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| CMS-MLG-23-005 ; CERN-EP-2025-005 | ||
| Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC | ||
| CMS Collaboration | ||
| 18 February 2025 | ||
| Submitted to Computing and Software for Big Science | ||
| Abstract: We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the $ \tau\tau $ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy. | ||
| Links: e-print arXiv:2502.13047 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Flow chart of a $ \text{CENNT} $ (upper) and $ \text{SANNT} $ (lower). In the figure $ D_{i} $ denotes the data set; $ n $ ($ d $) the number of events (observables) in the initial data set $ D_{X} $; $ l $ the number of classes after event classification; and $ h $ the number of histogram bins entering the statistical inference of the POIs. The function symbol $ \mathbb{P} $ represents the multinomial distribution, and the symbol $ \mathcal{L} $ has been defined in Eq. (3). |
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Figure 1-a:
Flow chart of a $ \text{CENNT} $ (upper) and $ \text{SANNT} $ (lower). In the figure $ D_{i} $ denotes the data set; $ n $ ($ d $) the number of events (observables) in the initial data set $ D_{X} $; $ l $ the number of classes after event classification; and $ h $ the number of histogram bins entering the statistical inference of the POIs. The function symbol $ \mathbb{P} $ represents the multinomial distribution, and the symbol $ \mathcal{L} $ has been defined in Eq. (3). |
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Figure 1-b:
Flow chart of a $ \text{CENNT} $ (upper) and $ \text{SANNT} $ (lower). In the figure $ D_{i} $ denotes the data set; $ n $ ($ d $) the number of events (observables) in the initial data set $ D_{X} $; $ l $ the number of classes after event classification; and $ h $ the number of histogram bins entering the statistical inference of the POIs. The function symbol $ \mathbb{P} $ represents the multinomial distribution, and the symbol $ \mathcal{L} $ has been defined in Eq. (3). |
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Figure 2:
Custom functions $ \mathcal{B}_{i} $ for the backward pass of the backpropagation algorithm, as used (left) in Ref. [14] and (right) in this paper. In the first row of each sub-figure, the same 20 random samples of a simple setup of pseudo-experiments, as described in Section 3.2, are shown. In the second row the resulting histogram $ H $, in the third and fourth rows the functions $ \mathcal{B}_{0} $ and $ \mathcal{B}_{1} $ for the individual bins $ H_{0} $ and $ H_{1} $, respectively, and in the last row the collective effect of $ \sum\mathcal{B}_{i} $ are shown. |
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Figure 2-a:
Custom functions $ \mathcal{B}_{i} $ for the backward pass of the backpropagation algorithm, as used (left) in Ref. [14] and (right) in this paper. In the first row of each sub-figure, the same 20 random samples of a simple setup of pseudo-experiments, as described in Section 3.2, are shown. In the second row the resulting histogram $ H $, in the third and fourth rows the functions $ \mathcal{B}_{0} $ and $ \mathcal{B}_{1} $ for the individual bins $ H_{0} $ and $ H_{1} $, respectively, and in the last row the collective effect of $ \sum\mathcal{B}_{i} $ are shown. |
png pdf |
Figure 2-b:
Custom functions $ \mathcal{B}_{i} $ for the backward pass of the backpropagation algorithm, as used (left) in Ref. [14] and (right) in this paper. In the first row of each sub-figure, the same 20 random samples of a simple setup of pseudo-experiments, as described in Section 3.2, are shown. In the second row the resulting histogram $ H $, in the third and fourth rows the functions $ \mathcal{B}_{0} $ and $ \mathcal{B}_{1} $ for the individual bins $ H_{0} $ and $ H_{1} $, respectively, and in the last row the collective effect of $ \sum\mathcal{B}_{i} $ are shown. |
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Figure 3:
Evolution of the loss functions CE, $ \Delta r_{s}^{\text{stat}} $, and $ \Delta r_{s} $ as used (left) in Ref. [14] and (right) for this paper. In the upper panels the evolution of $ \hat{y}(\,\cdot\,) $ for randomly selected 50 (blue) signal and 50 (orange) background samples during training is shown. Dashed horizontal lines indicate the boundaries of the histogram bins $ H_{i} $. The gray shaded area indicates the pre-training. In the second and third panels from above the evolution of CE and $ \Delta r_{s}^{\text{stat}} $ is shown. In the lowest panels the evolution of $ L_{\text{SANNT}}=\Delta r_{s} $ is shown. The evaluation on the training (validation) data set is indicated in blue (orange). The evaluation of the correspondingly inactive loss function, during or after pre-training, evaluated on the validation data set is indicated by the dashed orange curves. |
png pdf |
Figure 3-a:
Evolution of the loss functions CE, $ \Delta r_{s}^{\text{stat}} $, and $ \Delta r_{s} $ as used (left) in Ref. [14] and (right) for this paper. In the upper panels the evolution of $ \hat{y}(\,\cdot\,) $ for randomly selected 50 (blue) signal and 50 (orange) background samples during training is shown. Dashed horizontal lines indicate the boundaries of the histogram bins $ H_{i} $. The gray shaded area indicates the pre-training. In the second and third panels from above the evolution of CE and $ \Delta r_{s}^{\text{stat}} $ is shown. In the lowest panels the evolution of $ L_{\text{SANNT}}=\Delta r_{s} $ is shown. The evaluation on the training (validation) data set is indicated in blue (orange). The evaluation of the correspondingly inactive loss function, during or after pre-training, evaluated on the validation data set is indicated by the dashed orange curves. |
png pdf |
Figure 3-b:
Evolution of the loss functions CE, $ \Delta r_{s}^{\text{stat}} $, and $ \Delta r_{s} $ as used (left) in Ref. [14] and (right) for this paper. In the upper panels the evolution of $ \hat{y}(\,\cdot\,) $ for randomly selected 50 (blue) signal and 50 (orange) background samples during training is shown. Dashed horizontal lines indicate the boundaries of the histogram bins $ H_{i} $. The gray shaded area indicates the pre-training. In the second and third panels from above the evolution of CE and $ \Delta r_{s}^{\text{stat}} $ is shown. In the lowest panels the evolution of $ L_{\text{SANNT}}=\Delta r_{s} $ is shown. The evaluation on the training (validation) data set is indicated in blue (orange). The evaluation of the correspondingly inactive loss function, during or after pre-training, evaluated on the validation data set is indicated by the dashed orange curves. |
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Figure 4:
Expected distributions of $ \hat{y}(\,\cdot\,) $ for a binary classification task separating $ S $ from $ B $, for a (left) $ \text{CENNT} $ and (right) $ \text{SANNT} $, prior to any fit to $ D_{H}^{\mathcal{A}} $. The individual distributions for $ S $ and $ B $ are shown by the nonstacked open blue and filled orange histograms, respectively. In the lower panels of the figures the expected values of $ S/B+ $ 1 are shown. The gray bands correspond to the combined statistical and systematic uncertainty in $ B $. The boundaries of the histogram bins $ H_{i} $ are also given on an extra horizontal axis. |
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Figure 4-a:
Expected distributions of $ \hat{y}(\,\cdot\,) $ for a binary classification task separating $ S $ from $ B $, for a (left) $ \text{CENNT} $ and (right) $ \text{SANNT} $, prior to any fit to $ D_{H}^{\mathcal{A}} $. The individual distributions for $ S $ and $ B $ are shown by the nonstacked open blue and filled orange histograms, respectively. In the lower panels of the figures the expected values of $ S/B+ $ 1 are shown. The gray bands correspond to the combined statistical and systematic uncertainty in $ B $. The boundaries of the histogram bins $ H_{i} $ are also given on an extra horizontal axis. |
png pdf |
Figure 4-b:
Expected distributions of $ \hat{y}(\,\cdot\,) $ for a binary classification task separating $ S $ from $ B $, for a (left) $ \text{CENNT} $ and (right) $ \text{SANNT} $, prior to any fit to $ D_{H}^{\mathcal{A}} $. The individual distributions for $ S $ and $ B $ are shown by the nonstacked open blue and filled orange histograms, respectively. In the lower panels of the figures the expected values of $ S/B+ $ 1 are shown. The gray bands correspond to the combined statistical and systematic uncertainty in $ B $. The boundaries of the histogram bins $ H_{i} $ are also given on an extra horizontal axis. |
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Figure 5:
Impacts for the 20 nuisance parameters $ \theta_{j} $ with the largest impacts on $ r_{s} $. The gray lines refer to the $ \text{CENNT} $ and the colored bars to the $ \text{SANNT} $. The impacts can be read from the $ x $ axis. Labels shown on the $ y $ axis for each $ \theta_{j} $ are defined in Table 1. The entries are ordered by decreasing magnitude for $ \text{SANNT} $ when moving from the top to the bottom of the figure. The panel on the right shows the relative change of the symmetrized impact when moving from $ \text{CENNT} $ to $ \text{SANNT} $. A more detailed discussion is given in the text. |
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Figure 6:
Negative log of the profile likelihood $ -2\Delta\log\mathcal{L} $ as a function of $ r_{s} $, taking into account (red) all and (blue) only the statistical uncertainties in $ \Delta r_{s} $. The results obtained from the $ \text{CENNT} $ are indicated by the dashed lines, and the median expected result of an ensemble of 100 repetitions of the $ \text{SANNT} $ varying random initializations are indicated by the continuous lines. The red and blue shaded bands surrounding the median expectations indicate 68% confidence intervals (CI) from this ensemble. The lower panels show the distributions underlying these CI. |
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Figure 7:
Expected distributions of $ \hat{y}_{l}(\,\cdot\,) $ for multiclass classification, based on seven event classes, as used for a differential STXS cross section measurement of H production in Ref. [8], prior to any fit to $ D_{H}^{\mathcal{A}} $. In the upper (lower) part of the figure the results obtained after $ \text{CENNT} $ ($ \text{SANNT} $) are shown. The background processes of $ \Omega_{X} $ are indicated by stacked, colored, filled histograms. The expected $ \mathrm{g}\mathrm{g}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H} $ contributions are indicated by the nonstacked open histograms. In the lower panels of the figure the expected values of $ S/B+ $ 1 are shown. The gray bands correspond to the combined statistical and systematic uncertainty in the background model. |
png pdf |
Figure 7-a:
Expected distributions of $ \hat{y}_{l}(\,\cdot\,) $ for multiclass classification, based on seven event classes, as used for a differential STXS cross section measurement of H production in Ref. [8], prior to any fit to $ D_{H}^{\mathcal{A}} $. In the upper (lower) part of the figure the results obtained after $ \text{CENNT} $ ($ \text{SANNT} $) are shown. The background processes of $ \Omega_{X} $ are indicated by stacked, colored, filled histograms. The expected $ \mathrm{g}\mathrm{g}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H} $ contributions are indicated by the nonstacked open histograms. In the lower panels of the figure the expected values of $ S/B+ $ 1 are shown. The gray bands correspond to the combined statistical and systematic uncertainty in the background model. |
png pdf |
Figure 7-b:
Expected distributions of $ \hat{y}_{l}(\,\cdot\,) $ for multiclass classification, based on seven event classes, as used for a differential STXS cross section measurement of H production in Ref. [8], prior to any fit to $ D_{H}^{\mathcal{A}} $. In the upper (lower) part of the figure the results obtained after $ \text{CENNT} $ ($ \text{SANNT} $) are shown. The background processes of $ \Omega_{X} $ are indicated by stacked, colored, filled histograms. The expected $ \mathrm{g}\mathrm{g}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H} $ contributions are indicated by the nonstacked open histograms. In the lower panels of the figure the expected values of $ S/B+ $ 1 are shown. The gray bands correspond to the combined statistical and systematic uncertainty in the background model. |
png pdf |
Figure 8:
Negative log of the profile likelihood $ -2\Delta\log\mathcal{L} $ as a function of $ r_{s} $, for a differential STXS cross section measurement of H production in the $ \mathrm{H}\to\tau\tau $ decay channel, taking (red) all and (blue) only the statistical uncertainties in $ \Delta r_{s} $ into account. In the left plot $ r_{\text{inc}} $ for an inclusive measurement is shown, and in the middle and right plots $ r_{\mathrm{g}\mathrm{g}\mathrm{H}} $ and $ r_{\mathrm{q}\mathrm{q}\mathrm{H}} $ for a combined differential STXS measurement of these two contributions to the signal in two bins are shown. The results as obtained from $ \text{CENNT} $ are indicated by the dashed lines, and the median expected result of an ensemble of 100 repetitions of $ \text{SANNT} $ varying random initializations are indicated by the continuous lines. The red and blue shaded bands surrounding the median expectations indicate 68% confidence intervals (CI) of the ensemble. |
png pdf |
Figure 8-a:
Negative log of the profile likelihood $ -2\Delta\log\mathcal{L} $ as a function of $ r_{s} $, for a differential STXS cross section measurement of H production in the $ \mathrm{H}\to\tau\tau $ decay channel, taking (red) all and (blue) only the statistical uncertainties in $ \Delta r_{s} $ into account. In the left plot $ r_{\text{inc}} $ for an inclusive measurement is shown, and in the middle and right plots $ r_{\mathrm{g}\mathrm{g}\mathrm{H}} $ and $ r_{\mathrm{q}\mathrm{q}\mathrm{H}} $ for a combined differential STXS measurement of these two contributions to the signal in two bins are shown. The results as obtained from $ \text{CENNT} $ are indicated by the dashed lines, and the median expected result of an ensemble of 100 repetitions of $ \text{SANNT} $ varying random initializations are indicated by the continuous lines. The red and blue shaded bands surrounding the median expectations indicate 68% confidence intervals (CI) of the ensemble. |
png pdf |
Figure 8-b:
Negative log of the profile likelihood $ -2\Delta\log\mathcal{L} $ as a function of $ r_{s} $, for a differential STXS cross section measurement of H production in the $ \mathrm{H}\to\tau\tau $ decay channel, taking (red) all and (blue) only the statistical uncertainties in $ \Delta r_{s} $ into account. In the left plot $ r_{\text{inc}} $ for an inclusive measurement is shown, and in the middle and right plots $ r_{\mathrm{g}\mathrm{g}\mathrm{H}} $ and $ r_{\mathrm{q}\mathrm{q}\mathrm{H}} $ for a combined differential STXS measurement of these two contributions to the signal in two bins are shown. The results as obtained from $ \text{CENNT} $ are indicated by the dashed lines, and the median expected result of an ensemble of 100 repetitions of $ \text{SANNT} $ varying random initializations are indicated by the continuous lines. The red and blue shaded bands surrounding the median expectations indicate 68% confidence intervals (CI) of the ensemble. |
png pdf |
Figure 8-c:
Negative log of the profile likelihood $ -2\Delta\log\mathcal{L} $ as a function of $ r_{s} $, for a differential STXS cross section measurement of H production in the $ \mathrm{H}\to\tau\tau $ decay channel, taking (red) all and (blue) only the statistical uncertainties in $ \Delta r_{s} $ into account. In the left plot $ r_{\text{inc}} $ for an inclusive measurement is shown, and in the middle and right plots $ r_{\mathrm{g}\mathrm{g}\mathrm{H}} $ and $ r_{\mathrm{q}\mathrm{q}\mathrm{H}} $ for a combined differential STXS measurement of these two contributions to the signal in two bins are shown. The results as obtained from $ \text{CENNT} $ are indicated by the dashed lines, and the median expected result of an ensemble of 100 repetitions of $ \text{SANNT} $ varying random initializations are indicated by the continuous lines. The red and blue shaded bands surrounding the median expectations indicate 68% confidence intervals (CI) of the ensemble. |
png pdf |
Figure A1:
Evolution of the loss functions CE, $ \Delta r_{s}^{\text{stat}} $, and $ \Delta r_{s} $, as used for this paper. Instead of the custom functions $ \mathcal{B}_{i} $ the identity operation (the so-called straight-through estimator) is used for $ \text{SANNT} $. In the upper panel, the evolution of $ \hat{y} $ for randomly selected 50 (blue) signal and 50 (orange) background samples during training is shown. The gray shaded area indicates the pre-training. In the second panel from above the evolution of CE is shown. Though not actively used for the $ \text{SANNT} \Delta r_{s}^{\text{stat}} $ is also shown, in the third panel from above. In the lower panel, the evolution of $ \Delta r_{s} $ is shown. The evaluation on the training (validation) data set is indicated in blue (orange). The evolution of inactive loss functions, evaluated on the validation data set, is indicated by the dashed orange curves. |
| Tables | |
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Table 1:
Association of nuisance parameters $ \theta_{j} $ with the systematic variations they refer to for the 20 parameters with the largest impacts on $ r_{s} $, as shown in Fig. 5. The label of each corresponding uncertainty is given in the first column, the type of uncertainty, process that it applies to, and rank in Fig. 5 are given in the second, third, and fourth columns, respectively. The ``*'' in $ \epsilon_{\tau}^{\mathrm{ID}}( \text{1-prong}^{*}) $ refers to the fact that this is the decay channel with neutral pions in addition to the charged prong. The symbol EMB refers to $ \tau $-embedded event samples. A more detailed discussion is given in the text. |
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Table 2:
Expected combined statistical and systematic uncertainties $ \Delta r_{s} $ and statistical uncertainties $ \Delta r_{s}^{\text{stat}} $ in the parameters $ r_{\text{inc}} $ for an inclusive, and $ r_{\mathrm{g}\mathrm{g}\mathrm{H}} $ and $ r_{\mathrm{q}\mathrm{q}\mathrm{H}} $ for a differential STXS, cross section measurement of H production in the $ \mathrm{H}\to\tau\tau $ decay channel, as obtained from fits to $ D_{H}^{\mathcal{A}} $. In the second (third) column the results after $ \text{SANNT} $ ($ \text{CENNT} $) are shown. |
| Summary |
| We have proposed a neural network training method capable of accounting for the effects of systematic variations of the data model used in the training process and described its extension to neural network multiclass classification. The procedure has been evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the $ \tau\tau $ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy. This is the first time that a neural network training capable of accounting for the effects of systematic variations has been demonstrated on a data model of this complexity and the first time that such a training has been applied to multiclass classification. |
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