Determination of the strong coupling constant using inclusive jet cross section data from multiple experiments
Abstract
Inclusive jet cross section measurements from the ATLAS, CDF, CMS, D0, H1, STAR, and ZEUS experiments are explored for determinations of the strong coupling constant \(\alpha _{\text {s}} (M_{\text {Z}})\). Various jet cross section data sets are reviewed, their consistency is examined, and the benefit of their simultaneous inclusion in the \(\alpha _{\text {s}} (M_{\text {Z}})\) determination is demonstrated. Different methods for the statistical analysis of these data are compared and one method is proposed for a coherent treatment of all data sets. While the presented studies are based on nexttoleading order in perturbative quantum chromodynamics (pQCD), they lay the groundwork for determinations of \(\alpha _{\text {s}} (M_{\text {Z}})\) at nexttonexttoleading order.
1 Introduction
The strong coupling constant, \(\alpha _{\text {s}}\), is one of the least precisely known fundamental parameters in the StandardModel of particle physics. Because of its importance for precision phenomenology at the LHC and elsewhere, large efforts have been undertaken in the past decades to reduce uncertainties in determinations of \(\alpha _{\text {s}}\) [1, 2, 3, 4].
With the advent of modern particle detectors and sophisticated algorithms for their simulation and calibration, jet measurements have become very precise. Many determinations of \(\alpha _{\text {s}}\) in deepinelastic scattering (DIS) and in hadronhadron collisions are therefore based on measurements of the inclusive jet cross section, which is directly proportional to \(\alpha _{\text {s}}\) in DIS in the Breit frame and \(\alpha _{\text {s}} ^2\) in hadronhadron collisions. Using the most precise predictions of perturbative quantum chromodynamics (pQCD) available at the time, all previous \(\alpha _{\text {s}}\) extractions (except for Ref. [5]) were performed at nexttoleading order (NLO) in \(\alpha _{\text {s}}\). Their total uncertainty is dominated by the contribution related to the renormalisation scale dependence of the NLO pQCD results. The recent completion of nexttonexttoleading order (NNLO) predictions for the inclusive jet cross section [6, 7] promises a considerable reduction of the renormalisation scale dependence and will allow the inclusion of \(\alpha _{\text {s}}\) results from inclusive jet data in future determinations of the world average value of \(\alpha _{\text {s}}\) [1].
A determination of \(\alpha _{\text {s}}\) at NNLO from jet measurements in hadronhadron collisions is still not readily achievable, because the new NNLO pQCD calculations are computationally very demanding and cannot yet be repeated quickly for different parton distribution functions (PDFs) or values of \(\alpha _{\text {s}} (M_{\text {Z}})\). In preparation of such a determination, it is desirable to study a simultaneous analysis of data sets from different processes and experiments. This study includes an investigation of the consistency of the various data sets and an estimation of the reduction of the experimental contributions to the \(\alpha _{\text {s}}\) uncertainty. The groundwork for these two aspects is presented in this article.
We review inclusive jet cross section data over a wide kinematic range, from different experiments for various initial states and centreofmass energies, and study their potential for determinations of \(\alpha _{\text {s}}\). The consistency of the diverse data sets is examined and the benefit of their simultaneous inclusion is demonstrated. Different methods for the statistical analysis of the data are compared and one method is proposed for a coherent treatment of all data sets in an extraction of \(\alpha _{\text {s}} (M_{\text {Z}})\).
The article is structured as follows: The experimental data sets and the theoretical predictions are introduced in Sects. 2 and 3, respectively. Methods and results from previous \(\alpha _{\text {s}}\) determinations by different experimental collaborations are discussed and employed in Sect. 4. The strategy for a determination of \(\alpha _{\text {s}}\) from multiple data sets and the final result are presented in Sect. 5.
2 Experimental data

540 GeV, 546 GeV, and 630 GeV at the \(\text {p}\bar{\text {p}}\) collider \(\hbox {Sp}{\bar{\hbox {p}}}\hbox {S}\) by the UA1 [9, 10] and UA2 experiments [11],

546 GeV, 630 GeV, 1.8 TeV, and 1.96 TeVat the Tevatron \(\text {p}\bar{\text {p}}\) collider by the CDF [12, 13, 14, 15, 16, 17] and D0 experiments [18, 19, 20],

300 GeV and 320 GeV at the \(\text {ep}\) collider HERA by the H1 [21, 22, 23, 24, 25, 26, 27, 28] and ZEUS experiments [29, 30, 31, 32, 33, 34, 35],

200 GeV in \(\text {pp}\) collisions at RHIC by the STAR experiment [36],

and of 2.76 TeV, 7 TeV, 8 TeV, and 13 TeVin \(\text {pp}\) collisions at the LHC by the ALICE [37], ATLAS [38, 39, 40, 41, 42, 43], and CMS experiments [44, 45, 46, 47, 48, 49, 50].
Summary of previous determinations of \(\alpha _{\text {s}} (M_{\text {Z}})\) from inclusive jet cross sections. Rows 1–4 list the recent \(\alpha _{\text {s}} (M_{\text {Z}})\) extractions from doubledifferential inclusive jet cross sections by experimental collaborations that are studied in more detail in this work. Rows 5–12 and 13–16 summarise further determinations of \(\alpha _{\text {s}} (M_{\text {Z}})\) by experimental collaborations and by independent authors, respectively. The results in Refs. [61, 67] are reported for approximate NNLO (aNNLO) and NLO used for the pQCD predictions. In Ref. [61] only 22 out of the 110 D0 data points were used in the \(\alpha _{\text {s}} (M_{\text {Z}})\) extraction; the decomposition of the uncertainties is only provided for the aNNLO result. In case of Ref. [66], we only consider scale, PDF, and NP related uncertainties as theoretical uncertainty for reasons of comparability to the other listed results
Publication  Data  Comment  \({{\alpha _{\text {s}} (M_{\text {Z}})}}\) 

H1 [27]  H1 [27]  HERA II, high \(Q^2\)  \(0.1174(22)_{\text {exp}}(50)_{\text {theo}}\) 
D0 [61]  D0 [20]  aNNLO, 22 points  \(0.1161(^{+34}_{33})_{\text {exp}}(^{+29}_{35})_{\text {theo}}\) 
D0 [61]  D0 [20]  NLO, 22 points  \(0.1202(^{+72}_{59})_{\text {tot}}\) 
CMS [62]  CMS [46]  7 TeV, 5.0\(\,\text {fb}^{1}\)  \(0.1185(19)_{\text {exp}}(^{+60}_{37})_{\text {theo}}\) 
H1 [21]  H1 [21]  HERA I, \(\sqrt{s} = 300\,\text {GeV} \)  \(0.1186(30)_{\text {exp}}(51)_{\text {theo}}\) 
H1 [24]  H1 [24]  HERA I, \(\sqrt{s} = 320\,\text {GeV} \)  \(0.1193(14)_{\text {exp}}(^{+50}_{34})_{\text {theo}}\) 
H1 [26]  H1 [26]  HERA I, \(\sqrt{s} = 320\,\text {GeV} \), low \(Q^2\)  \(0.1180(18)_{\text {exp}}(^{+124}_{93})_{\text {theo}}\) 
H1 [5]  HERA I+II, NNLO  \(0.1152(20)_{\text {exp}}(27)_{\text {theo}}\)  
ZEUS [30]  ZEUS [30]  \(\sqrt{s} = 300\,\text {GeV} \), \(Q^2 >500{\,\text {GeV}^2} \)  \(0.1212(^{+29}_{35})_{\text {exp}}(^{+28}_{27})_{\text {theo}}\) 
ZEUS [33]  ZEUS [32]  \(d\sigma /dQ^2 \), \(Q^2 >500{\,\text {GeV}^2} \)  \(0.1207(^{+38}_{36})_{\text {exp}}(^{+22}_{23})_{\text {theo}}\) 
CDF [63]  CDF [63]  1.8 TeV, 87\(\,\text {pb}^{1}\)  \(0.1178(^{+81}_{95})_{\text {exp}}(^{+92}_{75})_{\text {theo}}\) 
CMS [50]  CMS [50]  8 TeV, 19.7\(\,\text {fb}^{1}\)  \(0.1164(^{+14}_{15})_{\text {exp}}(^{+59}_{40})_{\text {theo}}\) 
Giele et al. [64]  CDF [65]  1.8 TeV, 4.2\(\,\text {pb}^{1}\)  \(0.121(8)_{\text {exp}}(5)_{\text {theo}}\) 
Malaescu et al. [66]  ATLAS [39]  7 TeV, 37\(\,\text {pb}^{1}\)  \(0.1151(47)_{\text {exp}}(^{+51}_{40})_{\text {theo}}\) 
Biekötter et al. [67]  H1 [27]  aNNLO  \(0.122(2)_{\text {exp}}(13)_{\text {theo}}\) 
Biekötter et al. [67]  H1 [27]  NLO  \(0.115(2)_{\text {exp}}(5)_{\text {theo}}\) 
Overview of the inclusive jet data sets used in the \(\alpha _{\text {s}}\) determinations. For each data set the process (proc), the centreofmass energy \(\sqrt{s}\), the integrated luminosity \({\mathcal {L}}\), the number of data points, and the jet algorithm are listed. In case of \(\text {ep}\) collider data, the kinematic range may be defined by the fourmomentum transfer squared \(Q^2\), the inelasticity \(y_{\mathrm{DIS}}\), or the angle of the hadronic final state \(\cos \gamma _h\) of the NC DIS process. In all cases, jets are required to be within a given range of pseudorapidity \(\eta \) or rapidity y in the laboratory frame
Data  proc  \(\sqrt{{{s}}}\) (TeV)  \({\mathcal {L}} ({{fb}}^{1})\)  No. of points  Jet algorithm  \({{p_{\text {T}},\,E_{\text {T}}}}\)range (GeV)  Other kinematic ranges 

H1 [27]  \(\text {ep}\)  0.32  0.35  24  \(k_{\text {T}}\), \(R=1.0\)  \(7<p_{\text {T}} <50\)  \(150<Q^2<15\,000{\,\text {GeV}^2} 0.2<y_{\mathrm{DIS}}<0.7 1.0<\eta <2.5\) 
ZEUS [32]  \(\text {ep}\)  0.32  0.082  30  \(k_{\text {T}}\), \(R=1.0\)  \(E_{\text {T}} >8\)  \(Q^2 >125{\,\text {GeV}^2} \cos \gamma _h<0.65 2.0<\eta <1.5\) 
STAR [36]  \(\text {pp}\)  0.20  0.0003  9  MP, \(R=0.4\)  \(7.6< p_{\text {T}} <48.7\)  \(0.2<\eta <0.8\) 
CDF [16]  \(\text {p}\bar{\text {p}}\)  1.96  1.0  76  \(k_{\text {T}}\), \(R=0.7\)  \(54<p_{\text {T}} <527\)  \(y<2.1\) 
D0 [20]  \(\text {p}\bar{\text {p}}\)  1.96  0.7  110  MP, \(R=0.7\)  \(50<p_{\text {T}} <665\)  \(y<2.0\) 
ATLAS [41]  \(\text {pp}\)  7.0  4.5  140  anti\(k_{\text {T}}\), \(R=0.6\)  \(100<p_{\text {T}} <1992\)  \(y<3.0\) 
CMS [62]  \(\text {pp}\)  7.0  5.0  133  anti\(k_{\text {T}}\), \(R=0.7\)  \(114<p_{\text {T}} <2116\)  \(y<3.0\) 
Whenever experiments provide multiple measurements, we include those measured with a collinear and infraredsafe jet algorithm (\(k_{\text {T}}\) [68] or anti\(k_{\text {T}}\) [69]) and with a larger jet size parameter R, which improves the stability of fixedorder pQCD calculations. The STAR experiment published inclusive jet data collected by two different triggers with partially overlapping jet \(p_{\text {T}}\) ranges. We choose the data set collected with the trigger covering the higher jet \(p_{\text {T}}\) range from 7.6 GeV up to 50 GeV. The measurements from the STAR and D0 experiments are using the midpoint cone jet algorithms (MP) [55]. The infraredunsafety of this jet algorithm [54] prohibits NNLO pQCD predictions for these data sets, but it does not affect calculations at NLO. Four new measurements [28, 42, 43, 50] could not be included in this study; they are left for a future extension.
3 Theoretical predictions and tools
For transverse jet momenta at the TeVscale accessible at the LHC, electroweak (EW) treelevel effects of \(\mathcal {O}\left( \alpha \alpha _{\text {s}},\alpha ^2\right) \) and loop effects of \(\mathcal {O}\left( \alpha \alpha _{\text {s}} ^2\right) \) become sizeable [71]. A recent study of the complete set of QCD and EW NLO corrections has been presented in Ref. [72].
The partonic cross section is computed at NLO accuracy for five massless quark flavours using the NLOJet++ program version 4.1.3 [74, 75] within the fastNLO framework at version 2 [76, 77] to allow us fast recalculations for varying PDFs, scales \(\mu _{\text {r}}\) and \(\mu _{\text {f}}\), and assumptions on \(\alpha _{\text {s}} (M_{\text {Z}})\). Jet algorithms are taken either from the FastJet software library [78] or, for jet cross sections in DIS, from NLOJet++. The PDFs are evaluated via the LHAPDF interface [79, 80] at version 6. The running of \(\alpha _{\text {s}} (\mu _{\text {r}})\) is performed at 2loop order using the package CRunDec with five massless quark flavours [81, 82]. The minimal subtraction (\(\overline{\text {MS}}\)) scheme [83, 84, 85] has been adopted for the renormalisation procedure in these calculations.
For the computation of the inclusive jet cross section in hadronhadron collisions, the renormalisation and factorisation scales, \(\mu _{\text {r}}\) and \(\mu _{\text {f}}\), are identified with each jet’s \(p_{\text {T}}\), i.e. \(\mu _{\text {r}} = \mu _{\text {f}} = p_{\text {T}}^\mathrm{jet} \). In neutral current (NC) DIS, the scales are chosen to be \({\mu _{\text {r}}}^2 =\frac{1}{2}\left( Q^2 + ({p_{\text {T}}^\mathrm{jet}})^2\right) \) and \({\mu _{\text {f}}}^2 = Q^2\) as used by the H1 Collaboration [27]. Alternative scale choices have been discussed with respect to NNLO predictions [5, 7, 86, 87], but are beyond the scope of this article.
The EW corrections, \(c_{\text {EW}}\), relevant for the LHC data are provided by the experimental collaborations together with the data, based on Ref. [71]. These are considered to have negligible uncertainties. Due to restrictions of the scale choices in this calculation, the leading jet’s transverse momentum, \(p_{\text {T}}^\text {max}\), is used to define the scales \(\mu _{\text {r}}\) and \(\mu _{\text {f}}\). The NP correction factors \(c_{\text {NP}}\), except for the STAR data [88], are also provided by the experimental collaborations, together with an estimate of the corresponding uncertainty [16, 20, 27, 32, 33, 41, 46, 61, 62].
4 Comparison of three extraction methods for \({\varvec{\alpha _{\text {s}} (M_{\text {Z}})}}\)
Commonly, the value of \(\alpha _{\text {s}} (M_{\text {Z}})\) is determined from inclusive jet cross sections in a comparison of pQCD predictions to the measurements. These \(\alpha _{\text {s}} (M_{\text {Z}})\) results therefore depend on details of the extraction method such as the treatment of uncertainties in the characterisation of differences between theory and data, or the evaluation and propagation of theoretical uncertainties to the final result. An overview of previous determinations of \(\alpha _{\text {s}} (M_{\text {Z}})\) from fits to inclusive jet cross section data is provided in Table 1. We choose the three \(\alpha _{\text {s}} (M_{\text {Z}})\) determinations performed by the CMS [62], D0 [61], and H1 [27] collaborations listed in the upper part of Table 1 for further study.

the definition of the \(\chi ^2\) function to quantify the agreement between theory and data,

the uncertainties considered in the \(\chi ^2\) function,

the strategy to determine the central result for \(\alpha _{\text {s}} (M_{\text {Z}})\),

the propagation of the uncertainties to the value of \(\alpha _{\text {s}} (M_{\text {Z}})\),

the choice of PDF sets,

the consideration of the \(\alpha _{\text {s}} (M_{\text {Z}})\) dependence of the PDFs, and

the treatment of further theoretical uncertainties.
Values of \(\alpha _{\text {s}} (M_{\text {Z}})\) with experimental uncertainties obtained using the three extraction methods CMStype, D0type, and H1type together with the CT10 or MSTW2008 PDF set at NLO . The underlined values can be directly compared with the results published in Refs. [27, 61, 62]. Some fits to the STAR data do not exhibit a local minimum, in which case no value is listed
Fit method  CMStype  D0type  H1type  

PDF set  MSTW2008  CT10  MSTW2008  CT10  MSTW2008  CT10 
Data  \({{\alpha _{\text {s}} (M_{\text {Z}})}}\) values with experimental uncertainties  
H1  \(0.1172\,(28)\)  \(0.1172\,(28)\)  \(0.1161\,(27)\)  \(0.1164\,(26)\)  \(\underline{0.1174~(22)}\)  \(\underline{0.1180}\,(22)\) 
ZEUS  \(0.1213\,(28)\)  \(0.1223\,(29)\)  \(0.1210\,(^{+28}_{29})\)  \(0.1218\,(^{+30}_{29})\)  \(0.1231\,(30)\)  \(0.1236\,(30)\) 
STAR  –  \(0.1193\,(68)\)  –  \(0.1205\,(^{+54}_{111})\)  \(0.1159\,(116)\)  \(0.1280\,(111)\) 
CDF  \(0.1217\,(17)\)  \(0.1265\,(27)\)  \(0.1202\,(^{+10}_{27})\)  \(0.1162\,(^{+22}_{20})\)  \(0.1217\,(35)\)  \(0.1265\,(37)\) 
D0 (22 pts., NLO)  \(0.1226\,(32)\)  \(0.1237\,(36)\)  \(\underline{0.1203}\,(^{+40}_{42})\)  \(0.1191\,(^{+38}_{45})\)  \(0.1219\,(50)\)  \(0.1232\,(51)\) 
ATLAS  \(0.1220\,(9)\)  \(0.1258\,(15)\)  \(0.1204\,(^{+14}_{5})\)  \(0.1241\,(9)\)  \(0.1206\,(15)\)  \(0.1270\,(16)\) 
CMS  \(\underline{0.1162~(14)}\)  \(\underline{0.1188~(19)}\)  \(0.1158\,(12)\)  0.1162 (19)  \(0.1140\,(21)\)  0.1217 (23) 
In a second step, the \(\alpha _{\text {s}} (M_{\text {Z}})\) results and their experimental uncertainties are compared to each other and their dependencies on the extraction method and PDFs are studied. The \(\alpha _{\text {s}} (M_{\text {Z}})\) results determined for each data set are displayed in Fig. 1 (top row) for the three different extraction methods using CT10 PDFs (left) and MSTW2008 PDFs (right). For the STAR data, \(\alpha _{\text {s}} (M_{\text {Z}})\) results cannot be determined in case of the CMStype and D0type methods with MSTW2008 PDFs, since no local \(\chi ^2 \) minima are found. In all other cases the \(\alpha _{\text {s}} (M_{\text {Z}})\) results obtained with MSTW2008 PDFs are rather independent of the extraction method for all data sets. This is different when using CT10 PDFs: While in this case the extraction method has little impact on the \(\alpha _{\text {s}} (M_{\text {Z}})\) results from HERA data (H1 and ZEUS), it notably affects the results for the LHC data (ATLAS and CMS), and has large effects for the Tevatron data (CDF and D0). In the latter cases, the D0type method produces significantly lower \(\alpha _{\text {s}} (M_{\text {Z}})\) results as compared to the other two methods.
The \(\chi ^2/n_{\text {dof}} \) values for the \(\alpha _{\text {s}} (M_{\text {Z}})\) extractions are displayed in Fig. 1 (bottom row) for the three extraction methods using CT10 PDFs (left) and MSTW2008 PDFs (right). Overall, the fits exhibit reasonable values of\(\chi ^2/n_{\text {dof}} \), thus indicating agreement between theory and data. Exceptions are observed for the ZEUS data with rather low values of \(\chi ^2/n_{\text {dof}} \), and for the ATLAS data, where the values of \(\chi ^2/n_{\text {dof}} \) are large as also observed elsewhere [42, 91, 92].
The PDF dependence is displayed in Fig. 2, where the \(\alpha _{\text {s}} (M_{\text {Z}})\) results for CT10 and MSTW2008 PDFs are compared to each other, both obtained using the H1type method. While the PDF choice has no significant effect for the results from the H1, ZEUS, and D0 data, smaller variations are seen for the CDF data, and a large dependence for the ATLAS and CMS data. Reinvestigating this PDF dependence in the context of a common determination of \(\alpha _{\text {s}} (M_{\text {Z}})\) as described in the next section, we observe that differences with respect to the updated PDF sets, CT14 [93] and MMHT2014 [60], are reduced.
5 Determination of \({\varvec{\alpha _{\text {s}} (M_{\text {Z}})}}\) from multiple inclusive jet data sets
The analysis of multiple data sets requires their correlations to be taken into account. For the present study, measurements from different colliders are considered to be uncorrelated because of the largely complementary kinematic ranges of the data sets and different detector calibration techniques. Furthermore, investigations with respect to H1 and ZEUS data [58], CDF and D0 data [94], or ATLAS and CMS data [95, 96], did not identify a relevant source of experimental correlation. This only leaves theoretical uncertainties as a source of potential correlations in this study. For the determination of NP effects and their uncertainties various methods and MC event generators have been employed [16, 20, 27, 32, 36, 41, 61, 62, 88]. While a consistent derivation of these corrections with corresponding correlations is desirable, this is beyond the scope of this analysis. Hence, the NP correction factors and their uncertainties are considered to be uncorrelated between the different data sets. In contrast, the PDF uncertainties and the uncertainties due to the renormalisation and factorisation scale variations are treated as fully correlated; the relative variations with respect to the nominal scales are performed simultaneously for all data sets.
The method employed for the simultaneous \(\alpha _{\text {s}}\) extraction combines components of the individual methods outlined in the previous section and is referred to as “CMStype method”. The central \(\alpha _{\text {s}} (M_{\text {Z}})\) result is found in an iterative \(\chi ^2\) minimisation procedure adopted from the H1type method, where a normal distribution is assumed for the relative uncertainties. The exact \(\chi ^2\) formula is given by Eq. (4) of Appendix A. Whereas in the H1type \(\chi ^2\) expression, only experimental uncertainties are taken into account, the commontype method also accounts for the NP and PDF uncertainties in the \(\chi ^2\) expression, as in the CMStype and D0type methods. This \(\chi ^2\) definition treats variances as relative values and thus has advantages, e.g. when numerically inverting the covariance matrix. Moreover, uncertainties of experimental and theoretical origin are put on an equal footing. As in the H1type method, and in contrast to the CMStype and D0type ones, only PDF sets obtained with a fixed value of \(\alpha _{\text {s}} (M_{\text {Z}}) =0.1180\) are employed in the determination of the central \(\alpha _{\text {s}} (M_{\text {Z}})\) result, leaving the \(\alpha _{\text {s}}\) dependence of the PDFs to be treated as a separate uncertainty.

The “PDF\(\alpha _{\text {s}}\) ” uncertainty accounts for the initial assumption of \(\alpha _{\text {s}}^\text {PDF}(M_{\text {Z}}) =0.1180\) made in the PDF extraction, which is not necessarily consistent with the value of \(\alpha _{\text {s}} (M_{\text {Z}})\) used in the pQCD calculation. It is calculated as the maximal difference between any of the results obtained with PDF sets determined for \(\alpha _{\text {s}}^\text {PDF}(M_{\text {Z}}) = 0.1170\), 0.1180, and 0.1190, and therefore covers a difference of \(\varDelta \alpha _{\text {s}}^\text {PDF}(M_{\text {Z}}) =0.0020\), which is somewhat more conservative than the recommendation in Ref. [101].

The “\(\text{ PDFset }\) ” uncertainty covers differences due to the considered PDF set. These are caused by the data selection, assumptions made on parameterisation, parameter values, theory input, or the analysis method for the PDF determination. It also comprises the potential effect of the dual use of jet cross sections, first in the PDF determination and then in the fit of \(\alpha _{\text {s}} (M_{\text {Z}})\). Therefore, we define it as half of the width of the envelope constructed from the five values for the ABMP16, CT14, MMHT2014, and NNPDF3.0_nojet PDFs together with the NNPDF3.0 result. These global PDF sets either comprise a maximum of data other than jet cross sections or no jet cross sections at all.

The uncertainty due to variations of the renormalisation and factorisation scales customarily is taken as an estimate for the error of a fixedorder calculation caused by the truncation of the perturbative series. It is obtained using six additional \(\alpha _{\text {s}} (M_{\text {Z}})\) determinations, in which the nominal scales \((\mu _{\text {r}},\mu _{\text {f}})\) are varied by the conventional factors of (1 / 2, 1 / 2), (1 / 2, 1), (1, 1 / 2), (1, 2), (2, 1), and (2, 2). The scale factor combinations of (1 / 2, 2) and (2, 1 / 2) are customarily omitted [102, 103, 104].
Values of \(\alpha _{\text {s}} (M_{\text {Z}})\) for the simultaneous fit to the H1, ZEUS, STAR, CDF, D0, and CMS data using the commontype method for various PDF sets. The experimental, NP, \(\text{ PDFset }\), and scale uncertainties remain mostly unchanged under a change of the PDF set and are quoted only once for NNPDF3.0
PDF set  \({\alpha _{\text {s}} (M_{\text {Z}})}\)  Uncertainties (scaled by a factor of \(10^4\))  

exp  NP   PDF\(\alpha _{\text {s}}\)  \(\text{ PDFset }\)  scale  theo  total  
ABMP16  0.1203  4  3  \({}^{+63}_{46}\)  \({}^{+64}_{48}\)  
CT14  0.1206  10  2  \({}^{+57}_{46}\)  \({}^{+59}_{48}\)  
HERAPDF2.0  0.1184  6  2  \({}^{+62}_{50}\)  \({}^{+63}_{52}\)  
MMHT2014  0.1194  7  3  \({}^{+59}_{45}\)  \({}^{+60}_{47}\)  
NNPDF3.0_nojet  0.1196  12  9  \({}^{+60}_{44}\)  \({}^{+61}_{46}\)  
NNPDF3.0  0.1192  12  5  7  5  7  \({}^{+59}_{38}\)  \(^{+60}_{40}\)  \({}^{+61}_{42}\) 
In the previous section, cf. Fig. 1, it was found that the \(\chi ^2/n_{\text {dof}} \) values differ significantly from unity for some of the data sets. This necessitates to investigate in further detail the consistency of the data within an individual data set as well as among the different data sets. Therefore, the commontype method is employed to extract \(\alpha _{\text {s}} (M_{\text {Z}})\) from each individual data set and for each of the PDFs ABMP16, CT14, HERAPDF2.0, MMHT2014, and NNPDF3.0. The resulting \(\chi ^2/n_{\text {dof}} \) values are displayed in Fig. 3 left. Detailed listings of the \(\alpha _{\text {s}} (M_{\text {Z}})\) results and their uncertainties are given in Appendix B.
For a given data set, \(\chi ^2/n_{\text {dof}} \) is rather independent of the PDF set used for the predictions and varies between 0.8 and 1.2. These values indicate reasonable agreement of the predictions with the data. Exceptions are rather low values of \(\chi ^2/n_{\text {dof}} \) around 0.54 found for all PDF sets with the ZEUS data and large \(\chi ^2/n_{\text {dof}} \) values between 1.9 and 3.5 exhibited by the ATLAS data, also for all PDFs. Exceptionally large \(\chi ^2/n_{\text {dof}} \) values appear for the Tevatron or LHC data together with theory predictions using the HERAPDF2.0 set, and for the STAR data in conjunction with the ABMP16 PDF set.
The \(\alpha _{\text {s}} (M_{\text {Z}})\) values from fits using the various PDF sets given in Table 4 are found to be consistent within the experimental uncertainty. The NP, PDF, and PDF\(\alpha _{\text {s}}\) uncertainties are smaller than the experimental uncertainty, while the \(\text{ PDFset }\) uncertainty is of a similar size as the experimental one. The scale uncertainty is the largest individual uncertainty and is more than three times larger than any other uncertainty. Results of the \(\alpha _{\text {s}} (M_{\text {Z}})\) extractions from single data sets, cf. Appendix B, from the simultaneous \(\alpha _{\text {s}} (M_{\text {Z}})\) extraction from all data sets, and the world average [1] are compared in Fig. 4 and are seen to be consistent with each other.
The ratio of data to the predictions as a function of jet \(p_{\text {T}}\) for all selected data sets is presented in Fig. 5. The predictions are computed for \(\alpha _{\text {s}} (M_{\text {Z}}) =0.1192\) as obtained in this analysis. Visually, all data sets are well described by the theory predictions.
6 Summary and outlook
Inclusive jet cross section data from different experiments at various particle colliders with jet transverse momenta ranging from 7 GeV up to 2 TeVare explored for determinations of \(\alpha _{\text {s}} (M_{\text {Z}})\) using nexttoleading order predictions.
Previous \(\alpha _{\text {s}} (M_{\text {Z}})\) determinations reported by the CMS, D0, and H1 collaborations [27, 61, 62] are taken as a baseline, and these \(\alpha _{\text {s}} (M_{\text {Z}})\) extraction methods, which differ in various aspects, are applied to inclusive jet cross section data measured by the ATLAS, CDF, CMS, D0, H1, STAR, and ZEUS experiments [16, 20, 27, 32, 36, 41, 62]. Differencesamong the \(\alpha _{\text {s}} (M_{\text {Z}})\) results due to the extraction technique are found to be negligible in most cases. A new extraction method is proposed, which combines aspects of the baseline approaches above.
In a statistical analysis, data measured by the CDF, CMS, D0, H1, STAR, and ZEUS experiments are found to be well described by pQCD predictions at nexttoleading order, and hence are considered to be mutually consistent. Moreover, the values of \(\alpha _{\text {s}} (M_{\text {Z}})\) determined from each individual data set are found to be consistent among each other. By determining \(\alpha _{\text {s}} (M_{\text {Z}})\) simultaneously from these data, the experimental uncertainty of \(\alpha _{\text {s}} (M_{\text {Z}})\) is reduced to 1.0%, as compared to 1.9% when only the single most precise data set of that selection is considered.
The largest contribution to the uncertainty of \(\alpha _{\text {s}} (M_{\text {Z}})\) originates from the renormalisation scale dependence of the nexttoleading order pQCD calculation. This uncertainty is expected to be reduced once the nexttonexttoleading order predictions become available for such studies. Furthermore, a reevaluation of the nonperturbative corrections and their uncertainties for all data sets in a consistent manner is recommended for a determination of \(\alpha _{\text {s}} (M_{\text {Z}})\) at high precision. The presented study and the developed analysis framework provide a solid basis for future determinations of \(\alpha _{\text {s}} (M_{\text {Z}})\) and facilitate the inclusion of additional data sets, further observables, and improved theory predictions.
Footnotes
 1.
For the D0 analysis, the decomposition of uncertainties has been published only for their central result based on approximate NNLO pQCD and hence a comparison of the experimental uncertainty on \(\alpha _{\text {s}} (M_{\text {Z}})\) for the NLO result is not possible.
 2.
For HERAPDF2.0, the PDF uncertainty does not include the “model” or “parameterisation” uncertainties as those are represented here by the \(\text{ PDFset }\) uncertainty.
Notes
Acknowledgements
We thank our colleagues in the CMS, D0, and H1 collaborations for fruitful discussions, and K. Bjørke and D. Reichelt for early related studies. K. Rabbertz thanks G. Flouris, P. Kokkas, and the colleagues from the PROSA Collaboration. D. Savoiu acknowledges the support by the DFGfunded Doctoral School “Karlsruhe School of Elementary and Astroparticle Physics: Science and Technology”. M. Wobisch also wishes to thank the Louisiana Board of Regents Support Fund for the support through the Eva J. Cunningham Endowed Professorship.
Supplementary material
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