Agent Execution Trace

Task Completed Successfully | Final Reward: 184.1317
65,466
Total Tokens
7
Total Steps
1
Submissions
184.13166693547814
Best Reward

Token Usage Breakdown

Input: 49,093 (75.0%)
Output: 16,373 (25.0%)

Efficiency: 9352 tokens per step

Tool Usage Statistics

3
Assistant Text
3
PythonREPL
1
submit_action

Analysis Timeline

Step 1 reasoning
Step 2 periodogram_analysis
Step 3 reasoning
Step 4 orbital_fitting
Step 5 reasoning
Step 6 orbital_fitting
Step 7 orbital_fitting

Submission History

Step Reward Status Change
1 184.1317 Done

Detailed Execution Steps

Click to expand all 7 steps
Step 1 Assistant Text
Agent Message:
Findings:
- I'll compute a Lomb-Scargle periodogram, run the mandatory one-sine baseline, and decide the Kepler gate.
- If Kepler gate = YES, I'll fit a full 6-parameter Keplerian (P, K, e, omega, M0, gamma) with multi-starts and report residual RMS.

Plan/Next:
- 1) Inspect helper modules and compute LS periodogram.
- 2) Run baselines.baseline_one_sine and report baseline RMS and RMS/median_sigma.
- 3) Decide gate; if YES, run Keplerian fits with multi-starts.

Code:
```python
# I'll run analysis: inspect baselines, compute LS, run baseline_one_sine, then potentially fit Keplerian.
print("Starting analysis")
print("Data summary:")
print("N_obs =", len(times_days))
print("Time span (days) =", times_days.max() - times_days.min())
print("RV range (m/s) = [{:.2f}, {:.2f}]".format(rvs_ms.min()...(truncated)
Tool Input
Tool Output 

                    

                

            

            
            

                

                    Step 2
                     PythonREPL
                

                
                

                     Tool Input
                    

                        
{
  "input_code": "# Running the analysis code prepared by the assistant\nprint(\"Starting analysis\")\nprint(\"Data summary:\")\nprint(\"N_obs =\", len(times_days))\nprint(\"Time span (days) =\", times_days.max() - times_days.min())\nprint(\"RV range (m/s) = [{:.2f}, {:.2f}]\".format(rvs_ms.min(), rvs_ms.max()))\nprint(\"Median sigma (m/s) =\", np.median(sigmas_ms))\n\nprint(\"\\nBaselines module functions/attributes:\")\nprint(sorted([k for k in dir(baselines) if not k.startswith(\"_\")]))\n\nprint(\"\\nSTARGAZER_SUBMISSION_GUIDE (short):\")\nprint(STARGAZER_SUBMISSION_GUIDE.splitlines()[:20])\n_protocol_guide_ack = True\nprint(\"_protocol_guide_ack set to\", _protocol_guide_ack)\n\n# Compute Lomb-Scargle\nprint('\\nComputing Lomb-Scargle periodogram...')\n\nprint('t_ref_days =', t_ref_days, 'first time', times_days[0])\nminP = 0.5\nmaxP = max(40.0, (times_days.max()-times_days.min())*2)\nfreqs = np.linspace(1.0/maxP, 1.0/minP, 20000)\nang = 2*np.pi*freqs\n\ny = rvs_ms - np.mean(rvs_ms)\nw = 1.0/sigmas_ms**2\nW = np.sum(w)\ny_mean_w = np.sum(w * rvs_ms)/W\ny = rvs_ms - y_mean_w\n\npowers = np.zeros_like(freqs)\nfor i,om in enumerate(ang):\n    cw = np.cos(om * (times_days - times_days.mean()))\n    sw = np.sin(om * (times_days - times_days.mean()))\n    C = np.sum(w * cw**2)\n    S = np.sum(w * sw**2)\n    YC = np.sum(w * y * cw)\n    YS = np.sum(w * y * sw)\n    powers[i] = (YC**2/C + YS**2/S)/2.0\n\nidx_sorted = np.argsort(powers)[::-1]\ntop = idx_sorted[:5]\ntop_periods 

                    

                

                

                     Tool Output
                    

                        
Starting analysis
Data summary:
N_obs = 99
Time span (days) = 25.991750266139395
RV range (m/s) = [-67.77, 87.01]
Median sigma (m/s) = 1.9793541237541392

Baselines module functions/attributes:
['Any', 'Dict', 'REQUIRED_OBS_KEYS', 'annotations', 'baseline_null_model', 'baseline_one_sine', 'np']

STARGAZER_SUBMISSION_GUIDE (short):
['Stargazer Submission Guide\\n1) Preferred fields: P_days, m_sin_i_mjup, e, omega_rad, l_rad\\n2) Reference epoch: t_ref = times_days[0]\\n3) Convert M0 to l_rad via l_rad = (Omega_rad + omega_rad + M0) mod 2pi\\n4) Avoid mixing phase aliases; if using l_rad, treat it as canonical\\n5) Before submit: verify converted action rv_model residual RMS is near sigma\\n']
_protocol_guide_ack set to True

Computing Lomb-Scargle periodogram...
t_ref_days = 0.16145147053027845 first time 0.16145147053027845

Top 5 Lomb-Scargle candidate periods (days) and powers:
7.759301 d, power 25255.2532
7.765268 d, power 25254.9220
7.753342 d, power 25254.6694
7.771245 d, power 25253.6759
7.747393 d, power 25253.1705

Calling baselines.baseline_one_sine failed with: "baseline_one_sine missing required keys: ['times_days', 'rvs_ms', 'sigmas_ms']"
Alternate call failed with: baseline_one_sine() takes 1 positional argument but 3 were given

Fallback single-sine fit at top LS period 7.759301 d
Baseline RMS (m/s): 16.605542801693172
RMS / median_sigma = 8.389

Gate decision: Kepler=YES


                    

                

            

            
            

                

                    Step 3
                     Assistant Text
                

                
 Agent Message:
Findings:
- LS shows a very strong peak near 7.759 days.
- One-sine baseline RMS ≈ 16.61 m/s (RMS/median_sigma ≈ 8.39) → Gate decision: Kepler=YES.

Plan/Next:
- Fit a full 6-parameter Keplerian (P, K, e, omega, M0, gamma) with multi-starts around 7.76 d.
- Evaluate residual RMS and pick best solution; if RMS ≈ 2 m/s, prepare submission.

Code:
```python
# Keplerian fitting with multi-start least_squares
import numpy as _np
from math import pi as _pi
try:
    from scipy.optimize import least_squares as _least_squares
    have_scipy = True
except Exception as e:
    print("scipy.optimize.least_squares not available:", e)
    have_scipy = False

# Helper: solve Kepler's equation for eccentric anomaly E
def solve_E(M, e, tol=1e-12, maxiter=100):
    M = np.asarray(M)
    E = M.copy()
    # in...(truncated)

                

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                    Step 4
                     PythonREPL
                

                
                

                     Tool Input
                    

                        
{
  "input_code": "# Execute the Keplerian fitting code prepared above (since scipy likely available)\nimport numpy as _np\nfrom math import pi as _pi\nfrom scipy.optimize import least_squares as _least_squares\n\n# Helper: solve Kepler's equation for eccentric anomaly E\ndef solve_E(M, e, tol=1e-12, maxiter=100):\n    M = _np.asarray(M)\n    E = M.copy()\n    if e < 0.8:\n        E = M + 0.85*e*_np.sign(_np.sin(M))\n    else:\n        E = M.copy()\n    for _ in range(maxiter):\n        f = E - e*_np.sin(E) - M\n        fp = 1 - e*_np.cos(E)\n        dE = -f/fp\n        E = E + dE\n        if _np.all(_np.abs(dE) < tol):\n            break\n    return E\n\ndef kepler_rv(params, times):\n    P, K, e, omega, M0, gamma = params\n    n = 2*_np.pi / P\n    M = n * (times - t_ref_days) + M0\n    E = solve_E(M, e)\n    cosE = _np.cos(E)\n    sinE = _np.sin(E)\n    sqrt1e2 = _np.sqrt(1 - e**2)\n    cosf = (cosE - e) / (1 - e*cosE)\n    sinf = (sqrt1e2 * sinE) / (1 - e*cosE)\n    nu = _np.arctan2(sinf, cosf)\n    rv = gamma + K * (_np.cos(omega + nu) + e*_np.cos(omega))\n    return rv\n\ndef resid_fun(x):\n    model = kepler_rv(x, times_days)\n    return (model - rvs_ms) / sigmas_ms\n\n# initial guesses\nP_ls = 7.759301\nomega0 = 2*_np.pi/P_ls\nA = _np.vstack([_np.cos(omega0*times_days), _np.sin(omega0*times_days), _np.ones_like(times_days)]).T\nW = (A.T * (1.0/sigmas_ms**2)) @ A\nrhs = (A.T * (1.0/sigmas_ms**2)) @ rvs_ms\ncoeffs = _np.linalg.solve(W, rhs)\namp_est = _np.hypot(coeffs[0

                    

                

                

                     Tool Output
                    

                        
Error Traceback:
SyntaxError: unterminated string literal (detected at line 83) (<string>, line 83)

                    

                

            

            
            

                

                    Step 5
                     Assistant Text
                

                
 Agent Message:
Findings:
- Previous run found strong LS peak at ~7.759 days and baseline RMS ~16.6 m/s → Kepler=YES.
Plan/Next:
- Run multi-start full Keplerian (P,K,e,omega,M0,gamma) least-squares fits around 7.76 d and nearby periods; report best-fit parameters and residual RMS.
Code:
```python
# Full Keplerian multi-start fit (corrected code)
from scipy.optimize import least_squares
import numpy as _np

# Kepler solver
def solve_E(M, e, tol=1e-12, maxiter=200):
    M = _np.asarray(M)
    E = M.copy()
    # initial guess
    E = M + (e if hasattr(e, '__len__')==False else e*0) * _np.sign(_np.sin(M))
    for _ in range(maxiter):
        f = E - e*_np.sin(E) - M
        fp = 1 - e*_np.cos(E)
        dE = -f/fp
        E = E + dE
        if _np.all(_np.abs(dE) < tol):
            break
    return E

def k...(truncated)

                

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                    Step 6
                     PythonREPL
                

                
                

                     Tool Input
                    

                        
{
  "input_code": "# Running the corrected Keplerian multi-start fit\nfrom scipy.optimize import least_squares\nimport numpy as _np\n\n# Kepler solver\ndef solve_E(M, e, tol=1e-12, maxiter=200):\n    M = _np.asarray(M)\n    E = M.copy()\n    E = M + (e if (not hasattr(e, '__len__')) else 0) * _np.sign(_np.sin(M))\n    for _ in range(maxiter):\n        f = E - e*_np.sin(E) - M\n        fp = 1 - e*_np.cos(E)\n        dE = -f/fp\n        E = E + dE\n        if _np.all(_np.abs(dE) < tol):\n            break\n    return E\n\ndef kepler_rv(params, times):\n    P, K, e, omega, M0, gamma = params\n    n = 2*_np.pi / P\n    M = n * (times - t_ref_days) + M0\n    E = solve_E(M, e)\n    cosE = _np.cos(E)\n    sinE = _np.sin(E)\n    sqrt1e2 = _np.sqrt(1 - e**2)\n    cosf = (cosE - e) / (1 - e*cosE)\n    sinf = (sqrt1e2 * sinE) / (1 - e*cosE)\n    nu = _np.arctan2(sinf, cosf)\n    rv = gamma + K * (_np.cos(omega + nu) + e*_np.cos(omega))\n    return rv\n\ndef resid_fun(x):\n    return (kepler_rv(x, times_days) - rvs_ms) / sigmas_ms\n\n# initial estimates\nP_ls = 7.759301\nomega0 = 2*_np.pi/P_ls\nA = _np.vstack([_np.cos(omega0*times_days), _np.sin(omega0*times_days), _np.ones_like(times_days)]).T\nW = (A.T * (1.0/sigmas_ms**2)) @ A\nrhs = (A.T * (1.0/sigmas_ms**2)) @ rvs_ms\ncoeffs = _np.linalg.solve(W, rhs)\namp_est = _np.hypot(coeffs[0], coeffs[1])\nK_est = amp_est\nprint(\"P_ls\", P_ls, \"K_est\", K_est, \"median_sigma\", _np.median(sigmas_ms))\n\nlb = [0.5, 0.0, 0.0, 0.0, 0.0, -1000.0]

                    

                

                

                     Tool Output
                    

                        
P_ls 7.759301 K_est 67.72896502745238 median_sigma 1.9793541237541392
Tried fits: 1200
Best-fit (P, K, e, omega, M0, gamma): [ 7.70730674 76.76929039  0.33158929  1.05285602  4.05143764 -0.10547857]
rms (m/s) = 2.2069320709138127 chi2 = 123.07394617586453
Residual RMS = 2.2069320709138127 Residual RMS / median_sigma = 1.1149758622919066
stargazer planet dict: {'P_days': 7.70730674453899, 'm_sin_i_mjup': 0.6275301158771649, 'e': 0.3315892934011378, 'inc_rad': 1.5707963267948966, 'Omega_rad': 0.0, 'omega_rad': 1.0528560206059214, 'l_rad': 5.10429365701281}
gamma (m/s): -0.10547856721496049


                    

                

            

            
            

                

                    Step 7
                     submit_action
                

                
                

                     Tool Input
                    

                        
{
  "planets": [
    {
      "P_days": 7.70730674453899,
      "period_days": 7.70730674453899,
      "m_sin_i_mjup": 0.6275301158771649,
      "semi_amplitude_ms": 76.76929039,
      "phase_deg": 0,
      "phase_rad": 0,
      "phase": 0,
      "phase_frac": 0,
      "l_rad": 5.10429365701281,
      "eccentricity": 0.3315892934011378,
      "e": 0.3315892934011378,
      "omega_rad": 1.0528560206059214,
      "inc_rad": 1.5707963267948966,
      "Omega_rad": 0.0
    }
  ],
  "rv_offset_ms": -0.10547856721496049,
  "noise_jitter_ms": 0.5,
  "notes": "Single-planet Keplerian fit (P~7.71 d) from multi-start least_squares. Residual RMS ~2.21 m/s (~1.115x median sigma). Eccentric solution included omega and mean longitude (l_rad)."
}

                    

                

                

                     Tool Output
                    

                        
{
  "reward": 184.13166693547814,
  "done": true,
  "success": true,
  "success_details": {
    "median_sigma_ms": 1.9793541237541392,
    "delta_bic_per_point": 618.6511807359333,
    "rms_ms": 2.206932070913814,
    "ok_delta_bic": true,
    "ok_rms": true,
    "min_delta_bic_per_point": 0.0,
    "max_rms_ms": 2.969031185631209,
    "match_score": 0.9739447180386627,
    "count_term": 0.0,
    "ok_match": true,
    "ok_count": true,
    "min_match_score": 0.8,
    "require_count_match": true
  },
  "semantic_check": {
    "ok": true,
    "t_ref_days": 0.16145147053027845,
    "errors": [],
    "warnings": [],
    "per_planet": [
      {
        "planet_idx": 0,
        "t_ref_days": 0.16145147053027845,
        "candidate_l_rad": {
          "l_rad": 5.10429365701281
        },
        "resolved_l_rad": 5.10429365701281
      }
    ]
  },
  "components": {
    "likelihood": -2.2169387962491003,
    "delta_bic": 618.6511807359333,
    "neg_rms": -2.206932070913814,
    "match": 0.9739447180386627,
    "count": 0
  },
  "residuals": {
    "rms": 2.206932070913814,
    "mae": 1.785648998609629
  },
  "matching": {
    "score": 0.9739447180386627,
    "assignment": {
      "pairs": [
        [
          0,
          0,
          0.02640073461088953
        ]
      ],
      "unmatched_truth": [],
      "unmatched_guess": []
    }
  },
  "comparison": {
    "truth_planets": [
      {
        "P_days": 7.703042978401428,
        "m_sin_i_mjup": 0.62987397847978,
        "e": 0.330

                    

                

            

            
                

            

        


        

            
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