--- 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-seeds` is row *i* of ``, 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//-NNNNN-of-NNNNN.parquet data//seeds/-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 | |---|---| | `_` | 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.