bachelor_thesis/talk/vortrag.org

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What the heck should be in there. Let's draft up an outline.
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20 minutes: bloody short, so just results
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* Intro :1_30m:
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** Importance of MC Methods :SHORT:
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- important tool in particle physics
- not just numerical
- also applications in stat. phys and lattice QCD
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- somewhat romantic: distilling information with entropy
- interface with exp
- precision predictions within, beyond sm
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- validation of new theories
- some predictions are often more subtle than just the existense of
new particles
- backgrounds have to be substracted
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** Diphoton Process
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- feynman diags and reaction formula
- higgs decay channel
- dihiggs decay
- pure QED
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* Calculation of the XS :TOO_LONG: :5m:
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** Approach
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- formalism well separated from underlying theory
- but can fool intuition (spin arguments)
- in the course of semester: learned more about the theory :)
- translating feynman diagrams to abstract matrix elements straight
forward
- first try: casimir's trick
- error in calculation + one identity unknown
- second try: evaluating the matrices directly
- discovered a lot of tricks
- error prone
- back to studying the formalism: completeness relation for real
photons
- a matter of algebraic gymnastics
- boils down to some trace and dirac matrix gymnastics
- mixing terms cancel out, not zero in themselves
- resulting expression for ME essentially t/u channel propagator
(1/(t*u)) and spin correlation 1 + cos(x)^2
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- only angular dependencies, no kinematics, "nice" factors
- symmetric in θ
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** Result + Sherpa
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- apply the golden rule for 2->2 processes
- show plots and total xs
- shape verified later -> we need sampling techniques first
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* Monte Carlo Methods :8m:
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- one simple idea, can be exploited and refined
- how to extract information from a totally unknown function
- look at it -> random points are the most "symmetric" choice
- statistics to the rescue
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- what does this have to do with minecraft
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- theory deals with truly random (uncorrelated) so that statistics
apply, prng's cater to that: deterministic, efficient (we don't do
crypto)
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** Integration
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- integration as mean value
- convergence due to law of large numbers
- independent of dimension
- trivially parallelism
- result normal distributed with σ due to central limit theorem
- goal: speeding up convergence
1. modify distribution
2. integration variable
3. subdivide integration volume
- all those methods can be somewhat intertwined
- focus on some simple methods
*** Naive Integration
- why mix in that distribution: we choose it uniform
- integral is mean
- variance is variance of function: stddev linear in Volume!
- include result
- rediculous sample size
**** TODO compare to other numeric
*** Change of Variables
- drastic improvement by transf. to η
- only works by chance (more or less)
- pseudo rapidity eats up angular divergence
- can be shown: same effect as propability density
- implementation is different
*** VEGAS
- a simple ρ: step function on hypercubes, can be trivially generated
- effectively subdividing the integration volume
- optimal: same variance in every cube
- easier to optimize: approximate optimal rho by step function
- clarify: use rectangular grid and blank out unwated edges with θ
function
- nice feature: integrand does not have to be smooth :)
- similar efficiency as the travo case
- but a lot of room for parameter adjustment and tuning
**** TODO research the drawbacks that led to VEGAS
**** TODO nice visualization of vegas working
**** TODO look at original vegas
- in 70s/80s memory a constraint
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** Sampling
- why: generate events
- same as exp. measurements
- (includes statistical effects)
- events can be "dressed" with more effects
- usual case: we have access to uniformly distributed random values
- task: convert this sample into a sample of another distribution
- short: solve equation
*** Hit or Miss
- we don't always know f, may have complicated (inexplicit) form
- solve "by proxy": generate sample of g and accept with propability f/g
- the closer g to f, the better the efficiency
- simplest choice: flat upper bound
- show results etc
- one can optimize upper bound with VEGAS
*** Change of Variables
- reduction of variance similar to integration
- simplify or reduce variance
- one removes the step of generating g-samples
- show results etc
- hard to automate, but intuition and 'general rules' may serve well
- see later case with PDFs -> choose eta right away
*** Hit or Miss VEGAS
- use scaled vegas distribution as g and to hit or miss
- samples for g are trivial to generate
- vegas again approximates optimal distribution
- results etc
- advantage: no function specific input
- problem: isolated parts of the distribution can drag down
efficiency
- where the hypercube approx does not work well
- especially at discontinuities
**** TODO add pic that i've sent Frank
*** Stratified Sampling
- avoid global effects: subdivide integration interval and sample
independently
- first generate coarse samples and distribute them in the respective grid points
- optimizing: make cubes with low efficiency small! -> VEGAS
- this approach was used for the self-made event generator and
improved the efficiency greatly (< 1% to 30%)
- disadvantage: accuracies of upper bounds and grid weights has to be
good
- will come back to this
*** Observables
- particle identities and kinematics determine final state
- other observables can be calculated on a per-event base
- as can be shown, this results in the correct distributions
without knowledge of the Jacobian
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** Outlook
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- of course more methods
- Sherpa exploits form propagators etc
- multichannel uses multiple distributions for importance sampling
and can be optimized "live"
- https://www.sciencedirect.com/science/article/pii/0010465594900434
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*** TODO Other modern Stuff
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* Toy Event Generator :3m:
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** Basics :SHORT:
- just sampling the hard xs not realistic
1. free quarks do not occur in nature
2. hadron interaction more complicated in general
- we address the first problem here
- quarks in protons: no analytical bound state solution known so-far
*** Parton Density Functions
- in leading order, high momentum limit: propability to encounter
parton at some energy scale with some momentum fraction
- can not be calcualated from first principles
- have to be fitted from exp. data
- can be evolved to other Q^2 with DGLAP
- *calculated* with lattice QCQ: very recently
https://arxiv.org/abs/2005.02102
- scale has to be chosen appropriately: in deep inelastic scattering
-> momentum transfer
- p_T good choice
- here s/2 (mean of t and u in this case)
- xs formula
- here LO fit and evolution of PDFs
**** TODO check s/2
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** Implementation
- find xs in lab frame
- impose more cuts
- guarantee applicability of massless limit
- satisfy experimental requirements
- used vegas to integrate
- cuts now more complicated because photons not back to back
- apply stratified sampling variant along with VEGAS
- 3 dimensions: x1, x2 (symmetric), η
- use VEGAS to find grid, grid-weights and maxima
- improve maxima by gradient ascend (usually very fast)
- improve performance by cythonizing the xs and cut computation
- sampling routines JIT compiled with numba, especially performant
for loops and /very/ easy
- trivial parallelism through python multiprocessing
- overestimating the maxima corrects for numerical maximization
error
- assumptions: mc found maximum and VEGAS weights are precise enough
- most time consuming part: multidimensional implementation + debugging
- along the way: validation of kinematics and PDF values through sherpa
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** Results
*** Integration with VEGAS
- Python Tax: very slow, parallelism implemented, but omitted due
to complications with the PDF library
- also very inefficient memory management :P
- result compatible with sherpa
- that was the easy part
*** Sampling and Observables
- observables:
- usual: η and cosθ
- p_t of one photon and invariant mass are more interesting
- influence of PDF:
- more weight to the central angles (see eta)
- p_t cutoff due to cuts, very steep falloff due to pdf
- same picture in inv mass
- compatibilty problematic: just within acceptable limits
- for p_t and inv mass: low statistic and very steep falloff
- very sensitive to uncertainties of weights (can be improved by
improving accuracy of VEGAS)
- prompts a more rigorous study of uncertainties in the vegas step!
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* Pheno Stuff :2m:
- non LO effects completely neglected
- sherpa generator allows to model some of them
- always approximations
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** Short review of HO Effects
- always introduce stage and effects along with the nice event
picture
*** LO
- same as toy generator
*** LO+PS
- parton shower ~CSS~ (dipole) activated
- radiation of gluons, and splitting into quarks -> shower like
cascades QCD
- as there are no QCD particles in FS: initial state radiation
- due to 4-mom conservation: recoil momenta (and energies)
*** LO+PS+pT
- beam remnants and primordial transverse momenta simulated
- additinal radiation and parton showers
- primordial p_T due to localization of quarks, modeled like gaussian
distribution
- mean, sigma: .8 GeV, standard values in sherpa
- consistent with the notion of "fermi motion"
*** LO+PS+pT+Hadronization
- AHADIC activated (cluster hadr)
- jets of parton cluster into hadrons: non perturbative
- models inspired by qcd but still just models
- mainly affects isolation of photons (come back to that)
- in sherpa, unstable are being decayed (using lookup tables) with
correct kinematics
*** LO+PS+pT+Hadronization+MI
- Multiple Interactions (AMISIC) turned on
- no reason for just one single scattering in event
- based on overlap of hadrons and the most important QCD scattering
processes
- in sherpa: shower corrections
- generally more particles in FS, affects isolation
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** Presentation and Discussion of selected Histograms
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*** pT of γγ system
- Parton showers enhance at higher pT
- intrinsic pT at lower pT (around 1GeV)
- some isolation impact
- but highest in phase space cuts
- increase is almost one percent
- pT recoils to the diphoton system usually substract pT from one
photon -> harder to pass cuts -> amplified through big
probability of low pT events!
*** pT of leading and sub-leading photon
- shape similar to LO
- first photon slight pT boost
- second almost untouched
- cut bias to select events that have little effect on sub-lead
photon
*** Invariant Mass
- events with lower m are allowed throgh cuts
- events with very high recoil suppressed: colinear limit...
*** Angular Observables
- mostly untouched
- biggest difference: total xs and details
- but LO gives good qualitative picture
- reasonable, because LO should be dominating
*** Effects of Hadronization and MI
- fiducial XS differs because of isolation and cuts in the phase
space
- we've seen: parton shower affect kinematics and thus the shape of
observables and phase space cuts
- isolation critera:
- photon has to be isolated in detector
- allow only certain amount of energy in cone around photon
- force moinimum separation of photons to prevent cone overlap
- Hadronization spreads out FS particles (decay kinematics) and
produces particles like muons and neutrinos that aren't detectable
or easily filtered out -> decrase in isolation toll
- MI increases hadr activity in FS -> more events filtered out
*** Summary
- LO gives qualitative picture
- NLO affect observables shape, create new interesting observables
- some NLO effects affect mainly the isolation
- caveat: non-exhaustive, no QED radiation enabled
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* Wrap-Up
** Summary
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- calculated XS
- studied and applied simple MC methods
- built a basic working event generator
- looked at what lies beyond that simple generator
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** Lessons Learned (if any)
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- calculations have to be done verbose and explicit
- spending time on tooling is OK
- have to put more time into detailed diagnosis
- event generators are marvelously complex
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** Outlook
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- more effects
- multi channel mc
- better validation of vegas