PoS(ICRC2021)1103 International th 12–23 July 2021 July 12–23 37 SICS CONFERENCE SICS CONFERENCE Berlin | Germany Berlin Berlin | Germany Cosmic Ray Conference Ray Cosmic ICRC 2021 THE ASTROPARTICLE PHY ICRC 2021 THEASTROPARTICLE PHY https://pos.sissa.it/ ONLINE on behalf of the GENETIS Collaboration ∗ [email protected] . The GENETIS algorithm evolves antenna designs using simulated neutrino sensitivity as a 2 International Cosmic Ray Conference (ICRC 2021) Presenter ∗ th Evolutionary algorithms are a type ofefficiently artificial determine intelligence solutions that to utilize defined principlesat problems. of finding evolution These to solutions algorithms that are aresolutions particularly too found powerful with complex simplified to methods. solvegorithms with The (GAs) traditional GENETIS to collaboration techniques design is and antennas developing that atradio genetic are improving pulses al- more than sensitive to current ultra-high detectors. energytrinos neutrino-induced Improving are antenna rare sensitivity and is criticalkm require because massive UHE detector neu- volumes withmeasure of stations fitness dispersed by integrating over with hundreds XFdtd,and of a with finite-difference simulations time-domain of modeling neutrino program, experiments.initial The testing. best The antennas GA’s aim will is then to beARA create deployed experiment antennas in-ice by that for more improve than on a the factor designsantenna of used 2 sensitivities in in in the neutrino existing future sensitivities. experiments ThisThis and research is could thus the improve accelerate first the time discovery thatphysics of antennas outcome, have which UHE been will neutrinos. motivate designed the using continued GAs usephysics with and of beyond. GA-designed a instrumentation This fitness in proceeding score astro- will based reportimprove on on the a advancements GA to performance, the algorithm, the steps latest takenmap. results to from our evolutions, and the manufacturing road The Ohio State University, 281 W Lane Ave, Columbus, OH,United 43210 States of America E-mail: Copyright owned by the author(s) under the terms of the Creative Commons © Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). 37 July 12th – 23rd, 2021 Online – Berlin, Germany Julie A. Rolla (a complete list of authors can be found at the end of the proceedings) Evolving Antennas for Ultra-High Energy Neutrino Detection PoS(ICRC2021)1103 Julie A. Rolla eV. Due to the low neutrino cross-section, UHE 2 18 10 GAs are a type of evolutionary computation used to discover one or more sets of values that The GA workflow is presented in1. Figure First, a population of individuals is generated, each The GENETIS (Genetically Evolving NEuTrIno TeleScopes) project focuses on using genetic These proceedings describe some background information regarding GAs, as well as two main Neutrino astronomy is a field of particle astrophysics that seeks to investigate distant phenomena provide a best-fit to a large parameterthrough space more that traditional techniques would [6,8–10]. have otherwise Each been generation consistseach difficult of of or a not which population possible is of solutions, tested for itsof output an against individual, a a predefined fitness set functionthe of is optimal goals. created or to desired To quantify generate goals. the a Each performance fitnessto generation score of improve to individuals upon has test the the an previous potential, individual generation but against [11]. is not guaranteed, with randomly generated genes. Each individualfitness is score. tested through A the selection fitness method functionbe is to used implemented generate in to a creating decide the whichoffspring individuals, next individuals called generation. for parents, the An will next operator generation.threshold then This is uses process surpassed the is by parents’ iterated one genetic until orpassed. code a more to specified individuals, create fitness or score until a specified number of generations have 2. Genetic Algorithms algorithms (GAs) to improve the sensitivitiesused of by Askaryan radiation-based ANITA, detectors, ARA, such and asdetect the those ARIANNA. neutrino-induced These radio impulses experiments [2,4,5]. utilizethe GAs mechanisms various are of computational types biological techniques of evolution that by mimic problem antennas and creating then to evolving populations the population of over multiple potential generations to solutions findOur optimized to solutions research [6,7]. a seeks defined to improve experimentalin the sensitivities geometry with of consideration antennas deployed to inAskaryan current the radiation. ice, constraints and the signal characteristics of neutrino-generated projects: (1) the PhysicalEvolution Antenna Algorithm (AREA). Evolution In Algorithm PAEA, the (PAEA) physicalimproved properties and neutrino of (2) bicone sensitivities. the antennas are Antenna AREA evolvedsuited for Response is for neutrino focused detection. on evolving only gain patterns that are better neutrino detection experiments relydetect on when a massive neutrino detector directly strikes volumes,Askaryan an atom. such radiation. The as Askaryan collision the radiation of aattenuation is Antarctic neutrino lengths of ice, within in particular the pure to interest, ice ice produces on as the it order produces of radio 1 signals km [3]. with through the detection ofcosmic neutrinos. distances directly As from weakly theirsources interacting source, in particles, thus the neutrinos providing distant a are universeprobes unique able [1,2]. the means to most of travel The energetic exploring field events particle of at ultra-high above energy (UHE) neutrino astronomy Evolving Antennas for Ultra-High Energy Neutrino Detection 1. Introduction PoS(ICRC2021)1103 Julie A. Rolla is the value of the 8 6 . | 8 6 − # 8  | gene of the goal antenna, 1 Cℎ = # 3 8 Õ − is equal to 100 when a generated antenna perfectly  100 is the total number of genes. = General GA evolution procedure. #  is the value of the i Figure 1: 8  This investigation tested various combinations of GA parameters and found that the ideal The goal of this investigation is to evolve physical antennas for the best sensitivity to neutrinos Here, the individual’s fitness score GAs often combine different selection methods and operators. The two most common selection The proportions of selection methods and genetic operators used can have a large effect on the The simplified structure evolved antennas to a specific design. The fitness score was a measure gene of the generated antenna, and Cℎ i combination of selection methods is 80%the roulette optimal and combination 20% is tournament. 70% crossover, For 24% the mutation, genetic and operators, 6% reproduction. 3. Physical Antenna Evolution Algorithm (PAEA) in an in-ice array.simulation of PAEA antenna evolves performance. antenna geometries For PAEA, by a means seed of generation fitness of possible scores antenna based designs on is the matches the goal design. methods are roulette andindividual tournament being selection selected is [12]. proportional toindividuals In the are individual’s roulette randomly fitness selection, score. placed the intoindividual(s) In with probability tournament groups the selection, of highest (tournaments) fitness an score and inprimary compared each tournament operators by are are fitness then selected. used score. in Additionally,individuals three this swap The research: genes to crossover, form mutation, thegenes and offspring. [13]. reproduction. Finally, In in mutation, In reproduction genetic crossover, modification. an diversity individual is is introduced passed altering directly to the next generation without 2.1 Genetic Algorithm Parameter Optimization performance of a GA —fitness typically score measured achieved. by time To toframework optimize was converge created these to to parameters a assess for solution the the performance and of GA the the used maximum GA with in different Section3,of parameters. a how simplified similar amagnitude generated of antenna the differences was between to eachgenes: the of a specified generated goal. antenna’s genes and the This desired was design’s done by averaging the Evolving Antennas for Ultra-High Energy Neutrino Detection PoS(ICRC2021)1103 Julie A. Rolla f 78.36 6.12 0.0017 ` 89.92 2.08 0.0140 4 Length Minor Radius Opening Angle Gene A flow chart of the PAEA procedure with a detailed inset of the fitness score procedure. Summary of the mean and standard deviation for the newly selected parameters drawn from a In the results presented, the GENETIS algorithm evolved 50 individuals over 15 generations. The commercial antenna simulation software, XFdtd, is then used to model the associated In the current version of PAEA, we consider an asymmetric bicone antenna design as shown Figure 2: The results of thetoward algorithm improved are solutions. presented For in eachillustrated the generation, by violin the the plot range vertical in from lines,highest best4, Figure with at and which the the worst shows lowest top. fitness individual evolution The scores beingline vertical are at lines represents the are the not bottom probability error of density barsshown of the for as the line the the population. and mean black fitness the dotted The score. line currentin for The5. Figure fitness reference. width of The of the antenna the Ara with the antenna highest is fitness thus far is shown initialized, with values for eachThe gene ranges picked of the from uniform uniform distributions distributions, are as determined by presented the in constraints Table of 1. thefrequency-dependent antenna response design. patterns.Carlo Those neutrino antenna simulation response software. patternsdetector AraSim, is are sensitive in input to order into whenback to Monte that into predict the antenna the GA is that effective used evolves theis volume [14]. geometry generic of and of ice The can the be next the fitness generation adapted score of for many for antennas. types each The of antenna PAEA antenna process is designs. fed in3. Figure The biconeseparation design distance consists of of 3.0 two cm.minor truncated radius The cones of parameters sitting the of cone, back-to-back the the withdifferent design length a values of that for fixed the are each cone, parameter, subject and for to the a evolution cone’s total opening are: of angle. six the Each values cone that canTable define have 1: the antenna. Gaussian distribution Evolving Antennas for Ultra-High Energy Neutrino Detection PoS(ICRC2021)1103 ), 2 A , 1 A Julie A. Rolla ), minor radii ( 2 ! , 1 ! 5 ) fully define the geometry. In the results presented here, B A drawing of the highest scoring antenna after 15 generations. ), and separation distance ( 2 \ Initial results from PAEA showing evolution to antennas with improved fitness. , 1 Figure 5: \ The geometry of an asymmetric bicone antenna. The lengths ( Figure 4: The GENETIS project is currently working on allowing the GA to evolve bicone antennas that 3.1 Bicone Antennas with Nonlinear Sides have nonlinear sides. Bypossible increasing to the further parameter optimize space theimprovement, by additional design genes allowing and are nonlinear increase the sides, coefficients the it of fitness may a score polynomial be of that the describes the antennas. shape of With the this Figure 3: Evolving Antennas for Ultra-High Energy Neutrino Detection opening angles ( the separation distance was held constant, and the other six parameters were varied. PoS(ICRC2021)1103 Julie A. Rolla (the orbital angular ℓ 0 . This allows the convoluted 0 = < Operator Mutation Mutation Crossover Crossover 6 Roulette Roulette Tournament Tournament Selection Method 1/6 1/2 1/6 1/6 Fraction Summary of the selection methods and operators used in the AREA procedure Table 2: The fitness score for each individual is evaluated based on simulated neutrino detection rates for The results of an initial AREA test are presented in6. Figure On the left, the change in the These proceedings provide an overview of and update on the optimization projects currently AREA is an algorithm developed for the evolution of RF antenna responses for detecting UHE AREA uses a linear sum of 13 azimuthally symmetric, spherical harmonic functions to model various azimuthal angles. AREA usesLite a simplifies simplified AraSim’s version fitness of function AraSim, by omitting calledsignal the AraSim simulation’s polarization, Lite.AraSim ray-tracing, noise and waveforms, ice modeling,PAEA, AREA thereby uses reducing the the effective computational ice run volumeas the15]. time[ fitness score. As with maximum and average fitness scoresconvergence in of less a than population 100 arefinal generations given evolved with over radiation 500 minimal pattern improvement generations,to is thereafter. showing up-going shown. a signals On are the Results moreimprovements right, indicate to desirable, a that AREA which include antennas matches utilizing that thefor the more are current complete accurate results more fitness AraSim scores from sensitive instead for PAEA. of the Ongoing evolved AraSim antenna Lite, responses in allowing future5. runs. Conclusions being undertaken by the GENETIS collaboration. GAs are an efficient machine learning method for neutrinos. By evolving antenna responses insteadinto of the physical optimal antennas, AREA antenna can designtime provide by in insight removing the the need to absence simulate of antenna responses.insight constraints, into Optimizing while the the antenna also behavior response provides of decreasing an computation should optimal have antenna a that response may similar be to evolved the by optimized PAEA–the optimal pattern antenna from AREA. the gain or phaseconsider pattern spherical harmonics of with the an magnetic quantum antenna. number Since we are assuming azimuthal symmetry, we only Evolving Antennas for Ultra-High Energy Neutrino Detection side. Thus the prior gene ofand the the opening length angle are is still superseded included in indifferent the the polynomials newer evolution for design. and the the The top bicones minor and are radius bottom still cones. vertically asymmetric with 4. Antenna Response Evolution Algorithm (AREA) form of a gainmomentum or quantum number) phase of pattern the spherical to harmonics.with be AREA the follows proportions described the normal of with GA the procedure selection the methods 13 and coefficients genetic operators shown in Table 2. PoS(ICRC2021)1103 Julie A. Rolla eV: (a) The change in maximum and 18 10 7 Results of an example AREA procedure for energy Further research will involve increasing the parameter space being explored by PAEA and The GENETIS team is grateful for support from the Ohio State Department of Physics Sum- Figure 6: further improving the performance and efficiencylations of will the reduce GA. computation An improvement time toneutrino by the generation more fitness and efficiently calcu- the using detector AraSimin performance. to the separately GA Additionally, simulate new to the techniques improveother are the aspects being of performance. astrophysics tested experiments, Future suchtrigger as research systems. different will antenna seek designs, array to geometries, apply and similar techniques to Acknowledgements mer Undergraduate Research program, supportPhysics, from and the the Center Cal for Poly CosmologyCenter. Connect and Grant. Astroparticle J. We Rolla would also would1806923 like and like to the to Ohio thank thank State the University the Ohio Alumni Grants Supercomputing National for Science Graduate Research Foundation and for Scholarship. support under Award Evolving Antennas for Ultra-High Energy Neutrino Detection average fitness score over 500generations [15]. generations; (b) The radiation pattern with the best fitness scoremaximizing after the fitness 500 of the generated individuals.antenna PAEA designs, uses the using optimized six GA tothrough independent evolve bicone AraSim genes and for compared each to the antenna.generations antennas have currently been The in evolved, fitness with use in the scoreexceeding the average the is and ARA performance determined experiment. maximum of the fitness So ARA score far,antennas bicone. 15 of to be AREA the used is in evolution being UHE now neutrino used radio tofor detection experiments. optimize greater beam complexity Evolving for patterns and beam patterns for removes allows thetime, need to but simulate doesn’t antenna responses, result reducingoptimized in computation gain an pattern antenna without any geometry.the concern complete AraSim for This will antenna allow allows design more for constraints. accurate a fitness score Future better calculations. runs understanding utilizing of the PoS(ICRC2021)1103 , , 2015 , , pp. 93–96, ]. 3rd Julie A. Rolla ]. , in Neutrino , pp. 1115–1121, , ]. 1404.5285 [1507.08991] International Journal of , . MIT Press, Cambridge, MA, (2015) [ (2010) 451. (2016) [ 1607.08232 ]. 2 ]. 93 CIMCA-IAWTIC ’06 ]. , in , vol. 11, 2004. (2016) [ . CRC Press, 2018. 8 . Van Nostrand Reinhold, 2016. 1902.04005 Phys. Rev. D Optimization of Antennas in the Askaryan Radio Comparison of performance between different , 1903.01609 Astropart. Phys. J , Survey on multiobjective evolutionary and real coded Senior Thesis, California Polytechnic State University, (2019) [ astro-ph/0602132 , 99 Radio Detection of High Energy Neutrinos (2019) [ Complexity International (2006) 669 [ (2018) . 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Rolla Pennsylvania State 5 , Eliot Ferstl 1 , Tom Sinha 1 , Ethan Fahimi 1 Cade Sbrocco Arizona State University 3 4 , Leo Deer 1 , Carl Pfendner 1 , Ryan Debolt 1 Denison University 9 3 , Alex Patton 1 , Amy Connolly 1 5 , Alex Machtay 2 , Maximilian Clowdus 2 , Luke Letwin 2 , and Stephanie Wissel California Polytechnic State University 2 2 , Dean Arakaki , Corey Harris 1 1 Jacob Trevithick 4 Ohio State University Evolving Antennas for Ultra-High Energy Neutrino Detection Full Authors List: GENETIS Collaboration Julie Rolla Gourapura University 1 Staats