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icepeak update combined: Poisson loss, improved initial values, contr…
…ol param
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -7,15 +7,13 @@ | |
| # Tips: One can simplify the model by settings some parameters to | ||
| # their "symmetric" (e.g. 0) values and fixing those. | ||
| # | ||
| # [email protected], 2023 | ||
| # [email protected], 2024 | ||
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| input_path: './actions-stash/input/icefit/dqcd_2018_test/flat/muon/generalTracks/JPsi' | ||
| output_name: 'tune0' | ||
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| years: [2018] #[2016, 2017, 2018] | ||
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| fitrange: [2.91, 3.29] # Nominal fit window (GeV) | ||
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| systematics: ['Nominal'] # ['Nominal', 'nVertices_Up', 'nVertices_Down'] | ||
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| # List of systematic variations active | ||
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@@ -38,15 +36,30 @@ fit: | |
| pdf_pair: ['CB_pdf', 'asym_RBW_pdf'] # name of pdfs to be convoluted | ||
| par_index: [[0,1,2,3], [0,4,5]] # parameter indices per pdf (use same index for shared parameters) | ||
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| # Function weight (fit contribution) parameter | ||
| # ----------------------------------------------- | ||
| # See /docs/pdf/peakfit.pdf | ||
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| # `single`, `dual` and `dual-unitary-I` [signal weight] | ||
| theta_start: 100.0 | ||
| theta_limit: [1.0e-4, 1.0e+5] | ||
| theta_limit: [1.0e-4, 1.0e+9] | ||
| theta_fixed: False | ||
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| # `dual`, `dual-unitary-I` and `dual-unitary-II` [efficiency] | ||
| eps_start: 0.5 | ||
| eps_limit: [0.0, 1.0] | ||
| eps_fixed: False | ||
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| # `dual-unitary-II` [signal fraction] | ||
| f_start: 0.5 | ||
| f_limit: [0.0, 1.0] | ||
| f_fixed: False | ||
| # ----------------------------------------------- | ||
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| # Gamma(J/psi) = 9.32e-05 GeV (very narrow!) | ||
| # CB_param = par[0],par[1],par[2],par[3] ~ Crystal-Ball detector smearing x radiative | ||
| # asym_RBW = par[0],par[4],par[5] ~ J/Psi resonance ~ [initial state pdf x form factor x breit-wigner] | ||
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| # Fit function | ||
| p_name: ['M0', 'sigma', 'n', 'alpha', 'Gamma', 'asym'] | ||
| p_start: [3.097, 0.02, 1.001, 5.0, 1.0e-4, -0.01] | ||
| p_limit: [[3.085, 3.12], [0.01, 0.05], [1.001, 2.0], [0.001, 10.0], [9.32e-5, 0.01], [-3.0, 3.0]] | ||
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@@ -57,11 +70,21 @@ fit: | |
| args: | ||
| norm: True | ||
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| # Function weight (fit contribution) parameter | ||
| # ----------------------------------------------- | ||
| # See /docs/pdf/peakfit.pdf | ||
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| # `single` and `dual` [background weight] | ||
| theta_start: 0.1 | ||
| theta_limit: [1.0e-4, 1.0e+5] | ||
| theta_limit: [1.0e-4, 1.0e+9] | ||
| theta_fixed: False | ||
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| # `dual`, `dual-unitary-I` and `dual-unitary-II` [efficiency] | ||
| eps_start: 0.5 | ||
| eps_limit: [0.0, 1.0] | ||
| eps_fixed: False | ||
| # ----------------------------------------------- | ||
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| # Fit function | ||
| p_name: ['lambda'] | ||
| p_start: [1.0e-4] | ||
| p_limit: [[1.0e-5, 4.0]] | ||
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@@ -82,26 +105,28 @@ techno: | |
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| huber_delta: 1.0 # 'huber' loss threshold parameter | ||
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| # Use enough calls / iterations (otherwise can get stuck in bad local minima) | ||
| # Use enough 'trials' (otherwise can get stuck in bad local minima) | ||
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| ncall_scipy_simplex: 0 # Number of scipy search calls | ||
| ncall_mystic_diffev2: 1 # Number of Mystic solver calls | ||
| ncall_mystic_fmin_powell: 1 # Number of Mystic solver calls | ||
| ncall_scipy_simplex: 0 # Number of scipy search calls (recursively) | ||
| ncall_mystic_diffev2: 1 # Number of Mystic solver calls (recursively) | ||
| ncall_mystic_fmin_powell: 1 # Number of Mystic solver calls (recursively) | ||
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| ncall_minuit_scan: 0 # Number of brute force 1D-scan calls | ||
| ncall_minuit_simplex: 0 # Number of Nelder-Mead simplex search calls | ||
| ncall_minuit_gradient: 10000 # Number of Minuit gradient search calls | ||
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| minos: True # If True, Minuit MINOS uncertainties (slower but best), otherwise HESSE | ||
| minos: True # If True, Minuit MINOS uncertainties (slower but best), otherwise HESSE | ||
| migrad_first_step: 0.001 # First step size (relative percentage), set None for migrad heuristic | ||
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| min_ndof: 1 # Minimum number of d.o.f required | ||
| max_chi2: 1200 # Maximum chi2/ndf for a succesful fit | ||
| min_count: 5 # Minimum number of total entries (sum of bin content) in the histogram | ||
| max_trials: 3 # Number of restarts | ||
| trials: 10 # Number of random restarts (all are executed and inspected, the best is kept) | ||
| rand_sigma: 0.5 # Random perturbation scale for initial values per trial | ||
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| set_to_nan: True # Set parameters after the fit to NaN if not passing max_chi2 or min_count | ||
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| strategy: 1 # Default 1 | ||
| strategy: 2 # Default 1 (0 ~ fast, 1 ~ balanced, 2 ~ accurate) | ||
| tol: 0.1 # Default 0.1, https://iminuit.readthedocs.io/en/stable/reference.html#iminuit.Minuit.tol | ||
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| cov_eps: 0.0 # Covariance matrix post-regularization (added to diagonal) (set to 0 for none, can bias!) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -7,15 +7,13 @@ | |
| # Tips: One can simplify the model by settings some parameters to | ||
| # their "symmetric" (e.g. 0) values and fixing those. | ||
| # | ||
| # [email protected], 2023 | ||
| # [email protected], 2024 | ||
|
|
||
| input_path: './actions-stash/input/icefit/dqcd_2018_test/flat/muon/generalTracks/JPsi' | ||
| output_name: 'tune2' | ||
|
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||
| years: [2018] #[2016, 2017, 2018] | ||
|
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||
| fitrange: [2.91, 3.29] # Nominal fit window (GeV) | ||
|
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||
| systematics: ['Nominal'] # ['Nominal', 'nVertices_Up', 'nVertices_Down'] | ||
|
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||
| # List of systematic variations active | ||
|
|
@@ -33,11 +31,26 @@ fit: | |
| args: | ||
| norm: True | ||
|
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| # Function weight (fit contribution) parameter | ||
| # ----------------------------------------------- | ||
| # See /docs/pdf/peakfit.pdf | ||
|
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||
| # `single`, `dual` and `dual-unitary-I` [signal weight] | ||
| theta_start: 100.0 | ||
| theta_limit: [1.0e-4, 1.0e+5] | ||
| theta_limit: [1.0e-4, 1.0e+9] | ||
| theta_fixed: False | ||
|
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||
| # `dual`, `dual-unitary-I` and `dual-unitary-II` [efficiency] | ||
| eps_start: 0.5 | ||
| eps_limit: [0.0, 1.0] | ||
| eps_fixed: False | ||
|
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||
| # `dual-unitary-II` [signal fraction] | ||
| f_start: 0.5 | ||
| f_limit: [0.0, 1.0] | ||
| f_fixed: False | ||
| # ----------------------------------------------- | ||
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| # Fit function | ||
| p_name: ['M0', 'sigma', 'n_low', 'alpha_low', 'n_high', 'alpha_high'] | ||
| p_start: [3.097, 0.03, 1.5, 1.1, 1.5, 1.1] | ||
| p_limit: [[3.085, 3.12], [0.015, 0.05], [1.001, 20.0], [0.001, 2.0], [1.001, 20.0], [0.001, 2.0]] | ||
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@@ -48,11 +61,21 @@ fit: | |
| args: | ||
| norm: True | ||
|
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| # Function weight (fit contribution) parameter | ||
| # ----------------------------------------------- | ||
| # See /docs/pdf/peakfit.pdf | ||
|
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||
| # `single` and `dual` [background weight] | ||
| theta_start: 0.1 | ||
| theta_limit: [1.0e-4, 1.0e+5] | ||
| theta_limit: [1.0e-4, 1.0e+9] | ||
| theta_fixed: False | ||
|
|
||
| # `dual`, `dual-unitary-I` and `dual-unitary-II` [efficiency] | ||
| eps_start: 0.5 | ||
| eps_limit: [0.0, 1.0] | ||
| eps_fixed: False | ||
| # ----------------------------------------------- | ||
|
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| # Fit function | ||
| p_name: ['lambda'] | ||
| p_start: [1.0e-4] | ||
| p_limit: [[1.0e-5, 4.0]] | ||
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@@ -73,26 +96,28 @@ techno: | |
|
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| huber_delta: 1.0 # 'huber' loss threshold parameter | ||
|
|
||
| # Use enough calls / iterations (otherwise can get stuck in bad local minima) | ||
| # Use enough 'trials' (otherwise can get stuck in bad local minima) | ||
|
|
||
| ncall_scipy_simplex: 0 # Number of scipy search calls | ||
| ncall_mystic_diffev2: 1 # Number of Mystic solver calls | ||
| ncall_mystic_fmin_powell: 1 # Number of Mystic solver calls | ||
| ncall_scipy_simplex: 0 # Number of scipy search calls (recursively) | ||
| ncall_mystic_diffev2: 1 # Number of Mystic solver calls (recursively) | ||
| ncall_mystic_fmin_powell: 1 # Number of Mystic solver calls (recursively) | ||
|
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| ncall_minuit_scan: 0 # Number of brute force 1D-scan calls | ||
| ncall_minuit_simplex: 0 # Number of Nelder-Mead simplex search calls | ||
| ncall_minuit_scan: 0 # Number of brute force 1D-scan calls (0 or 1) | ||
| ncall_minuit_simplex: 0 # Number of Nelder-Mead simplex search calls (0 or 1) | ||
| ncall_minuit_gradient: 10000 # Number of Minuit gradient search calls | ||
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| minos: True # If True, Minuit MINOS uncertainties (slower but best), otherwise HESSE | ||
|
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| min_ndof: 1 # Minimum number of d.o.f required | ||
| max_chi2: 1200 # Maximum chi2/ndf for a succesful fit | ||
| min_count: 5 # Minimum number of total entries (sum of bin content) in the histogram | ||
| max_trials: 3 # Number of restarts | ||
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| set_to_nan: True # Set parameters after the fit to NaN if not passing max_chi2 or min_count | ||
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| strategy: 1 # Default 1 | ||
| tol: 0.1 # Default 0.1, https://iminuit.readthedocs.io/en/stable/reference.html#iminuit.Minuit.tol | ||
| minos: True # If True, Minuit MINOS uncertainties (slower but best), otherwise HESSE | ||
| migrad_first_step: 0.001 # First step size (relative percentage), set None for migrad heuristic | ||
|
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| min_ndof: 1 # Minimum number of d.o.f required | ||
| max_chi2: 1200 # Maximum chi2/ndf for a succesful fit | ||
| min_count: 5 # Minimum number of total entries (sum of bin content) in the histogram | ||
| trials: 10 # Number of random restarts (all are executed and inspected, the best is kept) | ||
| rand_sigma: 0.5 # Random perturbation scale for initial values per trial | ||
|
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| set_to_nan: True # Set parameters after the fit to NaN if not passing max_chi2 or min_count | ||
|
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| cov_eps: 0.0 # Covariance matrix post-regularization (added to diagonal) (set to 0 for none, can bias!) | ||
| strategy: 2 # Default 1 (0 ~ fast, 1 ~ balanced, 2 ~ accurate) | ||
| tol: 0.1 # Default 0.1, https://iminuit.readthedocs.io/en/stable/reference.html#iminuit.Minuit.tol | ||
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| cov_eps: 0.0 # Covariance matrix post-regularization (added to diagonal) (set to 0 for none, can bias!) | ||
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