file src/ColliderBit_InterpolatedYields.cpp
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Namespaces
| Name |
|---|
| Gambit TODO: see if we can use this one: |
| Gambit::ColliderBit |
Classes
| Name | |
|---|---|
| class | Gambit::ColliderBit::DMEFT_analysis_info |
| struct | Gambit::ColliderBit::_gsl_target_func_params A struct to contain parameters for the GSL optimiser target function. |
Detailed Description
Author:
- Martin White (martin.white@adelaide.edu.au)
- Andre Scaffidi (andre.scaffidi@adelaide.edu.au)
- Tomas Gonzalo (gonzalo@physik.rwth-aachen.de)
- Anders Kvellestad (anders.kvellestad@fys.uio.no)
Date:
- 2019 Aug
- 2021 Apr
- 2021 May
Functions for LHC analyses that use tabulated interpolations rather than direct MC simulation. For now this functionality is specific to the DMEFT model, but it will be turned into a general feature in ColliderBit.
Authors (add name and date if you modify):
Analyses based on: arxiv:1711.03301 and https://journals.aps.org/prd/abstract/10.1103/PhysRevD.97.092005 139invfb analysis based on arXiv:2102.10874
Source code
// GAMBIT: Global and Modular BSM Inference Tool
// *********************************************
/// \file
///
/// Functions for LHC analyses that use tabulated interpolations
/// rather than direct MC simulation. For now this functionality
/// is specific to the DMEFT model, but it will be turned into
/// a general feature in ColliderBit.
///
/// *********************************************
///
/// Authors (add name and date if you modify):
///
/// \author Martin White
/// (martin.white@adelaide.edu.au)
///
/// \author Andre Scaffidi
/// (andre.scaffidi@adelaide.edu.au)
/// \date 2019 Aug
///
/// \author Tomas Gonzalo
/// (gonzalo@physik.rwth-aachen.de)
/// \date 2021 Apr
///
/// \author Anders Kvellestad
/// (anders.kvellestad@fys.uio.no)
/// \date 2021 May
///
/// Analyses based on: arxiv:1711.03301 and https://journals.aps.org/prd/abstract/10.1103/PhysRevD.97.092005
/// 139invfb analysis based on arXiv:2102.10874
///
/// *********************************************
// Needs GSL 2
#include <gsl/gsl_math.h>
#include <gsl/gsl_interp2d.h>
#include <gsl/gsl_spline2d.h>
#include <gsl/gsl_sf_gamma.h>
#include "Eigen/Eigenvalues"
#include "Eigen/Eigen"
#include "multimin/multimin.hpp"
#include "gambit/ColliderBit/analyses/Analysis.hpp"
#include "gambit/Elements/gambit_module_headers.hpp"
#include "gambit/ColliderBit/ColliderBit_rollcall.hpp"
#include "gambit/Utils/interp_collection.hpp"
#include "gambit/Utils/file_lock.hpp"
#include "gambit/Utils/util_macros.hpp"
#include "gambit/ColliderBit/Utils.hpp"
// #define COLLIDERBIT_DEBUG_PROFILING
// #define COLLIDERBIT_DEBUG
// #define DEBUG_PREFIX "DEBUG: OMP thread " << omp_get_thread_num() << ": "
namespace Gambit
{
namespace ColliderBit
{
// =========== Useful stuff ===========
/// A minimal class with analysis info, maps for containing collections of 1D/2D interpolators
/// and some helper functions for adding and accessing the interpolators, and for
/// adding a background covariance matrix. Currently this class is tailored specifically
/// for the DMEFT model -- it will be generalized in the future.
class DMEFT_analysis_info
{
public:
// Standard analysis info:
str name;
double lumi_invfb;
size_t n_signal_regions;
std::vector<int> obsnum;
std::vector<double> bkgnum;
std::vector<double> bkgerr;
Eigen::MatrixXd bkgcov;
// A map to hold any extra non-standard numbers we might need for a given analysis.
// For the DMEFT-specific case we'll use this to store the MET spectrum bin limits
std::map<str, std::vector<double>> extra_info; // Any additional analysis-specific numbers
// Maps containing 1D and 2D interpolators
std::map<str,std::unique_ptr<Utils::interp1d_collection>> interp1d;
std::map<str,std::unique_ptr<Utils::interp2d_collection>> interp2d;
// Helper functions
void add_bkgcov(const std::vector< std::vector<double>>& bkgcov_in)
{
assert( bkgcov_in.size() > 0 && bkgcov_in.size() == n_signal_regions );
assert( bkgcov_in[0].size() > 0 && bkgcov_in[0].size() == n_signal_regions );
// Fill our Eigen matrix
bkgcov = Eigen::MatrixXd(n_signal_regions, n_signal_regions);
for (size_t i = 0; i < n_signal_regions; i++)
{
bkgcov.row(i) = Eigen::VectorXd::Map(&bkgcov_in[i][0], bkgcov_in[i].size());
}
}
void add_interp1d(str name, str filename, std::vector<str> colnames)
{
assert (interp1d.count(name) == 0); // Make sure we're not overwriting an existing entry
interp1d[name] = std::unique_ptr<Utils::interp1d_collection>(new Utils::interp1d_collection(name, filename, colnames));
}
void add_interp2d(str name, str filename, std::vector<str> colnames)
{
assert (interp2d.count(name) == 0); // Make sure we're not overwriting an existing entry
interp2d[name] = std::unique_ptr<Utils::interp2d_collection>(new Utils::interp2d_collection(name, filename, colnames));
}
const Utils::interp1d_collection& get_interp1d(str name) const
{
return *interp1d.at(name);
}
const Utils::interp2d_collection& get_interp2d(str name) const
{
return *interp2d.at(name);
}
};
/// A struct to contain parameters for the GSL optimiser target function
struct _gsl_target_func_params
{
double lambda;
AnalysisDataPointers adata_ptrs_original;
std::vector<str> skip_analyses;
bool use_covar;
bool use_marg;
bool combine_nocovar_SRs;
};
/// A global map from analysis name to DMEFT_analysis_info instance.
/// This map is initialized by the function fill_analysis_info_map,
/// which is called the first time DMEFT_results run.
std::map<str,DMEFT_analysis_info> analysis_info_map;
// =========== Forward declarations ===========
/// Forward declaration of funtion in LHC_likelihoods
AnalysisLogLikes calc_loglikes_for_analysis(const AnalysisData&, bool, bool, bool, bool);
/// Forward declarations of functions in this file
void fill_analysis_info_map();
void DMEFT_results(AnalysisDataPointers&);
void get_all_DMEFT_signal_yields(std::vector<double>&, const DMEFT_analysis_info&, const Spectrum&);
void get_DMEFT_signal_yields_dim6_operator(std::vector<double>&, const str, const DMEFT_analysis_info&, double, double, double, double);
void get_DMEFT_signal_yields_dim7_operator(std::vector<double>&, const str, const DMEFT_analysis_info&, double, double, double);
void DMEFT_results_profiled(AnalysisDataPointers&);
void DMEFT_results_cutoff(AnalysisDataPointers&);
void signal_modifier_function(AnalysisData&, double, double);
void signal_cutoff_function(AnalysisData&, double);
void _gsl_target_func(const size_t, const double*, void*, double*);
void calc_DMEFT_profiled_LHC_nuisance_params(map_str_dbl&);
void InterpolatedMCInfo(MCLoopInfo&);
// =========== Functions ===========
/// A function for filling the analysis_info_map.
/// This is where all the analysis-specific numbers and file names go.
void fill_analysis_info_map()
{
// Helper variables
str current_analysis_name;
std::vector<str> colnames;
DMEFT_analysis_info empty_analysis_info;
DMEFT_analysis_info* current_ainfo;
//
// New analysis: CMS_13TeV_MONOJET_36invfb_interpolated
//
// Analysis name
current_analysis_name = "CMS_13TeV_MONOJET_36invfb_interpolated";
// Create an entry in the global analysis_info_map and point the pointer current_ainfo to it
analysis_info_map[current_analysis_name] = DMEFT_analysis_info();
current_ainfo = &(analysis_info_map[current_analysis_name]);
current_ainfo->name = current_analysis_name;
current_ainfo->lumi_invfb = 36.1;
current_ainfo->obsnum = {136865, 74340, 42540, 25316, 15653, 10092, 8298, 4906, 2987, 2032, 1514,
926, 557, 316, 233, 172, 101, 65, 46, 26, 31, 29};
current_ainfo->bkgnum = {134500., 73400., 42320., 25490., 15430., 10160., 8480., 4865., 2970., 1915., 1506.,
844., 526., 325., 223., 169., 107., 88.1, 52.8, 25.0, 25.5, 26.9};
current_ainfo->bkgerr = {3700., 2000., 810., 490., 310., 170., 140., 95., 49., 33., 32.,
18., 14., 12., 9., 8., 6., 5.3, 3.9, 2.5, 2.6, 2.8};
assert(current_ainfo->obsnum.size() == current_ainfo->bkgerr.size());
assert(current_ainfo->obsnum.size() == current_ainfo->bkgerr.size());
current_ainfo->n_signal_regions = current_ainfo->obsnum.size(); // = 22
current_ainfo->extra_info["metmins"] = {250., 280., 310., 340., 370., 400., 430., 470., 510., 550., 590.,
640., 690., 740., 790., 840., 900., 960., 1020., 1090., 1160., 1250.};
assert(current_ainfo->obsnum.size() == current_ainfo->extra_info["metmins"].size());
// Construct the background covariance matrix
std::vector< std::vector<double>> bkgcov = {
{ 1.37e+07, 7.18e+06, 2.58e+06, 1.54e+06, 9.29e+05, 4.28e+05, 3.26e+05, 2.04e+05, 8.34e+04, 5.37e+04, 4.62e+04, 2.33e+04, 1.45e+04, 1.20e+04, 6.66e+03, 7.99e+03, 4.00e+03, 1.57e+03, 0.00e+00, 1.30e+03, 3.85e+02, -4.14e+02 },
{ 7.18e+06, 4.00e+06, 1.38e+06, 8.43e+05, 5.02e+05, 2.28e+05, 1.74e+05, 1.05e+05, 4.51e+04, 2.84e+04, 2.30e+04, 1.22e+04, 7.56e+03, 6.48e+03, 3.24e+03, 4.00e+03, 2.28e+03, 1.06e+03, 1.56e+02, 8.00e+02, 3.64e+02, -1.68e+02 },
{ 2.58e+06, 1.38e+06, 6.56e+05, 3.57e+05, 2.18e+05, 1.07e+05, 8.73e+04, 5.31e+04, 2.34e+04, 1.50e+04, 1.35e+04, 7.00e+03, 4.20e+03, 3.30e+03, 2.26e+03, 1.81e+03, 1.12e+03, 6.44e+02, 2.21e+02, 3.04e+02, 1.47e+02, 2.27e+01 },
{ 1.54e+06, 8.43e+05, 3.57e+05, 2.40e+05, 1.32e+05, 6.58e+04, 5.14e+04, 3.17e+04, 1.44e+04, 9.22e+03, 8.15e+03, 4.06e+03, 2.88e+03, 2.00e+03, 1.32e+03, 1.25e+03, 7.06e+02, 3.64e+02, 5.73e+01, 1.59e+02, 7.64e+01, -2.74e+01 },
{ 9.29e+05, 5.02e+05, 2.18e+05, 1.32e+05, 9.61e+04, 4.11e+04, 3.21e+04, 1.88e+04, 8.81e+03, 5.73e+03, 5.46e+03, 2.57e+03, 1.78e+03, 1.34e+03, 6.98e+02, 9.18e+02, 4.28e+02, 1.64e+02, 3.63e+01, 1.32e+02, 1.05e+02, -8.68e+00 },
{ 4.28e+05, 2.28e+05, 1.07e+05, 6.58e+04, 4.11e+04, 2.89e+04, 1.76e+04, 1.07e+04, 5.16e+03, 2.92e+03, 2.83e+03, 1.62e+03, 9.76e+02, 8.77e+02, 3.82e+02, 4.49e+02, 2.04e+02, 1.08e+02, 9.94e+01, 1.02e+02, 3.98e+01, 4.76e+00 },
{ 3.26e+05, 1.74e+05, 8.73e+04, 5.14e+04, 3.21e+04, 1.76e+04, 1.96e+04, 9.18e+03, 4.39e+03, 2.82e+03, 2.46e+03, 1.39e+03, 9.21e+02, 7.39e+02, 5.17e+02, 3.70e+02, 2.35e+02, 9.65e+01, 8.19e+01, 4.20e+01, 1.82e+01, 3.14e+01 },
{ 2.04e+05, 1.04e+05, 5.31e+04, 3.17e+04, 1.88e+04, 1.07e+04, 9.18e+03, 9.02e+03, 2.61e+03, 1.72e+03, 1.70e+03, 8.55e+02, 4.52e+02, 4.67e+02, 2.48e+02, 2.66e+02, 1.54e+02, 5.04e+01, 3.33e+01, 1.19e+01, 3.21e+01, 7.98e+00 },
{ 8.34e+04, 4.51e+04, 2.34e+04, 1.44e+04, 8.81e+03, 5.16e+03, 4.39e+03, 2.61e+03, 2.40e+03, 9.22e+02, 8.94e+02, 4.67e+02, 2.13e+02, 2.41e+02, 1.41e+02, 1.29e+02, 4.70e+01, 4.41e+01, 7.64e+00, 2.08e+01, 2.55e+01, 5.49e+00 },
{ 5.37e+04, 2.84e+04, 1.50e+04, 9.22e+03, 5.73e+03, 2.92e+03, 2.82e+03, 1.72e+03, 9.22e+02, 1.09e+03, 5.17e+02, 3.03e+02, 1.62e+02, 1.47e+02, 8.91e+01, 8.18e+01, 3.17e+01, 2.10e+01, 1.29e+00, 7.42e+00, 7.72e+00, 4.62e+00 },
{ 4.62e+04, 2.30e+04, 1.35e+04, 8.15e+03, 5.46e+03, 2.83e+03, 2.46e+03, 1.70e+03, 8.94e+02, 5.17e+02, 1.02e+03, 2.65e+02, 1.57e+02, 1.61e+02, 9.22e+01, 7.94e+01, 3.84e+01, 3.39e+00, -1.25e+00, 1.44e+01, 3.33e+00, -8.96e-01 },
{ 2.33e+04, 1.22e+04, 7.00e+03, 4.06e+03, 2.57e+03, 1.62e+03, 1.39e+03, 8.55e+02, 4.67e+02, 3.03e+02, 2.65e+02, 3.24e+02, 8.57e+01, 9.07e+01, 5.83e+01, 3.02e+01, 2.70e+01, 2.00e+01, 7.02e+00, 2.25e+00, 5.15e+00, 7.06e+00 },
{ 1.45e+04, 7.56e+03, 4.20e+03, 2.88e+03, 1.78e+03, 9.76e+02, 9.21e+02, 4.52e+02, 2.13e+02, 1.62e+02, 1.57e+02, 8.57e+01, 1.96e+02, 5.21e+01, 3.91e+01, 3.92e+01, 2.69e+01, 8.90e+00, 6.55e+00, 0.00e+00, 1.46e+00, 1.57e+00 },
{ 1.20e+04, 6.48e+03, 3.30e+03, 2.00e+03, 1.34e+03, 8.77e+02, 7.39e+02, 4.67e+02, 2.41e+02, 1.47e+02, 1.61e+02, 9.07e+01, 5.21e+01, 1.44e+02, 3.02e+01, 2.02e+01, 1.44e+01, 3.18e+00, 4.68e-01, 4.50e+00, 2.18e+00, 3.02e+00 },
{ 6.66e+03, 3.24e+03, 2.26e+03, 1.32e+03, 6.98e+02, 3.82e+02, 5.17e+02, 2.48e+02, 1.41e+02, 8.91e+01, 9.22e+01, 5.83e+01, 3.91e+01, 3.02e+01, 8.10e+01, 1.15e+01, 1.19e+01, 7.63e+00, 3.16e+00, -2.25e-01, 1.40e+00, 2.52e+00 },
{ 7.99e+03, 4.00e+03, 1.81e+03, 1.25e+03, 9.18e+02, 4.49e+02, 3.70e+02, 2.66e+02, 1.29e+02, 8.18e+01, 7.94e+01, 3.02e+01, 3.92e+01, 2.02e+01, 1.15e+01, 6.40e+01, 1.92e+00, -1.27e+00, -3.12e-01, 1.40e+00, 2.70e+00, -6.72e-01 },
{ 4.00e+03, 2.28e+03, 1.12e+03, 7.06e+02, 4.28e+02, 2.04e+02, 2.35e+02, 1.54e+02, 4.70e+01, 3.17e+01, 3.84e+01, 2.70e+01, 2.69e+01, 1.44e+01, 1.19e+01, 1.92e+00, 3.60e+01, 5.09e+00, 3.74e+00, -1.65e+00, 1.40e+00, 1.51e+00 },
{ 1.57e+03, 1.06e+03, 6.44e+02, 3.64e+02, 1.64e+02, 1.08e+02, 9.65e+01, 5.04e+01, 4.41e+01, 2.10e+01, 3.39e+00, 2.00e+01, 8.90e+00, 3.18e+00, 7.63e+00, -1.27e+00, 5.09e+00, 2.81e+01, 6.20e-01, -1.19e+00, 5.51e-01, -4.45e-01 },
{ 0.00e+00, 1.56e+02, 2.21e+02, 5.73e+01, 3.63e+01, 9.95e+01, 8.19e+01, 3.33e+01, 7.64e+00, 1.29e+00, -1.25e+00, 7.02e+00, 6.55e+00, 4.68e-01, 3.16e+00, -3.12e-01, 3.74e+00, 6.20e-01, 1.52e+01, 7.80e-01, 3.04e-01, 1.64e+00 },
{ 1.30e+03, 8.00e+02, 3.04e+02, 1.59e+02, 1.32e+02, 1.02e+02, 4.20e+01, 1.19e+01, 2.08e+01, 7.42e+00, 1.44e+01, 2.25e+00, 0.00e+00, 4.50e+00, -2.25e-01, 1.40e+00, -1.65e+00, -1.19e+00, 7.80e-01, 6.25e+00, 1.30e-01, 6.30e-01 },
{ 3.85e+02, 3.64e+02, 1.47e+02, 7.64e+01, 1.05e+02, 3.98e+01, 1.82e+01, 3.21e+01, 2.55e+01, 7.72e+00, 3.33e+00, 5.15e+00, 1.46e+00, 2.18e+00, 1.40e+00, 2.70e+00, 1.40e+00, 5.51e-01, 3.04e-01, 1.30e-01, 6.76e+00, 5.82e-01 },
{ -4.14e+02, -1.68e+02, 2.27e+01, -2.74e+01, -8.68e+00, 4.76e+00, 3.14e+01, 7.98e+00, 5.49e+00, 4.62e+00, -8.96e-01, 7.06e+00, 1.57e+00, 3.02e+00, 2.52e+00, -6.72e-01, 1.51e+00, -4.45e-01, 1.64e+00, 6.30e-01, 5.82e-01, 7.84e+00 }
};
// Save it
current_ainfo->add_bkgcov(bkgcov);
// Create interpolated functions for the CMS analysis:
// - 2d cross-sections
colnames = {"mass", "theta", "xsec"};
current_ainfo->add_interp2d("mass_theta_xsecpb_C61_C64", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_xsecpb_CMS_C61_C64.txt", colnames);
current_ainfo->add_interp2d("mass_theta_xsecpb_C62_C63", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_xsecpb_CMS_C62_C63.txt", colnames);
// - 1d cross-sections
colnames = {"mass", "xsec"};
current_ainfo->add_interp1d("mass_xsecpb_C71", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_CMS_C71.txt", colnames);
current_ainfo->add_interp1d("mass_xsecpb_C72", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_CMS_C72.txt", colnames);
current_ainfo->add_interp1d("mass_xsecpb_C73", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_CMS_C73.txt", colnames);
current_ainfo->add_interp1d("mass_xsecpb_C74", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_CMS_C74.txt", colnames);
// - 2d signal efficiencies
colnames = {"mass", "theta", "SR1", "SR2", "SR3", "SR4", "SR5", "SR6", "SR7", "SR8", "SR9", "SR10",
"SR11", "SR12", "SR13", "SR14", "SR15", "SR16", "SR17", "SR18", "SR19", "SR20", "SR21", "SR22"};
current_ainfo->add_interp2d("mass_theta_eff_C61_C64", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_eff_CMS_C61_C64.txt", colnames);
current_ainfo->add_interp2d("mass_theta_eff_C62_C63", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_eff_CMS_C62_C63.txt", colnames);
// - 1d signal efficiencies
colnames = {"mass", "SR1", "SR2", "SR3", "SR4", "SR5", "SR6", "SR7", "SR8", "SR9", "SR10",
"SR11", "SR12", "SR13", "SR14", "SR15", "SR16", "SR17", "SR18", "SR19", "SR20", "SR21", "SR22"};
current_ainfo->add_interp1d("mass_eff_C71", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_CMS_C71.txt", colnames);
current_ainfo->add_interp1d("mass_eff_C72", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_CMS_C72.txt", colnames);
current_ainfo->add_interp1d("mass_eff_C73", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_CMS_C73.txt", colnames);
current_ainfo->add_interp1d("mass_eff_C74", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_CMS_C74.txt", colnames);
// 'Clear' the pointer current_ainfo before moving on to the next analysis by pointing it to empty_analysis_info
current_ainfo = &empty_analysis_info;
//
// New analysis: ATLAS_13TeV_MONOJET_139invfb_interpolated
//
// Analysis name
current_analysis_name = "ATLAS_13TeV_MONOJET_139invfb_interpolated";
// Create an entry in the global analysis_info_map and point the reference current_ainfo to it
analysis_info_map[current_analysis_name] = DMEFT_analysis_info();
current_ainfo = &(analysis_info_map[current_analysis_name]);
current_ainfo->name = current_analysis_name;
current_ainfo->lumi_invfb = 139.0;
current_ainfo->obsnum = {1791624, 752328, 313912, 141036, 102888, 29458, 10203, 3986, 1663, 738, 413+187+207};
current_ainfo->bkgnum = {1783000., 753000., 314000., 140100., 101600., 29200., 10000., 3870., 1640., 754., 359.+182.+218.};
current_ainfo->bkgerr = {26000., 9000., 3500., 1600., 1200., 400., 180., 80., 40., 20., sqrt(10*10+6*6+9*9)};
assert(current_ainfo->obsnum.size() == current_ainfo->bkgnum.size());
assert(current_ainfo->obsnum.size() == current_ainfo->bkgerr.size());
current_ainfo->n_signal_regions = current_ainfo->obsnum.size();
current_ainfo->extra_info["metmins"] = {200., 250., 300., 350., 400., 500., 600., 700., 800., 900., 1000.};
assert(current_ainfo->obsnum.size() == current_ainfo->extra_info["metmins"].size());
// Create interpolated functions for the ATLAS analysis:
// - 2d cross-sections
colnames = {"mass", "theta", "xsec"};
current_ainfo->add_interp2d("mass_theta_xsecpb_C61_C64", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_xsecpb_ATLAS_C61_C64.txt", colnames);
current_ainfo->add_interp2d("mass_theta_xsecpb_C62_C63", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_xsecpb_ATLAS_C62_C63.txt", colnames);
// - 1d cross-sections
colnames = {"mass", "xsec"};
current_ainfo->add_interp1d("mass_xsecpb_C71", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_ATLAS_C71.txt", colnames);
current_ainfo->add_interp1d("mass_xsecpb_C72", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_ATLAS_C72.txt", colnames);
current_ainfo->add_interp1d("mass_xsecpb_C73", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_ATLAS_C73.txt", colnames);
current_ainfo->add_interp1d("mass_xsecpb_C74", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_xsecpb_ATLAS_C74.txt", colnames);
// - 2d signal efficiencies
colnames = {"mass", "theta", "SR1", "SR2", "SR3", "SR4", "SR5", "SR6", "SR7", "SR8", "SR9", "SR10", "SR11"};
current_ainfo->add_interp2d("mass_theta_eff_C61_C64", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_eff_ATLAS_C61_C64.txt", colnames);
current_ainfo->add_interp2d("mass_theta_eff_C62_C63", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_theta_eff_ATLAS_C62_C63.txt", colnames);
// - 1d signal efficiencies
colnames = {"mass", "SR1", "SR2", "SR3", "SR4", "SR5", "SR6", "SR7", "SR8", "SR9", "SR10", "SR11"};
current_ainfo->add_interp1d("mass_eff_C71", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_ATLAS_C71.txt", colnames);
current_ainfo->add_interp1d("mass_eff_C72", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_ATLAS_C72.txt", colnames);
current_ainfo->add_interp1d("mass_eff_C73", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_ATLAS_C73.txt", colnames);
current_ainfo->add_interp1d("mass_eff_C74", GAMBIT_DIR "/ColliderBit/data/DMEFT/mass_eff_ATLAS_C74.txt", colnames);
// 'Clear' the pointer current_ainfo before moving on to the next analysis by pointing it to empty_analysis_info
current_ainfo = &empty_analysis_info;
}
/// Results from DMEFT analyses before any modification of the MET spectrum
void DMEFT_results(AnalysisDataPointers& result)
{
using namespace Pipes::DMEFT_results;
static bool first = true;
// In this function we need to transfer info from the DMEFT-specific DMEFT_analysis_info objects
// to a set of ColliderBit-native AnalysisData objects, and also fill these with the DMEFT signal prediction.
// We need thread_local AnalysisData instances. Let's collect them in a map.
thread_local std::map<str,AnalysisData> analysis_data_map;
// The first time this function is run we must initialize the global analysis_info_map
// and the thread_local analysis_data_map
if (first)
{
fill_analysis_info_map();
for (const std::pair<str,const DMEFT_analysis_info&> aname_ainfo_pair : analysis_info_map)
{
// Extract analysis name and use it to create an AnalysisData element in the analysis_data_map
str aname = aname_ainfo_pair.first;
analysis_data_map[aname] = AnalysisData(aname);
}
first = false;
}
// Clear previous vectors, etc.
result.clear();
// Get the theory spectrum to pass on masses and parameters
const Spectrum& spec = *Dep::DMEFT_spectrum;
//
// Loop over the analyses registered in the analysis_info_map
//
for (const std::pair<str,const DMEFT_analysis_info&> aname_ainfo_pair : analysis_info_map)
{
// Extract analysis name and reference to the analysis_info instance
str aname = aname_ainfo_pair.first;
const DMEFT_analysis_info& ainfo = aname_ainfo_pair.second;
// Grab a reference to corresponding AnalysisData instance
// and clear it before we start filling it for the current parameter point
AnalysisData& adata = analysis_data_map.at(aname);
adata.clear();
// Vector to contain signal yield predictions
std::vector<double> sr_nums(ainfo.n_signal_regions, 0.);
// Fill the signal yield vector with DMEFT signal predictions
get_all_DMEFT_signal_yields(sr_nums, ainfo, spec);
// Create vector of SignalRegionData instances
std::vector<SignalRegionData> srdata_vector;
for (size_t sr_index = 0; sr_index < ainfo.n_signal_regions; ++sr_index)
{
// Generate an 'sr-N' label
std::stringstream ss; ss << "sr-" << sr_index;
// Construct a SignalRegionData instance and add it to srdata_vector
SignalRegionData sr;
sr.sr_label = ss.str();
sr.n_obs = ainfo.obsnum.at(sr_index);
sr.n_sig_MC = sr_nums.at(sr_index);
sr.n_sig_scaled = sr_nums.at(sr_index); // We have already scaled the signals in sr_nums to xsec * lumi
sr.n_sig_MC_sys = 0.;
sr.n_bkg = ainfo.bkgnum.at(sr_index);
sr.n_bkg_err = ainfo.bkgerr.at(sr_index);
srdata_vector.push_back(sr);
}
// Save our vector of SignalRegionData in the AnalysisData instance
adata.srdata = srdata_vector;
// If this analysis has a background covariance matrix, copy it to the AnalysisData instance
if (ainfo.bkgcov.size() > 0)
{
adata.srcov = ainfo.bkgcov;
}
// Save a pointer to our AnalysisData instance in the 'result' variable
result.push_back(&adata);
} // End loop over analyses
};
/// Fill the input vector with the total DMEFT signal prediction for each SR in the given LHC analysis
void get_all_DMEFT_signal_yields(std::vector<double>& sr_nums, const DMEFT_analysis_info& analysis_info, const Spectrum& spec)
{
// Get the parameters we need from the theory spectrum
double C61 = spec.get(Par::dimensionless, "C61");
double C62 = spec.get(Par::dimensionless, "C62");
double C63 = spec.get(Par::dimensionless, "C63");
double C64 = spec.get(Par::dimensionless, "C64");
double C71 = spec.get(Par::dimensionless, "C71");
double C72 = spec.get(Par::dimensionless, "C72");
double C73 = spec.get(Par::dimensionless, "C73");
double C74 = spec.get(Par::dimensionless, "C74");
double lambda = spec.get(Par::mass1, "Lambda");
double m = spec.get(Par::Pole_Mass, "chi");
// Get the dim-6 yields
// C61+C64
std::vector<double> sig_C61_C64(analysis_info.n_signal_regions, 0.);
get_DMEFT_signal_yields_dim6_operator(sig_C61_C64, "C61_C64", analysis_info, m, C61, C64, lambda);
// C62+C63
std::vector<double> sig_C62_C63(analysis_info.n_signal_regions, 0.);
get_DMEFT_signal_yields_dim6_operator(sig_C62_C63, "C62_C63", analysis_info, m, C62, C63, lambda);
// Get the dim-7 yields
// C71
std::vector<double> sig_C71(analysis_info.n_signal_regions, 0.);
get_DMEFT_signal_yields_dim7_operator(sig_C71, "C71", analysis_info, m, C71, lambda);
// C72
std::vector<double> sig_C72(analysis_info.n_signal_regions, 0.);
get_DMEFT_signal_yields_dim7_operator(sig_C72, "C72", analysis_info, m, C72, lambda);
// C73
std::vector<double> sig_C73(analysis_info.n_signal_regions, 0.);
get_DMEFT_signal_yields_dim7_operator(sig_C73, "C73", analysis_info, m, C73, lambda);
// C74
std::vector<double> sig_C74(analysis_info.n_signal_regions, 0.);
get_DMEFT_signal_yields_dim7_operator(sig_C74, "C74", analysis_info, m, C74, lambda);
// Add yields and save in sr_num
for (size_t i = 0; i < analysis_info.n_signal_regions; ++i)
{
sr_nums[i] = sig_C61_C64[i] + sig_C62_C63[i] + sig_C71[i] + sig_C72[i] + sig_C73[i] + sig_C74[i];
}
}
/// Fill the input vector with the DMEFT signal prediction for a given set of dim-6 operators
void get_DMEFT_signal_yields_dim6_operator(std::vector<double>& signal_yields, const str operator_key, const DMEFT_analysis_info& analysis_info, double m, double O1, double O2, double lambda)
{
// Calculate theta
double theta;
if (O2==0)
{
theta = 0.5 * pi;
}
else
{
theta = atan(O1 / O2);
if ( O1 / O2 < 0)
{
theta = theta + pi;
}
}
// Calculate normalisation
double norm = O1*O1 + O2*O2;
if (norm < 0.0)
{
ColliderBit_error().raise(LOCAL_INFO, "ERROR! norm < 0 in function get_DMEFT_signal_yields_dim6_operator.");
}
// Scaling with lambda, relative to lambda = 1000 GeV which was used to generate the data tables
double lambda_scaling = pow(1000.0 / lambda, 4);
// Get the interpolator collections for the given operator_key
const Utils::interp2d_collection& xsec_interp = analysis_info.get_interp2d("mass_theta_xsecpb_" + operator_key);
const Utils::interp2d_collection& eff_interp = analysis_info.get_interp2d("mass_theta_eff_" + operator_key);
// Compute the signal yield for each signal region
for (size_t sr_i = 0; sr_i < analysis_info.n_signal_regions; ++sr_i)
{
//
// Get the cross-section at the point (m,theta)
//
double xsec_pb = 0.;
// Check if (m,theta) point is inside interpolation region
if (not xsec_interp.is_inside_range(m,theta))
{
if (theta < xsec_interp.y_min || theta > xsec_interp.y_max)
{
ColliderBit_error().raise(LOCAL_INFO, "ERROR! Theta parameter out of range.");
}
if (m < xsec_interp.x_min)
{
ColliderBit_error().raise(LOCAL_INFO, "Mass parameter below lowest mass point in the cross-section table.");
}
if (m > xsec_interp.x_min)
{
// Set cross-section to 0 for masses above the tabulated range
xsec_pb = 0.;
}
}
else // All is OK, let's evaluate the cross-section
{
xsec_pb = xsec_interp.eval(m, theta);
}
//
// Get signal efficiency for signal region sr_i at the point (m,theta)
//
double eff = 0.;
// Check if (m,theta) point is inside interpolation region
if (not eff_interp.is_inside_range(m,theta))
{
if (theta < eff_interp.y_min || theta > eff_interp.y_max)
{
ColliderBit_error().raise(LOCAL_INFO, "ERROR! Theta parameter out of range.");
}
if (m < eff_interp.x_min)
{
ColliderBit_error().raise(LOCAL_INFO, "Mass parameter below lowest mass point in the signal efficiency table.");
}
if (m > eff_interp.x_min)
{
// Set efficiency to 0 for masses above the tabulated range
eff = 0.;
}
}
else // All is OK, let's evaluate the efficiency
{
eff = eff_interp.eval(m, theta, sr_i);
}
//
// Compute signal prediction and save it in the signal_yields vector
//
signal_yields[sr_i] = analysis_info.lumi_invfb * (xsec_pb * 1000.) * norm * lambda_scaling * eff; // converting cross-section from pb to fb
#ifdef COLLIDERBIT_DEBUG
{
cerr << std::scientific << "DEBUG:" << " operator:" << operator_key << ", analysis:" << analysis_info.name
<< ", sr_i:" << sr_i << ", m:" << m << ", theta:" << theta << ", xsec_pb:" << xsec_pb << ", eff:" << eff
<< ", lambda_scaling:" << lambda_scaling << ", norm:" << norm << ", signal:" << signal_yields[sr_i] << endl;
}
#endif
} // End loop over signal regions
}
/// Fill the input vector with the DMEFT signal prediction for a given dim-7 operator
void get_DMEFT_signal_yields_dim7_operator(std::vector<double>& signal_yields, const str operator_key, const DMEFT_analysis_info& analysis_info, double m, double O, double lambda)
{
// Calculate normalisation
double norm = O*O;
if (norm < 0.0)
{
ColliderBit_error().raise(LOCAL_INFO, "ERROR! norm < 0 in function get_DMEFT_signal_yields_dim7_operator.");
}
// Scaling with lambda, relative to lambda = 1000 GeV which was used to generate the data tables
double lambda_scaling = pow(1000.0 / lambda, 6);
// Get the interpolator collections for the given operator_key
const Utils::interp1d_collection& xsec_interp = analysis_info.get_interp1d("mass_xsecpb_" + operator_key);
const Utils::interp1d_collection& eff_interp = analysis_info.get_interp1d("mass_eff_" + operator_key);
// Compute the signal yield for each signal region
for (size_t sr_i = 0; sr_i < analysis_info.n_signal_regions; ++sr_i)
{
//
// Get the cross-section for mass m
//
double xsec_pb = 0.;
// Check if m is inside interpolation region
if (not xsec_interp.is_inside_range(m))
{
if (m < xsec_interp.x_min)
{
ColliderBit_error().raise(LOCAL_INFO, "Mass parameter below lowest mass point in the cross-section table.");
}
if (m > xsec_interp.x_min)
{
// Set cross-section to 0 for masses above the tabulated range
xsec_pb = 0.;
}
}
else // All is OK, let's evaluate the cross-section
{
xsec_pb = xsec_interp.eval(m);
}
//
// Get signal efficiency for signal region sr_i and mass m
//
double eff = 0.;
// Check if m point is inside interpolation region
if (not eff_interp.is_inside_range(m))
{
if (m < eff_interp.x_min)
{
ColliderBit_error().raise(LOCAL_INFO, "Mass parameter below lowest mass point in the signal efficiency table.");
}
if (m > eff_interp.x_min)
{
// Set efficiency to 0 for masses above the tabulated range
eff = 0.;
}
}
else // All is OK, let's evaluate the efficiency
{
eff = eff_interp.eval(m, sr_i);
}
//
// Compute signal prediction and save it in the signal_yields vector
//
signal_yields[sr_i] = analysis_info.lumi_invfb * (xsec_pb * 1000.) * norm * lambda_scaling * eff; // converting cross-section from pb to fb
} // End loop over signal regions
}
/// Results from DMEFT analyses after profiling over the 'a' parameter in the smooth cut-off of the MET spectrum
void DMEFT_results_profiled(AnalysisDataPointers& result)
{
using namespace Pipes::DMEFT_results_profiled;
// Clear previous vectors, etc.
result.clear();
// Get the original AnalysisDataPointers that we will adjust
result = *Dep::AllAnalysisNumbersUnmodified;
// Get the best-fit nuisance parameter(s)
map_str_dbl bestfit_nuisance_pars = *Dep::DMEFT_profiled_LHC_nuisance_params;
double a_bestfit = bestfit_nuisance_pars.at("a");
// Get Lambda
const Spectrum& spec = *Dep::DMEFT_spectrum;
double lambda = spec.get(Par::mass1, "Lambda");
// Recalculate AnalysisData instances in "result", using the best-fit a-value
for (AnalysisData* adata_ptr : result)
{
signal_modifier_function(*adata_ptr, lambda, a_bestfit);
}
}
/// Results from DMEFT analyses after imposing a hard cut-off of the MET spectrum
void DMEFT_results_cutoff(AnalysisDataPointers& result)
{
using namespace Pipes::DMEFT_results_cutoff;
// Clear previous vectors, etc.
result.clear();
// Get the original AnalysisDataPointers that we will adjust
result = *Dep::AllAnalysisNumbersUnmodified;
// Get Lambda
const Spectrum& spec = *Dep::DMEFT_spectrum;
double lambda = spec.get(Par::mass1, "Lambda");
// Apply the function signal_cutoff_function to each of the
// AnalysisData instances in "result"
for (AnalysisData* adata_ptr : result)
{
signal_cutoff_function(*adata_ptr, lambda);
}
}
/// Function to modify the DMEFT LHC signal prediction for ETmiss bins where ETmiss > Lambda.
/// Alt 1: Gradually turn off the ETmiss spectrum above Lambda by multiplying
/// the spectrum with (ETmiss/Lambda)^-a
void signal_modifier_function(AnalysisData& adata, double lambda, double a)
{
// Check that we have analysis info for the given analysis
if (analysis_info_map.count(adata.analysis_name) == 0)
{
ColliderBit_error().raise(LOCAL_INFO, "Unknown analysis '" + adata.analysis_name +"' encountered in signal_modifier_function!");
}
// Get a shorthand reference to the DMEFT_analysis_info instance
const DMEFT_analysis_info& ainfo = analysis_info_map.at(adata.analysis_name);
// Modify signals
for (size_t bin_index = 0; bin_index < ainfo.n_signal_regions; ++bin_index)
{
double MET_min = ainfo.extra_info.at("metmins")[bin_index];
double weight = 1.0;
if (lambda < MET_min)
{
weight = pow(MET_min / lambda, -a);
if (weight < 1.0e-8) { weight = 0.0; }
}
SignalRegionData& srdata = adata[bin_index];
srdata.n_sig_MC *= weight;
srdata.n_sig_scaled *= weight;
}
}
/// Function to modify the DMEFT LHC signal prediction for ETmiss bins where ETmiss > Lambda.
/// Alt 2: Simply put a hard cut-off in the ETmiss spectrum for ETmiss > Lambda
void signal_cutoff_function(AnalysisData& adata, double lambda)
{
// Check that we have analysis info for the given analysis
if (analysis_info_map.count(adata.analysis_name) == 0)
{
ColliderBit_error().raise(LOCAL_INFO, "Unknown analysis '" + adata.analysis_name +"' encountered in signal_modifier_function!");
}
// Get a shorthand reference to the DMEFT_analysis_info instance
const DMEFT_analysis_info& ainfo = analysis_info_map.at(adata.analysis_name);
// Modify signals with a hard cutoff
for (size_t bin_index = 0; bin_index < ainfo.n_signal_regions; ++bin_index)
{
double MET_min = ainfo.extra_info.at("metmins")[bin_index];
if (lambda < MET_min)
{
SignalRegionData& srdata = adata[bin_index];
srdata.n_sig_MC = 0.0;
srdata.n_sig_scaled = 0.0;
}
}
}
/// A target function for the GSL optimiser
void _gsl_target_func(const size_t /* n */ , const double* a, void* fparams, double* fval)
{
// Note: We don't use the first argument, it's just there for the GSL/multimin interface
double total_loglike = 0.0;
// Cast fparams to correct type
_gsl_target_func_params* fpars = static_cast<_gsl_target_func_params*>(fparams);
AnalysisLogLikes analoglikes;
// Create a vector with temp AnalysisData instances by copying the original ones
std::vector<AnalysisData> temp_adata_vec;
for (AnalysisData* adata_ptr : fpars->adata_ptrs_original)
{
const str& analysis_name = adata_ptr->analysis_name;
// If the analysis name is in skip_analyses, don't take it into account in this profiling
if (std::find(fpars->skip_analyses.begin(), fpars->skip_analyses.end(), analysis_name) != fpars->skip_analyses.end())
{
continue;
}
// Make a copy of the AnalysisData instance that adata_ptr points to
temp_adata_vec.push_back( AnalysisData(*adata_ptr) );
}
// Now loop over all the temp AnalysisData instances and calculate the total loglike for the current a-value
for (AnalysisData& adata : temp_adata_vec)
{
signal_modifier_function(adata, fpars->lambda, *a);
analoglikes = calc_loglikes_for_analysis(adata, fpars->use_covar, fpars->use_marg, fpars->combine_nocovar_SRs, false);
total_loglike += analoglikes.combination_loglike;
}
*fval = -total_loglike;
}
// DMEFT: Profile the 'a' nuisance parameter, which is used to smoothly
// suppress signal predictions for MET bins with MET > Lambda
void calc_DMEFT_profiled_LHC_nuisance_params(map_str_dbl& result)
{
using namespace Pipes::calc_DMEFT_profiled_LHC_nuisance_params;
static bool first = true;
// Check if user has requested a fixed value for the a parameter
static bool use_fixed_value_a = false;
static double fixed_a = -1e99;
if (first)
{
if (runOptions->hasKey("use_fixed_value_a"))
{
use_fixed_value_a = true;
fixed_a = runOptions->getValue<double>("use_fixed_value_a");
}
first = false;
}
if (use_fixed_value_a)
{
result["a"] = fixed_a;
return;
}
// Steal the list of skipped analyses from the options from the "calc_combined_LHC_LogLike" function
std::vector<str> default_skip_analyses; // The default is empty lists of analyses to skip
static const std::vector<str> skip_analyses = Pipes::calc_combined_LHC_LogLike::runOptions->getValueOrDef<std::vector<str> >(default_skip_analyses, "skip_analyses");
// Steal some settings from the "calc_LHC_LogLikes" function
static const bool use_covar = Pipes::calc_LHC_LogLikes::runOptions->getValueOrDef<bool>(true, "use_covariances");
// Use marginalisation rather than profiling (probably less stable)?
static const bool use_marg = Pipes::calc_LHC_LogLikes::runOptions->getValueOrDef<bool>(false, "use_marginalising");
// Use the naive sum of SR loglikes for analyses without known correlations?
static const bool combine_nocovar_SRs = Pipes::calc_LHC_LogLikes::runOptions->getValueOrDef<bool>(false, "combine_SRs_without_covariances");
// Clear previous result map
result.clear();
// Optimiser parameters
// Params: step1size, tol, maxiter, epsabs, simplex maxsize, method, verbosity
// Methods:
// 0: Fletcher-Reeves conjugate gradient
// 1: Polak-Ribiere conjugate gradient
// 2: Vector Broyden-Fletcher-Goldfarb-Shanno method
// 3: Steepest descent algorithm
// 4: Nelder-Mead simplex
// 5: Vector Broyden-Fletcher-Goldfarb-Shanno method ver. 2
// 6: Simplex algorithm of Nelder and Mead ver. 2
// 7: Simplex algorithm of Nelder and Mead: random initialization
static const double INITIAL_STEP = runOptions->getValueOrDef<double>(0.1, "nuisance_prof_initstep");
static const double CONV_TOL = runOptions->getValueOrDef<double>(0.01, "nuisance_prof_convtol");
static const unsigned MAXSTEPS = runOptions->getValueOrDef<unsigned>(10000, "nuisance_prof_maxsteps");
static const double CONV_ACC = runOptions->getValueOrDef<double>(0.01, "nuisance_prof_convacc");
static const double SIMPLEX_SIZE = runOptions->getValueOrDef<double>(1e-5, "nuisance_prof_simplexsize");
static const unsigned METHOD = runOptions->getValueOrDef<unsigned>(6, "nuisance_prof_method");
static const unsigned VERBOSITY = runOptions->getValueOrDef<unsigned>(0, "nuisance_prof_verbosity");
static const struct multimin::multimin_params oparams = {INITIAL_STEP, CONV_TOL, MAXSTEPS, CONV_ACC, SIMPLEX_SIZE, METHOD, VERBOSITY};
// Set fixed function parameters
_gsl_target_func_params fpars;
fpars.lambda = Dep::DMEFT_spectrum->get(Par::mass1, "Lambda");
fpars.adata_ptrs_original = *Dep::AllAnalysisNumbersUnmodified;
fpars.skip_analyses = skip_analyses;
fpars.use_covar = use_covar;
fpars.use_marg = use_marg;
fpars.combine_nocovar_SRs = combine_nocovar_SRs;
// Create a variable to store the best-fit loglike
double minus_loglike_bestfit = 50000.;
// Nuisance parameter(s) to be profiled
// NOTE: Currently we only profile one parameter ('a'), but the
// below setup can easily be extended to more parameters
static const std::vector<double> init_values_a = runOptions->getValue<std::vector<double>>("init_values_a");
static const std::pair<double,double> range_a = runOptions->getValue<std::pair<double,double>>("range_a");
// How many times should we run the optimiser?
static const size_t n_runs = init_values_a.size();
size_t run_i = 0;
double current_bestfit_a = init_values_a.at(0);
double current_bestfit_loglike = -minus_loglike_bestfit;
// Mute stderr while running multimin (due to verbose gsl output)?
static bool silence_multimin = runOptions->getValueOrDef<bool>(true, "silence_multimin");
// Do profiling n_runs times
while (run_i < n_runs)
{
std::vector<double> nuisances = {init_values_a[run_i]}; // set initial guess for each nuisance parameter
std::vector<double> nuisances_min = {range_a.first}; // min value for each nuisance parameter
std::vector<double> nuisances_max = {range_a.second}; // max value for each nuisance parameter
const size_t n_profile_pars = nuisances.size();
// Choose boundary type for each nuisance param (see comment below)
std::vector<unsigned int> boundary_types = {6};
/*
From multimin.cpp:
Interval: Transformation:
0 unconstrained x=y
1 semi-closed right half line [ xmin,+infty ) x=xmin+y^2
2 semi-closed left half line ( -infty,xmax ] x=xmax-y^2
3 closed interval [ xmin,xmax ] x=SS+SD*sin(y)
4 open right half line ( xmin,+infty ) x=xmin+exp(y)
5 open left half line ( -infty,xmax ) x=xmax-exp(y)
6 open interval ( xmin,xmax ) x=SS+SD*tanh(y)
where SS=.5(xmin+xmax) SD=.5(xmax-xmin)
*/
// Call the optimiser
if (silence_multimin)
{
CALL_WITH_SILENCED_STDERR(
multimin::multimin(n_profile_pars, &nuisances[0], &minus_loglike_bestfit,
&boundary_types[0], &nuisances_min[0], &nuisances_max[0],
&_gsl_target_func, nullptr, nullptr, &fpars, oparams)
)
}
else
{
multimin::multimin(n_profile_pars, &nuisances[0], &minus_loglike_bestfit,
&boundary_types[0], &nuisances_min[0], &nuisances_max[0],
&_gsl_target_func, nullptr, nullptr, &fpars, oparams);
}
double run_i_bestfit_a = nuisances[0];
double run_i_bestfit_loglike = -minus_loglike_bestfit;
// Save info for this run
result["a_run" + std::to_string(run_i)] = run_i_bestfit_a;
result["loglike_run" + std::to_string(run_i)] = run_i_bestfit_loglike;
// Update the global result?
if (run_i_bestfit_loglike > current_bestfit_loglike)
{
current_bestfit_loglike = run_i_bestfit_loglike;
current_bestfit_a = run_i_bestfit_a;
}
run_i++;
} // end optimisation loop
// Save the overall best-fit results
result["a"] = current_bestfit_a;
result["loglike"] = current_bestfit_loglike;
// DEBUG: Do a grid scan of a and Lambda parameter to investigate the profiled likelihood function
#ifdef COLLIDERBIT_DEBUG_PROFILING
double log10_a_min = -1.0;
double log10_a_max = 3.0;
double step_log10_a = 0.02;
double log10_a = log10_a_min;
std::vector<double> a = { pow(10., log10_a) };
double ll_val = 0.0;
double lambda_min = 670.0;
double lambda_max = 1070.0;
double step_lambda = 2.0;
double lambda = lambda_min;
ofstream f;
f.open("lambda_a_loglike.dat");
while (lambda <= lambda_max)
{
log10_a = log10_a_min;
while (log10_a <= log10_a_max)
{
cerr << "DEBUG: lambda, log10_a : " << lambda << ", " << log10_a << endl;
a[0] = pow(10., log10_a);
fpars.lambda = lambda;
_gsl_target_func(n_profile_pars, &a[0], &fpars, &ll_val);
f << fixed << setprecision(8) << fpars.lambda << " " << a[0] << " " << ll_val << "\n";
log10_a += step_log10_a;
}
lambda += step_lambda;
}
f.close();
#endif
}
/// This makes an MCLoopInfo object for satisfying the ColliderBit dependency chain
/// (This will not be needed once we have a general system for simulation-less analyses.)
void InterpolatedMCInfo(MCLoopInfo& result)
{
result.event_gen_BYPASS = true;
result.reset_flags();
}
} // namespace ColliderBit
} // namespace Gambit
Updated on 2022-08-03 at 12:58:08 +0000