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2b0bffa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 | """Built-in agents for evaluating CERNenv.
These do **not** use any neural model — they are deterministic / random
policies you can use as baselines and oracles. They consume a
``CollisionObservation`` and return an ``ExperimentAction``.
"""
from __future__ import annotations
import random
from dataclasses import dataclass
from typing import List, Optional, Protocol
from models import ActionType, CollisionObservation, ExperimentAction
class CernAgent(Protocol):
name: str
def reset(self) -> None: ...
def act(self, obs: CollisionObservation) -> ExperimentAction: ...
# ── Random agent ─────────────────────────────────────────────────────────
@dataclass
class RandomAgent:
"""Picks a uniformly random valid action; useful as a worst-case baseline."""
name: str = "random"
seed: int = 0
def __post_init__(self) -> None:
self._rng = random.Random(self.seed)
def reset(self) -> None:
self._rng = random.Random(self.seed)
def act(self, obs: CollisionObservation) -> ExperimentAction:
action_type = self._rng.choice(list(ActionType))
params: dict = {}
if action_type == ActionType.CONFIGURE_BEAM:
params = {"beam_energy": self._rng.choice(obs.task.beam_energy_options or ["13TeV"])}
elif action_type == ActionType.SELECT_CHANNEL:
params = {"channel": self._rng.choice(obs.task.available_channels or ["diphoton"])}
elif action_type == ActionType.SET_TRIGGER:
params = {"trigger": self._rng.choice(obs.task.available_triggers or ["high_pt"])}
elif action_type == ActionType.ALLOCATE_LUMINOSITY:
params = {"luminosity_fb": self._rng.uniform(20.0, 100.0)}
elif action_type == ActionType.COLLECT_COLLISIONS:
params = {"luminosity_fb": self._rng.uniform(20.0, 100.0)}
elif action_type == ActionType.BUILD_INVARIANT_MASS:
lo, hi = obs.task.mass_search_window_gev
params = {"mass_window_gev": [lo, hi]}
elif action_type == ActionType.SUBMIT_DISCOVERY_CLAIM:
mass = obs.candidate_masses_gev[-1] if obs.candidate_masses_gev else (
0.5 * (obs.task.mass_search_window_gev[0] + obs.task.mass_search_window_gev[1])
)
params = {
"claim": {
"mass_estimate_gev": mass,
"mass_uncertainty_gev": 5.0,
"significance_sigma": obs.cumulative_significance,
"decay_channel": obs.selected_channel or "diphoton",
"spin_hypothesis": int(self._rng.choice([0, 1, 2])),
"parity": self._rng.choice(["+", "-"]),
"confidence": self._rng.uniform(0.4, 0.9),
}
}
return ExperimentAction(
action_type=action_type,
parameters=params,
confidence=0.4,
justification="random baseline",
)
# ── Heuristic agent ──────────────────────────────────────────────────────
@dataclass
class HeuristicAgent:
"""A scripted analysis-flow agent using high-yield channels and
sensible default parameters. Acts as the strong non-LLM baseline.
"""
name: str = "heuristic"
def __post_init__(self) -> None:
self._reset_plan()
def reset(self) -> None:
self._reset_plan()
def _reset_plan(self) -> None:
self._plan: List[ExperimentAction] = [
ExperimentAction(
action_type=ActionType.CONFIGURE_BEAM,
parameters={"beam_energy": "13TeV"},
confidence=0.9,
justification="13 TeV maximises reach within budget",
),
ExperimentAction(
action_type=ActionType.SELECT_CHANNEL,
parameters={"channel": "diphoton"},
confidence=0.7,
justification="diphoton has clean low-background signature",
),
ExperimentAction(
action_type=ActionType.SET_TRIGGER,
parameters={"trigger": "diphoton_hlt"},
confidence=0.9,
justification="match trigger to channel",
),
ExperimentAction(
action_type=ActionType.ALLOCATE_LUMINOSITY,
parameters={"luminosity_fb": 80.0},
confidence=0.8,
justification="bulk allocation for the first run",
),
ExperimentAction(
action_type=ActionType.COLLECT_COLLISIONS,
parameters={"luminosity_fb": 80.0},
confidence=0.8,
justification="run physics",
),
ExperimentAction(
action_type=ActionType.RECONSTRUCT_TRACKS,
method="Athena",
confidence=0.9,
justification="reconstruct objects",
),
ExperimentAction(
action_type=ActionType.CALIBRATE_DETECTOR,
method="ECAL_calibration",
confidence=0.8,
justification="reduce systematic uncertainty",
),
ExperimentAction(
action_type=ActionType.BUILD_INVARIANT_MASS,
parameters={"mass_window_gev": [80.0, 800.0], "n_bins": 60},
confidence=0.8,
justification="broad-window histogram",
),
ExperimentAction(
action_type=ActionType.SUBTRACT_BACKGROUND,
confidence=0.7,
justification="smooth-fit subtraction",
),
ExperimentAction(
action_type=ActionType.SCAN_BUMP,
method="BumpHunter",
confidence=0.8,
justification="locate candidate peak",
),
ExperimentAction(
action_type=ActionType.FIT_RESONANCE,
method="ROOT_RooFit",
confidence=0.85,
justification="fit Breit-Wigner peak",
),
ExperimentAction(
action_type=ActionType.REQUEST_SYSTEMATICS,
method="Luminosity_calibration",
confidence=0.7,
justification="pin down dominant systematics",
),
ExperimentAction(
action_type=ActionType.ESTIMATE_SIGNIFICANCE,
method="Asimov_significance",
confidence=0.85,
justification="quantify discovery significance",
),
ExperimentAction(
action_type=ActionType.MEASURE_ANGULAR,
confidence=0.7,
justification="probe spin",
),
]
self._idx = 0
self._claim_submitted = False
def act(self, obs: CollisionObservation) -> ExperimentAction:
if self._idx < len(self._plan):
a = self._plan[self._idx]
self._idx += 1
return a
if not self._claim_submitted:
self._claim_submitted = True
mass = obs.candidate_masses_gev[-1] if obs.candidate_masses_gev else 125.0
sig = obs.cumulative_significance or 5.0
return ExperimentAction(
action_type=ActionType.SUBMIT_DISCOVERY_CLAIM,
parameters={
"claim": {
"mass_estimate_gev": mass,
"mass_uncertainty_gev": 1.0,
"width_estimate_gev": 0.01,
"significance_sigma": sig,
"decay_channel": obs.selected_channel or "diphoton",
"spin_hypothesis": 0,
"parity": "+",
"cross_section_fb": 50.0,
"confidence": 0.8,
}
},
confidence=0.85,
justification="submit best calibrated claim",
)
return ExperimentAction(
action_type=ActionType.REQUEST_THEORY_REVIEW,
confidence=0.3,
justification="filler step (claim already submitted)",
)
# ── Oracle agent ─────────────────────────────────────────────────────────
@dataclass
class OracleAgent:
"""An oracle that *peeks* at the latent particle truth (only available
for in-process evaluation; never used remotely). This is the upper bound
of what a perfect agent could achieve given the noise budget.
"""
name: str = "oracle"
truth: Optional[dict] = None # set externally before the episode
def reset(self) -> None:
self._stage = 0
self._claim_submitted = False
def act(self, obs: CollisionObservation) -> ExperimentAction:
truth = self.truth or {}
true_channel = truth.get("primary_channel", obs.selected_channel or "diphoton")
trigger_for_channel = {
"diphoton": "diphoton_hlt",
"dilepton_ee": "dilepton_hlt",
"dilepton_mumu": "dilepton_hlt",
"four_lepton": "dilepton_hlt",
"dijet": "jet_hlt",
"bb": "jet_hlt",
}.get(true_channel, "high_pt")
plan = [
ExperimentAction(action_type=ActionType.CONFIGURE_BEAM, parameters={"beam_energy": "13TeV"}, confidence=0.95),
ExperimentAction(action_type=ActionType.SELECT_CHANNEL, parameters={"channel": true_channel}, confidence=0.99),
ExperimentAction(action_type=ActionType.SET_TRIGGER, parameters={"trigger": trigger_for_channel}, confidence=0.95),
ExperimentAction(action_type=ActionType.ALLOCATE_LUMINOSITY, parameters={"luminosity_fb": 120.0}, confidence=0.9),
ExperimentAction(action_type=ActionType.COLLECT_COLLISIONS, parameters={"luminosity_fb": 120.0}, confidence=0.9),
ExperimentAction(action_type=ActionType.RECONSTRUCT_TRACKS, method="Athena", confidence=0.95),
ExperimentAction(action_type=ActionType.CALIBRATE_DETECTOR, method="ECAL_calibration", confidence=0.9),
ExperimentAction(
action_type=ActionType.BUILD_INVARIANT_MASS,
parameters={
"mass_window_gev": [
max(50.0, float(truth.get("mass_gev", 100.0)) - 50.0),
float(truth.get("mass_gev", 100.0)) + 80.0,
],
"n_bins": 80,
},
confidence=0.95,
),
ExperimentAction(action_type=ActionType.SUBTRACT_BACKGROUND, confidence=0.9),
ExperimentAction(action_type=ActionType.FIT_RESONANCE, method="ROOT_RooFit", confidence=0.95),
ExperimentAction(action_type=ActionType.REQUEST_SYSTEMATICS, method="Luminosity_calibration", confidence=0.9),
ExperimentAction(action_type=ActionType.ESTIMATE_SIGNIFICANCE, method="Asimov_significance", confidence=0.95),
ExperimentAction(action_type=ActionType.MEASURE_ANGULAR, confidence=0.85),
]
if self._stage < len(plan):
a = plan[self._stage]
self._stage += 1
return a
if not self._claim_submitted:
self._claim_submitted = True
return ExperimentAction(
action_type=ActionType.SUBMIT_DISCOVERY_CLAIM,
parameters={
"claim": {
"mass_estimate_gev": float(truth.get("mass_gev", 125.0)),
"mass_uncertainty_gev": 0.5,
"width_estimate_gev": float(truth.get("width_gev", 0.01)),
"significance_sigma": max(obs.cumulative_significance, 5.0),
"decay_channel": true_channel,
"spin_hypothesis": int(truth.get("spin", 0)),
"parity": str(truth.get("parity", "+")),
"cross_section_fb": float(truth.get("cross_section_fb", 50.0)),
"confidence": 0.95,
}
},
confidence=0.95,
justification="oracle claim from hidden truth",
)
return ExperimentAction(
action_type=ActionType.REQUEST_THEORY_REVIEW,
confidence=0.5,
justification="oracle filler",
)
__all__ = ["CernAgent", "RandomAgent", "HeuristicAgent", "OracleAgent"]
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