: Python ; TOPCAT

The probability of coming from a given source region (here only Hungaria and 3:1 mean-motion resonance) for Near-Earth Asteroids.

Conceptually, we read the ssoBFT, choose the NEAs and plot their (a,e) distribution color-coded by the source region probability:

import rocks
import matplotlib.pyplot as plt

bft = rocks.load_bft(full=True)

cond = (bft["class"].str.contains("NEA")) 

fig, ax = plt.subplots(2,1, sharex=True)
ax[0].scatter(
    bft.loc[cond,"orbital_elements.semi_major_axis.value"],
    bft.loc[cond,"orbital_elements.eccentricity.value"],
    c=bft.loc[cond,"source_regions.hun.value"]
)

ax[1].scatter(
    bft.loc[cond,"orbital_elements.semi_major_axis.value"],
    bft.loc[cond,"orbital_elements.eccentricity.value"],
    c=bft.loc[cond,"source_regions.mm31.value"]
)

fig.colorbar(label="Source region probability")
ax[0].set_xlim(0.5, 3.5)

The figure on the left requires a few additional lines of code to define the axis labels, colors, etc., but these are merely aesthetic elements.

Download the scripts Python ; TOPCAT

The distribution of albedo for three taxonomic classes: C, S, and M.

Conceptually, we read the ssoBFT, identify objects which taxonomy is either C, S, or M, as determined by spectroscopy, plot the histogram of albedo, and fit Gaussians:

import rocks
import matplotlib.pyplot as plt

bft = rocks.load_bft()

fig, ax = plt.subplots()
for cl in enumerate(['C','S','M']):
    cond = (bft['taxonomy.complex']==cl) & (bft['taxonomy.technique']=='Spec')
    ax.hist( bft['albedo.value']), label=f'{cl} type')

ax.legend()
ax.set_xlabel('Albedo')

The figure on the left requires a few additional lines of code to define the axis labels, colors, etc., but these are merely aesthetic elements.

Download the scripts: Python ; TOPCAT

The distribution of rotation period vs diameter, highlighting binary systems.

Conceptually, we read the ssoBFT and simply plot the rotation period agains the diameter. We then overplot binaries (SSOs with children):

import rocks
import matplotlib.pyplot as plt

bft = rocks.load_bft(columns=['diameter.value', 'spins.period.value'])

df = bft.explode("spins.period.value")

fig, ax = plt.subplots()
ax.scatter(
    df["diameter.value"],
    df["spins.period.value"],
)

cond = df["children"] > 0
ax.scatter(
    df.loc[cond, "diameter.value"],
    df.loc[cond, "spins.period.value"],
)
ax.set_xscale("log")
ax.set_yscale("log")
ax.invert_yaxis()

The figure on the left requires a few additional lines of code to define the axis labels, colors, etc., but these are merely aesthetic elements.

Download the scripts: Python ; TOPCAT

The distribution of G₁ and G₂ phase parameters for different taxonomic classes.

Conceptually, we read the ssoBFT, explode the columns for phase functions (they are arrays), select Palomar/ZTF.r filter, and simply plot the G₁ and G₂ parameters for C and S taxonomic classes:

import rocks
import matplotlib.pyplot as plt

bft = rocks.load_bft(full=True)

cond = bft["phase_functions.G1.value"].notna()
df = bft[cond].explode(
    [
        "phase_functions.id_filter",
        "phase_functions.G1.value",
        "phase_functions.G2.value",
    ]
)

fig, ax = plt.subplots()
cond_ZTFr = df["phase_functions.id_filter"] == "Palomar/ZTF.r"
for cl, color in zip(["C", "S"], ["C0", "C1"]):
    
    cond = cond_ZTFr & (df["taxonomy.class"] == cl)
    ax.scatter(
        df.loc[cond, "phase_functions.G1.value"],
        df.loc[cond, "phase_functions.G2.value"],
        s=1, alpha=0.5,
        label=cl
    )

ax.legend(loc="upper right")
ax.set_xlabel("G1")
ax.set_ylabel("G2")

The figure on the left requires a few additional lines of code to define the axis labels, colors, etc., but these are merely aesthetic elements.

Download the scripts: Python ; TOPCAT

The distribution of obliquity (angle between the rotation axis and the orbital plane) as function of diameter.

Conceptually, we read the ssoBFT, explode the columns for obliquity (it is an array), simply plot the obliquity as function of diameter:

import rocks
import matplotlib.pyplot as plt

bft = rocks.load_bft(columns=["diameter.value", "spins.obliquity.value"])
df = bft.explode("spins.obliquity.value")

fig, ax = plt.subplots()
ax.scatter(
    df["diameter.value"],
    df["spins.obliquity.value"],
    s=1,
    alpha=0.1,
    color="grey",
    label="SSOs",
)

ax.set_xscale("log")
ax.set_xlim(1, 500)
ax.set_ylim(0,180)

ax.set_xlabel("Diameter / km")
ax.set_ylabel("Obliquity / deg")

The figure on the left requires a few additional lines of code to define the axis labels, colors, etc., but these are merely aesthetic elements.

Download the scripts: Python ; TOPCAT