LEADER 01076nam a2200277 i 4500 001 991002917119707536 005 20250507101656.0 008 160418s2015 it 000 0 ita d 020 $a9788867602957 035 $ab14253781-39ule_inst 040 $aDip. di Studi Umanistici$bita 100 1 $aIbargüengoitia, Jorge$0444343 245 10$aTeatro /$cJorge Ibargüengoitia ; prefazione di Diego Símini ; traduzioni di Laura Pisanello, Fernanda Castellano e Diego Símini 260 $aLecce ;$aRovato :$bPensa multimedia,$c2015 300 $a190 p. ;$c21 cm 440 3$aLa Quinta del sordo ;$v8 500 $aContiene: Susana e i giovani ; L'attentato ; Il viaggio in superficie 700 1 $aSímini, Diego 700 1 $aPisanello, Laura 700 1 $aCastellano, Fernanda 907 $a.b14253781$b05-05-16$c18-04-16 912 $a991002917119707536 945 $aLE012 868.64 IBA$g1$i2007000269558$lle012$op$pE17.10$q-$rl$s-$t0$u0$v0$w0$x0$y.i15719534$z05-05-16 996 $aTeatro$9253507 997 $aUNISALENTO 998 $ale012$b18-04-16$cm$da$e-$fita$git$h0$i0 LEADER 03453nam 22006735 450 001 9910624314303321 005 20251202152304.0 010 $a9789811958809 010 $a9811958807 024 7 $a10.1007/978-981-19-5880-9 035 $a(MiAaPQ)EBC7130111 035 $a(Au-PeEL)EBL7130111 035 $a(CKB)25264904700041 035 $a(PPN)266350593 035 $a(BIP)86247817 035 $a(BIP)85148959 035 $a(DE-He213)978-981-19-5880-9 035 $a(EXLCZ)9925264904700041 100 $a20221101d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLarge-Scale Structure of the Universe $eCosmological Simulations and Machine Learning /$fby Kana Moriwaki 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (126 pages) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 311 08$a9789811958793 311 08$a9811958793 327 $aIntroduction -- Observations of the Large-Scale Structure of the Universe -- Modeling Emission Line Galaxies -- Signal Extraction from Noisy LIM Data -- Signal Separation from Confused LIM Data -- Signal Extraction from 3D LIM Data -- Application of LIM Data for Studying Cosmic Reionization -- Summary and Outlook -- Appendix. 330 $aLine intensity mapping (LIM) is an observational technique that probes the large-scale structure of the Universe by collecting light from a wide field of the sky. This book demonstrates a novel analysis method for LIM using machine learning (ML) technologies. The author develops a conditional generative adversarial network that separates designated emission signals from sources at different epochs. It thus provides, for the first time, an efficient way to extract signals from LIM data with foreground noise. The method is complementary to conventional statistical methods such as cross-correlation analysis. When applied to three-dimensional LIM data with wavelength information, high reproducibility is achieved under realistic conditions. The book further investigates how the trained machine extracts the signals, and discusses the limitation of the ML methods. Lastly an application of the LIM data to a study of cosmic reionization is presented. This book benefits students and researcherswho are interested in using machine learning to multi-dimensional data not only in astronomy but also in general applications. 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 606 $aCosmology 606 $aMachine learning 606 $aAstrophysics 606 $aAstronomy$vObservations 606 $aCosmology 606 $aMachine Learning 606 $aAstrophysics 606 $aAstronomy, Observations and Techniques 615 0$aCosmology. 615 0$aMachine learning. 615 0$aAstrophysics. 615 0$aAstronomy 615 14$aCosmology. 615 24$aMachine Learning. 615 24$aAstrophysics. 615 24$aAstronomy, Observations and Techniques. 676 $a520 700 $aMoriwaki$b Kana$01265910 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910624314303321 996 $aLarge-Scale Structure of the Universe$94300582 997 $aUNINA