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Sandbox Physics
Stellar Activity and Radial Velocity

AC18 · L4

Earth-mass signals and active stars

A metre-per-second signal is small enough that a choice about the star, the instrument or the observing window can matter as much as the orbit. Read the papers with those choices in view.

Begin with what the line model resolves

Read SOAP 2.0, arXiv 1409.3594v1, Sections 2.1–2.3 and Figure 1. Identify the distinction between removing flux and inhibiting convective blueshift. The authors use local-profile information richer than our equal Gaussian notches. Our live model reproduces a weighted first-moment mechanism; it does not inherit SOAP’s full bisector or cross-correlation fidelity.

Then read Haywood and colleagues, 1407.1044v1, Sections 2–3 and Table A1. Their contemporaneous photometry helps inform stellar variability. Our public replay retains the 2012 numerical table, but its RV–calcium process is a different, deliberately bounded analysis. Agreement with a published amplitude alone would not reproduce that paper’s photometric conditioning or complete inference.

Reading: Dumusque, Boisse & Santos — SOAP 2.0, arXiv:1409.3594v1 · Haywood et al. — CoRoT-7, arXiv:1407.1044v1

Follow the covariance into the result

In Rajpaul and colleagues, 1506.07304v1, follow Sections 3.1–3.4 from a shared latent process to multiple observables. Work through Appendix A.2 before looking at the tests in Sections 4.1–4.2 and Figures 3–4. Ask which observable is coupled to the process itself and which to its derivative. That question fixes the cross-covariance signs.

Separate a model’s own synthetic test from a test generated with a richer physical simulator. The former checks whether inference can recover its assumptions; the latter probes some consequences of misspecification. Neither alone demonstrates that all real stellar variability is captured. Our Gaussian and multimodal importance tests check integration, while the physical spot-only injection checks a different failure mode.

Reading: Rajpaul et al. — joint activity processes, arXiv:1506.07304v1 · Rasmussen & Williams — Gaussian Processes for Machine Learning, Chapter 2

Let the observing window enter the argument

Read “Ghost in the time series,” 1510.05598v1, Sections 2–3 and Figures 1–3. The important comparison keeps the sampling while changing the synthetic signal content. A selected periodic residual can survive a nuisance-removal procedure because of that procedure and the window. Our tutorial does not rerun the Alpha Centauri B data; its own injections demonstrate a related diagnostic on the CoRoT-7 dates.

Before treating a peak as evidence, specify the searched frequencies, nuisance model and selection rule. A conditional interval at the strongest peak does not account for the search. When an activity process absorbs an injected planet, inspect recovery across periods and priors. This tests how reliably the competing contributions can be distinguished.

Reading: Rajpaul, Aigrain & Roberts — Ghost in the time series, arXiv:1510.05598v1 · Rajpaul et al. — joint activity processes, arXiv:1506.07304v1

A modern example with several instrument states

Use Suárez Mascareño and colleagues, 2212.07332v2, especially Sections 2.1–2.2 and 4.1–4.4, Table 2, Table B.1 and Figures 9–10. The 139 observations combine CARMENES with ESPRESSO. ESPRESSO itself is separated into three states around hardware changes. Track the independent offsets and jitters before reading the orbital amplitudes.

This is published research in Astronomy & Astrophysics 670, A5 (2023); the reading link pins the corrected arXiv manuscript. The older abstract prints 20.202 days for the second planet, while the corrected version and parameter tables give 21.202. Checking a number against the tables resolves a typographical discrepancy before it becomes a physical puzzle.

Compare the raw time series, the GP components and the component-subtracted phase folds. A folded curve with other contributions removed is not the original observation. Table B.1’s priors are part of the model. The reported minimum masses near one Earth mass orbit a much lower-mass star at roughly 10.35 and 21.20 days; they are not Earth–Sun analogues with year-long periods and 0.089-metre-per-second amplitudes.

Our real tutorial contains one HARPS campaign, so it cannot empirically recover these multiple-instrument offsets. The separate artificial offset experiment is labeled synthetic. Likewise, an assumed inclination can turn minimum mass into a conditional mass, but it does not create a measured inclination.

Reading: Suárez Mascareño et al. — GJ 1002, arXiv:2212.07332v2 · GJ 1002 — published A&A 670, A5 (2023) · IAU 2015 Resolution B3 — nominal conversion constants

Write down what would change your conclusion

For each paper, record the observing span, independent channels, prior information, instrument divisions and synthetic checks. Then name one missing measurement that would most improve identifiability: longer phase coverage, an activity-sensitive line indicator with known errors, simultaneous photometry or an independent sky orbit. Choose a time and channel where the competing explanations predict different values.

The live Lab gives a surface mechanism, the frozen campaign gives an actual conditional posterior, and these papers show richer observational decisions. Keeping those three scopes explicit makes it possible to move between them without mistaking an attractive animation for a measured stellar map.

Reading: Haywood et al. — CoRoT-7, arXiv:1407.1044v1 · Suárez Mascareño et al. — GJ 1002, arXiv:2212.07332v2

Exercises and explanations

  1. A paper shows a clean folded planet curve after removing activity and another companion. What should you inspect before calling that a raw detection plot?

    Read the explanation

    Find the original time series, the subtracted model components and the posterior assumptions. The fold is a conditional visualization of an allocated component. Its clarity does not remove uncertainty in the terms that were subtracted.

  2. Why can a hardware change require a new zero point even if the instrument retains the same name?

    Read the explanation

    The measured line profile and velocity calibration can change with fibres or other optical elements. A common instrument label does not guarantee a common response. Separate offsets and jitters express that possibility; their need and priors should be justified by the actual instrument history.

Try the prediction

Plan a discriminating measurement

  1. Compare the Lab’s flux-only activity and bright magnetic-region presets.
  2. Choose an added observable that responds differently to the competing explanations. State its required error model before treating it as a new likelihood channel.

Sources and calculation scope

  1. Dumusque, Boisse & Santos — SOAP 2.0, arXiv:1409.3594v1

    11 September 2014 version. Sections 2.1–2.3, Equations 1–7 and Figure 1: surface-profile integration, flux contrast and convective blueshift. Our identical Gaussian local profiles are a simpler independent mechanism model.

  2. Haywood et al. — CoRoT-7, arXiv:1407.1044v1

    3 July 2014 version. Sections 2–3 and 4.2, Equations 1–12 and Table A1. All 71 HARPS2012 numerical rows are retained; the tutorial does not reproduce the full contemporaneous-photometry analysis.

  3. Rajpaul et al. — joint activity processes, arXiv:1506.07304v1

    24 June 2015 version. Sections 3.1–3.4 and 4.1–4.2, Figures 3–4, Appendix A.2. Derivative covariance signs follow a lag equal to first time minus second time; kernel-width normalizations must be kept with their equations.

  4. Rajpaul, Aigrain & Roberts — Ghost in the time series, arXiv:1510.05598v1

    20 October 2015 version. Sections 2–3 and Figures 1–3: sampling-window and synthetic-content tests. The Alpha Centauri B analysis is a source guide, not our reproduced dataset.

  5. Suárez Mascareño et al. — GJ 1002, arXiv:2212.07332v2

    25 January 2023 arXiv version; manuscript dated 26 January. Sections 2.1–2.2 and 4.1–4.4, Table 2, Table B.1, Figures 9–10. Multiple instrument states and minimum-mass interpretation are reading targets; no original figures are republished.

  6. IAU 2015 Resolution B3 — nominal conversion constants

    Official adopted table: nominal solar and terrestrial gravitational parameters and nominal solar radius are exact conversion constants, not uncertainty-free measurements of the real Sun and Earth.

  7. Rasmussen & Williams — Gaussian Processes for Machine Learning, Chapter 2

    Authors’ 2006 Chapter 2, Sections 2.1–2.2 and 2.7: Gaussian conditioning, normalized marginal densities and linear basis functions. The importance sampler and surface quadrature are independently authored here.

  8. GJ 1002 — published A&A 670, A5 (2023)

    Publisher version and DOI 10.1051/0004-6361/202244991 confirm publication status. Its older abstract retains a 20.202-day typo; the pinned arXiv v2 and parameter tables give 21.202 days. The guide reads the corrected author manuscript and preserves this version distinction.

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