Nova descoberta sobre gala¡xias distantes: as estrelas são mais pesadas do que pensa¡vamos
Uma equipe de astrofisicos da Universidade de Copenhague chegou a um resultado importante sobre as populaa§aµes de estrelas além da Via La¡ctea. O resultado pode mudar nossa compreensão de uma ampla gama de fena´menos...
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8gvG8fhMRcgcJ8jBXb+7oNJ/tVF21DrUO4KiaLrboK8Nuy5kwhJMD+VS2PT/wApjqSjeD+61xQtjD5Wx/8AoqPJO/u6Beo3Nbbc9B/tVF21DrU3uCEXOaZCNK+JiAAotNt8SoNgNiAGprKQtgGSRxWoJcRzQAoby3atITj5J393QKGsabNxkXqw1glt8gYaD/aqLtqHWpvcNIfksPgwgP8AW4D4QvnyTv7vpWqGiLXYE2heFYNDYHO1MqSqSRZDSm0paPDJ+0/2qi7ah1qb3DSLiMAg4UdAAfJAmdYrnfH87+76UeJRnqChaeyJ4BACIm+VTtlutyPwmUH/ALLmwnn2qi7ah1qb3DSnxHtA/amxS/qIImBwCF8+R/Kgb1dou+lDW2nRP0g2MW2tB/tVF21DrUzuGlZpXAWk6f1TSS2yDyCaBhHkneLtN30rLHWX2Tw4K1SOtOiBHDQf7VRdtX3UzuCvWqQVeYVxlRaE8kQMYXg/6Y2oj4QBxjR4/S2nE8AFiTUa/je7r+iddyTS51I0jCwEQ2kpXH+rQf7VR9tX3U3uCoWnBz71Awcy9Au/43qnpmbMXfKonjaBBlGzjFyf+Qy6j532k21jF+jjHyiRTUcn+lXGajVjo4V47n8VO+kBRsa7q6FbpA1urFkFSGl3RGjc2y4CUPwusnjIT/aqPpV91M7gowIMgo0jnS4iEGGlPyYxWvSWhEREJrS6WNMgIxjCDn0LrfE+HpwsYRbaNzsJUgHe+ZUuyrd9LxT+oawRF4Rd44pHRFyFoxJhETalsknEJj6BxmRF+Kff7GOq4rjo0fSr7qZ3DT2256biWv8ApXr55ogbzer7hyV2g76XD0yArXheGLMH5KhwkLVaApDBPROqO/ioaHCtnSr7qZ3DSd0QDv0zp4/jQ0jq/dus7v8AKvu+FcNJ30m0lGaMQINor8nhx/SdB1R38V4BYDRo+lX3UzuGkIdC/wByf/kaZi2f7qzud6ubmuAV7irvId9K05pc2wRhgUHgENDLN/HQd7VR9tX3UzuGkXcghYYCbFt16Duek4WGu6ms7lc3NScfKvqd0CbRh9gETKNGaS2InopKLbw4cCuMExPBP9qou2r7qZ3BS4wEbJwUkwEHNMgqA7H4RLyPtOsuEubha/hNui7S1bVnjcrjCsTxKk+deVcCv2q7y763dAmtNEaR8YLW/TFn9yH8J4drPdR3FMjG6Oqfy9qou2r7qZH7gqCf33oR/wBd6vTEw2g5sgYJ103K2LOymk4kaToDz/fCi/nep480QK5i7nViFiFeVirpKwAW1/C2ir5P2rh5l+i7oEwUZDDB1v8AC/JSB31ChytDHmSpj5jgn7qb1traW0tpbam0sVj5tF21fdVH3BWXIm8k8SrAc7rKsAmz8pt7iG4AlEnAJ7j+nAhtsCUHc9J2ow9a3LEq9xqwqwVwG436bugQtUZe4CcYhQxpBc2cdB+6uWCwqwWCiNA+XRdtX3VR9w0rLztL/eCzFn5hfjcCBy0SVPiuyrdv7ugVuidBgtvVqkdLogRw0H6OKx83FYo7rRdtX3VR9w0i7kF4v44ibMLxAILhowr6zud/mO6BEinezWN32talc/qpcYWqZhQKRpKcsVj5GPl85WC2VsoaqwWCwWHm0XbV91UfcNK97mxyKj/yfD58EI0nazz/AGq5GHGJuKkSZ393QKxxiVY4xKl3C9PpsA4QPlMLrLm3AwEbrqj52OiN1ou2r7qo+4aRbzXg2WREW5/wg3lolHUfHKFEwtozzRA8+/cHdAhSeFTGeAKMUVI083KyXEdESXvddF6aHPc5rcAU7cY0ButF21fdVH3BSUWwWuHAqSg8YFC42XGA5CRqwb0CXweinS2hWfNv3J3QIWHtshsCQvyOYR8DQf5/Fcd6ou2r7qo+4KgJwD71dwo71cgBjZKogMTZATicIXiA0cRNiOCDuekQJ53Vny790d0CsF7cLzKs0ZDhEyNB/tVF21fdVH3BQ4SFqiJVgN1eShjYVoNE1RafZ/bau0pU2Xj9wurPkY7u7oFe1tzCZQa8N1m2hHCuE/2qi7aj1qo+4aRPIIO8Zom+LKv0YmF6r/4WqrE3TipPHf3dAgPCD3AWr+CGreWTKFrgZVPSUexZzKo6UbdxlPu9qou2o9aqPuGkZwVoUNP4f7rSEYacAwhrXhQN/d0CI8YMeBB6Iu8W24CMIhWWPs/S13gtiIDYTaMumjacIT/aqLtqPWqj7hpFrjiF4XjUNnC1xQAwHknf3dArP+neSKS1P2rZonMFmL9B/tVF21HrVR9w0rbxfwulXUItx/18UJ5eSd/d0Gk/2qi7aj1FVH3BC6STACNG8Q6JQukkwAjRvEOAlA+GA0/1J0YwrXiCbP8AKbaxjyTv7ug0n+1UXbUeoqo+5UTuDH3q03BrIJVDStMsa+8ovF7W0cFUZ/TnWLhF6JPBOpG/pqKQLXygeemYTCDF0m5Y47+7oK4kjohJnFMNI2lB5l10p49qou2o9RVRdwquFVwUwE66bsE1zXubLYMHEIAYDTIQFrAQrI4b+7oK7sVYMdQmspLMAySOKf7VRdtR6iqi7hpMZG2nRRRZdCjyTv7ug0n1D2ii7aj1FVF3DSbrOaW8Qrqalvv2vKO/u6DSfUKj7NRdtR6iqi7huJ393QaT6h7RRdtTuoqou4eeSE3mW2kHfujf3dBXqmCvEeZN6ZTl2MSPhPHH2qi7andRVRd3nkJt94EKyOG/u6CvCV4T2kYplAW4RLvirBYLBYLBYImybIxMIFEeG8xyasFgsFgsEXEYKPDcOrUXHBWbNk4wQsFgsFgsEW+G4xyapAunkrPhucf6WqbOawWCwWCazifhCRecAE6BfxBQkXnABGBeMQVgsFgsFhoYLDcStoZraGa2hmtoZrbGa2hmtoZraGa2hmtoZrbGa2hmtoZraGa2hmtsZraGa2hmtoZraGa2xmtoZraGa2hmtoZrbGa2hmtoZraGa2hmnQZw800LOO0eQQAwi5UlJR0p4n4TXcxokF1meKYy0XB4OPBDlbE5qhjk6dI2do3BBvJOLaUg/CDnY6NB3qg5aypjwgKhPCCqflq73ZiUHDjoREygRo3CNCSsI0CeS4fWhhxTruMHQmJRMYY+aSW3n5TTGxgjq44ibtKy68KQL+ZRaG3HEKQL9IE8MELQmDKLiLzjfiobcNGXC8fKhwRbFxxQbFwwUNG9hw5Qg3loaolC6PjRh2M6EfMpxPEzoGEYEA8NCAOIThwcQdAhqixI67hQfL4RecAmCkaBbwgo0ob+MfN6t3X0ed6onOYA7xBdKNG9utEiDimtdZ1uTsE/w2AhhjHFWzd+L/Kl7bJ5SqRt1kAcVTODNh1+smt8Ma4lt6L3N1g6zHyvDdZkiRBVED/2BMocGWZMcU2w4Nd+2cU42Baa6zEo0YoxMTiv07i2HGZy9pdPAwsULPOE2MDKxToxC6IxwMXrFTKuRjAGFrXOiSEbLr95oyyzqGb0W0haGkf8VRmkswzlxTqEEWDx4qSGltiymNc5stMynOeQDYsiFRbA8P8AlP8ADLYeZv4K06yW2LKs0pBI4ovYRrRMqmbq/kKonauoIT2uLb32wpIYPhqoyATDwTCbSsucOaY99kBl9ytsdLIk9UaTVgiFQwDqzN3tJwvdKDZ2RCnomzGqolFt183og4cE4CLypu2pUmOKyTrMWXXpjZvbj8qlJi/yv//EACoQAAIBAgQEBgMBAAAAAAAAAAERACExEEFRYSBxobEwQIGR8PFQ0eHB/9oACAEBAAE/IcMoUMapytGkGiB7R7og1IbTqclPpM+kz6TPpM+kz6TPpM+kz6TPpM+kz6TPpM+kz6TPpM+kz6TPpM+kz6TPpM+kz6TPpM+kz6TPpM+kz6TPpM+mz6bPps+mz6TPpM+kz6TPpM+kwCICDYjBYQanGAECSJGQzhoed0IrBgFAFkLkQweHAlV5xtohrB8ISLFFoqYj2ChKB/2AOBzHJ0j/AKX6j/rfqP8ArfqP+t+o/wCt+o/636j/AK36j/rfqP8ArfqP+t+o/wCt+o/636j/AK36j/rfqP8ArfqP+t+o/wCt+o/636j/AK36j/rfqP8ArfqP+t+o/wCt+o/636j/AK36j/rfqP8ArfqP+t+o/wCt+o/636hYkCFY4mIZ+wgMmAMAbly9IAMoQM7Hv+K6Rh2WHwuogM2II12hqIs1+cNNuYFtCpK1X3Sg0KioQOXhEAhETLCgIgNvDqZXWV1ldZXWV1ldZXWV1ldZXWV1ldZXWV1ldZXWV1ldZVoz4jfi7fv+K6RhZyo5b8tR5zNhr6T7PPs8+zwYsAOYUEnWQRlDQIS80RKmaZD5QpuUEt4AhoRI9ZQaq3m4oKrlMxKwgJvOEgEASY6iUiE0QtCVEEASBXeOMJydVCDTyT9Yg+DqSBzE/wAT4jfi7Pv5+7A28HpGFvIjhU+So85hwnW5kIaJh9cKOQZbeK24YUbRMGLL3TVdCJRIWDaFyZUfbCpAoAeqGiBgIvSCWRJHIoqBkUbFuBBZTtwBFqMlc8HxG/F2ffyZ8YNvB6Rh0COfM34qbNCVClCWAQSjJQ815TLQyusrrK6yusrrK6yusrrK6yusrrK6yusrrK6yusrrK6xT4jfEyKAzA8AHpgThA1C7ynY9/IjyAbeD0jDpsc+BvxLqoTYSkO0H8RKBJCz42ZgmC4bq0hq1hsNEV5/4jfE4rkRNwaSHCjZCfsVUCGtYcQCJH18iPIBt4PTMOgxy/wCCvEQeGSFazT3k/p1hjYYAVp4AGCBeWYIoBALz/wARvxdj38iLeQBsfB6Jh0+OKeuxzhCCiA8gqaU5SIMBfgyAYAgTKgA6alGtIgMRHMHKwDkRDtKSZfDOAABYfiN8RvPpcBiADk7eDse/kR5AG3g9Ew6HHD8zWHbgCc6z0xuAIACEjSYptHJQtIHWIUJcF7Q5DYQoI3hqoADDpeAQiYCp/D4hzgiB8HHYhUOreUzzgxgwNA+Dse/im/ktrA28HomHS44/iZ8W6QNQqykugIR6Avr+F1oegWw6f/UODVQ+xaxzIqsBFAGIgKkwhFZJ5hOBFC16AbGdj343H4Y8ZG3g9Ew6LHAfmZ8RNI4wCMWAW46tNZcbMwh0wLvaA4warbDza8wh7Y9P/qCwpZkHvCUIYCS4EgHnDk3g5hL4l/BCS6jOEamCLBos9+Nx+KfEjY+D0TDpMc+M14qwBSS14IykZQdyISBCvr4AA0LnNnBHAgF5kkAVMOWjh6f/AFLkuK0gzgqUKxwUU7Hv5U+AL8BYeNxzpmHRY58hrxKxQIrnK3zIIGZYAAfwGAFTCHQJfi6f/UUMoqsDCrQCogHB2ffzovgLxmE04UiRCKdEwtcsc+I1hIBkhBTCaguBWINSYOYg1BcBlk3qw9CEQB0j5vCFerhFWAAnXheoWeowPL66ZMK15rlw4yAghjx8reHbm7N2Erjx1kBkdyG71gKuCxD2EE7QqBNOnB2ffjSJ4p4grEIAIhFikTGtubcSJOhYWeWOGkh5HrDEEgF6qDR2QGoMIitkcYCUdvMBeC9dzEk17wgPmvVARuBb1mWs5IvVwkKhM0BWEQuRVP2lDAl1iACxhLgtM5c2BDojlwnzgFmgGa1iaGWHBjWEgXMAtWFWpCSSz4JIAZsJW+mnWAK06f8A1DJkhmAzKaDBNbmFCQEgAQzDMLBEIEAQnY9/KngGAVigEUIpgLxCAEm11m11m11m11hFmJOn4W+XD4jWAREgAMiJmcYSAEclP/EhubEIARJLRj7VjVSYjFE1uG+ahIOvFXoaBkqVUVM5QIOgkBQb0BHt4toycz4oSSOgIDP2NGPT/wCpWzIE0raFoE4AhNDCc4r9nwRlyjPZKuVJsoQmYClNaxIQXGZ+IeEoMJEihFMBcRIMjOYT5DPkMWLC44XQcLHLh85rxkpEB5IKhjiuBA5QaRLCkAWQ2LLVgWEAKeZC1RqnyIsoDg6f/UIYIm0yNKw8jJBigiDQIgMhzdCJ+6hCmc13nOhIDoMVFFiwisUSJEiQjBRRRQ8AiTEgoSEYkAsACDec+AHrePCaM5GAQ1i4v2+HwmvEBIASC1RFicJmZi0ggqDir8yDRiHSCw8zQkkToE2doMsQOLp/9RzQGQYhbkGGE8HXDvh02IsWPAsSJEiRYFYoooeADAFUUGFFgIKLA+pFNQywkqjyJyIF2E2xCZNuHb4fGa8RSOIBYV8MgIAEs68S6Lq5R3gsPK26o650SrLrRjc9JZXHZBCCHT/UyzITaJhh3MiCbgJnl3mwIcYQjjjYGjRxx4HHwl3ENGPCuNpAZMcJwJAZsCMKCbAm0MTYE2BOhYdjh85rxUKbjARbQGwQY6wPiJlZoIfaC3k7dDXQXj/9Chal9nhGNbIArT4jeM/4Qip2EzO3FPZK/wBEoINmUGgyBFQMANALUec7fvgNEcccfAeXA8QmuAtgWwieeBIoGAVRQcAURnLG0j6QmrcKOOdMw7fD4TWHihnCQVLgQiIcjAuTMoMjAp40CqCiAihm2h2uO4m0emFNOKt9BCVpzEJcSsOooP3EoJJo/XxrESu66T0XWWV4RLBFVux+I3jgaEAUhCHnpZ+xhCAkBlNrC4GCgkBkpZ5XvBLMRY6b4HBRcBRYg3TmiDFBbgrJzznnPAkUNJywIbRtIYnSck5cFnH0jDt8Gsnmc5QrSNFoaazuUgCgHnOnPcxVqJFFDSkRyqRRzgMqQ4Ko1DDSAJHEcm1RIgJEwJuNQDN1b1QIwgBjvZAghgsQiNfegLb3obAE2cU28gRn3Bc1AHKGz6c1HMiyA5DwxWZvtgLBwfEbwW5MwbFIMyAyAkMhYBYqkYSgJMIsGAA2lGoOqnb9+DPwwKscBjBzzngSaUdImr2hEZ/aJr9oE5/aLq9oOomukWahHPEEXgLxQjj6Rh2eHyGsXCl6ZGMnRyZmJOAcyOa2ImouE+bwCAsIZhmlqZzFoMdYHxL7au77QWGBt6CUFM5oyu5GjlrQmLIQAAIBQ3AH0gsl6eQss56ABYcXxG8YxYIFBrAUFLkjTg7fvFFgLznnPFFFFwhRxCxs8AtHLM2Pm5lhWOVE5Zk2C6cs5cXHj0jDtcPmteISkCGhzGcuipFF6DmQZqrcJQpSGcbpAqgXdILYCxbeUAJsIUsoBvLeB8RvCXnlKEZxf4hUQDgFg5d5zw03nPKKuNc05pzznnPOec855rJzTXzmgfAXznhZnOeUZvWckpWnJHlTATPw+kYdrh8FrxAKBuMd77T84XZk+FqyeYisq8rQRsJme2AAWHh/EbwQANGBSqFJCEWAUigwtWPk5m0HThQAGChqR3jQQEcGgx55zx9Y8EIqKLAMKBKmcTmgtkQXX7wAze83HvBiNfWJBBQkAIjljAlHHHHx9Iwu8uHyWvENBBNlTPvV/IiBhhUPCbDWmgiQRm6D+pzneCmR0iZC33lMAF/IEJCb4fG+I3iQz/ZnxYoQhCtCcpk0m2GcJaCEJBGTcIFMwZe+D2SJpACUiaRNI44BSKBVFARF42sJjYm2hlMRtYSoxIEUDE0MCyM2DBiABjjjj8XpGF3kw+a14hnrARP0ySBjrAuHQk0sLx8pxSL1jZiICqFiVJaKA8Ylgmqgxp4/xG8NEoS7CvONvQ1XteAWNuu8G825sIElJaXpzhIm47zYMGTFbYWOEKsZtGAaLAJeLvKE4BtAzaMZsZsmEUBiwn5PpGF7kw+K1lWd0ALk6QzQJnQ1lc62DjbxqRhW4rExprGJY5CPsABIakAALGo4jWqEFJwW8e54RLyPxG8E7+Jv84c01R4/3gNG5d5zISDOJALm0ZzIsWcycyc6c6BVjNkwAAo2ofQDabRhRzi7wXlXSMLvJg5QUf8ASCBhgtDUT9hSBgQI2g3FI6wxcJzDghlgRI1gAKFXRODBWB8VoABoFvEoBJQldwABYeT+I3izIBElhdofiFxrHTg7HvgLx40cccccfA8AkPzLpGF3kw+W1hOIS4MHEBzN4kgaEUAOgiSTNwgEI2OEcAQQtwtyqtIgwRmWQW4ainACShAWZhyCICzM2QFesAAsPK/EbzMWUhW4hQEtVchhIAZgAGCxM4D0nY98LESMRGIxx8dnmzpGHSMPjteIgMMkK1lDa0HXWMAZnM8LxzZjKEoOcPi0Wt1IZmBJbgAf6ZrBRQbwKq3BvxBKABYeb+I3hha3VQAShMDg2WkpOxSZGUoQoALAuRBWKJ85JNe8UJi1X1wF444kSOOJExccBCQ35l0jDoGHz2vFcKSsRAvQrapy4WlOMxyCOYhGEjYKFIDLgvP/ABG8Fgz5m4RUEMroOUvUTU1MaQuqGlHeJLZC4VYEzse+GeLEY8Fxw+Z6Rh0TD4bXiSoHC85a3LOUtPiH4jfEbxQboIKj+IQ6CWpfB2Pf8V0jDpGHx2vEmEgCD2oApJOkA1RyjUQBRgZfiN8Rvxdj3/FdI4IfPawZlDa4mAChSiwRHyhtcYUEoKLBEfdcu5e0QQWxCJEdRo6NIZK6D5/iN8Rvxdj3xRiP4bpHAQWFkMLQz5JEJHSzhBsCuLMm0DGYA0qIhTUgatqZPkBURmDA2UDMJkE3JsPpLE0OnGZBkYGcNcRmEqtiI6+f+I3xKYN7dDJkLMTvBfyQa9hwbMFd8BeNpEdPw3SOBnwWsId4AQANogCQWkAIAG0AUCOqmYuOaADIH2gMFbQhxkEFMKAAVVQsoPIAAHn/AIjfGpUHQSpQvIaI7xdgAtk2ynY98LcR0hBRp+G6Rh8pr4CgkWDA7UlLrF5qoAAFh+I3xG/F2PfDqI2kI1Mvw3SMPhNfA1UBAwVGDddmVxgIA2tfxG+I34ux74dTh0n4bpGHxmsf5EW+I34ux74dTGIYfl4JvwHzPSMPmNfIyMoDAdYFoQZdIR1EIef+I3xYStQhwcKECJ5Exn5csttJUAMkPWIy3gbcZvgjpEdIjpEYjEdIjpEdIjpEdIjpEdIjpEdIjpEdIjpEdIjpEdIjpEdIjpEdIjpEdIjpEdIjpOkYfMaxHSCfQ+OQAUSFACgu6y+CBygAB5/4jfEhSC2CAEUoC1m444OGUQ0hA3Dm09ptPabT2m09ptPabT2g8z1WYljilRAwXyJaJsPabT2m09ptPabT2jk40CsP7HdUphA1MZ5pRJibT2m09ptPabT2m09o+LurQbNGolQYTqgZqRHQeQIzae02ntNp7Tae0M0M6pZB9QVAVMMChKEqJfyVAVMt4KgKibT2m09ptPabT2m09pbAgG82ntNh5EkQ+fMYxlGMYxlGMYyjGMZRjGMoxjGOggVnr4p0sx2P3R7R1AhmaBTSrSGKhERXChMQe6X7sC5M4bKKh6KK9Mbk4ikf60wV7AhKZmAISk1tgO6N+H52xl6cgeqlusCecv1wxzl+Mgeq82FKwglPSWmAcQiEQhilZSoNJA5RCIRCFAVggUKhcqRCIRCFQtpkqhyLiEQiEARFAccIamTBcQiEQiI1ggPcqEsFAEm5X8iiEQgi0whG3APt4tVqM1VmsoLbCzUE2GPLhYiCgmRjnKUwkr1gujydRwsqgTJZUfCDBk3sgEFABzi2VAgQ9UBRAFgOBhpwLMNioKBbgFq1EAHvUIHtUoAMs7+buLRJKhNFwWbxvVRQVcmXARAKmuUPXgoq4D2FwztNpQcAgCRMCbpSaPXgDmiy+hc1gKdEv1wDiZO8VAF2ubyBTNRW4Rlm1gEsXmI6GE4eSkagGcNqAbzWQ0XKQbrBhEZuAIS76U4lvGTMgmhIXUVgOxGphWGgaH1igmy8PUQiMXKhd/QxExdfdCkboDhygBIwREGGloEKjQByhJAhsUt0h7g5h+0ZtEXt7QaQ/E1Wpqgo/JFgiBpL1hBBAi9wlqitoEwwkmECAa3R5SArIrkKwV6zIChA5qor6whTBVQuRBoF5AQ2yRBbzBUgGbVMI7CE18jGhuRrRDvzs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)
Esquerda: temperatura de melhor ajuste de 10 a 50 K vs. tempo de retrospectiva de uma amostra de 139.535 gala¡xias COSMOS2015 com S/N > 10 na banda V (Laigle et al. 2016). A cada redshift, a distribuição énormalizada individualmente para enfatizar a distribuição de temperatura em todos os redshifts. Com o redshift aumentado, menos gala¡xias se encaixam em temperaturas mais baixas. Direita: média suavizada de vaga£o com desvio padrãoda temperatura do gás de melhor ajuste em diferentes tempos de retrospectiva (com a média determinada a partir de objetos em caixas de idade de 2 Gyr e não incluindo gala¡xias ajustadas nos limites da faixa de temperatura). A temperatura média aumenta de ∼28 a ∼36 K do presente para 12 Gyr, enquanto a propagação diminui. Crédito: The European Physical Journal E (2022). DOI: 10.1140/epje/s10189-022-00183-5
Uma equipe de astrofisicos da Universidade de Copenhague chegou a um resultado importante sobre as populações de estrelas além da Via La¡ctea. O resultado pode mudar nossa compreensão de uma ampla gama de fena´menos astrona´micos, incluindo a formação de buracos negros, supernovas e por que as gala¡xias morrem.
Desde que os humanos estudaram os canãus, a aparaªncia das estrelas em gala¡xias distantes tem sido um mistanãrio. Em um estudo publicado hoje no The Astrophysical Journal , uma equipe de pesquisadores do Instituto Niels Bohr da Universidade de Copenhague estãodesafiando entendimentos anteriores de estrelas além de nossa própria gala¡xia.
Desde 1955, assume-se que a composição das estrelas nas outras gala¡xias do universo ésemelhante a das centenas de bilhaµes de estrelas dentro da nossa osuma mistura de estrelas massivas, de massa média e de baixa massa. Mas com a ajuda de observações de 140.000 gala¡xias em todo o universo e uma ampla gama de modelos avana§ados, a equipe testou se a mesma distribuição de estrelas aparente na Via La¡ctea se aplica a outros lugares. A resposta énão. Estrelas em gala¡xias distantes são tipicamente mais massivas do que aquelas em nossa "vizinhana§a local". A descoberta tem um grande impacto sobre o que pensamos que sabemos sobre o universo.
"A massa das estrelas diz muito aos astra´nomos. Se vocêmudar a massa, vocêtambém muda o número de supernovas e buracos negros que surgem de estrelas massivas . Como tal, nosso resultado significa que teremos que revisar muitas das coisas uma vez presumimos, porque as gala¡xias distantes parecem bem diferentes das nossas", diz Albert Sneppen, estudante de pós-graduação do Instituto Niels Bohr e primeiro autor do estudo.
Analisada a luz de 140.000 gala¡xias
Os pesquisadores supunham que o tamanho e o peso das estrelas em outras gala¡xias eram semelhantes aos nossos por mais de cinquenta anos, pela simples razãode que eles eram incapazes de observa¡-los atravanãs de um telesca³pio, como podiam com as estrelas de nossa própria gala¡xia.
Gala¡xias distantes estãoa bilhaµes de anos-luz de distância. Como resultado, apenas a luz de suas estrelas mais poderosas chega a Terra. Isso tem sido uma dor de cabea§a para pesquisadores de todo o mundo hános, pois eles nunca conseguiram esclarecer com precisão como as estrelas em outras gala¡xias foram distribuadas, uma incerteza que os fora§ou a acreditar que elas eram distribuadas de maneira muito parecida com as estrelas da Via La¡ctea.
“Sa³ conseguimos ver a ponta do iceberg e sabemos hámuito tempo que esperar que outras gala¡xias se parea§am com a nossa não era uma suposição particularmente boa. gala¡xias formam diferentes populações de estrelas. Este estudo nos permitiu fazer exatamente isso, o que pode abrir as portas para uma compreensão mais profunda da formação e evolução das gala¡xias," disse o Professor Associado Charles Steinhardt, coautor do estudo.
No estudo, os pesquisadores analisaram a luz de 140.000 gala¡xias usando o cata¡logo COSMOS, um grande banco de dados internacional de mais de um milha£o de observações de luz de outras gala¡xias. Essas gala¡xias estãodistribuadas do mais pra³ximo ao mais distante alcance do universo, de onde a luz viajou doze bilhaµes de anos antes de ser observa¡vel na Terra.
Gala¡xias massivas morrem primeiro
Segundo os pesquisadores, a nova descoberta tera¡ uma ampla gama de implicações. Por exemplo, permanece sem solução por que as gala¡xias morrem e param de formar novas estrelas. O novo resultado sugere que isso pode ser explicado por uma tendaªncia simples.
"Agora que somos mais capazes de decodificar a massa das estrelas, podemos ver um novo padra£o; as gala¡xias menos massivas continuam a formar estrelas, enquanto as gala¡xias mais massivas param de gerar novas estrelas. Isso sugere uma tendaªncia notavelmente universal na morte de estrelas. gala¡xias", conclui Sneppen.