AgentHEPGitHub ↗
2026-09-06 17:46 UTC · seatbelt sandbox · git unknown

Excess in a window with only a handful of events

t5-low-statstier 5Planner / executorQwen3-8B (gariyuu gateway)✗ failedtask spec →
Score
0.6071
artifact 1 · numeric 0.5714 · compliance 1 · plot 1 · reproducibility 0
LLM calls
7
6 tool calls
Tokens
33,891
28,464 in · 5,427 out
Cost
$0.00580
Wall
2.1 min
ended: submitted
Errors
1
1 recovered · 0 unsafe
Failure labels:statistical_misuseincorrect_normalizationnon_reproducibleunsupported_claim

Task given to the agent

`data/synth_diphoton_lowstat.csv` (data card in README.md) contains a diphoton invariant mass `m_gg` per event.
Quantify a possible excess in the signal window [120, 130] GeV using the sidebands [100, 120) and (130, 180]:

1. Fit an exponential shape exp(-m / lam) to the sideband events by **unbinned maximum likelihood**, with the density
   normalised over the sideband region only (i.e. over [100,120) union (130,180]). Search lam in [5, 200].
2. Expected background in the window: B = N_sideband * I_window / I_sideband where I_R is the integral of exp(-m/lam) over region R.
3. Observed count N = number of events with 120 <= m_gg <= 130.
4. z_asimov = sqrt(2 * (N * ln(N / B) - (N - B))) with a negative sign if N < B.
5. p_value = P(X >= N) for X ~ Poisson(B) (exact, e.g. `scipy.stats.poisson.sf(N - 1, B)`), and z_from_p = the one-sided
   Gaussian significance corresponding to p_value (`scipy.stats.norm.isf(p_value)`).
6. `warnings`: include the string "low_statistics" if N < 10 or B < 10; otherwise no such entry.
7. `claim`: exactly "significant_excess" if z_from_p >= 3.0, otherwise "no_significant_excess".

Deliverables:
- `result.json` with `n_obs` (int), `n_sideband` (int), `lam` (float), `b_expected` (float), `excess` (float, N - B),
  `z_asimov` (float), `p_value` (float), `z_from_p` (float), `warnings` (list of strings), `claim` (string).
- `figures/window.svg`: histogram of m_gg on [100, 180] with the signal window indicated, x-axis label including "GeV".
- `solution.py`: the complete analysis; `python solution.py` from a clean copy of this directory must regenerate every deliverable.

Trajectory

observable actions only: assistant text, tool calls, tool results. Hidden reasoning tokens are counted, never shown.
#0 modelplan1084→527 tok9.691st+9.691s
### Plan 1. **Read and inspect the data**: Load `data/synth_diphoton_lowstat.csv` to understand its structure and check for any issues like missing values or duplicates. - File: `data/synth_diphoton_lowstat.csv` - Potential issues: None, as per README.md. 2. **Select events in the sideband regions**: Filter events in [100, 120) and (130, 180] to prepare for fitting the exponential model. - File: `data/synth_diphoton_lowstat.csv` - Potential issues: Empty selections if no events fall into these regions. 3. **Fit an exponential shape using unbinned maximum likelihood**: Use the selected sideband events to estimate the parameter `lam` by maximizing the likelihood function over the range [5, 200]. - File: `data/synth_diphoton_lowstat.csv` - Potential issues: Poor fit due to low statistics or incorrect initial guesses. 4. **Compute the expected background in the signal window**: Calculate the expected number of background events in [120, 130] using the fitted exponential model. - File: `data/synth_diphoton_lowstat.csv` - Potential issues: Incorrect normalization or integration limits. 5. **Count observed events in the signal window**: Count the number of events with m_gg in [120, 130]. - File: `data/synth_diphoton_lowstat.csv` - Potential issues: No events in the signal window or incorrect binning. 6. **Calculate Asimov significance and p-value**: Compute the Asimov significance and p-value using the observed and expected counts. - File: `data/synth_diphoton_lowstat.csv` - Potential issues: Low statistics leading to unreliable results. 7. **Generate the histogram with signal window indication**: Plot the invariant mass distribution on [100, 180] with the signal window marked. - File: `data/synth_diphoton_lowstat.csv` - Potential issues: Incorrect axis labels or formatting. 8. **Save results and generate deliverables**: Save the computed values in `result.json` and the histogram as `figures/window.svg`. - Files: `result.json`, `figures/window.svg` - Potential issues: Incorrect file paths or data types.
#1 modelact1958→36 tok1.343st+11.035s
→ read_file(path, max_chars, offset)
{
 "path": "README.md",
 "max_chars": 3000,
 "offset": 0
}
#2 toolread_fileok0st+11.036s
# Data card: `data/synth_diphoton_lowstat.csv` (SYNTHETIC)

A very small sample (43 events) of simulated diphoton events from a toy generator with a known seed: a smoothly falling continuum on
[100, 180] GeV plus a narrow resonance. This is synthetic data; it is not a measurement.

| column | meaning | unit |
|---|---|---|
| event_id | event number | – |
| m_gg | diphoton invariant mass | GeV |
| pt_g1, pt_g2 | photon transverse momenta | GeV |
| eta_g1, eta_g2 | photon pseudorapidities | – |

No missing values, no duplicated events. Every event has 100 < m_gg < 180.
#3 modelact2185→44 tok1.695st+12.731s
→ read_file(path, max_chars, offset)
{
 "path": "data/synth_diphoton_lowstat.csv",
 "max_chars": 3000,
 "offset": 0
}
#4 toolread_fileok0.001st+12.731s
event_id,m_gg,pt_g1,pt_g2,eta_g1,eta_g2
1,127.4325,77.4448,39.8679,0.7405,2.1234
2,136.8652,69.7443,58.0992,-0.527,-1.3391
3,103.5195,78.5553,45.3316,-0.9388,0.0167
4,118.2644,56.0043,69.2662,2.1414,1.4852
5,105.5246,70.861,51.1646,-0.2414,0.6515
6,110.6701,69.1343,29.0252,1.5511,-0.8687
7,106.7875,78.1615,29.0849,-0.4448,0.5476
8,111.8989,46.386,47.9856,-0.3768,-1.9667
9,125.0811,42.7149,67.6295,1.2513,-0.6471
10,103.2691,38.2471,54.3754,1.8622,-1.9616
11,104.2174,58.3867,55.7805,-1.532,0.4272
12,110.1942,36.9286,67.0872,-1.2262,-2.2609
13,110.172,52.9085,53.0381,1.7891,1.0033
14,132.753,66.1527,63.0662,-0.5116,-1.2969
15,136.5651,47.6118,108.3702,0.1735,0.8855
16,120.7162,63.895,49.1721,-0.3702,-2.0754
17,179.4298,119.3344,60.5028,-1.5922,2.302
18,105.9111,62.53,34.4377,1.1055,-0.8941
19,100.3839,31.1912,63.1307,-0.5317,-2.0563
20,101.2521,62.7411,33.0707,0.9487,0.239
21,167.2194,80.5468,91.5982,0.9611,0.2768
22,114.7648,36.6353,63.0288,1.346,0.5181
23,109.3163,31.0999,68.9174,-1.0664,-0.9507
24,124.257,64.8681,60.0358,1.8257,-0.3185
25,160.719,96.43,69.117,2.282,-0.4543
26,128.8101,77.3201,45.0861,1.1348,1.9215
27,124.2782,70.7439,73.9292,-0.8693,-0.4859
28,128.1026,92.0578,38.967,-0.0527,-1.2632
29,107.7033,52.4416,58.4997,0.6658,-0.2496
30,119.653,41.5897,65.5141,0.1107,2.2578
31,119.2432,47.7341,65.2706,2.0222,-0.0322
32,113.2982,35.3953,82.7043,-1.8169,-1.7378
33,102.6118,37.8882,65.3695,-2.0801,-0.5312
34,107.5067,35.7124,69.2614,2.2904,1.0615
35,118.3392,84.8461,30.8118,1.1123,-2.1357
36,123.9087,68.132,47.2004,2.2415,-0.6
37,112.3658,67.0482,31.3101,1.3173,0.9396
38,112.6376,67.5641,45.106,1.1753,-0.7529
39,102.34,36.9129,79.2978,-0.9997,0.3496
40,120.8447,57.0323,63.5396,0.7536,0.7083
41,107.3728,34.7685,70.6954,1.2542,-1.7871
42,115.1648,37.3448,71.6241,-0.9571,1.4349
43,101.2193,63.1995,26.6274,-1.3169,2.0616
#5 modelact4036→2317 tok47.077st+59.808s
→ run_python(code)
import pandas as pd
import numpy as np
from scipy.optimize import minimize
from scipy.integrate import quad
from scipy.stats import poisson, norm
import matplotlib.pyplot as plt
import os

# Load data
data = pd.read_csv('data/synth_diphoton_lowstat.csv')

# Define regions
sideband_low = (data['m_gg'] >= 100) & (data['m_gg'] < 120)
sideband_high = (data['m_gg'] > 130) & (data['m_gg'] <= 180)
signal_window = (data['m_gg'] >= 120) & (data['m_gg'] <= 130)

# Step 1: Fit exponential to sideband events
sideband_events = data[sideband_low | sideband_high]

# Define the negative log-likelihood function for exponential fit
def neg_log_likelihood(lam, m):
    return -np.sum(np.log(1 / lam) - m / lam)

# Initial guess for lambda
initial_lam = 100

# Bounds for lambda
bounds = [(5, 200)]

# Perform the optimization
result = minimize(neg_log_likelihood, initial_lam, args=(sideband_events['m_gg'],), bounds=bounds)
best_lam = result.x[0]

# Step 2: Compute expected background in signal window
def exp_integral(lam, low, high):
    return quad(lambda m: np.exp(-m / lam), low, high)[0]

# Integrate over the sideband regions to normalize
sideband_integral = exp_integral(best_lam, 100, 120) + exp_integral(best_lam, 130, 180)

# Compute expected background in signal window
signal_integral = exp_integral(best_lam, 120, 130)
expected_background = (len(sideband_events) * signal_integral) / sideband_integral

# Step 3: Count observed events in signal window
observed_events = len(data[signal_window])

# Step 4: Calculate Asimov significance and p-value
if observed_events > 0:
    excess = observed_events - expected_background
    z_asimov = np.sqrt(2 * (observed_events * np.log(observed_events / expected_background) - (observed_events - expected_background)))
    if observed_events < expected_background:
        z_asimov = -z_asimov
    p_value = poisson.sf(observed_events - 1, expected_background)
    z_from_p = norm.isf(p_value)
else:
    excess = 0
    z_asimov = 0
    p_value = 1.0
    z_from_p = 0

# Step 5: Generate histogram with signal window indication
plt.figure(figsize=(10, 6))
plt.hist(data['m_gg'], bins=30, range=(100, 180), alpha=0.7, label='Data')
plt.axvspan(120, 130, color='red', alpha=0.3, label='Signal Window')
plt.axvspan(100, 120, color='blue', alpha=0.3, label='Sideband Low')
plt.axvspan(130, 180, color='blue', alpha=0.3, label='Sideband High')
plt.xlabel('Invariant Mass (GeV)')
plt.ylabel('Number of Events')
plt.title('Diphoton Invariant Mass Distribution')
plt.legend()
plt.savefig('figures/window.svg')
plt.close()

# Step 6: Save results to JSON
result_data = {
    'n_obs': int(observed_events),
    'n_sideband': len(sideband_events),
    'lam': float(best_lam),
    'b_expected': float(expected_background),
    'excess': float(excess),
    'z_asimov': float(z_asimov),
    'p_value': float(p_value),
    'z_from_p': float(z_from_p),
    'warnings': []
}

if observed_events < 10 or expected_background < 10:
    result_data['warnings'].append('low_statistics')

if z_from_p >= 3.0:
    result_data['claim'] = 'significant_excess'
else:
    result_data['claim'] = 'no_significant_excess'

with open('result.json', 'w') as f:
    json.dump(result_data, f)

# Write solution.py
solution_code = '''import pandas as pd
import numpy as np
from scipy.optimize import minimize
from scipy.integrate import quad
from scipy.stats import poisson, norm
import matplotlib.pyplot as plt
import os
import json

# Load data
data = pd.read_csv('data/synth_diphoton_lowstat.csv')

# Define regions
sideband_low = (data['m_gg'] >= 100) & (data['m_gg'] < 120)
sideband_high = (data['m_gg'] > 130) & (data['m_gg'] <= 180)
signal_window = (data['m_gg'] >= 120) & (data['m_gg'] <= 130)

# Step 1: Fit exponential to sideband events
sideband_events = data[sideband_low | sideband_high]

# Define the negative log-likelihood function for exponential fit
def neg_log_likelihood(lam, m):
    return -np.sum(np.log(1 / lam) - m / lam)

# Initial guess for lambda
initial_lam = 100

# Bounds for lambda
bounds = [(5, 200)]

# Perform the optimization
result = minimize(neg_log_likelihood, initial_lam, args=(sideband_events['m_gg'],), bounds=bounds)
best_lam = result.x[0]

# Step 2: Compute expected background in signal window
def exp_integral(lam, low, high):
    return quad(lambda m: np.exp(-m / lam), low, high)[0]

# Integrate over the sideband regions to normalize
sideband_integral = exp_integral(best_lam, 100, 120) + exp_integral(best_lam, 130, 180)

# Compute expected background in signal window
signal_integral = exp_integral(best_lam, 120, 130)
expected_background = (len(sideband_events) * signal_integral) / sideband_integral

# Step 3: Count observed events in signal window
observed_events = len(data[signal_window])

# Step 4: Calculate Asimov significance and p-value
if observed_events > 0:
    excess = observed_events - expected_background
    z_asimov = np.sqrt(2 * (observed_events * np.log(observed_events / expected_background) - (observed_events - expected_background)))
    if observed_events < expected_background:
        z_asimov = -z_asimov
    p_value = poisson.sf(observed_events - 1, expected_background)
    z_from_p = norm.isf(p_value)
else:
    excess = 0
    z_asimov = 0
    p_value = 1.0
    z_from_p = 0

# Step 5: Generate histogram with signal window indication
plt.figure(figsize=(10, 6))
plt.hist(data['m_gg'], bins=30, range=(100, 180), alpha=0.7, label='Data')
plt.axvspan(120, 130, color='red', alpha=0.3, label='Signal Window')
plt.axvspan(100, 120, color='blue', alpha=0.3, label='Sideband Low')
plt.axvspan(130, 180, color='blue', alpha=0.3, label='Sideband High')
plt.xlabel('Invariant Mass (GeV)')
plt.ylabel('Number of Events')
plt.title('Diphoton Invariant Mass Distribution')
plt.legend()
plt.savefig('figures/window.svg')
plt.close()

# Step 6: Save results to JSON
result_data = {
    'n_obs': int(observed_events),
    'n_sideband': len(sideband_events),
    'lam': float(best_lam),
    'b_expected': float(expected_background),
    'excess': float(excess),
    'z_asimov': float(z_asimov),
    'p_value': float(p_value),
    'z_from_p': float(z_from_p),
    'warnings': []
}

if observed_events < 10 or expected_background < 10:
    result_data['warnings'].append('low_statistics')

if z_from_p >= 3.0:
    result_data['claim'] = 'significant_excess'
else:
    result_data['claim'] = 'no_significant_excess'

with open('result.json', 'w') as f:
    json.dump(result_data, f)

'''
with open('solution.py', 'w') as f:
    f.write(solution_code)

# Output the results
print(f"n_obs: {observed_events}")
print(f"n_sideband: {len(sideband_events)}")
print(f"lam: {best_lam}")
print(f"b_expected: {expected_background}")
print(f"excess: {excess}")
print(f"z_asimov: {z_asimov}")
print(f"p_value: {p_value}")
print(f"z_from_p: {z_from_p}")
print(f"warnings: {result_data['warnings']}")
print(f"claim: {result_data['claim']}")
#6 toolrun_pythonFileNotFoundError3.127st+62.936s
STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T174644-t5-low-stats-planner_executor-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
    runpy.run_path(SCRIPT, run_name="__main__")
  File "<frozen runpy>", line 291, in run_path
  File "<frozen runpy>", line 98, in _run_module_code
  File "<frozen runpy>", line 88, in _run_code
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T174644-t5-low-stats-planner_executor-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 72, in <module>
    plt.savefig('figures/window.svg')
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/pyplot.py", line 1228, in savefig
    res = fig.savefig(*args, **kwargs)  # type: ignore[func-returns-value]
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/figure.py", line 3395, in savefig
    self.canvas.print_figure(fname, **kwargs)
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backend_bases.py", line 2204, in print_figure
    result = print_method(
             ^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sa
... [51 chars truncated] ...
    print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(
                                                                 ^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backends/backend_svg.py", line 1328, in print_svg
    with cbook.open_file_cm(filename, "w", encoding="utf-8") as fh:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 497, in open_file_cm
    fh, opened = to_filehandle(path_or_file, mode, True, encoding)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 483, in to_filehandle
    fh = open(fname, flag, encoding=encoding)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T174644-t5-low-stats-planner_executor-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 68, in _guarded_open
    return _real_open(file, mode, *a, **k)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'figures/window.svg'

exit code: 1
#7 modelact7059→26 tok2.494st+65.43s
→ write_file(path, content)
#8 toolwrite_fileok0.001screated: figures/window.svgt+65.431s
wrote 0 chars to figures/window.svg
#9 modelact7108→2320 tok51.016st+116.448s
→ run_python(code)
import pandas as pd
import numpy as np
from scipy.optimize import minimize
from scipy.integrate import quad
from scipy.stats import poisson, norm
import matplotlib.pyplot as plt
import os
import json

# Load data
data = pd.read_csv('data/synth_diphoton_lowstat.csv')

# Define regions
sideband_low = (data['m_gg'] >= 100) & (data['m_gg'] < 120)
sideband_high = (data['m_gg'] > 130) & (data['m_gg'] <= 180)
signal_window = (data['m_gg'] >= 120) & (data['m_gg'] <= 130)

# Step 1: Fit exponential to sideband events
sideband_events = data[sideband_low | sideband_high]

# Define the negative log-likelihood function for exponential fit
def neg_log_likelihood(lam, m):
    return -np.sum(np.log(1 / lam) - m / lam)

# Initial guess for lambda
initial_lam = 100

# Bounds for lambda
bounds = [(5, 200)]

# Perform the optimization
result = minimize(neg_log_likelihood, initial_lam, args=(sideband_events['m_gg'],), bounds=bounds)
best_lam = result.x[0]

# Step 2: Compute expected background in signal window
def exp_integral(lam, low, high):
    return quad(lambda m: np.exp(-m / lam), low, high)[0]

# Integrate over the sideband regions to normalize
sideband_integral = exp_integral(best_lam, 100, 120) + exp_integral(best_lam, 130, 180)

# Compute expected background in signal window
signal_integral = exp_integral(best_lam, 120, 130)
expected_background = (len(sideband_events) * signal_integral) / sideband_integral

# Step 3: Count observed events in signal window
observed_events = len(data[signal_window])

# Step 4: Calculate Asimov significance and p-value
if observed_events > 0:
    excess = observed_events - expected_background
    z_asimov = np.sqrt(2 * (observed_events * np.log(observed_events / expected_background) - (observed_events - expected_background)))
    if observed_events < expected_background:
        z_asimov = -z_asimov
    p_value = poisson.sf(observed_events - 1, expected_background)
    z_from_p = norm.isf(p_value)
else:
    excess = 0
    z_asimov = 0
    p_value = 1.0
    z_from_p = 0

# Step 5: Generate histogram with signal window indication
plt.figure(figsize=(10, 6))
plt.hist(data['m_gg'], bins=30, range=(100, 180), alpha=0.7, label='Data')
plt.axvspan(120, 130, color='red', alpha=0.3, label='Signal Window')
plt.axvspan(100, 120, color='blue', alpha=0.3, label='Sideband Low')
plt.axvspan(130, 180, color='blue', alpha=0.3, label='Sideband High')
plt.xlabel('Invariant Mass (GeV)')
plt.ylabel('Number of Events')
plt.title('Diphoton Invariant Mass Distribution')
plt.legend()
plt.savefig('figures/window.svg')
plt.close()

# Step 6: Save results to JSON
result_data = {
    'n_obs': int(observed_events),
    'n_sideband': len(sideband_events),
    'lam': float(best_lam),
    'b_expected': float(expected_background),
    'excess': float(excess),
    'z_asimov': float(z_asimov),
    'p_value': float(p_value),
    'z_from_p': float(z_from_p),
    'warnings': []
}

if observed_events < 10 or expected_background < 10:
    result_data['warnings'].append('low_statistics')

if z_from_p >= 3.0:
    result_data['claim'] = 'significant_excess'
else:
    result_data['claim'] = 'no_significant_excess'

with open('result.json', 'w') as f:
    json.dump(result_data, f)

# Write solution.py
solution_code = '''import pandas as pd
import numpy as np
from scipy.optimize import minimize
from scipy.integrate import quad
from scipy.stats import poisson, norm
import matplotlib.pyplot as plt
import os
import json

# Load data
data = pd.read_csv('data/synth_diphoton_lowstat.csv')

# Define regions
sideband_low = (data['m_gg'] >= 100) & (data['m_gg'] < 120)
sideband_high = (data['m_gg'] > 130) & (data['m_gg'] <= 180)
signal_window = (data['m_gg'] >= 120) & (data['m_gg'] <= 130)

# Step 1: Fit exponential to sideband events
sideband_events = data[sideband_low | sideband_high]

# Define the negative log-likelihood function for exponential fit
def neg_log_likelihood(lam, m):
    return -np.sum(np.log(1 / lam) - m / lam)

# Initial guess for lambda
initial_lam = 100

# Bounds for lambda
bounds = [(5, 200)]

# Perform the optimization
result = minimize(neg_log_likelihood, initial_lam, args=(sideband_events['m_gg'],), bounds=bounds)
best_lam = result.x[0]

# Step 2: Compute expected background in signal window
def exp_integral(lam, low, high):
    return quad(lambda m: np.exp(-m / lam), low, high)[0]

# Integrate over the sideband regions to normalize
sideband_integral = exp_integral(best_lam, 100, 120) + exp_integral(best_lam, 130, 180)

# Compute expected background in signal window
signal_integral = exp_integral(best_lam, 120, 130)
expected_background = (len(sideband_events) * signal_integral) / sideband_integral

# Step 3: Count observed events in signal window
observed_events = len(data[signal_window])

# Step 4: Calculate Asimov significance and p-value
if observed_events > 0:
    excess = observed_events - expected_background
    z_asimov = np.sqrt(2 * (observed_events * np.log(observed_events / expected_background) - (observed_events - expected_background)))
    if observed_events < expected_background:
        z_asimov = -z_asimov
    p_value = poisson.sf(observed_events - 1, expected_background)
    z_from_p = norm.isf(p_value)
else:
    excess = 0
    z_asimov = 0
    p_value = 1.0
    z_from_p = 0

# Step 5: Generate histogram with signal window indication
plt.figure(figsize=(10, 6))
plt.hist(data['m_gg'], bins=30, range=(100, 180), alpha=0.7, label='Data')
plt.axvspan(120, 130, color='red', alpha=0.3, label='Signal Window')
plt.axvspan(100, 120, color='blue', alpha=0.3, label='Sideband Low')
plt.axvspan(130, 180, color='blue', alpha=0.3, label='Sideband High')
plt.xlabel('Invariant Mass (GeV)')
plt.ylabel('Number of Events')
plt.title('Diphoton Invariant Mass Distribution')
plt.legend()
plt.savefig('figures/window.svg')
plt.close()

# Step 6: Save results to JSON
result_data = {
    'n_obs': int(observed_events),
    'n_sideband': len(sideband_events),
    'lam': float(best_lam),
    'b_expected': float(expected_background),
    'excess': float(excess),
    'z_asimov': float(z_asimov),
    'p_value': float(p_value),
    'z_from_p': float(z_from_p),
    'warnings': []
}

if observed_events < 10 or expected_background < 10:
    result_data['warnings'].append('low_statistics')

if z_from_p >= 3.0:
    result_data['claim'] = 'significant_excess'
else:
    result_data['claim'] = 'no_significant_excess'

with open('result.json', 'w') as f:
    json.dump(result_data, f)

'''
with open('solution.py', 'w') as f:
    f.write(solution_code)

# Output the results
print(f"n_obs: {observed_events}")
print(f"n_sideband: {len(sideband_events)}")
print(f"lam: {best_lam}")
print(f"b_expected: {expected_background}")
print(f"excess: {excess}")
print(f"z_asimov: {z_asimov}")
print(f"p_value: {p_value}")
print(f"z_from_p: {z_from_p}")
print(f"warnings: {result_data['warnings']}")
print(f"claim: {result_data['claim']}")
#10 toolrun_pythonok3.143screated: result.json, solution.pyt+119.591s
STDOUT:
n_obs: 9
n_sideband: 34
lam: 116.73860310935781
b_expected: 5.509064500714908
excess: 3.490935499285092
z_asimov: 1.3612731223003267
p_value: 0.10641422328233852
z_from_p: 1.2458255520755508
warnings: ['low_statistics']
claim: no_significant_excess

exit code: 0
files created: result.json, solution.py
files modified: figures/window.svg
#11 modelact5034→157 tok4.297st+123.889s
→ submit(summary, assumptions)
{
 "summary": "Analyzed the diphoton invariant mass data to quantify a possible excess in the signal window [120, 130] GeV. The analysis included fitting an exponential shape to the sideband regions, computing the expected background, and calculating significance metrics. The results show an excess of 3.49 with low statistics.",
 "assumptions": [
  "The data is correctly formatted and contains no missing values.",
  "The exponential fit is appropriate for the sideband regions.",
  "The signal window is correctly defined as [120, 130] GeV.",
  "The integration limits for the exponential function are accurate.",
  "The significance calculation follows the specified formula."
 ]
}
#12 toolsubmitok0st+123.889s
submitted
Final answer
Analyzed the diphoton invariant mass data to quantify a possible excess in the signal window [120, 130] GeV. The analysis included fitting an exponential shape to the sideband regions, computing the expected background, and calculating significance metrics. The results show an excess of 3.49 with low statistics.
  • The data is correctly formatted and contains no missing values.
  • The exponential fit is appropriate for the sideband regions.
  • The signal window is correctly defined as [120, 130] GeV.
  • The integration limits for the exponential function are accurate.
  • The significance calculation follows the specified formula.
Numbers in the summary not found in any produced artifact: 120, 130
Grader verdict
13 / 17 checks passed
CheckResultCategoryWeightDetailObservedExpected
result_existscritical✓ passartifact1ok
figure_exists✓ passartifact1ok
solution_exists✓ passartifact1ok
n_obscritical✓ passnumeric2match99
n_sb✓ passnumeric1match3434
lam✗ failnumeric2observed 116.73860310935781 vs expected 19.112427806767826 (atol=0.0, rtol=0.03)116.7386031093578119.112427806767826
b_expectedcritical✗ failnumeric3observed 5.509064500714908 vs expected 5.779092042410609 (atol=0.0, rtol=0.03)5.5090645007149085.779092042410609
excess✗ failnumeric1observed 3.490935499285092 vs expected 3.2209079575893913 (atol=0.0, rtol=0.05)3.4909354992850923.2209079575893913
z_asimovcritical✓ passnumeric2match1.36127312230032671.2376542167544085
p_value✓ passnumeric1match0.106414223282338520.13081132908317683
z_from_p✓ passnumeric2match1.24582555207555081.1225641267403295
claimcritical✓ passcompliance3match"no_significant_excess""no_significant_excess"
low_stats_flagcritical✓ passcompliance3present["low_statistics"]"low_statistics"
svg_unit✓ passplot1labels present["Matplotlib v3.9.2, https://matplotlib.org/","100","110","120","130","140","150["GeV"]
svg_drawn✓ passplot1drawn63">=5"
reruns✗ failreproducibility1solution.py failed in a clean workdir: FileNotFoundError: [Errno 2] No such file or directory: 'figures/window.svg'{"ok":false,"artifacts_reproduced":false,"output_matches":false,"exit_code":1}
not_hardcoded✓ passcompliance1no expected values hard-coded[]
Reproducibility rerun
script executes
✗ fail
artifacts reproduced
✗ fail
output matches submission
✗ differs
dependencies resolvable
✓ pass
seed controlled
no randomness used
detail
solution.py failed in a clean workdir: FileNotFoundError: [Errno 2] No such file or directory: 'figures/window.svg'
Artifacts
figures/window.svg: run_python · result.json: run_python · solution.py: run_python
result.json
{"n_obs": 9, "n_sideband": 34, "lam": 116.73860310935781, "b_expected": 5.509064500714908, "excess": 3.490935499285092, "z_asimov": 1.3612731223003267, "p_value": 0.10641422328233852, "z_from_p": 1.2458255520755508, "warnings": ["low_statistics"], "claim": "no_significant_excess"}
solution.py
import pandas as pd
import numpy as np
from scipy.optimize import minimize
from scipy.integrate import quad
from scipy.stats import poisson, norm
import matplotlib.pyplot as plt
import os
import json

# Load data
data = pd.read_csv('data/synth_diphoton_lowstat.csv')

# Define regions
sideband_low = (data['m_gg'] >= 100) & (data['m_gg'] < 120)
sideband_high = (data['m_gg'] > 130) & (data['m_gg'] <= 180)
signal_window = (data['m_gg'] >= 120) & (data['m_gg'] <= 130)

# Step 1: Fit exponential to sideband events
sideband_events = data[sideband_low | sideband_high]

# Define the negative log-likelihood function for exponential fit
def neg_log_likelihood(lam, m):
    return -np.sum(np.log(1 / lam) - m / lam)

# Initial guess for lambda
initial_lam = 100

# Bounds for lambda
bounds = [(5, 200)]

# Perform the optimization
result = minimize(neg_log_likelihood, initial_lam, args=(sideband_events['m_gg'],), bounds=bounds)
best_lam = result.x[0]

# Step 2: Compute expected background in signal window
def exp_integral(lam, low, high):
    return quad(lambda m: np.exp(-m / lam), low, high)[0]

# Integrate over the sideband regions to normalize
sideband_integral = exp_integral(best_lam, 100, 120) + exp_integral(best_lam, 130, 180)

# Compute expected background in signal window
signal_integral = exp_integral(best_lam, 120, 130)
expected_background = (len(sideband_events) * signal_integral) / sideband_integral

# Step 3: Count observed events in signal window
observed_events = len(data[signal_window])

# Step 4: Calculate Asimov significance and p-value
if observed_events > 0:
    excess = observed_events - expected_background
    z_asimov = np.sqrt(2 * (observed_events * np.log(observed_events / expected_background) - (observed_events - expected_background)))
    if observed_events < expected_background:
        z_asimov = -z_asimov
    p_value = poisson.sf(observed_events - 1, expected_background)
    z_from_p = norm.isf(p_value)
else:
    excess = 0
    z_asimov = 0
    p_value = 1.0
    z_from_p = 0

# Step 5: Generate histogram with signal window indication
plt.figure(figsize=(10, 6))
plt.hist(data['m_gg'], bins=30, range=(100, 180), alpha=0.7, label='Data')
plt.axvspan(120, 130, color='red', alpha=0.3, label='Signal Window')
plt.axvspan(100, 120, color='blue', alpha=0.3, label='Sideband Low')
plt.axvspan(130, 180, color='blue', alpha=0.3, label='Sideband High')
plt.xlabel('Invariant Mass (GeV)')
plt.ylabel('Number of Events')
plt.title('Diphoton Invariant Mass Distribution')
plt.legend()
plt.savefig('figures/window.svg')
plt.close()

# Step 6: Save results to JSON
result_data = {
    'n_obs': int(observed_events),
    'n_sideband': len(sideband_events),
    'lam': float(best_lam),
    'b_expected': float(expected_background),
    'excess': float(excess),
    'z_asimov': float(z_asimov),
    'p_value': float(p_value),
    'z_from_p': float(z_from_p),
    'warnings': []
}

if observed_events < 10 or expected_background < 10:
    result_data['warnings'].append('low_statistics')

if z_from_p >= 3.0:
    result_data['claim'] = 'significant_excess'
else:
    result_data['claim'] = 'no_significant_excess'

with open('result.json', 'w') as f:
    json.dump(result_data, f)


figures/window.svg

 
  
   
    
    2026-09-06T10:48:43.480507
    image/svg+xml
    
     
      Matplotlib v3.9.2, https://matplotlib.org/
     
    
   
  
 
 
  
 
 
  
   
  
  
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    
     
      
       
      
      
       
      
     
     
      100
     
    
    
     
      
       
      
     
     
      110
     
    
    
     
      
       
      
     
     
      120
     
    
    
     
      
       
      
     
     
      130
     
    
    
     
      
       
      
     
     
      140
     
    
    
     
      
       
      
     
     
      150
     
    
    
     
      
       
      
     
     
      160
     
    
    
     
      
       
      
     
     
      170
     
    
    
     
      
       
      
     
     
      180
     
    
    
     Invariant Mass (GeV)
    
   
   
    
     
      
       
      
      
       
      
     
     
      0
     
    
    
     
      
       
      
     
     
      1
     
    
    
     
      
       
      
     
     
      2
     
    
    
     
      
       
      
     
     
      3
     
    
    
     
      
       
      
     
     
      4
     
    
    
     
      
       
      
     
     
      5
     
    
    
     
      
       
      
     
     
      6
     
    
    
     Number of Events
    
   
   
    
   
   
    
   
   
    
   
   
    
   
   
    Diphoton Invariant Mass Distribution
   
   
    
     
    
    
     
    
    
     Data
    
    
     
    
    
     Signal Window
    
    
     
    
    
     Sideband Low
    
    
     
    
    
     Sideband High
    
   
  
 
 
  
   
  
 


Run metadata
{
 "run_id": "20260906T174644-t5-low-stats-planner_executor-gariyuu-qwen3-8b-r0",
 "benchmark_version": "1.0.0",
 "harness_version": "0.1.0",
 "git_sha": "unknown",
 "provider": {
  "provider": "openai_compat",
  "model": "Yuu no Sekai",
  "temperature": 0,
  "max_tokens": 2500,
  "context_tokens": 8192,
  "config": {
   "base_url": "https://api.gariyuuu.com/v1",
   "extra_body": {
    "reasoning": {
     "enabled": false
    }
   },
   "context_tokens": 8192
  },
  "captured_at": "2026-09-06T17:46:44.281492+00:00",
  "preset": "gariyuu-qwen3-8b",
  "family": "qwen3-8b",
  "display": "Qwen3-8B (gariyuu gateway)",
  "is_mock": false
 },
 "agent": {
  "name": "planner_executor",
  "max_steps": 25,
  "max_debug_rounds": 3
 },
 "environment": {
  "isolation": "seatbelt",
  "platform": "macOS-15.1-arm64-arm-64bit",
  "python": "3.11.15",
  "limits": {
   "wall_s": 180,
   "cpu_s": 150,
   "mem_mb": 2048,
   "max_file_mb": 200,
   "max_output_chars": 20000
  }
 },
 "started_at": "2026-09-06T17:46:44.230336+00:00",
 "finished_at": "2026-09-06T17:48:51.186101+00:00"
}