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• Corpus ID: 238634350

# First search for exclusive diphoton production at high mass with tagged protons in proton-proton collisions at $\sqrt{s}$ = 13 TeV

@inproceedings{Cms2021FirstSF,
title={First search for exclusive diphoton production at high mass with tagged protons in proton-proton collisions at \$\sqrt\{s\}\$ = 13 TeV},
author={Cms and Totem Collaborations},
year={2021}
}
• Published 12 October 2021
• Physics
A search for exclusive two-photon production via photon exchange in proton-proton collisions, pp → pγγp with intact protons, is presented. The data correspond to an integrated luminosity of 9.4 fb−1 collected in 2016 using the CMS and TOTEM detectors at a center-of-mass energy of 13 TeV at the LHC. Events are selected with a diphoton invariant mass above 350 GeV and with both protons intact in the final state, to reduce backgrounds from strong interactions. The events of interest are those…
2 Citations

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## References

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Observation of proton-tagged, central (semi)exclusive production of high-mass lepton pairs in pp collisions at 13 TeV with the CMS-TOTEM precision proton spectrometer
A bstractThe process pp → pℓ+ℓ−p(*), with ℓ+ℓ− a muon or an electron pair produced at midrapidity with mass larger than 110 GeV, has been observed for the first time at the LHC in pp collisions at
Search for high-mass diphoton resonances in proton-proton collisions at 13 TeV and combination with 8 TeV search
A search for the resonant production of high-mass photon pairs is presented. The search focuses on spin-0 and spin-2 resonances with masses between 0.5 and 4.5 TeV, and with widths, relative to the
Evidence for light-by-light scattering and searches for axion-like particles in ultraperipheral PbPb collisions at sNN=5.02TeV
Abstract Evidence for the light-by-light scattering process, γ γ → γ γ , in ultraperipheral PbPb collisions at a centre-of-mass energy per nucleon pair of 5.02 TeV is reported. The analysis is
Light-by-light scattering with intact protons at the LHC: from standard model to new physics
• Physics
• 2014
A bstractWe discuss the discovery potential of light-by-light scattering at the Large Hadron Collider (LHC), induced by the Standard Model (SM) and by new exotic charged particles. Our simulation
Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at s = 8 TeV
A description is provided of the performance of the CMS detector for photon reconstruction and identification in proton-proton collisions at a centre-of-mass energy of 8 TeV at the CERN LHC. Details
Probing new physics in diphoton production with proton tagging at the Large Hadron Collider
• Physics
• 2014
The sensitivities to anomalous quartic photon couplings at the Large Hadron Collider are estimated using diphoton production via photon fusion. The tagging of the protons proves to be a very powerful
Precision luminosity measurement in proton-proton collisions at s = 13 TeV in 2015 and 2016 at CMS.
• A. Sirunyan, +2,320 authors W. Vetens
• Medicine, Physics
The European physical journal. C, Particles and fields
• 2021
The measurement of the luminosity recorded by the CMS detector installed at LHC interaction point 5, using proton-proton collisions at s = 13 TeV in 2015 and 2016, is reported, and are among the most precise luminosity measurements at bunched-beam hadron colliders.
CMS-TOTEM Precision Proton Spectrometer
TheCT-PPS (CMS-TOTEMPrecision Proton Spectrometer) detector system consists of silicon tracking stations as well as timing detectors to measure both the position and direction of protons and their
The physics of ultraperipheral collisions at the LHC
Abstract We discuss the physics of large impact parameter interactions at the LHC: ultraperipheral collisions (UPCs). The dominant processes in UPCs are photon–nucleon (nucleus) interactions. The
FPMC: A Generator for forward physics
We present the Forward Physics Monte Carlo (FPMC) designed to simulated central particle production with one or two leading intact protons and some hard scale in the event. The underlying interaction


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Accelerating the pace of engineering and science

# kaiserord

Kaiser window FIR filter design estimation parameters

## Syntax

[n,Wn,beta,ftype] = kaiserord(f,a,dev)
[n,Wn,beta,ftype] = kaiserord(f,a,dev,fs)
c = kaiserord(f,a,dev,fs,'cell')

## Description

kaiserord returns a filter order n and beta parameter to specify a Kaiser window for use with the fir1 function. Given a set of specifications in the frequency domain, kaiserord estimates the minimum FIR filter order that will approximately meet the specifications. kaiserord converts the given filter specifications into passband and stopband ripples and converts cutoff frequencies into the form needed for windowed FIR filter design.

[n,Wn,beta,ftype] = kaiserord(f,a,dev) finds the approximate order n, normalized frequency band edges Wn, and weights that meet input specifications f, a, and dev. f is a vector of band edges and a is a vector specifying the desired amplitude on the bands defined by f. The length of f is twice the length of a, minus 2. Together, f and a define a desired piecewise constant response function. dev is a vector the same size as a that specifies the maximum allowable error or deviation between the frequency response of the output filter and its desired amplitude, for each band. The entries in dev specify the passband ripple and the stopband attenuation. You specify each entry in dev as a positive number, representing absolute filter gain (not in decibels).

 Note   If, in the vector dev, you specify unequal deviations across bands, the minimum specified deviation is used, since the Kaiser window method is constrained to produce filters with minimum deviation in all of the bands.

fir1 can use the resulting order n, frequency vector Wn, multiband magnitude type ftype, and the Kaiser window parameter beta. The ftype string is intended for use with fir1; it is equal to 'high' for a highpass filter and 'stop' for a bandstop filter. For multiband filters, it can be equal to 'dc-0' when the first band is a stopband (starting at f = 0) or 'dc-1' when the first band is a passband.

To design an FIR filter b that approximately meets the specifications given by kaiser parameters f, a, and dev, use the following command.

```b = fir1(n,Wn,kaiser(n+1,beta),ftype,'noscale')
```

[n,Wn,beta,ftype] = kaiserord(f,a,dev,fs) uses a sampling frequency fs in Hz. If you don't specify the argument fs, or if you specify it as the empty vector [], it defaults to 2 Hz, and the Nyquist frequency is 1 Hz. You can use this syntax to specify band edges scaled to a particular application's sampling frequency. The frequency band edges in f must be from 0 to fs/2.

c = kaiserord(f,a,dev,fs,'cell') is a cell-array whose elements are the parameters to fir1.

 Note   In some cases, kaiserord underestimates or overestimates the order n. If the filter does not meet the specifications, try a higher order such as n+1, n+2, and so on, or a try lower order.Results are inaccurate if the cutoff frequencies are near 0 or the Nyquist frequency, or if dev is large (greater than 10%).

## Examples

expand all

### Kaiser Window Filter Design

Design a lowpass filter with passband defined from 0 to 1 kHz and stopband defined from 1500 Hz to 4 kHz. Specify a passband ripple of 5% and a stopband attenuation of 40 dB.

```fsamp = 8000;
fcuts = [1000 1500];
mags = [1 0];
devs = [0.05 0.01];

[n,Wn,beta,ftype] = kaiserord(fcuts,mags,devs,fsamp);
hh = fir1(n,Wn,ftype,kaiser(n+1,beta),'noscale');

freqz(hh)
```

Design an odd-length bandpass filter. Note that odd length means even order, so the input to fir1 must be an even integer.

```fsamp = 8000;
fcuts = [1000 1300 2210 2410];
mags = [0 1 0];
devs = [0.01 0.05 0.01];

[n,Wn,beta,ftype] = kaiserord(fcuts,mags,devs,fsamp);
n = n + rem(n,2);
hh = fir1(n,Wn,ftype,kaiser(n+1,beta),'noscale');

[H,f] = freqz(hh,1,1024,fsamp);
plot(f,abs(H))
grid
```

Design a lowpass filter with a passband cutoff of 1500 Hz, a stopband cutoff of 2000 Hz, a passband ripple of 0.01, a stopband ripple of 0.1, and a sample rate of 8000 Hz. Design an equivalent filter using the 'cell' option.

```[n,Wn,beta,ftype] = kaiserord([1500 2000],[1 0],...
[0.01 0.1],8000);
b = fir1(n,Wn,ftype,kaiser(n+1,beta),'noscale');

c = kaiserord([1500 2000],[1 0],[0.01 0.1],8000,'cell');
bcell = fir1(c{:});

hfvt = fvtool(b,1,bcell,1);
legend(hfvt,'b','bcell')
```

expand all

### Tips

Be careful to distinguish between the meanings of filter length and filter order. The filter length is the number of impulse response samples in the FIR filter. Generally, the impulse response is indexed from n = 0 to n = L–1 where L is the filter length. The filter order is the highest power in a z-transform representation of the filter. For an FIR transfer function, this representation is a polynomial in z, where the highest power is zL–1 and the lowest power is z0. The filter order is one less than the length (L–1) and is also equal to the number of zeros of the z polynomial.

### Algorithms

kaiserord uses empirically derived formulas for estimating the orders of lowpass filters, as well as differentiators and Hilbert transformers. Estimates for multiband filters (such as bandpass filters) are derived from the lowpass design formulas.

The design formulas that underlie the Kaiser window and its application to FIR filter design are

$\beta =\left\{\begin{array}{ll}0.1102\left(\alpha -8.7\right),\hfill & \alpha >50\hfill \\ 0.5842{\left(\alpha -21\right)}^{0.4}+0.07886\left(\alpha -21\right),\hfill & 50\ge \alpha \ge 21\hfill \\ 0,\hfill & \alpha <21\hfill \end{array}$

where α = –20log10δ is the stopband attenuation expressed in decibels (recall that δp = δs is required).

The design formula is

$n=\frac{\alpha -7.95}{2.285\left(\Delta \omega \right)}$

where n is the filter order and Δω is the width of the smallest transition region.

## References

[1] Kaiser, James F. "Nonrecursive Digital Filter Design Using the I0-sinh Window Function." Proceedings of the 1974 IEEE International Symposium on Circuits and Systems. 1974, pp. 20–23.

[2] Digital Signal Processing Committee of the IEEE Acoustics, Speech, and Signal Processing Society, eds. Selected Papers in Digital Signal Processing. Vol. II. New York: IEEE Press, 1976, pp. 123–126.

[3] Oppenheim, Alan V., Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Upper Saddle River, NJ: Prentice Hall, 1999.

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