README.md

---
license: cc0-1.0
language:
- en
task_categories:
- tabular-regression
- tabular-classification
pretty_name: Trigger Anomaly Detection for New Physics at the Large Hadron Collider
size_categories:
- 10M<n<100M
tags:
- physics
- particle-physics
- anomaly-detection
- cms
- level-1-trigger
configs:
- config_name: GluGluHTo2B_Par-MH-125
  data_files:
  - split: test
    path: data/GluGluHTo2B_Par-MH-125/test-*.parquet
  - split: validation
    path: data/GluGluHTo2B_Par-MH-125/valid-*.parquet
- config_name: GluGluHTo2B_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/GluGluHTo2B_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/GluGluHTo2B_Par-MH-125/seeds/valid-*.parquet
- config_name: GluGluHto2G_Par-MH-125
  data_files:
  - split: test
    path: data/GluGluHto2G_Par-MH-125/test-*.parquet
  - split: validation
    path: data/GluGluHto2G_Par-MH-125/valid-*.parquet
- config_name: GluGluHto2G_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/GluGluHto2G_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/GluGluHto2G_Par-MH-125/seeds/valid-*.parquet
- config_name: GluGluHto2G_Par-MH-90
  data_files:
  - split: test
    path: data/GluGluHto2G_Par-MH-90/test-*.parquet
  - split: validation
    path: data/GluGluHto2G_Par-MH-90/valid-*.parquet
- config_name: GluGluHto2G_Par-MH-90-seeds
  data_files:
  - split: test
    path: data/GluGluHto2G_Par-MH-90/seeds/test-*.parquet
  - split: validation
    path: data/GluGluHto2G_Par-MH-90/seeds/valid-*.parquet
- config_name: GluGluHto2Tau_Par-MH-125
  data_files:
  - split: test
    path: data/GluGluHto2Tau_Par-MH-125/test-*.parquet
  - split: validation
    path: data/GluGluHto2Tau_Par-MH-125/valid-*.parquet
- config_name: GluGluHto2Tau_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/GluGluHto2Tau_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/GluGluHto2Tau_Par-MH-125/seeds/valid-*.parquet
- config_name: GluGlutoHHto2B2WtoLNu2Q_Par-c2-0-kl-1-kt-1
  data_files:
  - split: test
    path: data/GluGlutoHHto2B2WtoLNu2Q_Par-c2-0-kl-1-kt-1/test-*.parquet
  - split: validation
    path: data/GluGlutoHHto2B2WtoLNu2Q_Par-c2-0-kl-1-kt-1/valid-*.parquet
- config_name: GluGlutoHHto2B2WtoLNu2Q_Par-c2-0-kl-1-kt-1-seeds
  data_files:
  - split: test
    path: data/GluGlutoHHto2B2WtoLNu2Q_Par-c2-0-kl-1-kt-1/seeds/test-*.parquet
  - split: validation
    path: data/GluGlutoHHto2B2WtoLNu2Q_Par-c2-0-kl-1-kt-1/seeds/valid-*.parquet
- config_name: HHHto4B2Tau_Par-c3-0-d4-0
  data_files:
  - split: test
    path: data/HHHto4B2Tau_Par-c3-0-d4-0/test-*.parquet
  - split: validation
    path: data/HHHto4B2Tau_Par-c3-0-d4-0/valid-*.parquet
- config_name: HHHto4B2Tau_Par-c3-0-d4-0-seeds
  data_files:
  - split: test
    path: data/HHHto4B2Tau_Par-c3-0-d4-0/seeds/test-*.parquet
  - split: validation
    path: data/HHHto4B2Tau_Par-c3-0-d4-0/seeds/valid-*.parquet
- config_name: HHHto6B_Par-c3-0-d4-0
  data_files:
  - split: test
    path: data/HHHto6B_Par-c3-0-d4-0/test-*.parquet
  - split: validation
    path: data/HHHto6B_Par-c3-0-d4-0/valid-*.parquet
- config_name: HHHto6B_Par-c3-0-d4-0-seeds
  data_files:
  - split: test
    path: data/HHHto6B_Par-c3-0-d4-0/seeds/test-*.parquet
  - split: validation
    path: data/HHHto6B_Par-c3-0-d4-0/seeds/valid-*.parquet
- config_name: SUSYGluGluToBBHTo2B_Par-M-1200
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHTo2B_Par-M-1200/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHTo2B_Par-M-1200/valid-*.parquet
- config_name: SUSYGluGluToBBHTo2B_Par-M-1200-seeds
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHTo2B_Par-M-1200/seeds/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHTo2B_Par-M-1200/seeds/valid-*.parquet
- config_name: SUSYGluGluToBBHToBB_Par-M-120
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHToBB_Par-M-120/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHToBB_Par-M-120/valid-*.parquet
- config_name: SUSYGluGluToBBHToBB_Par-M-120-seeds
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHToBB_Par-M-120/seeds/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHToBB_Par-M-120/seeds/valid-*.parquet
- config_name: SUSYGluGluToBBHToBB_Par-M-350
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHToBB_Par-M-350/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHToBB_Par-M-350/valid-*.parquet
- config_name: SUSYGluGluToBBHToBB_Par-M-350-seeds
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHToBB_Par-M-350/seeds/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHToBB_Par-M-350/seeds/valid-*.parquet
- config_name: SUSYGluGluToBBHToBB_Par-M-600
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHToBB_Par-M-600/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHToBB_Par-M-600/valid-*.parquet
- config_name: SUSYGluGluToBBHToBB_Par-M-600-seeds
  data_files:
  - split: test
    path: data/SUSYGluGluToBBHToBB_Par-M-600/seeds/test-*.parquet
  - split: validation
    path: data/SUSYGluGluToBBHToBB_Par-M-600/seeds/valid-*.parquet
- config_name: SingleNeutrino_E-10-gun
  data_files:
  - split: test
    path: data/SingleNeutrino_E-10-gun/test-*.parquet
  - split: validation
    path: data/SingleNeutrino_E-10-gun/valid-*.parquet
- config_name: SingleNeutrino_E-10-gun-seeds
  data_files:
  - split: test
    path: data/SingleNeutrino_E-10-gun/seeds/test-*.parquet
  - split: validation
    path: data/SingleNeutrino_E-10-gun/seeds/valid-*.parquet
- config_name: TTHTo2C_Par-MH-125
  data_files:
  - split: test
    path: data/TTHTo2C_Par-MH-125/test-*.parquet
  - split: validation
    path: data/TTHTo2C_Par-MH-125/valid-*.parquet
- config_name: TTHTo2C_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/TTHTo2C_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/TTHTo2C_Par-MH-125/seeds/valid-*.parquet
- config_name: TTHto2B_Par-MH-125
  data_files:
  - split: test
    path: data/TTHto2B_Par-MH-125/test-*.parquet
  - split: validation
    path: data/TTHto2B_Par-MH-125/valid-*.parquet
- config_name: TTHto2B_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/TTHto2B_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/TTHto2B_Par-MH-125/seeds/valid-*.parquet
- config_name: VBFHTo2C_Par-MH-125
  data_files:
  - split: test
    path: data/VBFHTo2C_Par-MH-125/test-*.parquet
  - split: validation
    path: data/VBFHTo2C_Par-MH-125/valid-*.parquet
- config_name: VBFHTo2C_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/VBFHTo2C_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/VBFHTo2C_Par-MH-125/seeds/valid-*.parquet
- config_name: VBFHto2B_Par-MH-125
  data_files:
  - split: test
    path: data/VBFHto2B_Par-MH-125/test-*.parquet
  - split: validation
    path: data/VBFHto2B_Par-MH-125/valid-*.parquet
- config_name: VBFHto2B_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/VBFHto2B_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/VBFHto2B_Par-MH-125/seeds/valid-*.parquet
- config_name: VBFHto2Tau_Par-MH-125
  data_files:
  - split: test
    path: data/VBFHto2Tau_Par-MH-125/test-*.parquet
  - split: validation
    path: data/VBFHto2Tau_Par-MH-125/valid-*.parquet
- config_name: VBFHto2Tau_Par-MH-125-seeds
  data_files:
  - split: test
    path: data/VBFHto2Tau_Par-MH-125/seeds/test-*.parquet
  - split: validation
    path: data/VBFHto2Tau_Par-MH-125/seeds/valid-*.parquet
- config_name: WtoTauto3Mu
  data_files:
  - split: test
    path: data/WtoTauto3Mu/test-*.parquet
  - split: validation
    path: data/WtoTauto3Mu/valid-*.parquet
- config_name: WtoTauto3Mu-seeds
  data_files:
  - split: test
    path: data/WtoTauto3Mu/seeds/test-*.parquet
  - split: validation
    path: data/WtoTauto3Mu/seeds/valid-*.parquet
- config_name: ZB_run396102
  data_files:
  - split: test
    path: data/ZB_run396102/test-*.parquet
  - split: train
    path: data/ZB_run396102/train-*.parquet
  - split: validation
    path: data/ZB_run396102/valid-*.parquet
- config_name: ZB_run396102-seeds
  data_files:
  - split: test
    path: data/ZB_run396102/seeds/test-*.parquet
  - split: train
    path: data/ZB_run396102/seeds/train-*.parquet
  - split: validation
    path: data/ZB_run396102/seeds/valid-*.parquet
- config_name: ZB_run398183
  data_files:
  - split: test
    path: data/ZB_run398183/test-*.parquet
  - split: train
    path: data/ZB_run398183/train-*.parquet
  - split: validation
    path: data/ZB_run398183/valid-*.parquet
- config_name: ZB_run398183-seeds
  data_files:
  - split: test
    path: data/ZB_run398183/seeds/test-*.parquet
  - split: train
    path: data/ZB_run398183/seeds/train-*.parquet
  - split: validation
    path: data/ZB_run398183/seeds/valid-*.parquet
- config_name: ggH-suep-decay
  data_files:
  - split: test
    path: data/ggH-suep-decay/test-*.parquet
  - split: validation
    path: data/ggH-suep-decay/valid-*.parquet
- config_name: ggH-suep-decay-seeds
  data_files:
  - split: test
    path: data/ggH-suep-decay/seeds/test-*.parquet
  - split: validation
    path: data/ggH-suep-decay/seeds/valid-*.parquet
- config_name: haa-4b-ma15
  data_files:
  - split: test
    path: data/haa-4b-ma15/test-*.parquet
  - split: validation
    path: data/haa-4b-ma15/valid-*.parquet
- config_name: haa-4b-ma15-seeds
  data_files:
  - split: test
    path: data/haa-4b-ma15/seeds/test-*.parquet
  - split: validation
    path: data/haa-4b-ma15/seeds/valid-*.parquet
- config_name: smj-case-A
  data_files:
  - split: test
    path: data/smj-case-A/test-*.parquet
  - split: validation
    path: data/smj-case-A/valid-*.parquet
- config_name: smj-case-A-seeds
  data_files:
  - split: test
    path: data/smj-case-A/seeds/test-*.parquet
  - split: validation
    path: data/smj-case-A/seeds/valid-*.parquet
---

# Trigger Anomaly Detection for New Physics at the Large Hadron Collider

*This dataset is a mirror of the Zenodo record: https://doi.org/10.5281/zenodo.21787779*

This dataset contains Level-1 Trigger objects from the CMS experiment at the CERN Large
Hadron Collider, assembled for research on unsupervised anomaly detection in the
trigger. The goal of unsupervised anomaly detection in this context is the discovery of
new physics. **This data set does not contain new physics. It is meant for research on
anomaly detectors.**

In the new physics search context, recording true anomalies or simulating them is
impossible, compared to other settings (e.g. industrial applications of anomaly
detection) where this is commonly done. Anomaly simulation data sets are provided, but
they should be used with the aforementioned caveat in mind. Aside from its high
statistics, **this data set uniquely provides simulations of the normal data**.

* **normal data**: zero-bias events recorded during 2025 proton–proton running (runs
396102, 398183); these events are chosen at random from all the proton-proton collisions
that happen inside the CMS detector and are recorded by it.

* **anomaly simulations**: 20 simulated signal data sets covering Higgs, multi-Higgs,
SUSY and exotic scenarios from the CMS Run 3 Winter25 campaign.

* **normal data simulation**: one simulated zero-bias-like background sample
(SingleNeutrino).

The normal data has 20,887,636 events. The normal data simulation has 2,000,000 events.
The 20 anomaly simulations amount to 13,012,931 events.

Each event provides particle level and event information, as recorded by the trigger.
The data is published pre-partitioned into training, validation and test splits: the
zero-bias data 60/20/20, the simulated samples 60/40 between validation and test. All
the feature values are in the trigger-native format of hardware integers.

Specific technical data can be found in the github repo that was used to produce this
data: https://github.com/bb511/adl1t_datamaker

## Comparison with other algorithms

The level-1 trigger contains around 180 algorithms that take data like the one in this
record and output a decision. These algorithms were applied to every event in the
presented data sets as well. Their results are in the `-seeds` configs and can be used
to do comparative studies with other live algorithms currently running in the CMS
trigger. Skip them if you want kinematics alone and do not want to compare your anomaly
detector with the rest of the algorithms.

Row *i* of `<data set>-seeds` is row *i* of `<data set>`, which is the correspondence
that always holds. Neither column is a key on its own: every event the standard
preprocessing dropped carries `order = -1`, and `event` cycles over a hundred values in
`ggH-suep-decay`, `haa-4b-ma15` and `smj-case-A`. Among the rows with `order >= 0` it is
unique within a split, so a filter or a shuffle can be undone by joining on `order` once
those rows are set aside.

## Loading

Every data set is a configuration of its own, and every one of them has a `-seeds` twin
holding the other algorithm decisions for the same events in the same order.

```python
from datasets import load_dataset
normal = load_dataset("podagiu/anomaly_detection_cmsl1t", "ZB_run396102", split="train")
signal = load_dataset("podagiu/anomaly_detection_cmsl1t", "WtoTauto3Mu", split="validation")
menu = load_dataset("podagiu/anomaly_detection_cmsl1t", "WtoTauto3Mu-seeds", split="validation")
```

HuggingFace calls the middle split `validation` while our files call it 'valid'. We also
include a dataloader that we used for performing anomaly detection trigger development
on this data. It reads the data, applies our cuts and normalisation, and stacks the
collections into pytorch tensors that can be directly fed into models.

```python
import sys
from huggingface_hub import snapshot_download
from hydra import compose, initialize_config_dir
from hydra.utils import instantiate
record = snapshot_download("podagiu/anomaly_detection_cmsl1t", repo_type="dataset")
sys.path.insert(0, record)  # the configs name loader.*, so the record has to be importable
with initialize_config_dir(config_dir=record + "/configs", version_base=None):
    cfg = compose("config", overrides=["paths.root_dir=" + record])
data = instantiate(cfg.data)
data.prepare()
train = data.load("train")
```

`prepare` reads the whole record and caches each stage under `./cache`, which the
`ADL1T_CACHE` environment variable moves elsewhere; allow it a few times the record's
size on disk. `train` then has `x`, the model input, of shape (events, 39, 3) under the
`basis` configuration, its padding `mask`, whether the event passed ANY other algorithm
in the trigger `l1bit` and the label `y` (0 for zerobias, > 0 for anomalies, < 0 for
zerobias simulation). `data.load_aux("valid")` returns the same for every simulated
sample. The pipeline needs python 3.10 or newer with `awkward`, `pyarrow`, `numpy`,
`torch`, `omegaconf` and `hydra-core`, which `pip install -r requirements.txt` at the
record's root installs. **If you would just rather read the raw data, you need none of
the above dependencies.** Just call `load_dataset`.

## Layout

```
data/<data set>/<split>-NNNNN-of-NNNNN.parquet
data/<data set>/seeds/<split>-NNNNN-of-NNNNN.parquet
loader/
configs/
requirements.txt
```

One row is one event. The four object collections are jagged, holding one entry per
in-time object up to the global trigger's capacity of 8 muons, 12 jets, 12 e-gammas and
12 taus, with no padding and no truncation. The energy sums are collections too, of one
entry each. The event information, the trigger's verdict and every seed are plain
values.

## Columns

| column | holds |
|---|---|
| `<collection>_<branch>` | one collection's features, e.g. `muons_muonIEt` or `jets_jetIEta`. The collections are `muons`, `jets`, `egammas`, `taus` and the energy sums `ET`, `HT`, `MET`, `MHT`, `FET`, `FHT`, and the branch names are the trigger's own |
| `run`, `lumi`, `event`, `bx`, `orbit`, `time`, `nPV_True` | event information, carried without a prefix |
| `split`, `order` | the published partition and the position within it, described below |
| `L1bit` | whether the trigger menu accepted the event, i.e. the OR over its algorithms |
| `dataset` | the data set the row came from, so that a concatenation stays self-describing |
| `label` | 0 for zero bias, negative for a simulated background, positive for a signal |

**A column prefixed by a collection is a list, and every other column is a plain
value.** The collections have one entry per object in the event. The energy sums have a
single entry (list with one value). Everything else holds one value: `row["L1bit"]` is
`True`, `row["event"]` is an integer, and `ds.filter(lambda r: r["L1bit"])` selects the
events the trigger accepted. A `-seeds` config has one boolean column per trigger
algorithm and four other columns: `L1bit`, `dataset`, `event` and `order`.

## Data sets

| config | label | train | valid | test |
|---|---|---|---|---|
| `GluGluHTo2B_Par-MH-125` | 1 |   | 293,231 | 195,736 |
| `GluGluHto2G_Par-MH-125` | 2 |   | 598,666 | 399,132 |
| `GluGluHto2G_Par-MH-90` | 3 |   | 443,336 | 295,444 |
| `GluGluHto2Tau_Par-MH-125` | 4 |   | 35,706 | 23,805 |
| `GluGlutoHHto2B2WtoLNu2Q_Par-c2-0-kl-1-kt-1` | 5 |   | 179,378 | 119,714 |
| `HHHto4B2Tau_Par-c3-0-d4-0` | 6 |   | 1,200,063 | 800,123 |
| `HHHto6B_Par-c3-0-d4-0` | 7 |   | 383,502 | 255,858 |
| `SUSYGluGluToBBHTo2B_Par-M-1200` | 8 |   | 29,588 | 19,676 |
| `SUSYGluGluToBBHToBB_Par-M-120` | 9 |   | 299,996 | 200,004 |
| `SUSYGluGluToBBHToBB_Par-M-350` | 10 |   | 119,971 | 80,029 |
| `SUSYGluGluToBBHToBB_Par-M-600` | 11 |   | 30,000 | 20,000 |
| `SingleNeutrino_E-10-gun` | -1 |   | 1,200,002 | 799,998 |
| `TTHTo2C_Par-MH-125` | 12 |   | 1,199,636 | 800,464 |
| `TTHto2B_Par-MH-125` | 13 |   | 1,200,165 | 800,225 |
| `VBFHTo2C_Par-MH-125` | 14 |   | 299,566 | 199,720 |
| `VBFHto2B_Par-MH-125` | 15 |   | 299,564 | 199,733 |
| `VBFHto2Tau_Par-MH-125` | 16 |   | 955,535 | 636,965 |
| `WtoTauto3Mu` | 17 |   | 60,002 | 39,998 |
| `ZB_run396102` | 0 | 6,220,470 | 2,071,292 | 2,072,007 |
| `ZB_run398183` | 0 | 6,312,116 | 2,106,228 | 2,105,523 |
| `ggH-suep-decay` | 18 |   | 59,640 | 39,760 |
| `haa-4b-ma15` | 19 |   | 59,942 | 39,958 |
| `smj-case-A` | 20 |   | 59,458 | 39,642 |

## Units

Each feature of each object has values that are integer hardware units, as the trigger
produces them. Nothing in the files is scaled. Multiply by the following to get GeV,
radians and pseudorapidity:

| collection | Et | eta | phi |
|---|---|---|---|
| muons | 0.5 GeV | 0.010875 | 0.0109083 rad |
| jets | 0.5 GeV | 0.0435 | 0.0436332 rad |
| e-gammas | 0.5 GeV | 0.0435 | 0.0436332 rad |
| taus | 0.5 GeV | 0.0435 | 0.0436332 rad |
| ET, HT | 0.5 GeV |   |   |
| MET, MHT, FET, FHT | 0.5 GeV |   | 0.0436332 rad |

The decimals are rounded. The steps are exact fractions: calorimeter eta is 0.0870/2,
muon eta is 0.0870/8, calorimeter phi is 2pi/144, and muon phi is 2pi/576.
`muons_muonIEtaAtVtx` and `muons_muonIPhiAtVtx` take the same scales as muon eta and
phi. Two energies are missing from the table: `muons_muonIEtUnconstrained` is 1 GeV per
unit rather than 0.5, and `jets_jetRawEt` has no documented scale. Quality, charge,
isolation, index and tower-count fields are already integers and unscaled, as is every
event information field and every seed.

## Caveats

**Ordering.** Objects arrive in the trigger's readout order, which is ET-descending for
the calorimeter objects and not for the muons. The shipped loader sorts objects by ET
before processing them.

**The menu differs between data and simulation.** Zero bias data has 183 algorithm
columns and the simulations have 161, of which 147 are shared. Additionally, the order
of the other trigger algorithm decisions is not the same between zerobias and
simulations.

**`nPV_True` has two types.** It is float32 in zero bias and int32 in simulation.

**Simulation carries no beam coordinates.** Every simulated sample has `run` of 1, `bx`
of 4294967295 and `orbit` of 18446744073709551615, the all-ones codes of their types, in
place of the values a collision would have. Only the zero bias has non-trivial values in
these fields.

**`jetRawEt` is zero throughout the zero-bias data.** The branch is unfilled in original
data ntuples, though it has real values in simulation.

## Standard preprocessing

Multiple studies were done internally at CERN on this data set. A number of conventional
preprocessing steps were applied in each of these studies. Therefore the record contains
two columns that the raw data does not: `split`, so a file separated from its directory
is still self-describing, and `order`, the position in that ordering, which is `-1` for
the events that the conventional preprocessing removed. Links to the papers detailing
these studies will be attached here once these studies become public.

The split was drawn once with NumPy's PCG64 generator seeded with **42**, over the two
zero-bias runs concatenated in the order `ZB_run396102 then ZB_run398183`.

**A split can span two configs.** The two zero-bias runs were permuted together. Their
training rows interleave and `order` counts across the whole split rather than within
one run. To rebuild the same order as in previous studies, read both zero-bias configs,
concatenate them, then stable-sort by `order` with the `-1` rows left at the end.
Concatenating one run after the other gives the right rows in the wrong order.

The pipeline in `loader/` goes through the standard preprocessing steps. In the four
stages the studies used: read the tables into one array per collection, drop the events
saturated in ET and mask the objects above a cut, fit the normalisation on the training
split alone and apply it to every other split, then pad each collection to a fixed
number of constituents and stack them into one tensor. That object cut is `Et < 511` for
muons, e-gammas, jets and taus; 511 is the all-ones code of the 9-bit muon, e-gamma and
tau energies, so this cut removes saturated objects. For the jet energy, the full bit
width is 11, which would saturate at 2047. The loader cuts any jets above 255.5 GeV, and
not at the hardware limit. The events cut by this pipeline have the `order` set to `-1`:
run over the zero bias it removes the 457 events marked `-1`, 0.0022% of them.

## Provenance

Zero-bias data: CMS, 2025, runs 396102, 398183. Simulated samples: CMS Run 3 Winter25
campaign. The values are the Level-1 trigger's own reconstructed objects rather than
offline reconstruction.

## Citation

Cite the Zenodo record that this dataset mirrors. The data descriptor is not published
yet; its citation will be added here once it is.

```bibtex
@dataset{cms_l1t_anomaly_2026,
  author    = {{CMS Collaboration}},
  title     = {Trigger Anomaly Detection for New Physics at the Large Hadron Collider},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0},
  doi       = {10.5281/zenodo.21787779},
  url       = {https://doi.org/10.5281/zenodo.21787779}
}
```

## Licence

CC0 1.0, a public domain dedication with no restrictions on reuse. See `LICENSE`.
Citation by DOI is requested as a courtesy, not required.

## Contact

Questions and problems are welcome as a discussion on this dataset's page, or as an
issue on the repository that produced it: https://github.com/bb511/adl1t_datamaker.