ScoutingVVVTools

CMS scouting VVV analysis workflow for pileup reweighting, branch conversion, optional sample-entry mixing, BDT/NN training, signal-region optimization, QCD ABCD estimation, data/MC plotting, and CMS combine significance/limits.

Pipeline order

1. mode=1: build pileup-weight CSV files.
2. mode=0: convert the selected NanoAOD-style inputs into fat2 and fat3 ROOT trees.
3. mode=6: shuffle each selected MC sample across its ROOT chunks while preserving chunk sizes and filenames, using a deterministic block shuffle that keeps ROOT reads mostly sequential.
4. mode=2: train the BDT or PyTorch NN and save the model plus copied configs. The checked-in config reads from dataset/{signal_mixed|bkg_mixed} by default and uses model_type: "bdt" unless changed.
5. mode=3: scan the test split and write signal_region.csv.
6. mode=5: run the QCD ABCD validation on the MC test split.
7. mode=4: make data/MC comparison plots.
8. mode=7: run the CMS combine wrapper to compute expected significance and AsymptoticLimits per signal class / signal sample / combined, both with the MC-true QCD class yields and with the merged ABCD QCD prediction.

selections/b_veto/ and systematics/ are separate studies and are not launched through run.sh.

run.sh

Usage:

./run.sh <mode> [config.json] [sample1 sample2 ...]

Examples:

./run.sh 1
./run.sh 0
./run.sh 0 selections/convert/config.json www qcd_ht2000
./run.sh 6
./run.sh 6 selections/mix/config.json www
./run.sh 2
./run.sh 3 selections/signal_region/config.json
./run.sh 5 background_estimation/config.json
./run.sh 4 plotting/config.json
./run.sh 7 combine/config.json

Modes:

• mode=0: compile and run selections/convert/convert_branch.C. Input: selections/convert/config.json, selections/convert/branch.json, selections/convert/selection.json, src/sample.json, and the pileup CSVs from mode=1. Output: converted ROOT files under {output_root}/{signal|bkg|data}/, plus selections/convert/log.txt. The final writer streams each tree through a TChain clone owned by the chain itself, so branch addresses stay correct when ROOT advances across temp-file boundaries, and it aborts on any chunk/tree entry-count mismatch instead of silently writing too few or repeated entries. The convert expression engine includes collection helpers such as sum, max_value, min_value, nth_max_value(collection, expr, rank, default), value_at_max(collection, key_expr, value_expr, default), and value_at_nth_max(collection, key_expr, value_expr, rank, default); the checked-in output branch config writes per-AK8 WvsQCD and VvsQCD scores, event-level ScoutingFatPFJetRecluster_{WvsQCD,VvsQCD}_{sum,max} summaries, and AK4 b-tag summaries including ScoutingPFJetRecluster2_scoutUParT_probb_max_pt, ScoutingPFJetRecluster2_scoutUParT_probb_sum, ScoutingPFJetRecluster2_scoutUParT_probb_second_max, and ScoutingPFJetRecluster2_scoutUParT_probb_second_max_pt for both fat2 and fat3.
• mode=1: compile and run selections/weight/weight.C. Input: selections/weight/config.json and src/sample.json. Output: pileup CSV files under the configured output_root, plus selections/weight/log.txt.
• mode=6: compile and run selections/mix/mix.C. Input: selections/mix/config.json, src/sample.json, and the converted MC ROOT files from mode=0. Output: shuffled ROOT files under the configured output_root/{signal_mixed|bkg_mixed}/, preserving the original chunk filenames and per-chunk entry counts while applying a deterministic per-tree block shuffle for each selected MC sample, plus selections/mix/log.txt. Missing input files for a requested sample are fatal. The implementation scans input chunk entry counts first (warming ROOT serially, then using OpenMP across the remaining files when available) and then rewrites each tree through a TChain using shuffled contiguous entry blocks plus an in-block cyclic rotation. Each non-empty output chunk is cloned from the TChain itself so ROOT keeps branch addresses synced across input-file boundaries, and the writer validates both per-chunk and total entry counts before closing. The block size is chosen as clamp(total_entries / 512, min_block_entries, max_block_entries); the checked-in defaults are 32 and 4096. This keeps the output format unchanged while avoiding the old fully random entry-by-entry ROOT read pattern.
• mode=2: run selections/BDT/train.py. Input: selections/BDT/config.json, the ROOT files resolved from its input_root / input_pattern (the checked-in config points to the mixed mode=6 output under ../../dataset/{sample_group}_mixed), and src/sample.json. Output: one trained model directory per tree under output_root. With model_type: "bdt" it contains {tree}_model.json and {tree}_model_stage1.json; with model_type: "nn" it contains PyTorch checkpoints {tree}_model.pt and {tree}_model_stage1.pt and requires PyTorch at training/inference time. The rest of the output layout is shared: feature_corr.pdf, loss plots (loss_mlogloss.pdf / loss_classification.pdf / loss_total.pdf for BDT or loss_weighted_ce.pdf / loss_objective.pdf for NN, plus loss_decorrelation.pdf; NN x-axes are epochs, BDT x-axes are boosting rounds), stage-tagged comparison plots such as importance_cls.pdf / importance_decorr.pdf, roc_*_cls.pdf / roc_*_decorr.pdf, score_*_cls.pdf / score_*_decorr.pdf, decor_corr_{train,test}_cls.pdf / decor_corr_{train,test}_decorr.pdf, decor_score_vs_branch_cls.pdf / decor_score_vs_branch_decorr.pdf, and decor_branch_shapes_by_signal_score_cls.pdf / decor_branch_shapes_by_signal_score_decorr.pdf. BDT feature importance uses XGBoost gain; NN feature importance uses deterministic permutation importance while preserving the same filenames. The output also includes a branches/ subdirectory with one normalized per-class input-distribution PDF per training branch, copied config.json / branch.json / selection.json, test_ranges.json, and the saved test-set prediction references test_reference_signal_region.npz, test_reference_qcd_est.npz, and test_reference_qcd_est_full.npz, plus selections/BDT/log.txt. Both model types keep the same sample splitting, clipping/log preprocessing, event-weight construction, dynamic learning-rate reduction, prediction-reference validation, and downstream score interface. When a per-tree decorrelate branch also appears in selection.json thresholds, only that branch's threshold is omitted from the training/early-stopping objective splits and their class balancing; ordinary branch, ROC, score, feature-importance, feature-correlation, and downstream reference outputs still use the full threshold set, while decorrelation diagnostics use the training-threshold view without that decorrelate-branch cut. BDT logs sum-scale classification_loss and stage 1 monitors test classification_loss; NN uses class-balanced mini-batches with sampling-corrected event weights for backward() and logs epoch-end eval-mode train_objective_loss / test_objective_loss, where stage 1 uses objective_loss = weighted_CE and stage 2 uses objective_loss = weighted_CE + scaled_smooth_CvM_decorrelation. NN early stopping monitors epoch-end test objective_loss in both stages, and AdamW weight_decay remains native decoupled optimizer behavior rather than a logged loss term. In NN mode, per-tree fat2_nn / fat3_nn blocks configure the PyTorch MLP (hidden_layers, dropout, batch_norm, batch_size, learning rates, weight decay, epochs, and permutation-importance event count); batch_size is the total size of each class-balanced mini-batch, the checked-in NN blocks set batch_norm: false, and smooth-CvM decorrelation is supported in the second NN stage with decorrelation scale calibrated against the stage-1 batch-averaged weighted CE.
• mode=3: run selections/signal_region/signal_region.py. Input: selections/signal_region/config.json and one trained model directory from mode=2. Output: sr_score_*.pdf, scores_no_regions.pdf, scores.pdf, scores_no_regions_radial_equalized.pdf, scores_radial_equalized.pdf, and signal_region.csv inside the configured output_dir, plus selections/signal_region/log.txt. The 2D score PDFs use the shared class palette, hide coordinate axes/ticks/frames, and include both the original regular-polygon simplex projection and a radial-equalized projection that keeps polygon vertices fixed while spreading points outward. The script builds one shared candidate pool of general high-dimensional rectangles over the configured BDT score_axes (all in the checked-in config), where every score axis has an independent [low, high) interval and can therefore represent simultaneous lower and upper cuts. Candidate generation combines dense per-axis boundary sets from total/signal/background weighted quantiles plus tail-heavy quantiles, explicit low/mid/high-tail single-axis seeds from seed_quantiles, exact single-axis bounded interval seeds, optional bounded multi-axis seed combinations, deterministic beam coordinate search where every per-axis update scans exact [edges[a], edges[b]) intervals, compatibility expansion that optimizes regions constrained to be non-overlapping with high-Z anchor candidates, and local event-threshold refinement. Independent edge building, beam updates, compatibility updates, local-refinement workers, and event-mask canonicalization can use max_threads, but each stage merges results in deterministic scan order so the candidate set is unchanged by threading. No candidate-pool or global-candidate count limit is applied; candidates are removed only by rounded geometry duplicates and by final exact event-mask/canonical-box deduplication. Before global selection, every retained candidate is shrunk to an event-preserving minimal score box; candidates are deduped only when they select the same exact event mask and shrink to the same canonical box. The final selection is global rather than sequential: it searches K mutually non-overlapping candidates and maximizes the existing combined objective sqrt(sum Z_i^2), with Python and OpenMP compatibility checks using the same exact half-open [low, high) overlap semantics as event-mask membership. The OpenMP helper first finds a beam incumbent and then runs branch-and-bound; if the search completes, the result is exact for the finite candidate list, while node caps produce a conservative upper-bound certificate. After the K non-overlapping SRs are selected, an empty-bin expansion step runs in order SR1, SR2, …: each SR has its signal-class score-axis upper bounds pushed toward 1.0 and its background-class score-axis lower bounds pushed toward 0.0, but only into space that is empty in MC under the SR's other-axis cuts and only as far as the expanded box remains geometrically non-overlapping with every other selected SR. The expansion is constrained to keep each SR's exact selected event mask, so per-bin S/B/Z are numerically unchanged; only the empty-side score-axis bounds in signal_region.csv are widened. Before scanning, the script reloads the saved model, reproduces the saved full-threshold signal-region test prediction, and aborts if it does not match test_reference_signal_region.npz within the stored tolerances.
• mode=4: run plotting/data_mc.py. Input: plotting/config.json, plotting/branch.json, the converted ROOT files from mode=0, and one trained model directory from mode=2. Output: one PDF per plotted branch under the configured output_root, plus plotting/log.txt. Data and MC both use the configured trained-model input pattern for ordinary ROOT branches; missing sample files, empty MC trees/classes before filtering, and non-positive MC raw_entries are treated as fatal errors. In addition to ROOT branches, one derived model score branch per class is plotted as score_{class_name}. For those score branches, MC is read from bdt_root/test_ranges.json, validated against test_reference_signal_region.npz, and normalised to the same full-sample target total while data is predicted from the full configured data_samples input. The script loads .json/.pkl BDT models or .pt NN checkpoints according to the copied model_type.
• mode=5: run background_estimation/qcd_est.py. Input: background_estimation/config.json, one trained model directory from mode=2, and the signal_region.csv written by mode=3. Output: ABCD summary PDFs and one ROOT file under the configured output_dir, plus background_estimation/log.txt. The validation PDFs keep the finite-MC / ABCD-propagated uncertainties based on sum(w^2). The qcd_abcd_region_counts.pdf and qcd_abcd_region_fractions.pdf summaries are stacked by the BDT class_groups classes with a shared fixed class palette; additional colors are generated deterministically when there are more classes than base colors, and qcd_abcd_region_counts_linear.pdf saves the same counts with a linear y axis. The QCD-merged summaries (qcd_abcd_region_counts_qcd_merged.pdf, qcd_abcd_region_counts_qcd_merged_linear.pdf, and qcd_abcd_region_fractions_qcd_merged.pdf) combine every class_groups class whose name contains qcd case-insensitively into one QCD stack color and legend entry, leaving non-QCD classes unchanged. The ROOT file stores combine-facing bundles for every saved category (samples/*, groups/*, qcd_predict, qcd_true, total_predict, and total_true) as srN/yield, srN/stat_error, and srN/scale_error one-bin histograms plus a category-level covariance_total TH2. The combine-facing convention is stat_error = sqrt(yield), scale_error = 0, and diagonal covariance_total = diag(yield), so the weighted yield is treated as the Poisson event count. QCD classes are identified by class_groups names containing qcd case-insensitively; all samples from those classes are summed for the ABCD A/B/C/D totals, qcd_true, qcd_predict, and total_predict, while the per-class groups/* outputs remain MC-true. The signal-region score axes are detected from the CSV {axis}_low / {axis}_high columns, so the ABCD step can consume either the legacy independent axes or the new all-score-axis regions. The non-score ABCD dimension is configured by required per-tree abcd_branches; every listed branch must have a threshold in the copied BDT selection.json, A/B require all listed branches to pass, C/D require all listed branches to fail, and partial pass/fail events are excluded. Before building the ABCD regions, the script reloads the saved model, validates against test_reference_qcd_est_full.npz when present (falling back to the legacy filtered test_reference_qcd_est.npz), and aborts on mismatch.
• mode=7: compile and run combine/combine.C. Input: combine/config.json (lists one or more channels, each pointing to a qcd_abcd_yields.root from mode=5) plus one trained bdt_root directory from mode=2. combine.C reads class_groups from bdt_root/config.json, then resolves that copied config's sample_config the same way as qcd_est.py so the signal/background-class split still comes from sample.json. Group names from class_groups are matched against groups/* in the ROOT file by the same slugified lowercase convention used by qcd_est.py, and QCD classes are the class names containing qcd case-insensitively. Must be executed inside a CMSSW area that has HiggsAnalysis/CombinedLimit built, so that combine and combineCards.py are on $PATH. Output: significance.csv, limits.csv, significance_abcd_mc.csv, limits_abcd_mc.csv, significance_by_channel.csv, limits_by_channel.csv, significance_by_channel_abcd_mc.csv, and limits_by_channel_abcd_mc.csv under the configured output_dir, plus combine/log.txt. Each CSV has one row per signal scenario (combined / per-signal-class / per-signal-sample); the per-channel CSVs add a channel column and rerun the same scenarios using one channel at a time. The wrapper reads every SR from the new srN/yield one-bin histograms and, by default (use_root_covariance=false), writes pure counting datacards with one independent bin per SR (<channel>_sr<N>). Combine's native Poisson likelihood then provides the statistical uncertainty; this matches the signal-region Asimov significance convention, avoids double-counting the same counting fluctuation as a Gaussian nuisance, and avoids the old multi-bin shape-PDF factorization warning. The _abcd_mc.csv CSVs replace all QCD classes with the single merged qcd_predict block while keeping the non-QCD processes on their MC-true group/sample blocks. The wrapper validates every required signal sample/group and aborts on missing inputs or invalid shapes instead of regularizing the yields. If use_root_covariance=true, the full per-process covariance between signal regions is additionally injected via an eigen-decomposition of each process's covariance_total, one Gaussian shape nuisance per retained eigenmode, using one-bin shape templates for each SR. In that optional mode, when rescale_shape_modes_to_positive is true (default), any background process whose nominal yields and covariance are identically zero across all SRs is dropped before writing the datacard, and any eigenmode whose raw ±1σ templates would make a varying bin negative or a total template norm non-positive is shrunk to the largest safe step below that boundary; the datacard shape coefficient is rescaled by 1/a, zero-valued bins are allowed as long as the varied templates stay non-negative and keep strictly positive total norms, and every drop/rescale is written as a warning to combine/log.txt. If a per-sample signal process is identically zero in some channels, those channels are skipped with a warning; if it is identically zero in every channel, the wrapper records 0 significance and inf expected limits for that row and continues. If AsymptoticLimits finishes successfully but its ROOT output still lacks the expected quantiles, the wrapper logs a warning, records 0 for that row's significance and inf for its expected limits in the CSVs, and continues. A temporary work directory is kept under output_dir/work/ with the generated datacards and combine outputs; optional covariance-nuisance shape ROOT files are written only when use_root_covariance=true.

Sample arguments:

• Extra sample names are supported only for mode=0, mode=1, and mode=6.
• If no sample names are given, run.sh uses submit_samples from the chosen config.
• If submit_samples is empty or missing, all MC samples in src/sample.json are used.

Log archiving:

• After the final [finished …] line is written, run.sh copies the per-mode log.txt into the program's configured output directory.
• Applies to mode=1 (output_root), mode=2 / mode=4 (per-tree output_root expanded over submit_trees), and mode=3 / mode=5 / mode=7 (output_dir). mode=0 (convert_branch) and mode=6 (mix) are intentionally skipped.
• The copy runs on both success and failure; if the resolved output dir does not yet exist (e.g., the run aborted before creating it), the copy is skipped with a warning instead of erroring.

Main JSON files

• src/sample.json: master sample registry with name, path, sample_ID, is_MC, is_signal, xsection, lumi, and raw_entries.
• selections/weight/config.json: pileup histogram inputs and pileup-weight output paths.
• selections/convert/config.json: convert-step paths, threading, file-size splitting, and pileup CSV pattern.
• selections/mix/config.json: mix-step tree selection, input/output ROOT roots and patterns, sample config, threading for the input-chunk scan, deterministic random_state, and the block-size bounds min_block_entries / max_block_entries (default 32 / 4096).
• selections/convert/selection.json: event selection and tree split (fat2 / fat3).
• selections/convert/branch.json: input branches to read and output branches to write.
• selections/BDT/config.json: model inputs, input_root / input_pattern, class groups, training settings, output directories, top-level model_type (bdt or nn, default bdt), top-level decor_loss_mode, decor_lambda, decor_n_bins, decor_n_thresholds, decor_score_tau, decor_bin_tau_scale, per-tree BDT hyperparameters (n_estimators, n_estimators_decorr, max_depth, learning_rate, learning_rate_decorr, optional min_learning_rate / lr_reduce_patience, gamma, reg_lambda, reg_alpha, min_child_weight, subsample, colsample_bytree, optional colsample_bynode, early_stopping_rounds), per-tree NN hyperparameters in fat2_nn / fat3_nn (epochs, epochs_decorr, hidden_layers, activation, dropout, batch_norm, batch_size, learning_rate, learning_rate_decorr, optional min_learning_rate / lr_reduce_patience, weight_decay, optional grad_clip_norm defaulting off, early_stopping_rounds, permutation_importance_events; checked-in NN values use learning_rate: 0.001, learning_rate_decorr: 0.0005, and min_learning_rate: 0.00002), decorrelate, decor_efficiencies, and event_reweight_branches.
• selections/signal_region/config.json: signal-region scan settings: lumi, n_signal_regions / N, bdt_root, output_dir, score_axes, min_bkg_weight, min_signal_weight, optional entry minima, max_edge_candidates_per_axis, beam_width, top_intervals_per_axis, coordinate_rounds, seed_intervals_per_axis, multi_axis_seed_max_axes, multi_axis_seed_max_seeds, compatibility_seed_anchors, compatibility_seed_rounds, local_refine_rounds, local_refine_neighbor_edges, local_refine_top_candidates, local_refine_diverse_masks, local_refine_candidate_overscan, global_beam_width, branch_bound_max_nodes, branch_bound_time_limit_seconds, deduplicate_event_masks, require_exact_n_regions, max_threads, progress_every_seconds, and seed_quantiles.
• background_estimation/config.json: qcd_est.py settings, including bdt_root, signal_region_csv, output_dir, root_file_name, and required per-tree abcd_branches entries whose thresholds are read from the copied BDT selection.json.
• plotting/config.json: data_mc.py settings, including bdt_root, output_root, data_samples, and per-tree event_reweight_branches (applied to MC events only; data weights stay 1.0).
• plotting/branch.json: per-tree plot overrides such as skip_branches, bins, x_range, y_range, logx, and logy; derived model score branches use names like score_VVV and accept the same overrides.
• combine/config.json: combine.C settings: channels (list of {name, root_file, bdt_root} — each ROOT file is the qcd_abcd_yields.root output of mode=5 for one channel, and each bdt_root is that channel's trained tree output directory), output_dir, optional combine_cmd / combine_cards_cmd, use_root_covariance (default false; combine uses binned Poisson statistics from the yields and does not turn ROOT covariance into nuisances), eigen_rel_cutoff (used only when use_root_covariance=true, dropping eigenmodes with λ_k ≤ cutoff × max(diag(cov)); default 1e-10), rescale_shape_modes_to_positive (used only when covariance nuisances are enabled; default true), and keep_work (keep the generated datacards under output_dir/work/; default true).

Step-by-step file flow

• mode=1 writes the pileup CSVs used by mode=0 for MC samples.
• mode=0 writes the converted fat2 and fat3 ROOT trees used directly by mode=4, and as the input source for mode=6 or any mode=2 config that still points input_root at the unmixed dataset.
• mode=6 can rewrite those converted MC trees into {signal_mixed|bkg_mixed} directories under the same dataset root with the same chunk layout using a deterministic block shuffle, which the checked-in model-training config uses by default.
• mode=2 writes the model, copied configs, and saved test-set prediction references used by mode=3, mode=4, and mode=5.
• mode=3 writes signal_region.csv, which defines the A-region score bins for mode=5.
• mode=5 writes the ABCD validation ROOT file and PDFs for the chosen tree.
• mode=4 writes one data/MC comparison PDF per branch.
• mode=7 reads one or more mode=5 ROOT files (one per channel) plus the BDT and sample configs, generates per-scenario datacards, and calls combine to fill significance.csv, limits.csv, significance_abcd_mc.csv, and limits_abcd_mc.csv under its output_dir; it also reruns the same scenarios per individual channel and writes the matching *_by_channel*.csv files. Combined/per-class scenarios use the stored groups/*/srN/yield one-bin histograms, per-sample scenarios use samples/*/srN/yield, and ABCD-mode scenarios replace every QCD class with the merged qcd_predict/srN/yield. By default each SR is written as an independent counting bin and ROOT covariance fields are not encoded as nuisances; combine's Poisson likelihood supplies the counting statistics from the weighted yields. Setting use_root_covariance=true restores the optional eigen-decomposed covariance shape nuisances.