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تسجيل مستخدم جديدProperties of potential eco-friendly gas replacements for particle detectors in high-energy physics
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Modern gas detectors for detection of particles require F-based gases for optimal performance. Recent regulations demand the use of environmentally unfriendly F-based gases to be limited or banned. This review studies properties of potential eco-friendly gas candidate replacements.
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The Multigap Resistive Plate Chambers (MRPC) are used as a timing detector in several particle physics and cosmic ray experiments. The gas mixture of MRPC at current experiments is a mixture containing $rm C_2F_4H_2$ and in some cases $rm SF_6$. $rm
C_2F_4H_2$ and $rm SF_6$ have a Global Warming Potential (GWP) of 1430 and 23900 respectively, therefore they are classified as greenhouse gases. The studies to reduce the amount of emission of the greenhouse gas in high energy experiments are underway; the present contribution has been performed as part of this effort. The results have been obtained from the beam test of a small MRPC which has 6 gaps of 220 $mu$m and an sensitive area of 20 $times$ 20 cm$^2$. It has been operated with the ecological HFO-1234ze gas ($rm C_3F_4H_2$), and with the $rm C_2F_4H_2/SF_6$ mixture. We have found that the ecological gas can substitute for the $rm C_2F_4H_2$-based gas mixture without significantly compromising the current level of performance.
Modern gas detectors for detection of particles require F-based gases for optimal performance. Recent regulations demand the use of environmentally unfriendly F-based gases to be limited or banned. This review studies properties of potential eco-friendly gas candidate replacements.
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy
collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.
The ATLAS RPC standard mixture, mainly based on C$_{2}$H$_{2}$F$_{4}$, has a high Global Warming Potential (GWP) and therefore the search for RPC eco friendly gases is mandatory. In this work we present the results on the detector performances in ter
ms of efficiency, prompt and ionic charge, with different gas mixtures.
Resistive Plate Chambers (RPCs), used for the Muon Spectrometer of the ALICE experiment at CERN LHC, are currently operated in maxi-avalanche mode with a low threshold value and without amplification in the front-end electronics. RPC detectors have s
hown a good operation stability with the current gas mixture during the entire Run 1 (2010$-$2013) and the ongoing Run 2 (2015$-$2018) at the LHC. The gas mixture is made up of $C_{2}H_{2}F_{4}$, $SF_{6}$ and $iC_{4}H_{10}$. Since the first two gases have high Global Warming Potentials (GWPs), there is the risk that they will be phased out of production in the next years, due to the recent restrictions and regulations of the European Union. Therefore, finding a new eco-friendly gas mixture has become extremely important in order to reduce the emissions of greenhouse gases. In addition, the present $iC_{4}H_{10}$ contribution makes the current gas mixture flammable. Non-flammable components, or at least in non-flammable concentrations, would be advisable to make the operation of detectors simpler and safer. In order to identify a gas mixture suited to cope with the requirements of the ALICE Muon Identifier in the forthcoming High-Luminosity runs, a dedicated experimental set-up has been used to carry out R&D studies on promising gas mixtures with small-size RPCs. Hydrofluoroolefins ($HFOs$) are appropriate candidates to replace the $C_{2}H_{2}F_{4}$ thanks to their very low GWPs, especially $HFO1234ze$ which is not flammable at room temperature. Several tests on $HFO$-based mixtures with addition of various gases are ongoing and encouraging results have already been obtained. Furthermore, the use of $CO_{2}$ as a quencher has been studied as it might represent a valid solution to avoid flammability of the mixture. Finally, medium-term stability of detectors exposed to the cosmic-ray flux will be shown in this paper.
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