You will be presented with the full text of a science research paper. Please write an abstract for this paper. Your response should include the abstract and no additional text.

Paper text:
hadron colliders will supply an increasing amount of data with the upcoming high luminosity tevatron run and the lhc project .
methods for extracting @xmath0 and parton density functions from these data sets can therefore expect a steady improvement in the precision over the coming decades .    often it is claimed that hadron colliders can not do such precision measurements .
a few comments can be made in answer to this .
first of all , a hadron collider measures the value of @xmath0 at many different values of the ( partonic ) center of mass energy .
this is in contrast to @xmath1-colliders where the center of mass energy is fixed .
in fact , at the tevatron the partonic center of mass energy useful for the @xmath0-extraction can go as high as 1 tev ( and at the lhc this will increase by an order of magnitude ) . secondly , a hadron collider can make accurate measurements by selecting appropriate observables .
for this purpose we select in this talk the _
normalized _ one jet inclusive transverse energy distribution .
the value of @xmath0 will be determined from the shape of the distribution .
the major factor determining the shape is the fraction of quarks in the colliding hadrons . due to the evolution of the parton density functions the quark fraction at moderate parton fractions decreases as the jet energy increases .
this depletion is controlled by the strength of the strong coupling constant .    because we look at a normalized quantity both the theoretical ( renormalization scale dependence ) and experimental uncertainties ( e.g. luminosity uncertainty ) are much smaller than one would expect .
as mentioned in the introduction , the observable used is the normalized one jet inclusive transverse energy distribution . as an example we use the published run 1a results from the cdf collaboration @xcite . for the theoretical prediction we use the jetrad monte carlo @xcite with the cuts and jet algorithm as close as possible to the experimental setup .
the mrsa parton density functions @xcite , which allows varying @xmath0 , were used .
the renormalization / factorization scale , @xmath2 , was chosen to be a constant , @xmath3 , times the maximum jet transverse energy , @xmath4 , in the event .
both data and theory are divided by the `` reference '' theory prediction which is given by : @xmath5 , @xmath6 . to normalize the distribution we choose the ratio to be equal to unity at @xmath7 gev .
we show the @xmath0-dependence in fig . 1a and the scale dependence in fig 1b .
as can be seen the dependence on @xmath0 is quite substantial compared to both the experimental and theoretical uncertainties .
also note that the leading order ( lo ) and next - to - leading order ( nlo ) results are quite close .
the only difference between the two predictions is the ( expected ) reduced scale dependence at nlo .
the method to extract @xmath0 is now quite simple : we minimize the @xmath8 to fit the theory to the data in fig
. 1 between 30 gev @xmath9 200 gev by varying both @xmath0 and @xmath3 . the results and , more importantly , the interpretation are discussed in the next section .
note that we do not consider the systematic uncertainties at this point .
they can be easily included in the @xmath8-fit by building up the correlation matrix out of the systematic uncertainties @xcite .
their inclusion is better left to the experimenters .
here we want to concentrate on the methodology .
[ fig2 ]    in fig .
2a we show the results of the minimalization procedure to fit to the data . both the minimum and the 1-@xmath10 uncertainty ellipse is shown .
the figure contains all the information we can extract from the data . while the central value is quite trivial to determine , the interpretation of the uncertainty is not .
the perfect answer ( that is no renormalization scale uncertainty ) would be a vertical strip .
the uncertainty would then simply be the width of the strip independent of the choice of @xmath3 .
however , in fixed order perturbative qcd we have a residual scale sensitivity due to the truncation of the series .
this is reflected in the slope of the ellipse - axis .
in fact one could argue that the slope is the correct measure of the theoretical uncertainty . when comparing results from different experiments this slope could be used to weight different experiments on their theoretical uncertainty .
this all implies that the parameter @xmath3 can not be considered a fitting parameter as @xmath0 is , nor is it in a direct manner related to the theoretical uncertainty .
note that when the data accuracy increases ( e.g. the cdf / d0 run 1b data ) the fit of the theory to the data will become more strained and the variation of @xmath3 will become more constrained .
this does not indicate that the theoretical uncertainty is decreasing . on the contrary , this means that the nlo prediction is becoming more and more inadequate to describe the data and even higher order calculations are needed .    for the moment we use a naive procedure to quote the theoretical uncertainty .
the experimental uncertainty is taken to be the width of the ellipse at the minimum , while the theoretical uncertainty is taken as the variation within 1-@xmath10 for scales between @xmath11 .
the results are @xmath12 alternatively , one could argue that the difference between the lo and nlo value of @xmath0 should be larger than the difference between the nlo and nnlo value of @xmath0 , giving an alternative , but equal , estimate on the theoretical uncertainty of @xmath13 .
we have used the normalized one jet inclusive transverse energy distribution to extract @xmath14 .
the fact that we used the normalized distribution reduces the experimental and theoretical uncertainty significantly . to improve the results we need to fit the parton density functions ( specifically the gluon ) together with the value of @xmath0
this should remedy the obvious discrepancies between the data and theory for @xmath15 gev ( see ref .
@xcite ) . such a measurement would not only give us @xmath0 , but simultaneously a _ true _ nlo determination of the gluon parton density function . in fig .
2b we finally show , as a cross check , the comparison between the @xmath0 extracted in this talk and the @xmath0-values determined in each @xmath4-bin , obtained using the methods of ref .
@xcite ( incorporating parton density functions with varying @xmath0 ) .
as can be seen the agreement between the two methods is quite good .
the new run 1b data from the cdf and d0 collaboration will severely test the nlo description of the data and higher order calculations might be needed to describe these results and extract the gluon parton density function and @xmath0 .
9 f.  abe et al . , _ phys .
lett . _ * 77 * 438 ( 1996 ) .
w.  t.  giele et al . , _ nucl .
phys . _ * b403 * 633 ( 1993 ) .
a.  d.  martin et al . , _ phys .
lett . _ * b387 * 41 ( 1996 ) .
see e.g. s.  alekhin , _
hep - ph _ 9611213 ( 1996 ) .
j.  huston et al . , _ phys .
lett . _ * 77 * 444 ( 1996 ) .
w.  t.  giele et al . , _ phys .
rev . _ * d53 * 120 ( 1996 ) .
in this contribution we explore one of the many possibilities of determining the strong coupling constant @xmath0 at hadron colliders . 
 the method considered is quite unique compared to other methods in that the value of @xmath0 is determined by the `` evolution rate '' of the parton density functions rather than by the `` event rate '' .    
 fermilab - conf-97/240-t +    * extracting @xmath0 from tevatron data + * +