12 research outputs found
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
There are many cases in collider physics and elsewhere where a calibration
dataset is used to predict the known physics and / or noise of a target region
of phase space. This calibration dataset usually cannot be used out-of-the-box
but must be tweaked, often with conditional importance weights, to be maximally
realistic. Using resonant anomaly detection as an example, we compare a number
of alternative approaches based on transporting events with normalizing flows
instead of reweighting them. We find that the accuracy of the morphed
calibration dataset depends on the degree to which the transport task is set up
to carry out optimal transport, which motivates future research into this area.Comment: 7 pages, 3 figures. Presented at the Machine Learning and the
Physical Sciences Workshop at the 36th conference on Neural Information
Processing Systems (NeurIPS
FETA: Flow-Enhanced Transportation for Anomaly Detection
Resonant anomaly detection is a promising framework for model-independent
searches for new particles. Weakly supervised resonant anomaly detection
methods compare data with a potential signal against a template of the Standard
Model (SM) background inferred from sideband regions. We propose a means to
generate this background template that uses a flow-based model to create a
mapping between high-fidelity SM simulations and the data. The flow is trained
in sideband regions with the signal region blinded, and the flow is conditioned
on the resonant feature (mass) such that it can be interpolated into the signal
region. To illustrate this approach, we use simulated collisions from the Large
Hadron Collider (LHC) Olympics Dataset. We find that our flow-constructed
background method has competitive sensitivity with other recent proposals and
can therefore provide complementary information to improve future searches.Comment: 13 pages, 11 figure
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
Many components of data analysis in high energy physics and beyond require
morphing one dataset into another. This is commonly solved via reweighting, but
there are many advantages of preserving weights and shifting the data points
instead. Normalizing flows are machine learning models with impressive
precision on a variety of particle physics tasks. Naively, normalizing flows
cannot be used for morphing because they require knowledge of the probability
density of the starting dataset. In most cases in particle physics, we can
generate more examples, but we do not know densities explicitly. We propose a
protocol called flows for flows for training normalizing flows to morph one
dataset into another even if the underlying probability density of neither
dataset is known explicitly. This enables a morphing strategy trained with
maximum likelihood estimation, a setup that has been shown to be highly
effective in related tasks. We study variations on this protocol to explore how
far the data points are moved to statistically match the two datasets.
Furthermore, we show how to condition the learned flows on particular features
in order to create a morphing function for every value of the conditioning
feature. For illustration, we demonstrate flows for flows for toy examples as
well as a collider physics example involving dijet eventsComment: 15 pages, 17 figures. This work is a merger of arXiv:2211.02487 and
arXiv:2212.0615
Exploring the Space of Jets with CMS Open Data
We explore the metric space of jets using public collider data from the CMS
experiment. Starting from 2.3/fb of 7 TeV proton-proton collisions collected at
the Large Hadron Collider in 2011, we isolate a sample of 1,690,984 central
jets with transverse momentum above 375 GeV. To validate the performance of the
CMS detector in reconstructing the energy flow of jets, we compare the CMS Open
Data to corresponding simulated data samples for a variety of jet kinematic and
substructure observables. Even without detector unfolding, we find very good
agreement for track-based observables after using charged hadron subtraction to
mitigate the impact of pileup. We perform a range of novel analyses, using the
"energy mover's distance" (EMD) to measure the pairwise difference between jet
energy flows. The EMD allows us to quantify the impact of detector effects,
visualize the metric space of jets, extract correlation dimensions, and
identify the most and least typical jet configurations. To facilitate future
jet studies with CMS Open Data, we make our datasets and analysis code
available, amounting to around two gigabytes of distilled data and one hundred
gigabytes of simulation files.Comment: 37 pages, 25 figures, 5 tables; v2: updated to match PRD version;
code available at https://energyflow.networ
The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Machine learning--based anomaly detection (AD) methods are promising tools
for extending the coverage of searches for physics beyond the Standard Model
(BSM). One class of AD methods that has received significant attention is
resonant anomaly detection, where the BSM is assumed to be localized in at
least one known variable. While there have been many methods proposed to
identify such a BSM signal that make use of simulated or detected data in
different ways, there has not yet been a study of the methods' complementarity.
To this end, we address two questions. First, in the absence of any signal, do
different methods pick the same events as signal-like? If not, then we can
significantly reduce the false-positive rate by comparing different methods on
the same dataset. Second, if there is a signal, are different methods fully
correlated? Even if their maximum performance is the same, since we do not know
how much signal is present, it may be beneficial to combine approaches. Using
the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative
answers to these questions. We find that there are significant gains possible
by combining multiple methods, which will strengthen the search program at the
LHC and beyond.Comment: 23 pages, 17 figure
Self-supervised Anomaly Detection for New Physics
We investigate a method of model-agnostic anomaly detection through studying
jets, collimated sprays of particles produced in high-energy collisions. We
train a transformer neural network to encode simulated QCD "event space" dijets
into a low-dimensional "latent space" representation. We optimize the network
using the self-supervised contrastive loss, which encourages the preservation
of known physical symmetries of the dijets. We then train a binary classifier
to discriminate a BSM resonant dijet signal from a QCD dijet background both in
the event space and the latent space representations. We find the classifier
performances on the event and latent spaces to be comparable. We finally
perform an anomaly detection search using a weakly supervised bump hunt on the
latent space dijets, finding again a comparable performance to a search run on
the physical space dijets. This opens the door to using low-dimensional latent
representations as a computationally efficient space for resonant anomaly
detection in generic particle collision events.Comment: 12 pages, 11 figure
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Anomaly detection under coordinate transformations
There is a growing need for machine-learning-based anomaly detection strategies to broaden the search for beyond-the-Standard-Model physics at the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly detection approach is to specify observables and then use them to decide on a set of anomalous events. One common choice is to select events that have low probability density. It is a well-known fact that probability densities are not invariant under coordinate transformations, so the sensitivity can depend on the initial choice of coordinates. The broader machine learning community has recently connected coordinate sensitivity with anomaly detection and our goal is to bring awareness of this issue to the growing high-energy physics literature on anomaly detection. In addition to analytical explanations, we provide numerical examples from simple random variables and from the LHC Olympics dataset that show how using probability density as an anomaly score can lead to events being classified as anomalous or not depending on the coordinate frame
Recommended from our members
Anomaly Detection under Coordinate Transformations
There is a growing need for machine learning-based anomaly detection
strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at
the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly
detection approach is to specify observables and then use them to decide on a
set of anomalous events. One common choice is to select events that have low
probability density. It is a well-known fact that probability densities are not
invariant under coordinate transformations, so the sensitivity can depend on
the initial choice of coordinates. The broader machine learning community has
recently connected coordinate sensitivity with anomaly detection and our goal
is to bring awareness of this issue to the growing high energy physics
literature on anomaly detection. In addition to analytical explanations, we
provide numerical examples from simple random variables and from the LHC
Olympics Dataset that show how using probability density as an anomaly score
can lead to events being classified as anomalous or not depending on the
coordinate frame