Abstract
We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network ...containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimizing the difference in response of the network to background samples originating from different Monte Carlo models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the Large Hadron Collider with
t
t
ˉ
H
signal versus
t
t
ˉ
b
b
ˉ
background classification and discuss implications and limitations of the method.
We consider the impact of varying
α
s
choices (and scales) on each side of the so-called “matching scale” in MLM-matched matrix-element + parton-shower predictions of collider observables. We explain ...how inconsistent prescriptions can lead to counter-intuitive results and present a few explicit examples, focusing mostly on
W
/
Z
+jets processes. We give a specific prescription for how to improve the consistency of the matching and also address how to perform consistent tune variations (e.g., of the renormalization scale) around a central choice. Comparisons to several collider processes are included to illustrate the properties of the resulting improved matching, relying on AlpGen +
Pythia
6, with the latter using the so-called Perugia 2011 tunes, developed as part of this effort. Our observations, nevertheless, apply to the large class of tools where matrix-element generators are merged with independent codes for the parton-shower evolution.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
HepMCAnalyser is a tool for Monte Carlo (MC) generator validation and comparisons. It is a stable, easy-to-use and extendable framework allowing for easy access/integration to generator level ...analysis. It comprises a class library with benchmark physics processes to analyse MC generator HepMC output and to fill root histograms. A web-interface is provided to display all or selected histogramms, compare to references and validate the results based on Kolmogorov Tests. Steerable example programs can be used for event generation. The default steering is tuned to optimally align the distributions of the different MC generators. The tool will be used for MC generator validation by the Generator Services (GENSER) LCG project, e.g. for version upgrades. It is supported on the same platforms as the GENSER libraries and is already in use at ATLAS.
Charged particle multiplicity distributions in positron-proton deep inelastic scattering at a centre-of-mass energy
s
=
319
GeV are measured. The data are collected with the H1 detector at HERA ...corresponding to an integrated luminosity of 136 pb
-
1
. Charged particle multiplicities are measured as a function of photon virtuality
Q
2
, inelasticity
y
and pseudorapidity
η
in the laboratory and the hadronic centre-of-mass frames. Predictions from different Monte Carlo models are compared to the data. The first and second moments of the multiplicity distributions are determined and the KNO scaling behaviour is investigated. The multiplicity distributions as a function of
Q
2
and the Bjorken variable
x
bj
are converted to the hadron entropy
S
hadron
, and predictions from a quantum entanglement model are tested.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event ...and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimizing the difference in response of the network to background samples originating from different Monte Carlo models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the Large Hadron Collider with \(t\bar{t}H\) signal versus \(t\bar{t}b\bar{b}\) background classification and discuss implications and limitations of the method.
A precision measurement of jet cross sections in neutral current deep-inelastic scattering for photon virtualities
5.5
<
Q
2
<
80
GeV
2
and inelasticities
0.2
<
y
<
0.6
is presented, using data taken ...with the H1 detector at HERA, corresponding to an integrated luminosity of
290
pb
-
1
. Double-differential inclusive jet, dijet and trijet cross sections are measured simultaneously and are presented as a function of jet transverse momentum observables and as a function of
Q
2
. Jet cross sections normalised to the inclusive neutral current DIS cross section in the respective
Q
2
-interval are also determined. Previous results of inclusive jet cross sections in the range
150
<
Q
2
<
15
,
000
GeV
2
are extended to low transverse jet momenta
5
<
P
T
jet
<
7
GeV
. The data are compared to predictions from perturbative QCD in next-to-leading order in the strong coupling, in approximate next-to-next-to-leading order and in full next-to-next-to-leading order. Using also the recently published H1 jet data at high values of
Q
2
, the strong coupling constant
α
s
(
M
Z
)
is determined in next-to-leading order.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Monte Carlo generators in ATLAS software Ay, C; Buckley, A; Butterworth, J ...
Journal of physics. Conference series,
04/2010, Letnik:
219, Številka:
3
Journal Article
Recenzirano
Odprti dostop
This document describes how Monte Carlo (MC) generators can be used in the ATLAS software framework (Athena). The framework is written in C++ using Python scripts for job configuration. Monte Carlo ...generators that provide the four-vectors describing the results of LHC collisions are written in general by third parties and are not part of Athena. These libraries are linked from the LCG Generator Services (GENSER) distribution. Generators are run from within Athena and the generated event output is put into a transient store, in HepMC format, using StoreGate. A common interface, implemented via inheritance of a GeneratorModule class, guarantees common functionality for the basic generation steps. The generator information can be accessed and manipulated by helper packages like TruthHelper. The ATLAS detector simulation as well access the truth information from StoreGate1. Steering is done through specific interfaces to allow for flexible configuration using ATLAS Python scripts. Interfaces to most general purpose generators, including: Pythia6, Pythia8, Herwig, Herwig++ and Sherpa are provided, as well as to more specialized packages, for example Phojet and Cascade. A second type of interface exist for the so called Matrix Element generators that only generate the particles produced in the hard scattering process and write events in the Les Houches event format. A generic interface to pass these events to Pythia6 and Herwig for parton showering and hadronisation has been written.
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