Optimise a MET cut for S/sqrt(B) with cross-section normalisation
0.95
4
17,241
$0.00351
1.6 min
3
Task given to the agent
Using `data/synth_sigbkg.csv` (data card in README.md, including the normalisation rule), scan a lower
threshold t on `met` over t = 50, 75, 100, ..., 300 GeV (inclusive, step 25). For each threshold compute
the expected signal yield S(t) and background yield B(t) after the selection `met > t`, normalised to
sigma * L exactly as the data card prescribes (including the per-event `weight`), and the figure of merit
Z(t) = S(t) / sqrt(B(t)).
Deliverables:
- `result.json` with `scan` (list of objects `{"threshold": float, "S": float, "B": float, "Z": float}` in ascending
threshold order), `best_threshold` (float, the threshold maximising Z), `S_best`, `B_best`, `Z_best` (floats),
and `Z_no_cut` (float, S/sqrt(B) with no selection).
- `figures/scan.svg`: Z(t) versus t with an x-axis label that includes "GeV".
- `solution.py`: the complete analysis; `python solution.py` from a clean copy of this directory must regenerate every deliverable.
Trajectory
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": t,
"S": S,
"B": B,
"Z": Z
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": best_threshold,
"S_best": S_best,
"B_best": B_best,
"Z_best": Z_best,
"Z_no_cut": Z_no_cut
}, f)
# Plot Z(t) vs t
plt.figure(figsize=(10, 6))
thresholds_array = np.array([r['threshold'] for r in scan_results])
Z_values = np.array([r['Z'] for r in scan_results])
plt.plot(thresholds_array, Z_values, marker='o', linestyle='-')
plt.xlabel('met (GeV)')
plt.ylabel('Z(t) = S / sqrt(B)')
plt.title('Z(t) vs met Threshold')
plt.grid(True)
plt.savefig("figures/scan.svg")
# Save solution.py
solution_path = "solution.py"
with open(solution_path, 'w') as f:
f.write('''import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": t,
"S": S,
"B": B,
"Z": Z
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": best_threshold,
"S_best": S_best,
"B_best": B_best,
"Z_best": Z_best,
"Z_no_cut": Z_no_cut
}, f)
# Plot Z(t) vs t
plt.figure(figsize=(10, 6))
thresholds_array = np.array([r['threshold'] for r in scan_results])
Z_values = np.array([r['Z'] for r in scan_results])
plt.plot(thresholds_array, Z_values, marker='o', linestyle='-')
plt.xlabel('met (GeV)')
plt.ylabel('Z(t) = S / sqrt(B)')
plt.title('Z(t) vs met Threshold')
plt.grid(True)
plt.savefig("figures/scan.svg")
''')
# Submit the solution
print("Solution submitted with the following assumptions:")
print("- Used the provided data card to compute the expected yields.")
print("- Applied the normalization rule as specified in the data card.")
print("- Computed Z(t) as S / sqrt(B) for each threshold.")
print("- Selected the threshold that maximizes Z(t).")
```
wrote 5054 chars to solution.py
STDERR:
Traceback (most recent call last):
File "/Users/gariyuu/Projects/agenthep/runs/20260906T175128-t4-cut-optimization-self_debug-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/20260906T175128-t4-cut-optimization-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 66, in <module>
json.dump({
File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 179, in dump
for chunk in iterable:
File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 432, in _iterencode
yield from _iterencode_dict(o, _current_indent_level)
File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
yield from chunks
File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 326, in _iterencode_list
yield from chunks
File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
yield from chunks
File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 439, in _iterencode
o = _default(o)
^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.15_4/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 180, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type int64 is not JSON serializable
exit code: 1
files created: result.json```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": float(t),
"S": float(S),
"B": float(B),
"Z": float(Z)
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": float(best_threshold),
"S_best": float(S_best),
"B_best": float(B_best),
"Z_best": float(Z_best),
"Z_no_cut": float(Z_no_cut)
}, f)
# Plot Z(t) vs t
plt.figure(figsize=(10, 6))
thresholds_array = np.array([r['threshold'] for r in scan_results])
Z_values = np.array([r['Z'] for r in scan_results])
plt.plot(thresholds_array, Z_values, marker='o', linestyle='-')
plt.xlabel('met (GeV)')
plt.ylabel('Z(t) = S / sqrt(B)')
plt.title('Z(t) vs met Threshold')
plt.grid(True)
plt.savefig("figures/scan.svg")
# Save solution.py
solution_path = "solution.py"
with open(solution_path, 'w') as f:
f.write('''import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": float(t),
"S": float(S),
"B": float(B),
"Z": float(Z)
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": float(best_threshold),
"S_best": float(S_best),
"B_best": float(B_best),
"Z_best": float(Z_best),
"Z_no_cut": float(Z_no_cut)
}, f)
# Plot Z(t) vs t
plt.figure(figsize=(10, 6))
thresholds_array = np.array([r['threshold'] for r in scan_results])
Z_values = np.array([r['Z'] for r in scan_results])
plt.plot(thresholds_array, Z_values, marker='o', linestyle='-')
plt.xlabel('met (GeV)')
plt.ylabel('Z(t) = S / sqrt(B)')
plt.title('Z(t) vs met Threshold')
plt.grid(True)
plt.savefig("figures/scan.svg")
''')
# Submit the solution
print("Solution submitted with the following assumptions:")
print("- Used the provided data card to compute the expected yields.")
print("- Applied the normalization rule as specified in the data card.")
print("- Computed Z(t) as S / sqrt(B) for each threshold.")
print("- Selected the threshold that maximizes Z(t).")
```
wrote 5180 chars to solution.py
STDERR:
Traceback (most recent call last):
File "/Users/gariyuu/Projects/agenthep/runs/20260906T175128-t4-cut-optimization-self_debug-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/20260906T175128-t4-cut-optimization-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 84, in <module>
plt.savefig("figures/scan.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
... [79 chars truncated] ...
s.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/20260906T175128-t4-cut-optimization-self_debug-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/scan.svg'
exit code: 1
files modified: result.json```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": float(t),
"S": float(S),
"B": float(B),
"Z": float(Z)
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": float(best_threshold),
"S_best": float(S_best),
"B_best": float(B_best),
"Z_best": float(Z_best),
"Z_no_cut": float(Z_no_cut)
}, f)
# Plot Z(t) vs t
plt.figure(figsize=(10, 6))
thresholds_array = np.array([r['threshold'] for r in scan_results])
Z_values = np.array([r['Z'] for r in scan_results])
plt.plot(thresholds_array, Z_values, marker='o', linestyle='-')
plt.xlabel('met (GeV)')
plt.ylabel('Z(t) = S / sqrt(B)')
plt.title('Z(t) vs met Threshold')
plt.grid(True)
plt.savefig("figures/scan.svg")
# Submit the solution
print("Solution submitted with the following assumptions:")
print("- Used the provided data card to compute the expected yields.")
print("- Applied the normalization rule as specified in the data card.")
print("- Computed Z(t) as S / sqrt(B) for each threshold.")
print("- Selected the threshold that maximizes Z(t).")
```
wrote 2708 chars to solution.py
STDERR:
Traceback (most recent call last):
File "/Users/gariyuu/Projects/agenthep/runs/20260906T175128-t4-cut-optimization-self_debug-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/20260906T175128-t4-cut-optimization-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 84, in <module>
plt.savefig("figures/scan.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
... [79 chars truncated] ...
s.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/20260906T175128-t4-cut-optimization-self_debug-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/scan.svg'
exit code: 1
files modified: result.json```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Ensure the figures directory exists
os.makedirs("figures", exist_ok=True)
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": float(t),
"S": float(S),
"B": float(B),
"Z": float(Z)
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": float(best_threshold),
"S_best": float(S_best),
"B_best": float(B_best),
"Z_best": float(Z_best),
"Z_no_cut": float(Z_no_cut)
}, f)
# Plot Z(t) vs t
plt.figure(figsize=(10, 6))
thresholds_array = np.array([r['threshold'] for r in scan_results])
Z_values = np.array([r['Z'] for r in scan_results])
plt.plot(thresholds_array, Z_values, marker='o', linestyle='-')
plt.xlabel('met (GeV)')
plt.ylabel('Z(t) = S / sqrt(B)')
plt.title('Z(t) vs met Threshold')
plt.grid(True)
plt.savefig("figures/scan.svg")
# Submit the solution
print("Solution submitted with the following assumptions:")
print("- Used the provided data card to compute the expected yields.")
print("- Applied the normalization rule as specified in the data card.")
print("- Computed Z(t) as S / sqrt(B) for each threshold.")
print("- Selected the threshold that maximizes Z(t).")
```
wrote 2785 chars to solution.py
STDOUT: Solution submitted with the following assumptions: - Used the provided data card to compute the expected yields. - Applied the normalization rule as specified in the data card. - Computed Z(t) as S / sqrt(B) for each threshold. - Selected the threshold that maximizes Z(t). exit code: 0 files created: figures/scan.svg files modified: result.json
Final answer
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Ensure the figures directory exists
os.makedirs("figures", exist_ok=True)
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": float(t),
"S": float(S),
"B": float(B),
"Z": float(Z)
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": float(best_threshold),
"S_best": float(S_best),
"B_best": float(B_be
Grader verdict
| Check | Result | Category | Weight | Detail | Observed | Expected |
|---|---|---|---|---|---|---|
| result_existscritical | ✓ pass | artifact | 1 | ok | ||
| figure_exists | ✓ pass | artifact | 1 | ok | ||
| solution_exists | ✓ pass | artifact | 1 | ok | ||
| scan_len | ✓ pass | numeric | 1 | ok | 11 | ">=11" |
| best_tcritical | ✓ pass | numeric | 2 | match | 100 | 100 |
| S_bestcritical | ✓ pass | numeric | 2 | match | 4008.105962479735 | 4008.105962479735 |
| B_bestcritical | ✓ pass | numeric | 2 | match | 570601.9668501167 | 570601.9668501167 |
| Z_best | ✓ pass | numeric | 1 | match | 5.306064956839125 | 5.306064956839125 |
| Z_no_cut | ✗ fail | numeric | 1 | observed 0.0 vs expected 2.6457513110645907 (atol=0.0, rtol=0.0001) | 0 | 2.6457513110645907 |
| S_first | ✓ pass | numeric | 1 | match | 6383.664148540839 | 6383.664148540839 |
| B_last | ✓ pass | numeric | 1 | match | 5188.848044105208 | 5188.848044105208 |
| svg_unit | ✓ pass | plot | 1 | labels present | ["Matplotlib v3.9.2, https://matplotlib.org/","50","100","150","200","250","300" | ["GeV"] |
| svg_drawn | ✓ pass | plot | 1 | drawn | 44 | ">=3" |
| reruns | ✓ pass | reproducibility | 1 | reproduced exactly | {"ok":true,"artifacts_reproduced":true,"output_matches":true,"exit_code":0} | |
| not_hardcoded | ✓ pass | compliance | 1 | no expected values hard-coded | [] |
Reproducibility rerun
✓ pass
✓ pass
✓ exact
✓ pass
no randomness used
reproduced exactly
Artifacts
result.json
{"scan": [{"threshold": 50.0, "S": 6383.664148540839, "B": 1954399.414612395, "Z": 4.566288724730452}, {"threshold": 75.0, "S": 5303.613480240965, "B": 1051646.1079389919, "Z": 5.171744441498639}, {"threshold": 100.0, "S": 4008.105962479735, "B": 570601.9668501167, "Z": 5.306064956839125}, {"threshold": 125.0, "S": 2788.084117029789, "B": 318410.707206491, "Z": 4.9409679942263}, {"threshold": 150.0, "S": 1824.3464109391339, "B": 179135.3670226506, "Z": 4.310390706626355}, {"threshold": 175.0, "S": 1149.91192462522, "B": 102334.71236984505, "Z": 3.5946208664889046}, {"threshold": 200.0, "S": 705.2840392367898, "B": 58447.329996802306, "Z": 2.917304201947774}, {"threshold": 225.0, "S": 396.99604950750984, "B": 29749.380752869736, "Z": 2.3016920627478483}, {"threshold": 250.0, "S": 221.74814494562958, "B": 14732.2001252237, "Z": 1.8269480176683435}, {"threshold": 275.0, "S": 121.500396196249, "B": 8549.271572668807, "Z": 1.3140537732996704}, {"threshold": 300.0, "S": 68.30519132923702, "B": 5188.848044105208, "Z": 0.9482399225248805}], "best_threshold": 100.0, "S_best": 4008.105962479735, "B_best": 570601.9668501167, "Z_best": 5.306064956839125, "Z_no_cut": 0.0}solution.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import os
# Ensure the figures directory exists
os.makedirs("figures", exist_ok=True)
# Read the data
data_path = "data/synth_sigbkg.csv"
df = pd.read_csv(data_path)
# Extract signal and background
signal_df = df[df['label'] == 1]
background_df = df[df['label'] == 0]
# Compute normalization factors
sigma_S = 0.05 # pb
sigma_B = 50.0 # pb
L = 140.0 # fb^-1 (convert to pb^-1)
L_pb_inv = L * 1000 # Convert to pb^-1
# Compute total weights for normalization
total_signal_weight = signal_df['weight'].sum()
total_background_weight = background_df['weight'].sum()
# Define thresholds
thresholds = np.arange(50, 301, 25)
# Initialize results
scan_results = []
Z_no_cut = 0.0
# Compute Z(t) for each threshold
for t in thresholds:
# Signal yield after selection
selected_signal = signal_df[signal_df['met'] > t]
S = sigma_S * L_pb_inv * selected_signal['weight'].sum() / total_signal_weight
# Background yield after selection
selected_background = background_df[background_df['met'] > t]
B = sigma_B * L_pb_inv * selected_background['weight'].sum() / total_background_weight
# Compute Z(t)
Z = S / np.sqrt(B) if B > 0 else 0.0
# Store results
scan_results.append({
"threshold": float(t),
"S": float(S),
"B": float(B),
"Z": float(Z)
})
# Update Z_no_cut
if t == 0:
Z_no_cut = S / np.sqrt(B)
# Find best threshold
best_threshold = max(scan_results, key=lambda x: x['Z'])['threshold']
S_best = max(scan_results, key=lambda x: x['Z'])['S']
B_best = max(scan_results, key=lambda x: x['Z'])['B']
Z_best = max(scan_results, key=lambda x: x['Z'])['Z']
# Save results to JSON
result_path = "result.json"
with open(result_path, 'w') as f:
json.dump({
"scan": scan_results,
"best_threshold": float(best_threshold),
"S_best": float(S_best),
"B_best": float(B_best),
"Z_best": float(Z_best),
"Z_no_cut": float(Z_no_cut)
}, f)
# Plot Z(t) vs t
plt.figure(figsize=(10, 6))
thresholds_array = np.array([r['threshold'] for r in scan_results])
Z_values = np.array([r['Z'] for r in scan_results])
plt.plot(thresholds_array, Z_values, marker='o', linestyle='-')
plt.xlabel('met (GeV)')
plt.ylabel('Z(t) = S / sqrt(B)')
plt.title('Z(t) vs met Threshold')
plt.grid(True)
plt.savefig("figures/scan.svg")
# Submit the solution
print("Solution submitted with the following assumptions:")
print("- Used the provided data card to compute the expected yields.")
print("- Applied the normalization rule as specified in the data card.")
print("- Computed Z(t) as S / sqrt(B) for each threshold.")
print("- Selected the threshold that maximizes Z(t).")
figures/scan.svg
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