data:
- - Lhash: '1835966171918955607'
    abs_disc: 8910
    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,-2]],[3,[1,2,1]],[5,[1,-3,7,-5]],[11,[1,5,15,11]]]'
    bad_primes:
    - 2
    - 3
    - 5
    - 11
    class: 990.a
    cond: 990
    disc_sign: 1
    end_alg: Q x Q
    eqn: '[[0,3,4,7,4,3],[0,1,1]]'
    g2_inv: '[''364007458703857/8910'',''4713906106372/4455'',''-57404054'']'
    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: 155
    igusa_clebsch_inv: '[''3268'',''252577'',''318023313'',''1140480'']'
    igusa_inv: '[''817'',''17288'',''-766260'',''-231227341'',''8910'']'
    is_gl2_type: true
    is_simple_base: false
    is_simple_geom: false
    label: 990.a.8910.1
    leading_coeff:
      __RealLiteral__: 0
      data: '0.3859335527311484550245760450697409080045574838549403946'
      prec: 190
    locally_solvable: true
    modell_images:
    - 2.180.3
    - 3.90.1
    mw_rank: 0
    mw_rank_proved: true
    non_solvable_places: []
    num_rat_pts: 2
    num_rat_wpts: 2
    real_geom_end_alg: R x R
    real_period:
      __RealLiteral__: 0
      data: '6.1749368436983752803932167211'
      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: 4
    torsion_order: 8
    torsion_subgroup: '[2,4]'
    two_selmer_rank: 2
    two_torsion_field:
    - 4.0.193600.4
    - - 146
      - -32
      - 33
      - -2
      - 1
    - - 4
      - 2
    - true
- - 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: 54020
    is_simple_base: false
    is_simple_geom: false
    label: 990.a.8910.1
    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:
    - - - 241
      - - -3689
    - - - 1057
      - - 34255
    spl_facs_condnorms:
    - 15
    - 66
    spl_facs_labels:
    - 15.a6
    - 66.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: 154
    label: 990.a.8910.1
    mw_gens:
    - - - - 1
          - 1
        - - 2
          - 3
        - - 1
          - 1
      - - - 1
          - 2
        - - -1
          - 6
        - - 0
          - 1
        - - 0
          - 1
    - - - - 1
          - 1
        - - 1
          - 3
        - - 1
          - 1
      - - - 0
          - 1
        - - 0
          - 1
        - - 0
          - 1
        - - 0
          - 1
    mw_gens_v: true
    mw_heights: []
    mw_invs:
    - 2
    - 4
    num_rat_pts: 2
    rat_pts:
    - - 0
      - 0
      - 1
    - - 1
      - 0
      - 0
    rat_pts_v: true
- - conductor: 990
    id: 246
    lmfdb_label: 990.a.8910.1
    modell_image: 2.180.3
    prime: 2
  - conductor: 990
    id: 247
    lmfdb_label: 990.a.8910.1
    modell_image: 3.90.1
    prime: 3
- - id: 171772
    label: 990.a.8910.1
    p: 2
    tamagawa_number: 1
  - cluster_label: c1c2c2_1_1_-1
    id: 171773
    label: 990.a.8910.1
    p: 3
    tamagawa_number: 4
  - cluster_label: c3c2_1~2_0
    id: 171774
    label: 990.a.8910.1
    p: 5
    tamagawa_number: 1
  - cluster_label: c3c2_1~2_0
    id: 171775
    label: 990.a.8910.1
    p: 11
    tamagawa_number: 1
- - id: 155
    label: 990.a.8910.1
    plot: 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wGkIgABc59Ahac0aX9j7/HPT43e%2Bli2llBQT9Pr0Mc/t8cgtgGBAAAQQ1M6eNbdy16zxhb6ybuXWr2%2Be10tJ8TVG5AIIVgRAAEHDsqR9%2B6TVq32Bb%2BPG0mvk1qoldeliQl7fvuY1Pp7n9gC4BwHQYTwejzwejwrLmkEWcJkzZ8ztW2/gW71aOm9VRklmvr2%2BfX2td28pKirw9QJATcE8gA7FPIBwo/37TcjLyDCvGzaYiZdLCguTevSQ%2BvUzYa9fP6ltW6ZgAYCS6AEEUCMVFJgVNDIyfG3//tLHNW9uQp63JSVJdeoEvl4AcBICIIAaITvb9OqtXCmtWmXm3Tt/RY1atcyce/37%2B1qbNvTuAcDFIgACCDjLMiNxV60ygW/lSmnLFrO9pIYNTa%2BeN%2Bz16WNG6wIAKocACKDaFRWZgLdypbRihXk9cKD0ce3bS6mpvhYfb3r9AABViwAIoMrl5Unr10vLl5vAl5EhnTzpf0xoqFkv94orTOvf3zzPBwCofgRAAJX2/ffm%2Bb3ly0377DMzAXNJ9eqZ27lXXCENGGDm3qtXz556AcDtCIAALlp2trmNu3y5tGyZ6e07fzqWJk1M0PO2Hj3MFC0AAPvx1zGAH/Tdd%2BZW7rJlpm3aZJ7rKykuTho40LQBA6TOnRmdCwA1FQEQQCknTpjevaVLTfvii9IjdNu3lwYNMm3gQDPZMgDAGQiAAJSdbQLfkiWmlRX4OnWSBg/2Bb6WLW0pFQBQBQiAgAt9/715hm/JEunTT80zfOff0vUGviFDTOhjhC4ABA8CoMN4PB55PB4VFhbaXQocJD9fWrPGhL1PPzW/LijwP6ZDBxP2hgwxwY/ABwDBK8Syzr/RAyfIyclRdHS0srOz1aBBA7vLQQ1TVCR9%2BaW0eLH0ySfm9u75y6q1bi1deaVpQ4ZIrVrZUysAIPDoAQSCxL59Unq6aZ9%2BKh075r%2B/WTNf4LvySulHP2KULgC4FQEQcKjsbPMMX3q6tGiRtHu3//569cyze0OHSlddJXXtSuADABgEQMAhCguldetM2Fu0yDzHV/JR0NBQs7rG0KGmpaRI4eH21QsAqLkIgEANduCA9PHHpi1ebObnK6lDB2n4cGnYMDNwIzraljIBAA5DAARqkLw8Mz3LRx9JH34obd3qv79hQ3M7d8QIE/zatLGnTgCAsxEAAZtlZpqwt3ChGbH7/fe%2BfbVqScnJJvBdfbX5NevpAgAqi39KgAArKJBWr5Y%2B%2BMC083v5mjf3Bb5hw6TLLrOnTgBA8CIAAgFw7Jjp5Xv/ffM8X3a2b1%2BtWlK/ftI115jWo4fZBgBAdSEAAtXAsqRt26T33jNt9Wr/tXWbNDE9fNdea57la9zYvloBAO5DAASqSEGBtGKF9O67pu3d67%2B/WzfpuutM69PHTNsCAIAdCIBAJeTmmhG7//mPeZ7v5EnfvogIs%2BLG9deb0BcXZ1%2BdAACURAB0GI/HI4/Ho8KSMwAjoI4cMT1877xj5ubLz/fta9LEhL0bbjADOOrXt69OAADKE2JZJZ9MglPk5OQoOjpa2dnZatCggd3lBL3MTOntt01btUoqKvLtu/xyadQoaeRIM5iDW7sAgJqOHkCgHLt3S2%2B%2BaULfunX%2B%2B5KSpNGjTfBLSGCNXQCAsxAAgRJ27ZLeeMO0L77wba9VS7riCuknPzGhjxU4AABORgCE6%2B3eLf3736aVDH2hodKQIdJNN5nQFxNjX40AAFQlAiBcKTNTev116bXXpA0bfNvDwsxauzffbEIfq3AAAIIRARCucfSoubX76qtmIIdXaKgJfbfcYp7rY1JmAECwIwAiqH3/vbRggfTKK9KiRZJ39pyQEGngQGnMGOnGG6WmTe2tEwCAQGLF0Soye/ZstWvXTpGRkUpKStKKFSvKPfaFF17QgAED1KhRIzVq1EhDhw7V2rVrA1htcCssNGFv7Fjz3N7tt5t1eAsLzejdP/5R2r9fWrpUuucewh8AwH3oAawCr7/%2BuiZNmqTZs2crNTVVc%2BbM0TXXXKNt27apdevWpY5funSpbrvtNvXv31%2BRkZF68sknNXz4cG3dulUtW7a04RsEh61bpZdekl5%2BWTp0yLe9fXvpZz8zrWNH%2B%2BoDAKCmYCLoKpCSkqJevXrp%2BeefL94WHx%2BvUaNGKS0t7QfPLywsVKNGjfTcc89p3LhxFfpMJoI2vvtOmj9f%2Bsc/pM8/921v3Njc3r39dqlvX%2BbpAwCgJHoAKyk/P1/r16/X1KlT/bYPHz5cGRkZFXqP06dPq6CgQI0vMPogLy9PeXl5xT/n5ORcWsFBoKhI%2BuQTad4883yf97KEhUnXXiuNG2deIyLsrRMAgJqKAFhJx44dU2FhoWLOmyQuJiZGWVlZFXqPqVOnqmXLlho6dGi5x6SlpWnGjBmVqtXpDh6U/v530775xre9Wzfp5z83t3h5ng8AgB9GAKwiIefdY7Qsq9S2sjz55JOaP3%2B%2Bli5dqsjIyHKPmzZtmiZPnlz8c05OjuLi4i69YIcoLJQ%2B%2BkiaM0f64APfGrzR0Sbw3XGH1KuXvTUCAOA0BMBKatKkiUJDQ0v19h05cqRUr%2BD5/vCHP2jmzJlavHixunXrdsFjIyIiFOGie5qHD0t/%2B5s0d66ZtNlr4EDprrvM1C116thXHwAATkYArKTw8HAlJSUpPT1do0ePLt6enp6ukSNHlnveU089pccee0wff/yxevfuHYhSazzLkjIypOeek956SyooMNsbN5bGjzfBr3NnW0sEACAoEACrwOTJkzV27Fj17t1b/fr109y5c5WZmal77rlHkjRu3Di1bNmyeETwk08%2Bqd/%2B9rd69dVX1bZt2%2BLew/r166t%2B/fq2fQ%2B7nD1rRvL%2B5S/Sxo2%2B7X37Svfea5Zlo7cPAICqQwCsArfeequOHz%2BuRx99VIcOHVJiYqIWLlyoNm3aSJIyMzNVq5Zvzu3Zs2crPz9fN910k9/7/P73v9f06dMDWbqtDh2SZs%2BW/vpX6dgxsy0yUvrpT6WJE6WePe2tDwCAYMU8gA7l5HkAN282q3G8%2BqrvNm/r1tJ990l33ilddpm99QEAEOzoAURAWJa0ZIn05JPSxx/7tvfvLz3wgDRqlJnHDwAAVD/%2ByUW1KioykzU/8YS0bp3ZVquWGcX7619LKSn21gcAgBsRAFEtzp0zAzvS0qTt2822OnWk//1fafJk6Uc/src%2BAADcjACIKlVQIL38svT449KePWZbw4bShAnS/fezUgcAADUBARBV4tw5M6jj0Ud9wa9JE9PbN2GC5LBxKgAABDUCICqlqMhM2vzb30o7d5ptTZtKDz9s5vCrV8/e%2BgAAQGkEQIfxeDzyeDwqLCy0uxR98ok0ZYq0fr35uXFjE/wmTiT4AQBQkzEPoEPZOQ/g5s0m6H30kfm5fn0zonfyZG71AgDgBPQAosKOHpUeeUT629/Mrd/ataV77jHbmjWzuzoAAFBRBED8oIICs2Tb738vZWebbTfeKM2aJbVvb29tAADg4hEAcUErVpgl2rZsMT/37Ck9%2B6w0YIC9dQEAgEtXy%2B4CUDMdPy7dcYc0cKAJf5ddJs2da1bzIPwBAOBs9ADCj2VJ//639Mtfmmf%2BJOmuu8xSbo0b21sbAACoGgRAFDt82Mzd98475ueEBOmFF6T%2B/e2tCwAAVC1uAUOStGCBlJhowl9YmBnwsWED4Q8AgGBED6DLff%2B9NGmSmdpFkrp1k/75T6l7d3vrAgAA1YceQBfbulVKTjbhLyTETO68di3hDwCAYEcPoEu98or0i19Ip09LLVpIL78sXXml3VUBAIBAoAfQZc6dM0u23X67CX/DhkmbNhH%2BAABwEwKgw3g8HiUkJCg5Ofmiz83Olq67TvrTn8zPv/mN9OGHLOMGAIDbhFiWZdldBC5eTk6OoqOjlZ2drQYNGvzg8QcOSD/%2BsbR5s1S3rhnoceONASgUAADUODwD6AI7d5pbvfv3S82bS%2B%2B/LyUl2V0VAACwCwEwyG3eLA0dKh05InXqJH38sdSmjd1VAQAAOxEAg9iWLWZwx7FjUs%2BeJvw1bWp3VQAAwG4EwCC1Z4/p%2BTt2TOrdW1q0SGrUyO6qAABATcAo4CB09Kg0YoRZ27dbN9PzR/gDAABeBMAgk5cnjRplegDbtjXhr3Fju6sCAAA1CQEwyNx/v5SRIUVHmzn%2Bmje3uyIAAFDTEACDyPz50pw5Zl3f116TOne2uyIAAFATEQCDxP790r33ml8/8oh09dX21gMAAGouAmAQsCzpvvvMUm8pKdLvfmd3RQAAoCYjADpMWWsBv/eeWd2jdm3pxRelMCb3AQAAF8BawA7lXQv4%2BPFs9evXQLt2SVOnSmlpdlcGAABqOnoAHe6NN6Rdu6QmTaRp0%2ByuBgAAOAEB0OGefda8/vrXUoMG9tYCAACcgVvADuW9BSxlq169BjpwQGrY0O6qAACAE9ADGARuvZXwBwAAKo4A6FBFRb5f33abfXUAAADnIQA61JYt5rVuXWngQHtrAQAAzkIAdKKVKxU67WFJ0u9azlN4/imbCwIAAE7CIBAnyc%2BXxoyR3nlHOZLMEBCpQZMmZibolBSbCwQAAE5AD6CTTJ8uvfNO6e3HjknXXy%2BdPh3wkgAAgPMQAJ0iL0%2BaM6f8/UePSvPnB64eAADgWKwaWwmWZSk3Nzcgn5W/davCv/uu%2BOec814lSatXSzffHJB6AABwuqioKIWEhNhdhi14BrASfJMxAwAAp8nOzlYDly6jRQCshIr0ACYnJ2vdunWV/qy8vDyFXn%2B9wj77TJLp%2BYuTtF9S8X%2B6K1dKXbtW%2BrOkqqs70O9dne%2Bfk5OjuLg47d%2B/v1r%2BwnDqNed6B/a9q/P9ueaBfW%2Bud%2BDf//xr7uYeQG4BV0JISMgP/qENDQ2tuj/Yf/qTdOWV0tmzxZsa/Ldp/HgpNbVqPkdVXHcA3zsQ79%2BgQYNqeX%2BnXnOud2DfOxDvzzUP3HtLXG873r%2B6rrmTMAikmk2YMKHq3qxfP2npUmnoUHkXAvkusrmUlibNm1d1n6MqrjuA7x2I968uTr3mXO/Avncg3r%2B6OPWac70D%2B96BeH9wC9ix/vXMVo17IFGd2h/Rjt1N7S7HFbzPfLr5mZFA4noHHtc8sLjegcc19wmdPn36dLuLwMWLbBSi5557SsdPPKJf/CJCUVF2V%2BQOoaGhGjx4sMLCeHoiELjegcc1Dyyud%2BBxzQ16AB3KNwI5W7NnN9C999pdEQAAcAqeAQwC//qX3RUAAAAnIQA6XGiomf/5iy/srgQAADgFAdDhRo40r089ZW8dAADAOQiADnf//eZ1/nxp%2B3Z7a3Ga2bNnq127doqMjFRSUpJWrFhxwePfeustJSQkKCIiQgkJCXrnnXeK9xUUFGjKlCnq2rWr6tWrp9jYWI0bN07ffvttdX8NR6nKa36%2Bu%2B%2B%2BWyEhIXrmmWequmzHqo7rvX37dt1www2Kjo5WVFSU%2Bvbtq8zMzOr6Co5T1df81KlTmjhxolq1aqU6deooPj5ezz//fHV%2BBce7mN%2BDrVu36sYbb1Tbtm3d9/eHBUfKzs62JFnZ2dnWqFGWJVnWtdfaXZVzvPbaa1bt2rWtF154wdq2bZt1//33W/Xq1bP27dtX5vEZGRlWaGioNXPmTGv79u3WzJkzrbCwMGvNmjWWZVnWyZMnraFDh1qvv/66tWPHDmv16tVWSkqKlZSUFMivVaNV9TUv6Z133rG6d%2B9uxcbGWn/605%2Bq%2B6s4QnVc7927d1uNGze2HnroIWvDhg3Wnj17rPfff986fPhwoL5WjVYd1/zOO%2B%2B02rdvby1ZssTau3evNWfOHCs0NNRasGBBoL6Wo1zs78HatWutBx980Jo/f77VvHlzV/39QQB0mOeee86Kj4%2B3OnbsWBwAd%2BywrLAwEwL/8x%2B7K3SGPn36WPfcc4/fts6dO1tTp04t8/hbbrnFuvrqq/22jRgxwhozZky5n7F27VpLUrl/8bhNdV3zAwcOWC1btrS2bNlitWnTxlV/gV9IdVzvW2%2B91br99turvtggUR3XvEuXLtajjz7qd0yvXr2sRx55pIqqDi4X%2B3tQktv%2B/uAWsMNMmDBB27Zt81sjsVMn6de/Nr%2B%2B7z7p5EmbinOI/Px8rV%2B/XsOHD/fbPnz4cGVkZJR5zurVq0sdP2LEiHKPl8wi4yEhIWrYsGHli3a46rrmRUVFGjt2rB566CF16dKl6gt3qOq43kVFRfrggw/UsWNHjRgxQs2aNVNKSooWLFhQPV/CYapfGdmhAAAgAElEQVTrv/ErrrhC7777rg4ePCjLsrRkyRLt2rVLI0aMqPov4XCX8nvgZgTAIPG730kdOkgHD5oQyOyO5Tt27JgKCwsVExPjtz0mJkZZWVllnpOVlXVRx589e1ZTp07VT3/6U9fPNi9V3zWfNWuWwsLC9Ktf/arqi3aw6rjeR44c0alTp/TEE0/o6quv1qJFizR69Gj95Cc/0bJly6rnizhIdf03/uc//1kJCQlq1aqVwsPDdfXVV2v27Nm64oorqv5LONyl/B64mbunwQ4ideua%2BQBTU82AkCuvlO680%2B6qaraQkBC/ny3LKrXtUo4vKCjQmDFjVFRUpNmzZ1dNsUGiKq/5%2BvXr9eyzz2rDhg0XfA83q8rrXVRkViAfOXKkHnjgAUlSjx49lJGRob/%2B9a8aNGhQVZbuWFX998qf//xnrVmzRu%2B%2B%2B67atGmj5cuX67777lOLFi00dOjQqi0%2BSFzs74Fb0QMYRFJSpMceM7%2BeOFFau9beemqqJk2aKDQ0tNT/ER45cqTU/zl6NW/evELHFxQU6JZbbtHevXuVnp5O799/Vcc1X7FihY4cOaLWrVsrLCxMYWFh2rdvn37961%2Brbdu21fI9nKI6rneTJk0UFhamhIQEv2Pi4%2BMZBazqueZnzpzR//3f/%2Bnpp5/W9ddfr27dumnixIm69dZb9Yc//KF6voiDXcrvgZsRAIPMww9LN9wg5eVJo0ZJ%2B/fbXVHNEx4erqSkJKWnp/ttT09PV//%2B/cs8p1%2B/fqWOX7Rokd/x3vD31VdfafHixbrsssuqvniHqo5rPnbsWH355ZfatGlTcYuNjdVDDz2kjz/%2BuHq%2BiENUx/UODw9XcnKydu7c6XfMrl271KZNmyqs3pmq45oXFBSooKBAtWr5/1MdGhpa3CMLn0v5PXA1u0afoHJKTgNTep9lJSaaUcFduljWd9/ZUGAN550qYN68eda2bdusSZMmWfXq1bO%2B%2BeYby7Isa%2BzYsX6jxlatWmWFhoZaTzzxhLV9%2B3briSee8JuuoaCgwLrhhhusVq1aWZs2bbIOHTpU3PLy8mz5jjVNVV/zsrhtFN%2BFVMf1fvvtt63atWtbc%2BfOtb766ivrL3/5ixUaGmqtWLEi4N%2BvJqqOaz5o0CCrS5cu1pIlS6yvv/7aevHFF63IyEhr9uzZAf9%2BTnCxvwd5eXnWxo0brY0bN1otWrSwHnzwQWvjxo3WV199ZddXCBgCoENdKABalmXt22dZsbEmBPbvb1mnTgW4QAfweDxWmzZtrPDwcKtXr17WsmXLivcNGjTI%2Bp//%2BR%2B/49944w2rU6dOVu3ata3OnTtbb731VvG%2BvXv3WpLKbEuWLAnQN6r5qvKal4UA6K86rve8efOsyy%2B/3IqMjLS6d%2B/OfHTnqeprfujQIWv8%2BPFWbGysFRkZaXXq1Mn64x//aBUVFQXi6zjSxfwelPd396BBgwJfeICFWBbjRZ0oJydH0dHRys7OLvc5s82bpYEDzbQwQ4ZI779vBosAAAB34xnAINa1q/TRR1JUlLRkiXTttdKpU3ZXBQAA7EYADHIpKdLHH5sQuHSpNHSodPy43VUBAAA7EQBdoF8/6ZNPpMaNpc8%2BkwYMkJi1AQAA9yIAukRysrR8udSqlbR9u9S3r7R%2Bvd1VAQAAOxAAHcbj8SghIUHJyckXfW6XLlJGhpSYKB06ZHoC33qrGooEAAA1GqOAHaoio4DLk50tjRljBohI0m9%2BI82YIYWGVkOhAACgxqEH0IWio6X33pP%2Bu5ynHn9cuu46BocAAOAWBECXCguTnn5a%2Bte/pDp1TG9gz57S6tV2VwYAAKobAdDlbr/dhL7LLzfrBg8YID3xhMQykwAABC8CINS9uxkRPGaMVFgoTZsmXXWVCYQAACD4EAAhSWrQQHr1VWnePKlePTNpdNeu5hYxw4QAAAguBEAUCwmR/vd/pY0bzQoi2dnSuHHST34iZWXZXR0AAKgqBECU0qGDtHKl9P/%2Bn1S7trRggZSQIL30Er2BAAAEAwIgyhQWJj3yiPT551KvXtKJE9L48dLw4dKePXZXBwAAKoMAiAvq1s2sH5yWJkVGSosXm5VEHn9cysuzuzoAAHApCID4QWFh0tSp0ubNZnTw2bOmd7B7dyk93e7qAADAxSIAosIuv9wEvpdflmJipJ07zS3hm26SvvnG7uoAAEBFEQAdxuPxKCEhQcnJybZ8fkiI9LOfSTt2SL/8pVSrlvTWW1J8vPS730nff29LWQAA4CKEWBbjOp0oJydH0dHRys7OVoMGDWyrY/Nm6Ve/MvMGSlJsrDRzpjR2rAmHAACg5uGfaFRK167Sp59Kb74ptWsnffutGS2clGQGjAAAgJqHAIhKCwmRbrxR2rZNevJJs6rIpk3SsGHSiBFmYmkAAFBzEABRZSIjpYceMvME/upXZhLpRYvMPIJjxkhffWV3hQAAQCIAoho0aSI9%2B6wZKHLbbWbb66%2BbgSJ33SVlZtpbHwAAbkcARLX50Y%2BkV181t4B//GOpsFD629/MUnMTJkgHD9pdIQAA7kQARLXr0UP64AOzvvCQIVJ%2BvjR7ttS%2BvblVTBAEACCwCICVZFmWpk%2BfrtjYWNWpU0eDBw/W1q1bL3hOWlqakpOTFRUVpWbNmmnUqFHauXNngCq2T2qqGTH86afSgAFmKbm//MX0FN53n7Rvn90VAgDgDgTASnryySf19NNP67nnntO6devUvHlzDRs2TLm5ueWes2zZMk2YMEFr1qxRenq6zp07p%2BHDh%2Bt7l8yiPGSItGyZ9Mkn0sCBpkfw%2BefNSiN33MFgEQAAqhsTQVeCZVmKjY3VpEmTNGXKFElSXl6eYmJiNGvWLN19990Vep%2BjR4%2BqWbNmWrZsmQYOHFihc2rKRNBVYelS6bHHTCCUzATSN91k1h/u2dPW0gAACEr0AFbC3r17lZWVpeHDhxdvi4iI0KBBg5SRkVHh98nOzpYkNW7cuNxj8vLylJOT49eCxeDBZtLojAzpuuukoiLp3/8208eMGGFuGfO/KQAAVB0CYCVkZWVJkmJiYvy2x8TEFO/7IZZlafLkybriiiuUmJhY7nFpaWmKjo4ubnFxcZdeeA3Vr5/03nvSF1%2BY6WNq1TLzCF51lZScbKaSOXfO7ioBAHA%2BAuBFeOWVV1S/fv3iVlBQIEkKCQnxO86yrFLbyjNx4kR9%2BeWXmj9//gWPmzZtmrKzs4vb/v37L%2B1LOEC3bmb6mK%2B%2BMoND6tSR1q83k0l36CA984x0gUcsAQDAD%2BAZwIuQm5urw4cPF/%2Bcl5enxMREbdiwQT1LPKw2cuRINWzYUC%2B99NIF3%2B%2BXv/ylFixYoOXLl6tdu3YXVUswPQP4Q44eNdPGPPecdOyY2RYdbSaV/uUvpdat7a0PAACnoQfwIkRFRenyyy8vbgkJCWrevLnS09OLj8nPz9eyZcvUv3//ct/HsixNnDhRb7/9tj799NOLDn9u07Sp9PvfmxVE/vpXqWNHKTtb%2BsMfzBQyt9xi5hjkf2UAAKgYAmAlhISEaNKkSZo5c6beeecdbdmyRePHj1fdunX105/%2BtPi4q666Ss8991zxzxMmTNDLL7%2BsV199VVFRUcrKylJWVpbOnDljx9dwjDp1pLvvlrZvl95/X7rySrO6yBtvmHkFk5Olf/7TzC8IAADKxy3gSrIsSzNmzNCcOXN04sQJpaSkyOPx%2BA3oaNu2rcaPH6/p06dLKv3MoNeLL76o8ePHV%2Bhz3XQL%2BEI2bzbPBL7yii/4NW0q/eIX0j33SK1a2VsfAAA1EQHQoQiA/o4dk%2BbONRNKHzhgtoWGSiNHmnWHhwyRKjguBwCAoEcAdCgCYNnOnZP%2B8x8zYGTpUt/2zp1Nj%2BC4cVKjRraVBwBAjUAAdCgC4A/butWMHv7nP6VTp8y2OnXMdDJ33y316UOvIADAnQiADkUArLicHOnll80I4s2bfdt79DDPCv7sZxKXEADgJgRAhyIAXjzLMsvNzZljlprzDhqpW9f0Ct55p9S3L72CAIDgRwB0KAJg5Xz3nbk1PGeOtGOHb3uXLiYI3n671KSJffUBAFCdCIAORQCsGpYlrVolvfCC6RU8e9ZsDw83I4jvuEMaOtSMKAYAIFgQAB3G4/HI4/GosLBQu3btIgBWoZMnzRrE8%2BZJGzb4tsfFmdHD48dLl19uW3kAAFQZAqBD0QNYvTZtMkHwlVekEyd82wcMMEHw5pulqCjbygMAoFIIgA5FAAyMs2fNvIIvvigtWuRbb7huXenGG03P4JVXSrVYVBEA4CAEQIciAAbegQNm4MhLL0m7dvm2t2oljR1rWny8ffUBAFBRBECHIgDax7KkNWtMEHz9dfPsoFfv3iYIjhkjNWtmX40AAFwIAdChCIA1w9mz0nvvSf/6l/Thh2YpOsmMGh4xwkwnM3KkuWUMAEBNQQB0KAJgzXP0qPTaayYMrlvn216/vjRqlFlxZOhQKSzMvhoBAJAIgI5FAKzZdu40I4hfeUX6%2Bmvf9mbNpFtukW67TerXj1VHAAD2IAA6FAHQGbzPC778splo%2Btgx3762bc2zgrfdJnXtShgEAAQOAdChCIDOU1AgpadL8%2BdL77wjff%2B9b19CggmDY8ZIHTrYVyMAwB0IgA5FAHS206el9983YXDhQik/37evVy/p1lvNreK2bW0rEQAQxAiADkUADB7Z2aZH8LXXpMWLpcJC374%2BfUwYvPlmsyQdAABVgQDoMKwFHNyOHpXeftvML7h0qW/lEckMGrnlFummm8zk0wAAXCoCoEPRAxj8srKkt94yYXDlytJh8KabTGvd2r4aAQDORAB0KAKgu3z7rfTmm9Ibb0irVvmHwZQUEwRvvFFq186%2BGgEAzkEAdCgCoHt9%2B63pGXzjjdI9g716%2BcJgx4721QgAqNkIgA5FAIQkHTpkBpC8%2Baa0bJlUVOTbl5go/eQnpnXrxjyDAAAfAqBDEQBxvqNHpQULTO/gJ5/41iWWpPbtpdGjTRhMSZFq1bKvTgCA/QiADkUAxIWcOCG9954ZUfzxx9LZs759LVpII0eaMDh4sFS7tm1lAgBsQgB0KAIgKurUKemjj0wY/OADKSfHty86WrruOtM7OGKEVL%2B%2BfXUCAAKHAOhQBEBcirw86dNPzXOD//mPdOSIb19EhDRsmOkdvP56KSbGvjoBANWLAOhQBEBUVmGhtHq1eW5wwQJpzx7fvpAQM9fgyJGmdepkX50AgKpHAHQoAiCqkmVJW7f6wuD69f77O3WSbrjBhMG%2BfaXQUHvqBABUDQKgQxEAUZ0OHJDefdfcJl6yRCoo8O1r2lS69loTCIcN47lBAHAiAqBDEQARKNnZZhDJu%2B9KCxdKJ0/69oWHS1deacLgdddJcXH21QkAqDgCoMN4PB55PB4VFhZq165dBEAEVEGBtGKFmWLm3Xelr7/239%2BjhxlAct11Uu/ezDcIADUVAdCh6AGE3SxL2r7dBMH33jMDSkr%2BbRITY24VX3cdt4oBoKYhADoUARA1zdGj5hbx%2B%2B%2Bbyadzc337wsPNpNPXXmta%2B/a2lQkAEAHQsQiAqMny86Xly00YfO%2B90reKO3f2hcErrmA1EgAINAKgQxEA4RSWJe3cacLgBx%2BYZwgLC337GzSQhg%2BXfvxj6ZprpObN7asVANyCAOhQBEA41cmT0qJF5nbxwoXm1nFJSUkmCP74x1KfPsw5CADVgQDoUARABIOiImndOl8Y/Pxz//2XXebrHRwxwsxBCACoPAKgQxEAEYwOH5Y%2B/NCEwfR0/zkHQ0LM1DLeW8W9e9M7CACXilm6KsmyLE2fPl2xsbGqU6eOBg8erK1bt1b4/LS0NIWEhGjSpEnVWCXgDDEx0vjx0r//bW4NL18uTZ0qde9uniVct06aMcMsRxcTI/30p9I//ykdOWJ35QDgLATASnryySf19NNP67nnntO6devUvHlzDRs2TLkl58Aox7p16zR37lx169YtAJUCzhIWJg0YIKWlSZs2SQcPSvPmSTfdJEVHS8ePS/PnS//zPyYM9u4tPfKIGWRScuk6AEBp3AKuBMuyFBsbq0mTJmnKlCmSpLy8PMXExGjWrFm6%2B%2B67yz331KlT6tWrl2bPnq3HHntMPXr00DPPPFPhz%2BYWMNysoEBas8YsUffhh9LGjf77GzSQhg41zw1efbXUurU9dQJATUUPYCXs3btXWVlZGj58ePG2iIgIDRo0SBkZGRc8d8KECbr22ms1dOjQ6i4TCDq1a5vewccflzZskLKypJdeksaMMQNHcnKkt9%2BW7r5batNGio%2BXJk0yYfH0aburBwD7hdldgJNlZWVJkmJiYvy2x8TEaN%2B%2BfeWe99prr2nDhg1at25dhT8rLy9PeXl5xT/n5ORcZLVA8IqJkcaNM62wUFq/3qxG8tFHpqdwxw7Tnn1WioiQBg40vYPDh0uJiWaACQC4CT2AF%2BGVV15R/fr1i1vBfx80CjnvXw/Lskpt89q/f7/uv/9%2Bvfzyy4qMjKzwZ6elpSk6Orq4xcXFXfoXAYJYaKiZP/C3v5VWrTLPCr7xhnTnnVJcnJSXZ0YYP/ig1K2b1LKlGXgyfz6DSQC4B88AXoTc3FwdPny4%2BOe8vDwlJiZqw4YN6tmzZ/H2kSNHqmHDhnrppZdKvceCBQs0evRohZaYv6KwsFAhISGqVauW8vLy/PaV/KzzewDj4uJ4BhC4CJZlegI//thMRr10qXTmjP8xPXv6egf79zc9hgAQbAiAleAdBPLAAw/o4YcfliTl5%2BerWbNm5Q4Cyc3NLXV7%2BOc//7k6d%2B6sKVOmKDExsUKfzSAQoPLOnpVWrjRhcNEi6Ysv/PfXrSsNGiQNG2YCYUICt4sBBAcCYCXNmjVLaWlpevHFF9WhQwfNnDlTS5cu1c6dOxUVFSVJuuqqqzR69GhNnDixzPcYPHgwo4CBGiAry9we9rb/PuZbLDbWjC4eNsy8sm4xAKdiEEglPfzwwzpz5ozuu%2B8%2BnThxQikpKVq0aFFx%2BJOkPXv26NixYzZWCaAimjeXxo41zbKkzZtNEFy0yExK/e23ZuLpf/7THN%2B1qy8MDhwo1atnb/0AUFH0ADoUPYBAYHlvF3t7B8%2BfezA83Dwz6O0hTEpiqToANRcB0KEIgIC9jh6VPvlEWrzYBMLMTP/9DRtKQ4aYQDh0qNShA88PAqg5CIAORQAEag7LknbvNkFw8WJpyRLp5En/Y%2BLipKuuMmHwqqt4fhCAvQiADkUABGou72TU3kCYkSHl5/sf06WLCYJXXSUNHmyWrwOAQCEAOhQBEHCO06fN84OLF5u2aZPpNfQKDZWSk32BsF8/6SLmiQeAi0YAdCgCIOBcx4%2Bb28SLF5vnCHfv9t8fGSmlpvoCYa9eUhhzNgCoQgRAhyIAAsFj3z4TBD/91LyeP/9ggwbmNvGVV5rG%2BsUAKosA6DAej0cej0eFhYXatWsXARAIMpYlbd/uC4NLl5YeUNKsmX8gvPxyAiGAi0MAdCh6AAF3KCw0cw56ewhXrjTPFJbUqpUJgkOGmNfWre2pFYBzEAAdigAIuFN%2BvvTZZyYMfvqptHq1VFDgf0z79iYMeluLFvbUCqDmIgA6FAEQgGR6AzMyfIHw889Nr2FJnTv7wuDgwVLTpraUCqAGIQA6FAEQQFlycqQVK8wo408/LT3ljGQGkXgD4aBBUuPG9tQKwD4EQIciAAKoiO%2B%2Bk5YvN4FwyRJp82b//SEhUrduvt7BgQOlRo1sKRVAABEAHYoACOBSHD0qLVvmC4Tbt/vvDwmRevQwYdAbCBs2tKNSANWJAOhQBEAAVeHwYTPVzNKlJhDu3Om/v2QgHDJEGjCAQAgEAwKgQxEAAVSHQ4d8PYRLl0q7dvnvP7%2BHcMAAbhkDTkQAdCgCIIBA%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%2BoFAoEA6FAEQAC4NKdOmdHF3kD42WelRxrXrSv17esLhX37MrAEwYUA6FAEQACoGvn50oYNvkC4apX03Xf%2Bx4SGSj17%2BgJhaqoUE2NPvUBVIAA6FAEQAKpHUZF5bnDFCt%2Bt47IGlnTo4LtlnJpqfuY5QjgFAdChCIAAEDiZmSYMegPhli2lj2nWzIRBb%2BvRg%2BcIUXMRAB3G4/HI4/GosLBQu3btIgACgA1OnDBL2HkD4bp15lZySSWfI0xNNb/mr2vUFARAh6IHEABqjrNnpc8/9/USrlolnTzpf0ytWmYd49RUXy9hy5b21AsQAB2KAAgANVdRkbRtmwmC3lD4zTelj2vTxtdDmJoqdeliBpwA1Y0A6FAEQABwloMHfYFw1Spp0yYTFEuKjjZrGXtDYZ8%2B5lYyUNUIgA5FAAQAZ8vNNXMQensI16yRvv/e/5iwMDP9jPe2cWqq1Ly5PfUiuBAAHYoACADB5dw56csvfT2Eq1aZXsPz/ehHvlvGqalmneNatQJfL5yNAOhQBEAACG6W5Zt%2BxhsIN28220tq2NCMMPYGwj59pHr17KkZzkEAdCgCIAC4T3a2uVXsDYRr1kinT/sfExpq5iD0BsL%2B/aVWreypFzUXAbCSLMvSjBkzNHfuXJ04cUIpKSnyeDzq0qXLBc87ePCgpkyZog8//FBnzpxRx44dNW/ePCUlJVXocwmAAIBz56QvvjBzEnoHmJR127h1axMEvaGwa1fzfCHciwBYSbNmzdLjjz%2Buf/zjH%2BrYsaMee%2BwxLV%2B%2BXDt37lRUVFSZ55w4cUI9e/bUkCFDdO%2B996pZs2bas2eP2rZtq/bt21focwmAAICyZGb6eggzMkxAPH%2B0cf36UkqKLxT27WtGIMM9CICVYFmWYmNjNWnSJE2ZMkWSlJeXp5iYGM2aNUt33313medNnTpVq1at0ooVKy75swmAAICKyM2V1q71BcLVq6WcHP9jQkKkxEQzBY03FLZvz9rGwYwAWAlff/212rdvrw0bNqhnz57F20eOHKmGDRvqpZdeKvO8hIQEjRgxQgcOHNCyZcvUsmVL3XfffbrrrrvK/ay8vDzl5eUV/5yTk6O4uDgCIADgohQWSlu3mjDobXv2lD6uaVMTBPv1M69JSVJkZODrRfUgAFZCRkaGUlNTdfDgQcXGxhZv/8UvfqF9%2B/bp448/LvO8yP/%2BCZo8ebJuvvlmrV27VpMmTdKcOXM0bty4Ms%2BZPn26ZsyYUWo7ARAAUFmHD/vC4KpV0vr1pdc2rl1b6tXL9BB6ewlbtLCnXlQeAfAivPLKK363dT/44AMNHjxY3377rVqU%2BFNw1113af/%2B/froo4/KfJ/w8HD17t1bGRkZxdt%2B9atfad26dVq9enWZ59ADCAAIlLw8EwJL9hIePlz6uDZtfIGwf3%2BpWzcGlzgFv00X4YYbblBKSkrxz95AlpWV5RcAjxw5opiYmHLfp0WLFkpISPDbFh8fr7feeqvccyIiIhQREXGppQMAUGEREb5QJ5m5B/fu9e8l3LJF2rfPtPnzzXF165p5CL3n9u0rXXaZfd8D5SMAXoSoqCi/kb2WZal58%2BZKT08vfgYwPz9fy5Yt06xZs8p9n9TUVO3cudNv265du9SmTZvqKRwAgEoICTErkPzoR9Ltt5tt3qXsvKFwzRozT%2BHSpaZ5derkG1zSv78UH8/KJTUBt4AradasWUpLS9OLL76oDh06aObMmVq6dKnfNDBXXXWVRo8erYkTJ0qS1q1bp/79%2B2vGjBm65ZZbtHbtWt11112aO3eufvazn1XocxkFDACoSYqKpO3bfYFw9WrpvL4OSWa6mb59TSjs189MR8MUNIFHAKwk70TQc%2BbM8ZsIOjExsfiYtm3bavz48Zo%2BfXrxtvfff1/Tpk3TV199pXbt2mny5MkXHAV8PgIgAKCmO37cBEFvW7tW%2Bv57/2NCQqQuXXy9hP36SR07MgVNdSMAOhQBEADgNOfOmfWMS/YS7t1b%2BrjGjX09hP36mecK69cPfL3BjADoUARAAEAwyMry7yX8/HPp7Fn/Y2rVMsvXeXsI%2B/VjourKIgA6FAEQABCM8vOlTZv8Q2FmZunjmjTx7yVMTpbq1Qt8vU5FAHQoAiAAwC0OHvQPhGVNVB0aanoJ77tPuohH6l2LaWAAAECN1rKldNNNpklmouqNG/1D4YEDpufw5El7a3UKegAdih5AAAB8DhwwcxH27GmeD8SFEQAdigAIAAAuFXNxO4zH41FCQoKSk5PtLgUAADgUPYAORQ8gAAC4VPQAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgA7DWsAAAKCyWAvYoVgLGAAAXCp6AAEAAFyGAAgAAOAyBO%2BnEQcAAAH%2BSURBVEAAAACXIQACAAC4DAEQAADAZQiAAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQA6jMfjUUJCgpKTk%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%3D
label_cols:
- label
- label
- label
- lmfdb_label
- label
- label
labels:
- 990.a.8910.1
- 990.a.8910.1
- 990.a.8910.1
- 990.a.8910.1
- 990.a.8910.1
- 990.a.8910.1
tables:
- g2c_curves
- g2c_endomorphisms
- g2c_ratpts
- g2c_galrep
- g2c_tamagawa
- g2c_plots
timestamp: '2024-05-21T19:52:12.061532'