A shortcut to truth

Article title: “Automated detector simulation and reconstruction
parametrization using machine learning”

Authors: D. Benjamin, S.V. Chekanov, W. Hopkins, Y. Li, J.R. Love

Reference: https://arxiv.org/abs/2002.11516 (https://iopscience.iop.org/article/10.1088/1748-0221/15/05/P05025)

Demonstration of probability density function as the output of a neural network. (Source: paper)

The simulation of particle collisions at the LHC is a pharaonic task. The messy chromodynamics of protons must be modeled; the statistics of the collision products must reflect the Standard Model; each particle has to travel through the detectors and interact with all the elements in its path. Its presence will eventually be reduced to electronic measurements, which, after all, is all we know about it.

The work of the simulation ends somewhere here, and that of the reconstruction starts; namely to go from electronic signals to particles. Reconstruction is a process common to simulation and to the real world. Starting from the tangle of statistical and detector effects that the actual measurements include, the goal is to divine the properties of the initial collision products.

Now, researchers at the Argonne National Laboratory looked into going from the simulated particles as produced in the collisions (aka “truth objects”) directly to the reconstructed ones (aka “reco objects”): bypassing the steps of the detailed interaction with the detectors and of the reconstruction algorithm could make the studies that use simulations much more speedy and efficient.

Display of a collision event involving hadronic jets at ATLAS. Each colored block corresponds to interaction with a detector element. (Source: ATLAS experiment)

The team used a neural network which it trained on simulations of the full set. The goal was to have the network learn to produce the properties of the reco objects when given only the truth objects. The process succeeded in producing the transverse momenta of hadronic jets, and looks suitable for any kind of particle and for other kinematic quantities.

More specifically, the researchers began with two million simulated jet events, fully passed through the ATLAS experiment and the reconstruction algorithm. For each of them, the network took the kinematic properties of the truth jet as input and was trained to achieve the reconstructed transverse momentum.

The network was taught to perform multi-categorization: its output didn’t consist of a single node giving the momentum value, but of 400 nodes, each corresponding to a different range of values. The output of each node was the probability for that particular range. In other words, the result was a probability density function for the reconstructed momentum of a given jet.

The final step was to select the momentum randomly from this distribution. For half a million of test jets, all this resulted in good agreement with the actual reconstructed momenta, specifically within 5% for values above 20 GeV. In addition, it seems that the training was sensitive to the effects of quantities other than the target one (e.g. the effects of the position in the detector), as the neural network was able to pick up on the dependencies between the input variables. Also, hadronic jets are complicated animals, so it is expected that the method will work on other objects just as well.

Comparison of the reconstructed transverse momentum between the full simulation and reconstruction (“Delphes”) and the neural net output. (Source: paper)

All in all, this work showed the perspective for neural networks to imitate successfully the effects of the detector and the reconstruction. Simulations in large experiments typically take up loads of time and resources due to their size, intricacy and frequent need for updates in the hardware conditions. Such a shortcut, needing only small numbers of fully processed events, would speed up studies such as optimization of the reconstruction and detector upgrades.

More reading:

Argonne Lab press release: https://www.anl.gov/article/learning-more-about-particle-collisions-with-machine-learning

Intro to neural networks: https://physicsworld.com/a/neural-networks-explained/

Crystals are dark matter’s best friends

Article title: “Development of ultra-pure NaI(Tl) detector for COSINE-200 experiment”

Authors: B.J. Park et el.

Reference: arxiv:2004.06287

The landscape of direct detection of dark matter is a perplexing one; all experiments have so far come up with deafening silence, except for a single one which promises a symphony. This is the DAMA/LIBRA experiment in Gran Sasso, Italy, which has been seeing an annual modulation in its signal for two decades now.

Such an annual modulation is as dark-matter-like as it gets. First proposed by Katherine Freese in 1987, it would be the result of earth’s motion inside the galactic halo of dark matter in the same direction as the sun for half of the year and in the opposite direction during the other half. However, DAMA/LIBRA’s results are in conflict with other experiments – but with the catch that none of those used the same setup. The way to settle this is obviously to build more experiments with the DAMA/LIBRA setup. This is an ongoing effort which ultimately focuses on the crystals at its heart.

Cylindrical crystals wrapped in reflector, bounded by photomultipliers (PMTs) and surrounded by scintillators. (COSINE-100)

The specific crystals are made of the scintillating material thallium-doped sodium iodide, NaI(Tl). Dark matter particles, and particularly WIMPs, would collide elastically with atomic nuclei and the recoil would give off photons, which would eventually be captured by photomultiplier tubes at the ends of each crystal.

Right now a number of NaI(Tl)-based experiments are at various stages of preparation around the world, with COSINE-100 at the Yangyang mountain, S.Korea, already producing negative results. However, these are still not on equal footing with DAMA/LIBRA’s because of higher backgrounds at COSINE-100. What is the collaboration to do, then? The answer is focus even more on the crystals and how they are prepared.

Setup of the COSINE-100 experiment. (COSINE-100)

Over the last couple of years some serious R&D went into growing better crystals for COSINE-200, the planned upgrade of COSINE-100. Yes, a crystal is something that can and does grow. A seed placed inside the raw material, in this case NaI(Tl) powder, leads it to organize itself around the seed’s structure over the next hours or days.

In COSINE-100 the most annoying backgrounds came from within the crystals themselves because of the production process, because of natural radioactivity, and because of cosmogenically induced isotopes. Let’s see how each of these was tackled during the experiment’s mission towards a radiopure upgrade.

Improved techniques of growing and preparing the crystals reduced contamination from the materials of the grower device and from the ambient environment. At the same time different raw materials were tried out to put the inherent contamination under control.

Among a handful of naturally present radioactive isotopes particular care was given to 40K. 40K can decay characteristically to an X-ray of 3.2keV and a γ-ray of 1,460keV, a combination convenient for tagging it to a large extent. The tagging is done with the help of 2,000 liters of liquid scintillator surrounding the crystals. However, if the γ-ray escapes the crystal then the left-behind X-ray will mimic the expected signal from WIMPs… Eventually the dangerous 40K was brought down to levels comparable to those in DAMA/LIBRA through the investigation of various techniques and first materials.

But the main source of radioactive background in COSINE-100 was isotopes such as 3H or 22Na created inside the crystals by cosmic ray muons, after their production. Now, their abundance was reduced significantly by two simple moves: the crystals were grown locally at a very low altitude and installed underground within a few weeks (instead of being transported from a lab at 1,400 meters above sea in Colorado). Moreover, most of the remaining cosmogenic background is to decay away within a couple of years.

Components of the background, and temporal evolution of the cosmogenic radioactivity. (Source)

Where are these efforts standing? The energy range of interest for testing the DAMA/LIBRA signal is 1-6keV. This corresponds to a background target of 1 count/kg/day/keV. After the crystals R&D, the achieved contamination was less than about 0.34 counts. In short, everything is ready for COSINE-100 to upgrade to COSINE-200 and test the annual modulation without the previous ambiguities that stood in the way.

Learn more:

More on DAMA/LIBRA in ParticleBites.

Cross-checking the modulation.

The COSINE-100 experiment.

First COSINE-100 results.

Listening for axions

If dark matter actually consists of a new kind of particle, then the most up-and-coming candidate is the axion. The axion is a consequence of the Peccei-Quinn mechanism, a plausible solution to the “strong CP problem,” or why the strong nuclear force conserves the CP-symmetry although there are no reasons for it to. It is a very light neutral boson, named by Frank Wilczek after a detergent brand (in a move that obviously dates its introduction in the ’70s).

Axion decay in a magnetic field: the result is a photon. (Source.)

Most experiments that try to directly detect dark matter have looked for WIMPs (weakly interacting massive particles). However, as those searches have not borne fruit, the focus started turning to axions, which make for good candidates given their properties and the fact that if they exist, then they exist in multitudes throughout the galaxies. Axions “speak” to the QCD part of the Standard Model, so they can appear in interaction vertices with hadronic loops. The end result is that axions passing through a magnetic field will convert to photons.

In practical terms, their detection boils down to having strong magnets, sensitive electronics and an electromagnetically very quiet place at one’s disposal. One can then sit back and wait for the hypothesized axions to pass through the detector as earth moves through the dark matter halo surrounding the Milky Way. Which is precisely why such experiments are known as “haloscopes.”

Now, the most veteran haloscope of all published significant new results. Alas, it is still empty-handed, but we can look at why its update is important and how it was reached.

ADMX (Axion Dark Matter eXperiment) of the University of Washington has been around for a quarter-century. By listening for signals from axions, it progressively gnaws away at the space of allowed values for their mass and coupling to photons, focusing on an area of interest:

ADMX_results_2020
Latest exclusion limits on the axion mass and coupling to photons.

Unlike higher values, this area is not excluded by astrophysical considerations (e.g. stars cooling off through axion emission) and other types of experiments (such as looking for axions from the sun). In addition, the bands above the lines denoted “KSVZ” and “DFSZ” are special. They correspond to the predictions of two models with favorable theoretical properties. So, ADMX is dedicated to scanning this parameter space. And the new analysis added one more year of data-taking, making a significant dent in this ballpark.

As mentioned, the presence of axions would be inferred from a stream of photons in the detector. The excluded mass range was scanned by “tuning” the experiment to different frequencies, while at each frequency step longer observation times probed smaller values for the axion-photon coupling.

Two things that this search needs is a lot of quiet and some good amplification, as the signal from a typical axion is expected to be as weak as the signal from a mobile phone left on the surface of Mars (around 10-23W). The setup is indeed stripped of noise by being placed in a dilution refrigerator, which keeps its temperature at a few tenths of a degree above absolute zero. This is practically the domain governed by quantum noise, so advantage can be taken of the finesse of quantum technology: for the first time ADMX used SQUIDs, superconducting quantum interference devices, for the amplification of the signal.

The heart of the experiment inside the refrigerator. The resonant frequency of the cavity is tuned to match the photons -hopefully- given off by axions. (Source.)




In the end, a good chunk of the parameter space which is favored by the theory might have been excluded, but the haloscope is ready to look at the rest of it. Just think of how, one day, a pulse inside a small device in a university lab might be a messenger of the mysteries unfolding across the cosmos.

References:

Publication by the ADMX collaboration. (arXiv)

Learn more:

  1. The theory behind axions.
  2. The hitchhiker’s guide to the dilution refrigerator.
  3. Intro to KSVZ and DFSZ axions (and more).
  4. Resonant cavities.