Reproduce the table with photometric uncertainties on the Gaia science performance pages

This notebook below shows how to reproduce the numbers in the table of photometric uncertainties on the Gaia science performance web pages. This also serves as a test to verify that the numbers on the science performance pages are correctly reproduced.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from pygaia.errors.photometric import magnitude_uncertainty as magunc
from pygaia.photometry.transformations import gbrminv_from_vminic

plt.style.use("./agab.mplstyle")

Calculating the uncertainties at fixed \(G\) for different spectral types

The code below uses the transformations from the Johnson-Cousins to the Gaia photometric systems to assign the correct values of \(G_\mathrm{BP}\) and \(G_\mathrm{RP}\), given \(G\), for stars of different spectral types (B1V, G2V, M6V).

NOTE The values of \((V-I)_\mathrm{c}\) for the spectal types above are as used on the Gaia science performance pages. They are slightly different from the result of vminic_from_spt() from the pygaia.photometry.utils module.

gmags = np.arange(13, 21)
gbpmags = {}
grpmags = {}
spt_vminic = {"B1V": -0.22, "G2V": 0.75, "M6V": 3.85}

for spt in spt_vminic.keys():
    gminv, bpminv, rpminv, _ = gbrminv_from_vminic(spt_vminic[spt])
    gbpmags[spt] = gmags + bpminv - gminv
    grpmags[spt] = gmags + rpminv - gminv

dfs = {}
for spt in spt_vminic.keys():
    dfs[spt] = pd.DataFrame({"G": gmags})
    for release in ["dr3", "dr4", "dr5"]:
        dfs[spt][f"sigG_{release.upper()}"] = magunc("g", gmags, release=release)
    for release in ["dr3", "dr4", "dr5"]:
        valid = gbpmags[spt] <= 21.0
        uncs = np.zeros(gmags.size)
        uncs[valid] = magunc("bp", gbpmags[spt][valid], release=release)
        uncs[np.logical_not(valid)] = np.nan
        dfs[spt][f"sigBP_{release.upper()}"] = uncs
    for release in ["dr3", "dr4", "dr5"]:
        valid = grpmags[spt] <= 21.0
        uncs = np.zeros(gmags.size)
        uncs[valid] = magunc("rp", grpmags[spt][valid], release=release)
        uncs[np.logical_not(valid)] = np.nan
        dfs[spt][f"sigRP_{release.upper()}"] = uncs

Recreate the tables from the Gaia performance pages

spt = "B1V"
dfs[spt].style.format("{:.1f}").hide(axis="index").set_caption(spt)

B1V

G	sigG_DR3	sigG_DR4	sigG_DR5	sigBP_DR3	sigBP_DR4	sigBP_DR5	sigRP_DR3	sigRP_DR4	sigRP_DR5
13.0	0.2	0.1	0.1	0.6	0.5	0.3	0.6	0.4	0.3
14.0	0.2	0.1	0.1	0.8	0.6	0.4	1.0	0.7	0.5
15.0	0.3	0.2	0.1	1.2	0.9	0.6	1.8	1.4	1.0
16.0	0.4	0.3	0.2	2.2	1.7	1.2	3.8	2.9	2.0
17.0	0.7	0.6	0.4	4.3	3.3	2.3	8.4	6.3	4.5
18.0	1.3	1.0	0.7	8.9	6.7	4.8	17.6	13.3	9.4
19.0	2.4	1.8	1.3	19.7	14.9	10.5	38.9	29.4	20.8
20.0	4.5	3.4	2.4	41.8	31.6	22.4	82.6	62.5	44.2

spt = "G2V"
dfs[spt].style.format("{:.1f}").hide(axis="index").set_caption(spt)

G2V

G	sigG_DR3	sigG_DR4	sigG_DR5	sigBP_DR3	sigBP_DR4	sigBP_DR5	sigRP_DR3	sigRP_DR4	sigRP_DR5
13.0	0.2	0.1	0.1	0.6	0.5	0.3	0.4	0.3	0.2
14.0	0.2	0.1	0.1	0.9	0.7	0.5	0.6	0.5	0.3
15.0	0.3	0.2	0.1	1.5	1.2	0.8	1.1	0.9	0.6
16.0	0.4	0.3	0.2	2.9	2.2	1.6	2.2	1.7	1.2
17.0	0.7	0.6	0.4	5.8	4.4	3.1	4.7	3.5	2.5
18.0	1.3	1.0	0.7	12.4	9.4	6.6	10.2	7.7	5.4
19.0	2.4	1.8	1.3	27.2	20.5	14.5	21.3	16.1	11.4
20.0	4.5	3.4	2.4	58.4	44.1	31.2	48.8	36.9	26.1

spt = "M6V"
dfs[spt].style.format("{:.1f}", na_rep="-").hide(axis="index").set_caption(spt)

M6V

G	sigG_DR3	sigG_DR4	sigG_DR5	sigBP_DR3	sigBP_DR4	sigBP_DR5	sigRP_DR3	sigRP_DR4	sigRP_DR5
13.0	0.2	0.1	0.1	1.6	1.2	0.9	0.4	0.3	0.2
14.0	0.2	0.1	0.1	3.1	2.3	1.7	0.4	0.3	0.2
15.0	0.3	0.2	0.1	6.2	4.7	3.3	0.7	0.5	0.4
16.0	0.4	0.3	0.2	13.4	10.1	7.2	1.2	0.9	0.6
17.0	0.7	0.6	0.4	29.1	22.0	15.6	2.3	1.8	1.2
18.0	1.3	1.0	0.7	62.9	47.5	33.6	5.0	3.8	2.7
19.0	2.4	1.8	1.3	-	-	-	10.9	8.2	5.8
20.0	4.5	3.4	2.4	-	-	-	22.8	17.2	12.2

Show the photometric uncertainties as a function of \(G\)

gmags = np.linspace(4, 21, 17 * 50)
gbpmags = {}
grpmags = {}

for spt in spt_vminic.keys():
    gminv, bpminv, rpminv, _ = gbrminv_from_vminic(spt_vminic[spt])
    gbpmags[spt] = gmags + bpminv - gminv
    grpmags[spt] = gmags + rpminv - gminv

dfs = {}
for spt in spt_vminic.keys():
    dfs[spt] = pd.DataFrame({"G": gmags})
    for release in ["dr3", "dr4", "dr5"]:
        dfs[spt][f"sigG_{release.upper()}"] = magunc("g", gmags, release=release)
    for release in ["dr3", "dr4", "dr5"]:
        valid = (gbpmags[spt] <= 21.0) & (gbpmags[spt] >= 4.0)
        uncs = np.zeros(gmags.size)
        uncs[valid] = magunc("bp", gbpmags[spt][valid], release=release)
        uncs[np.logical_not(valid)] = np.nan
        dfs[spt][f"sigBP_{release.upper()}"] = uncs
    for release in ["dr3", "dr4", "dr5"]:
        valid = (grpmags[spt] <= 21.0) & (grpmags[spt] >= 4.0)
        uncs = np.zeros(gmags.size)
        uncs[valid] = magunc("rp", grpmags[spt][valid], release=release)
        uncs[np.logical_not(valid)] = np.nan
        dfs[spt][f"sigRP_{release.upper()}"] = uncs

fig, ax = plt.subplots(1, 1, figsize=(16, 9))

release = "dr3"
ax.plot(gmags, np.log10(dfs["B1V"][f"sigG_{release.upper()}"]), "-", lw=3, label=r"$G$")
for spt, kleur in zip(spt_vminic.keys(), ["C1", "C2", "C3"]):
    ax.plot(
        gmags,
        np.log10(dfs[spt][f"sigBP_{release.upper()}"]),
        "-",
        c=kleur,
        lw=3,
        label=rf"{spt}: $G_\mathrm{{BP}}$",
    )
    ax.plot(
        gmags,
        np.log10(dfs[spt][f"sigRP_{release.upper()}"]),
        "--",
        c=kleur,
        lw=3,
        label=rf"{spt}: $G_\mathrm{{RP}}$",
    )
ax.grid(True)
ax.set_xlabel(r"$G$ [mag]")
ax.set_ylabel(r"$\log10(\sigma)$ [mmag]")
ax.legend()
ax.set_title(f"Gaia {release.upper()} photometric uncertainties")

plt.show()