data:
- - Lhash: '1221962300074229217'
    abs_disc: 562432
    analytic_rank: 0
    analytic_rank_proved: true
    analytic_sha: 1
    aut_grp_id: '[4,2]'
    aut_grp_label: '4.2'
    aut_grp_tex: C_2^2
    bad_lfactors: '[[2,[1,0,-1]],[13,[1,0,-1]]]'
    bad_primes:
    - 2
    - 13
    class: 676.a
    cond: 676
    disc_sign: 1
    end_alg: Q x Q
    eqn: '[[0,2,2,4,2,2],[1,0,0,1]]'
    g2_inv: '[''10896201253125/562432'',''5912281125/10816'',''-492075/208'']'
    geom_aut_grp_id: '[4,2]'
    geom_aut_grp_label: '4.2'
    geom_aut_grp_tex: C_2^2
    geom_end_alg: Q x Q
    globally_solvable: 1
    has_square_sha: true
    hasse_weil_proved: true
    id: 67
    igusa_clebsch_inv: '[''1620'',''52953'',''29527389'',''71991296'']'
    igusa_inv: '[''405'',''4628'',''-8112'',''-6175936'',''562432'']'
    is_gl2_type: true
    is_simple_base: false
    is_simple_geom: false
    label: 676.a.562432.1
    leading_coeff:
      __RealLiteral__: 0
      data: '0.3201552354797366231840162692222260046864076699129857330'
      prec: 190
    locally_solvable: true
    modell_images:
    - 2.60.2
    - 3.2160.20
    mw_rank: 0
    mw_rank_proved: true
    non_solvable_places: []
    num_rat_pts: 4
    num_rat_wpts: 0
    real_geom_end_alg: R x R
    real_period:
      __RealLiteral__: 0
      data: '6.7232599450744690868643416537'
      prec: 100
    regulator:
      __RealLiteral__: 0
      data: '1.0'
      prec: 10
    root_number: 1
    st_group: SU(2)xSU(2)
    st_label: 1.4.B.1.1a
    st_label_components:
    - 1
    - 4
    - 1
    - 1
    - 1
    - 0
    tamagawa_product: 21
    torsion_order: 21
    torsion_subgroup: '[21]'
    two_selmer_rank: 0
    two_torsion_field:
    - 6.0.86528.1
    - - 1
      - 0
      - 2
      - -2
      - 2
      - 0
      - 1
    - - 6
      - 3
    - false
- - factorsQQ_base:
    - - 1.1.1.1
      - - 0
        - 1
      - -1
    - - 1.1.1.1
      - - 0
        - 1
      - -1
    factorsQQ_geom:
    - - 1.1.1.1
      - - 0
        - 1
      - -1
    - - 1.1.1.1
      - - 0
        - 1
      - -1
    factorsRR_base:
    - RR
    - RR
    factorsRR_geom:
    - RR
    - RR
    fod_coeffs:
    - 0
    - 1
    fod_label: 1.1.1.1
    id: 64978
    is_simple_base: false
    is_simple_geom: false
    label: 676.a.562432.1
    lattice:
    - - - 1.1.1.1
        - - 0
          - 1
        - - 0
      - - - 1.1.1.1
          - - 0
            - 1
          - -1
        - - 1.1.1.1
          - - 0
            - 1
          - -1
      - - RR
        - RR
      - - 2
        - -1
      - G_{3,3}
    ring_base:
    - 2
    - -1
    ring_geom:
    - 2
    - -1
    spl_facs_coeffs:
    - - - 217
      - - 6371
    - - - 129
      - - -2241
    spl_facs_condnorms:
    - 26
    - 26
    spl_facs_labels:
    - 26.a2
    - 26.b2
    spl_fod_coeffs:
    - 0
    - 1
    spl_fod_gen:
    - 0
    spl_fod_label: 1.1.1.1
    st_group_base: G_{3,3}
    st_group_geom: G_{3,3}
- - id: 66
    label: 676.a.562432.1
    mw_gens:
    - - - - 3
          - 2
        - - 1
          - 2
        - - 1
          - 1
      - - - -3
          - 4
        - - 1
          - 4
        - - 0
          - 1
        - - 0
          - 1
    mw_gens_v: true
    mw_heights: []
    mw_invs:
    - 21
    num_rat_pts: 4
    rat_pts:
    - - 0
      - -1
      - 1
    - - 0
      - 0
      - 1
    - - 1
      - -1
      - 0
    - - 1
      - 0
      - 0
    rat_pts_v: true
- - conductor: 676
    id: 104
    lmfdb_label: 676.a.562432.1
    modell_image: 2.60.2
    prime: 2
  - conductor: 676
    id: 105
    lmfdb_label: 676.a.562432.1
    modell_image: 3.2160.20
    prime: 3
- - id: 131755
    label: 676.a.562432.1
    p: 2
    tamagawa_number: 7
  - cluster_label: cc2_1~2c2_1~2c2_1~2_0
    id: 131756
    label: 676.a.562432.1
    p: 13
    tamagawa_number: 3
- - id: 67
    label: 676.a.562432.1
    plot: 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nn8wW2vLy80w8PnAIKYATr2FFq3Vo6eFCaNs12GgAIH7m5udq9e7fat2%2BvmjVrqmbNmlq6dKkmTpyomjVrKiEhQYcOHVLBUVMt7N69OzDql5iYeMxIX0FBgUpLS48ZGawQGxur%2BPj4ShsQDBTACObxHB4F5GYQADh11157rdatW6e1a9cGtssvv1xDhw4NHEdHR2vhwoWBn9m5c6fWr1%2BvTp06SZLS0tK0fv167dy5M3DOggULFBsbq/bt21f7ewKO5PH7qQWRbN8%2BKSnJjAIuWyZ17mw7EQCEp65du6pt27Z65plnJEl33HGH5s6dqylTpqhhw4a65557tGfPHuXm5ioqKkplZWVq27atEhIS9Pjjj2vv3r0aMWKE0tPT9eyzz57S7ywsLJTX65XP52M0EFWKEcAIV7/%2B4ZVBXnjBbhYAiCRPP/200tPTNXDgQHXu3Fl16tTRnDlzFBUVJUmKiorSvHnzVKtWLXXu3FkDBw5Uenq6nnjiCcvJAUYAnfDJJ2Zy6NhYaccO6ZxzbCcCAJwKRgARLIwAOiA1VWrXTiopkV5/3XYaAABgGwXQAdwMAgAAjkQBdMTQoVLdutLmzdLixbbTAAAAmyiAjqhb1ywPJ3EzCAAArqMAOuSOO8x%2B1izpBKsQAQAAB1AAHXLppVJamvTDD9LLL9tOAwA4kaysLKWkpCg1NdV2FEQopoFxzLRp0rBhUnKytHWr9ON0VQCAEMQ0MAgWRgAdc%2BONZh7AvDxp3jzbaQAAgA0UQMfUqiXdcos5fv55u1kAAIAdFEAHVcwJ%2BMEH0hdf2M0CAACqHwXQQRdcIPXqZSaEZkoYAADcQwF0VMWUMK%2B%2BKn3/vd0sAACgelEAHdWnj3TeedLevdJbb9lOAwAAqhMF0FFRUYevBXzuObtZAABA9aIAOuzXv5ZiYqRPPpHWrLGdBgAAVBcKoMOaNJFuuMEcMwoIAIA7KICOGzXK7N98U9qzx24WAABQPSiAjktLk9q2NXcCv/aa7TQAAIm1gBF8rAUMvfyyNHKk1KKFtGUL6wMDQKhgLWAECyOA0JAhUoMG0tat0vz5ttMAAIBgowBCdepIN99sjrOy7GYBAADBRwGEJOnOOyWPR8rJkf77X9tpAABAMFEAIcmsD9y7tzlmFBAAgMhGAUTAXXeZ/WuvScXFdrMAAIDgoQAioGdP6cILJZ9Pmj7ddhoAABAsFEAE1KhxeGLoSZMkJggCACAyUQBRyYgRUlyctGGDtGSJ7TQAACAYKICopH59adgwc/zss3azAACA4KAA4hgVN4O8%2B660bZvdLAAAoOpRAHGMlBTp2mul8nLpuedspwEA97AWMIKNtYBxXO%2B9Jw0YIDVsKOXlmdVCAADVi7WAESyMAOK4%2BvSRWrSQ9u6V3njDdhoAAFCVKIA4rqiow1PCTJzIlDAAAEQSCiBO6JZbzFe/69YxJQwAAJGEAogTatDg8JQwEyfazQIAAKoOBRAnNXq02b/3nvTVV1ajAACAKkIBxEmlpEjdu5spYSZNsp0GAABUBQog/qff/tbsX35ZKi62mwUAAJw9CiD%2Bp969pYsuknw%2BaepU22kAAMDZogDif6pR4/C1gBMnmq%2BDAQBA%2BKIA4pSMGCHFx0ubNkkffGA7DQAAOBsUQJySevXMvICSNGGC3SwAEOlYCxjBxlrAOGVffildeKFZFeQ//5FatbKdCAAiG2sBI1gYAcQpO/98qX9/c8zE0AAAhC8KIE7L2LFm//rr0p49drMAAIAzQwHEabnySqltW%2BngQWnyZNtpAADAmaAA4rR4PFJmpjmeNEkqLbWbBwAAnD4KIE7b4MFSQoK0Y4f09tu20wAAgNNFAcRpi42V7rjDHD/9tN0sAADg9FEAcUbuuEOKiZE%2B%2BURavtx2GgCoWs8//7wuvfRSxcfHKz4%2BXmlpaZo/f37g%2BZKSEo0ePVqNGjVSXFyc%2Bvfvr%2B3bt1d6ja%2B//lr9%2BvVTXFycGjVqpDFjxujQoUPV/VaA46IA4ow0aSLddJM5ZhQQQKRp1qyZHnnkEa1Zs0Zr1qzRNddcowEDBmjDhg2SpMzMTM2aNUvZ2dlatmyZiouL1bdvX5WVlUmSysrK1KdPH%2B3fv1/Lli1Tdna23n77bd1999023xYQwETQOGPr1kmXXmrWCv7yS6l5c9uJACB4GjZsqMcff1w33HCDGjdurGnTpmnQoEGSpG%2B%2B%2BUbJycl6//331bNnT82fP199%2B/ZVXl6ekpKSJEnZ2dkaMWKEdu/efcqTOjMRNIKFEUCcsTZtpGuvlcrLmRgaQOQqKytTdna29u/fr7S0NOXm5qq0tFQ9evQInJOUlKTWrVtr%2BY/XxKxYsUKtW7cOlD9J6tmzp0pKSpSbm1vt7wE4GgUQZ6ViYuiXX5aKiuxmAYCqtG7dOtWtW1exsbH6zW9%2Bo1mzZiklJUX5%2BfmKiYlRgwYNKp2fkJCg/Px8SVJ%2Bfr4SEhIqPd%2BgQQPFxMQEzjmekpISFRYWVtqAYKAA4qz07i21bCkVFkqvvmo7DQBUnZYtW2rt2rVauXKl7rjjDg0fPlz/%2Bc9/Tni%2B3%2B%2BXx%2BMJ/PnI4xOdc7Tx48fL6/UGtuTk5LN7E8AJUABxVmrUODwx9IQJ0o/XPwNA2IuJidGFF16oyy%2B/XOPHj9dll12mCRMmKDExUYcOHVJBQUGl83fv3h0Y9UtMTDxmpK%2BgoEClpaXHjAweady4cfL5fIEtLy%2Bv6t8YIAogqsCwYVLDhtLWrdK779pOAwDB4ff7VVJSovbt2ys6OloLFy4MPLdz506tX79enTp1kiSlpaVp/fr12rlzZ%2BCcBQsWKDY2Vu3btz/h74iNjQ1MPVOxAcFAAcRZq1NH%2Bs1vzDFTwgCIBH/84x/1z3/%2BU1999ZXWrVun%2B%2B%2B/X0uWLNHQoUPl9Xp166236u6779aHH36ozz77TDfddJPatGmjbt26SZJ69OihlJQUZWRk6LPPPtOHH36oe%2B65RyNHjqTUISRQAFElRo2SoqOlZcuk1attpwGAs7Nr1y5lZGSoZcuWuvbaa7Vq1Srl5OSoe/fukqSnn35a6enpGjhwoDp37qw6depozpw5ioqKkiRFRUVp3rx5qlWrljp37qyBAwcqPT1dTzzxhM23BQQwDyCqzLBh0rRpZq3gN9%2B0nQYAwh/zACJYGAFElamYEmbmTInrlgEACF0UQFSZdu2krl3NncDPPms7DQAAOBEKIKrU735n9i%2B9xMTQAACEKgogqlSfPtLFF0s%2Bn/Taa7bTAACA46EAokodOTH0M88wMTQAAKGIAogqN3w4E0MDABDKKICockdODP3UU3azAEA4ysrKUkpKilJTU21HQYRiHkAExc6dUvPmUmmptGqVdMUVthMBQPhhHkAECyOACIqmTaUhQ8wxo4AAAIQWCiCCpmJi6L//Xdq2zW4WAABwGAUQQXPZZdI11zAxNAAAoYYCiKCqmBh68mQmhgYAIFRQABFUvXtLLVtKhYXSK6/YTgMAACQKIIKsRo3D1wJOnMjE0AAAhAIKIIIuI0M65xwzMfTs2bbTAAAACiCC7siJoZ9%2B2m4WAABAAUQ1GTVKio6WPv5YWr3adhoAANxGAUS1aNpUGjzYHDMxNAAAdlEAUW0qbgaZOVPKy7ObBQBCGWsBI9hYCxjVqmtXaelS6b77pEcesZ0GAEIbawEjWBgBRLWqGAV86SVp/367WQAAcBUFENWqb1/pggukggJp6lTbaQAAcBMFENUqKkoaM8YcT5gglZfbzQMAgIsogKh2N98sxcdLmzZJH3xgOw0AAO6hAKLa1asn3XqrOZ4wwW4WAABcRAGEFXfdJXk8ZgTw889tpwEAwC0UQFhx/vlSv37meOJEu1kAAHANBRDW/Pa3Zj91qrRvn90sAAC4hAIIa66%2BWrrkEjMf4Guv2U4DAIA7KICwxuORRo82x1lZTAkDAEB1oQDCqptukurXl774QsrJsZ0GAEIDawEj2FgLGNb97nfS009LvXtL779vOw0AhA7WAkawMAII6%2B680%2BxzcsxIIAAACC4KIKy78EKpVy/J75deeMF2GgAAIh8FECGhYhTw1Vel77%2B3mwUAgEhHAURIuO466bzzpL17pZkzbacBACCyUQAREqKipNtuM8cvvmg3CwAAkY4CiJBxyy2mCH78sbRhg%2B00AABELgogQkbTplL//uZ48mS7WQAAiGQUQISUkSPNfto0qaTEbhYAACIVBRAhpUcPqVkzczPI7Nm20wAAEJkogAgpUVHSiBHm%2BLXXrEYBACBiUQARcoYPN/uFC6UdO%2BxmAQAbWAsYwcZawAhJP/%2B5tGyZ9Nhj0r332k4DAHawFjCChRFAhKSMDLOfPt1uDgAAIhEFECHpxhul6Gjp3/9mTkAAAKoaBRAhqUEDqVcvc5ydbTcLAACRhgJYDTZskDIzpa%2B/tp0kvAwaZPYzZ0pcqQoAQNWhAFaDxx6TJkyQzj9fuukmae1a24nCQ79%2BUkyMtGmTtHGj7TQAAEQOCmA1yMiQrrlGKiuTZsyQ2rWTuneXPviAka2TiY%2BXrr3WHL/7rt0sANwyfvx4paamql69emrSpInS09O1adOmSueUlJRo9OjRatSokeLi4tS/f39t37690jlff/21%2BvXrp7i4ODVq1EhjxozRoUOHqvOtAMdFAawG3bpJH34orVkjDR4s1aghLVpkrnG79FIz4THLnh3fgAFmP2eO3RwA3LJ06VKNGjVKK1eu1MKFC/XDDz%2BoR48e2r9/f%2BCczMxMzZo1S9nZ2Vq2bJmKi4vVt29flZWVSZLKysrUp08f7d%2B/X8uWLVN2drbefvtt3X333bbeFhDAPIAWfPWV9Mwz0iuvSMXF5rGEBGnUKOk3v5EaN7YaL6Tk5UnnnWdK87ffSg0b2k4EwEXffvutmjRpoqVLl%2BrKK6%2BUz%2BdT48aNNW3aNA368YLlb775RsnJyXr//ffVs2dPzZ8/X3379lVeXp6SkpIkSdnZ2RoxYoR27959SvP6MQ8ggoURQAt%2B8hNTAPPypEcfNWvf7tol/b//JyUnS7feaqY/gfk8WrWSysulxYttpwHgKp/PJ0lq%2BOO/QnNzc1VaWqoePXoEzklKSlLr1q21fPlySdKKFSvUunXrQPmTpJ49e6qkpES5ubnVmB44FgXQovr1pd//XvryS3NtYGqq%2BSr41Velyy4z1w3Onm2uHXRZxXWAS5ZYjQHAUX6/X7/73e/UpUsXtW7dWpKUn5%2BvmJgYNWjQoNK5CQkJys/PD5yTkJBQ6fkGDRooJiYmcM7RSkpKVFhYWGkDgoECGAKio6UhQ6RVq8zyZzfeKEVFmRGv66%2BXLrjA3Em8Z4/tpHZceaXZL1tmNwcAN911113697//rTfffPN/nuv3%2B%2BXxeAJ/PvL4ROccafz48fJ6vYEtOTn5zIMDJ0EBDCEej9S5s/S3v0lbt0r33Weuedu2zRw3aybdcovk2jcHaWlmv26ddPCg3SwA3DJ69Gi99957Wrx4sZo1axZ4PDExUYcOHVJBQUGl83fv3h0Y9UtMTDxmpK%2BgoEClpaXHjAxWGDdunHw%2BX2DLy8ur4ncEGBTAEJWcLD3yiLR9u7lZpF076fvvzR3Dl18udeggTZniRiE691xzY0xZmbR%2Bve00AFzg9/t111136Z133tFHH32kFi1aVHq%2Bffv2io6O1sKFCwOP7dy5U%2BvXr1enTp0kSWlpaVq/fr127twZOGfBggWKjY1V%2B/btj/t7Y2NjFR8fX2kDgoG7gMOE3y%2BtWCFlZZmVMUpLzeMNGkjDhkm3325ulohU11xjvhJ//XXzfgEgmO6880698cYbevfdd9WyZcvA416vV7Vr15Yk3XHHHZo7d66mTJmihg0b6p577tGePXuUm5urqKgolZWVqW3btkpISNDjjz%2BuvXv3asSIEUpPT9ezzz57Sjm4CxjBwghgmPB4pE6dzM0i27dLDz9s7iYuKDCrjKSkmGvlpk2LzFHBCy4w%2B61b7eYA4Ibnn39ePp9PXbt2VdOmTQPbW2%2B9FTjn6aefVnp6ugYOHKjOnTurTp06mjNnjqKioiRJUVFRmjdvnmrVqqXOnTtr4MCBSk9P1xNPPGHrbQEBjACGsbIyacEC6cUXpblzD98t7PVKQ4ea6WTatTPlMdw9%2BKD00ENmpPOFF2ynAYDqwQgggoURwDAWFSX17m2mitm2TfrLX8yooM8nPfec1L69KYATJphJlMNZxUwL%2B/bZzQEAQCSgAEaIc8%2BV/vQ0Yz4fAAAR0ElEQVQn6YsvzKjgoEFSTIz0r39JmZlSUpKUni698054Ljv34zcqzs%2BJCABAVaAARpgaNaTu3aXsbGnnTmnSJDMS%2BMMP0rvvSr/8pdS0qfkq9R//MCtshIOK5Tfj4uzmAAAgElAAI1jDhmZ94TVrzPQpv/%2B9GSksKJBeekm66iqpeXPp3nvNOaF8Nei2bWbftKndHAAARAIKoCMuucSsO7xtm7RokXTzzVJ8vLmj%2BIknzDJ0F15oJpxevTr0yuDq1Wbfpo3dHAAARALuAnbY999L8%2Bebr4vnzKk8fUxyslmGbsAA6ec/N8vV2fLll2YaGI9H2rGDUUAA7uAuYAQLBRCSzDV28%2BZJf/%2B79P77h6%2B5k8y0Mr16SX36SD17Sk2aVG%2B2ESPMBNA9ekgffFC9vxsAbMjKylJWVpbKysq0efNmCiCqHAUQxzh4UFq40EwvM3fusVPI/Oxnpoxde62ZnLpOneBlee01s/6xZFZC6dgxeL8LAEINI4AIFgogTqqsTFq1yowOvv%2B%2BtHZt5eejo6UrrpC6dDFlsEMH6QRrnJ%2BW/fvNvIaPPmr%2B/Mc/Sn/969m/LgCEEwoggoUCiNOSn29GBxctkj76yNxEcrTkZKltW%2Bmyy8wSdRdfbK7h83pPvirJgQPmbuQ5c8xXvhUjj5mZ0pNPmiluAMAlFEAECwUQZ8zvNzdo/OMf0scfm69oN2488R3EdetKiYnSOeeY4%2BhoMz9hUZEplnl5leclPP98U/zS06vn/QBAqKEAIlgogKhSRUXSZ5%2BZr4rXrTOFcMsWaffuU/v5xETp6qulG2%2BU%2BvWTatYMbl4ACGUUQAQLBRDV4sAB83Xxrl3S3r3mGr/SUrPEW9265rrB8883dxif7GtiAHAJBRDBwvgKqkWdOuZawIsvtp0EAABwWT0AAIBjKIAAAACOoQACAAA4hgIIAADgGAogAAAhJisrSykpKUpNTbUdBRGKaWAAAAhRTAODYGEEEAAAwDEUQAAAAMdQAAEAABxDAQQAAHAMBRAAAMAxFEAAAADHUAABAAAcQwEEAABwDAUQAADAMRRAAAAAx1AAAQAIMawFjGBjLWAAAEIUawEjWBgBBAAAcAwFEAAAwDEUQAAAAMdQAAEAABxDAQQAAHAMBRAAAMAxFEAAAADHUAABAAAcQwEEAABwDAUQAADAMRRAAABCDGsBI9hYCxgAgBDFWsAIFkYAAQAAHEMBBAAAcAwFEAAAwDEUQAAAAMdQAAEAABxDAQQAAHAMBRAAgKP84x//UL9%2B/ZSUlCSPx6PZs2dXet7v9%2BvBBx9UUlKSateura5du2rDhg2VzikoKFBGRoa8Xq%2B8Xq8yMjK0b9%2B%2B6nwbwAlRAAEAOMr%2B/ft12WWXadKkScd9/rHHHtNTTz2lSZMmafXq1UpMTFT37t1VVFQUOGfIkCFau3atcnJylJOTo7Vr1yojI6O63gJwUkwEDQDASXg8Hs2aNUvp6emSzOhfUlKSMjMzdd9990mSSkpKlJCQoEcffVS33367Nm7cqJSUFK1cuVIdOnSQJK1cuVJpaWn6/PPP1bJly1P63UwEjWBhBBAAgNOwdetW5efnq0ePHoHHYmNjddVVV2n58uWSpBUrVsjr9QbKnyR17NhRXq83cM7xlJSUqLCwsNIGBAMFEACA05Cfny9JSkhIqPR4QkJC4Ln8/Hw1adLkmJ9t0qRJ4JzjGT9%2BfOCaQa/Xq%2BTk5CpMDhxGAQQA4Ax4PJ5Kf/b7/ZUeO/r5451ztHHjxsnn8wW2vLy8qgsMHKGm7QAAAISTxMRESWaUr2nTpoHHd%2B/eHRgVTExM1K5du4752W%2B//faYkcMjxcbGKjY2tooTA8diBBAAgNPQokULJSYmauHChYHHDh06pKVLl6pTp06SpLS0NPl8Pn3yySeBc1atWiWfzxc4B7CJEUAAAI5SXFys//73v4E/b926VWvXrlXDhg113nnnKTMzUw8//LAuuugiXXTRRXr44YdVp04dDRkyRJLUqlUr9erVSyNHjtSLL74oSbrtttvUt2/fU74DGAgmpoEBAOAoS5Ys0dVXX33M48OHD9eUKVPk9/v10EMP6cUXX1RBQYE6dOigrKwstW7dOnDu3r17NWbMGL333nuSpP79%2B2vSpEmqX7/%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%2Bpk9S2re10oY8CCABAqPn4Y%2BnBB6WPPjJ/Tk%2BX/u//TLuJMH6/dOCAVFBgCt3eveZ4zx5zvGfP4e277w5ve/aYnz3a449TAE8FBRAAgFAyc6Y0aFDldrN4sfTzn5vnfvELe9mOUl4u7d8vFRWZrbDQbEVFks9njiv2%2B/aZ4337Km8FBVJp6ZlnqF9fatxYatLEbC1aVN37i2RMBA0AQKgoLZW8XungQUlSoSSvJJ%2BkeEmKizOtqeaJx2/8fvP16PffSyUllfcV28GDx24HDhzeHzhgil3Fvrj42H3FcVWpWVNq0EBq2LDyds45ZmvU6PC%2BUSNT%2Bs45R4qOrroMLmEE8Cz4/X4VFRXZjgEACHMrV0ozZkjnr5yusT%2BWP8kUwCP32r9ff2k5VQvq3qCSEtMXS0p0zHF1D%2B14PFK9elLdumZfr54UH2%2B2I4/r1zd7r9ds9eubzes1P%2BvxnN7vrSivZ6pevXrynO4vjRCMAJ6FiiV6AABA%2BHF5iT0K4Fk40xHA1NRUrV69usrzBON1CwsLlZycrLy8vCr/H0k4fQ7Bel0%2B3%2BC/Np9xcF%2BXz/fsX9fvl%2B69V5o8WfqD/qpxeizwXKGkZEl5%2BvErYEkP6U96SvdWReQT8niOP1LXsOHhr2mP/Dq2USNz/V2dOqH3%2BZ6MyyOAfAV8Fjwezxn9By8qKioo/%2BII1utKUnx8fJW/drh9Dny%2BwX3dYL82nzGfbyi/7ksvmU35Y6Wmjx3zfPyPm9/j0agVY3RL3XgdOnT4K99Tudav4tq%2Biuv6Fi1aqTZtOqqoyFzPV3ETR1mZKaU%2Bn9lOR926UknJAvXtG6%2BmTaVzzzVbs2ZmS06WkpJOegnjCQXz76%2BLKIAWjBo1KqxeN1jC7XPg8w3u6wb7tYMh3D5jPt8weN3ERGnwYCk7%2B7hPe266Sed3aHz2v0dSVlauRo3qWOkxv9%2BUxYryV3GX7r59h6doOXJKloq593bvNuWzuFiSmuuf/zzx742KMmXwJz8x2/nnm%2B2CC6QLLzSjiccblAu3v7%2Bhjq%2BAcVIV1zm6fJ1EMPH5Bh%2BfcXDx%2BQbBDz9Id94pvfqqCsvK5JW0r0YNeUeOlCZNOrPhsyDz%2B80IYn6%2B2XbulL75xmw7dkjbt0t5eeb4f0354vVKF18stWwp/fSnUqtWZrvwQu74rUqh97cIISU2NlZ//vOfFRsbaztKROLzDT4%2B4%2BDi8w2CmjXN98F//atKZ8%2BWbrtNhzZsMG0oRFVcMxgfb8rbiZSVmYK4bZv01VfS1q3Sl1%2Ba7b//NUXR55NWrzbbkaKjzUfQpo3ZLrvMTPicmHj6dw%2BDEUAAAEKWayOsBw%2BaIrh5s7Rpk/T559LGjWY70ZyDTZpIP/uZ1L69dPnlUseOphTi5CiAAACEKNcK4ImUl0tffy2tXy%2BtWyf9%2B9/Sv/5lSmJ5eeVzx46VnnrKTs5wwlfAAAAgpNWocfimkb59Dz9%2B4IAphLm5Zlu92owA4n9jBBAAgBDFCCCCpYbtAAAAAKheFECcVHFxse666y41a9ZMtWvXVqtWrfT888/bjhVRNm7cqP79%2B8vr9apevXrq2LGjvv76a9uxIs7tt98uj8ejZ555xnaUiFFaWqr77rtPbdq0UVxcnJKSkjRs2DB98803tqMB%2BB8ogDipsWPHKicnR9OnT9fGjRs1duxYjR49Wu%2B%2B%2B67taBHhiy%2B%2BUJcuXfTTn/5US5Ys0b/%2B9S898MADqlWrlu1oEWX27NlatWqVkpKSbEeJKAcOHNCnn36qBx54QJ9%2B%2Bqneeecdbd68Wf3797cdLexlZWUpJSVFqamptqMgQnENIE6qdevWGjRokB544IHAY%2B3bt9d1112nv/zlLxaTRYbBgwcrOjpa06ZNsx0lYu3YsUMdOnTQBx98oD59%2BigzM1OZmZm2Y0Ws1atX64orrtC2bdt03nnn2Y4T9rgGEMHCCCBOqkuXLnrvvfe0Y8cO%2Bf1%2BLV68WJs3b1bPnj1tRwt75eXlmjdvni6%2B%2BGL17NlTTZo0UYcOHTR79mzb0SJGeXm5MjIydO%2B99%2BqSSy6xHccJPp9PHo9H9evXtx0FwElQAHFSEydOVEpKipo1a6aYmBj16tVLzz33nLp06WI7WtjbvXu3iouL9cgjj6hXr15asGCBrr/%2Bev3iF7/Q0qVLbceLCI8%2B%2Bqhq1qypMWPG2I7ihO%2B//15/%2BMMfNGTIEEargBBHAUTAjBkzVLdu3cD2z3/%2BUxMnTtTKlSv13nvvKTc3V08%2B%2BaTuvPNOLVq0yHbcsHP057tp0yZJ0oABAzR27Fi1bdtWf/jDH9S3b1%2B98MILltOGn6M/36VLl2rChAmaMmWKPKwTVSWO99%2BICqWlpRo8eLDKy8v13HPPWUwJ4FRwDSACioqKtGvXrsCfzz33XHm9Xs2aNUt9%2BvQJPP7rX/9a27dvV05Ojo2YYevoz7dx48Zq1KiR/vznP%2BtPf/pT4PH77rtPy5Yt08cff2wjZtg6%2BvOdOXOm7r//ftWocfjfuWVlZapRo4aSk5P11VdfWUgZ3o7334jatWurtLRUAwcO1JdffqmPPvpI55xzjsWUkcXv96uoqEj16tXjHzKoUqwEgoB69eqpXr16gT8XFhaqtLS00v%2BBSlJUVJTKj157B//T0Z%2BvJKWmpgZGAits3rxZzZs3r85oEeHoz/e2225Tv379Kp3Ts2dPZWRk6Oabb67ueBHheH%2BHK8rfli1btHjxYspfFfN4PHydjqCgAOKE4uPjddVVV%2Bnee%2B9V7dq11bx5cy1dulRTp07VUyy0WCXuvfdeDRo0SFdeeaWuvvpq5eTkaM6cOVqyZIntaGHvnHPOOaaMREdHKzExUS1btrSUKrL88MMPuuGGG/Tpp59q7ty5KisrU35%2BviSpYcOGiomJsZwQwInwFTBOKj8/X%2BPGjdOCBQu0d%2B9eNW/eXLfddpvGjh3L1xFV5NVXX9X48eO1fft2tWzZUg899JAGDBhgO1ZE%2BslPfsI0MFXoq6%2B%2BUosWLY773OLFi9W1a9fqDQTglFEAAQAAHMNdwAAAAI6hAAIAADiGAggAAOAYCiAAAIBjKIAAAACOoQACAAA4hgIIAADgGAogAACAYyiAAAAAjqEAAgAAOIYCCAAA4BgKIAAAgGMogAAAAI6hAAIAADiGAggAAOAYCiAAAIBjKIAAAACOoQACAAA4hgIIAADgGAogAACAYyiAAAAAjqEAAgAAOIYCCAAA4BgKIAAAgGMogAAAAI6hAAIAADiGAggAAOAYCiAAAIBjKIAAAACOoQACAAA4hgIIAADgGAogAACAYyiAAAAAjqEAAgAAOIYCCAAA4BgKIAAAgGMogAAAAI6hAAIAADiGAggAAOAYCiAAAIBjKIAAAACOoQACAAA4hgIIAADgGAogAACAYyiAAAAAjqEAAgAAOIYCCAAA4Jj/D3NBQ7rLRaTTAAAAAElFTkSuQmCC
label_cols:
- label
- label
- label
- lmfdb_label
- label
- label
labels:
- 676.a.562432.1
- 676.a.562432.1
- 676.a.562432.1
- 676.a.562432.1
- 676.a.562432.1
- 676.a.562432.1
tables:
- g2c_curves
- g2c_endomorphisms
- g2c_ratpts
- g2c_galrep
- g2c_tamagawa
- g2c_plots
timestamp: '2024-04-18T11:51:55.377466'