data:
- - Lhash: '1221962300074229217'
    abs_disc: 5408
    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: '[[1,3,3,1],[0,1,1,1]]'
    g2_inv: '[''345025251/5408'',''-928557/1352'',''0'']'
    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: 66
    igusa_clebsch_inv: '[''204'',''3273'',''161211'',''692224'']'
    igusa_inv: '[''51'',''-28'',''0'',''-196'',''5408'']'
    is_gl2_type: true
    is_simple_base: false
    is_simple_geom: false
    label: 676.a.5408.1
    leading_coeff:
      __RealLiteral__: 0
      data: '0.3201552354797366231840162692222260046864076699129857330'
      prec: 190
    locally_solvable: true
    modell_images:
    - 2.60.2
    - 3.2160.21
    mw_rank: 0
    mw_rank_proved: true
    non_solvable_places: []
    num_rat_pts: 6
    num_rat_wpts: 0
    real_geom_end_alg: R x R
    real_period:
      __RealLiteral__: 0
      data: '20.169779835223407260593024961'
      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: 7
    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: 39770
    is_simple_base: false
    is_simple_geom: false
    label: 676.a.5408.1
    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:
    - - - -23
      - - -181
    - - - 129
      - - -2241
    spl_facs_condnorms:
    - 26
    - 26
    spl_facs_labels:
    - 26.a3
    - 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: 65
    label: 676.a.5408.1
    mw_gens:
    - - - - 0
          - 1
        - - 1
          - 1
        - - 1
          - 1
      - - - -1
          - 1
        - - -2
          - 1
        - - 0
          - 1
        - - 0
          - 1
    mw_gens_v: true
    mw_heights: []
    mw_invs:
    - 21
    num_rat_pts: 6
    rat_pts:
    - - -1
      - 0
      - 1
    - - -1
      - 1
      - 1
    - - 0
      - -1
      - 1
    - - 0
      - 1
      - 1
    - - 1
      - -1
      - 0
    - - 1
      - 0
      - 0
    rat_pts_v: true
- - conductor: 676
    id: 102
    lmfdb_label: 676.a.5408.1
    modell_image: 2.60.2
    prime: 2
  - conductor: 676
    id: 103
    lmfdb_label: 676.a.5408.1
    modell_image: 3.2160.21
    prime: 3
- - id: 131753
    label: 676.a.5408.1
    p: 2
    tamagawa_number: 7
  - cluster_label: c2c2_1~2c2_1~2_0
    id: 131754
    label: 676.a.5408.1
    p: 13
    tamagawa_number: 1
- - id: 66
    label: 676.a.5408.1
    plot: 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P80Wc/z1DH9AADy3Eyek3btN27Mnt%2B3dm3t7%2BHDRXy842IwaxMSYVrt2bqtVy7SaNc1tjRrmOjcgkBEA4XcsS1q82KwbOHt27vmLLpIeeEAaMIC/xOGbkpKSlJSUpKysLG3atMmxATA72/TM7dxp2q5d%2BduePebauqIIDZViY02rUyf/safFxJjeuiBWvgVyEADh137/XRo/3iwj41mHt0YN6e67zYSR%2BvXtrQ84m0DvAczONsOv27dLO3bkb57Al55%2B7tepXFmKizPLQuVtdeuaVq%2BeCXb0/APFRwBEQDhyRHr3XSkpyQwZSeav/WuvNUGwRw/%2B%2BofvCIQAmJwsbduWv23fnhv6zhXwgoJMgKtf37QGDXKP4%2BJMi44m3AHlhQCIgJKZKc2aZYLgd9/lnm/Y0OwucvvtZngIsJM/BEDLMsO0W7ZIW7ea5jnetu3cQ7TBwSbENWwoxceb24YNTdBr2ND04IWElP/PAeDsCIAIWL//Lr31lvTBB2bRVcn0OvTqZRaWvuYas2Ap4G2%2BEgAtywzVbt5s2pYtubdbt5rdJQpTs6bZx9vT4uNzW1wcAQ/wZQRABLyTJ6XPPzeLSy9dmnu%2BVi3pllvMcjIXXmhbeXAgbwfAI0fMEkqbNpmWN/AVFvKCgsyQbKNGUuPG5tbTzjuPRdkBf0YAhKNs2CC9/7704YdmAViPCy%2BUbr3VzCCuW/ccL3L4sPlfs149ujhQfG63UjZuVHSrVmUaAE%2BeNKEub9DzHB87VvDzgoJMj13jxqY1aZJ7HB/PcihAoCIAwpEyMqS5c6XJk81SMp4L1l0u6YorpIEDpX79zOKuOZYtkx5/XFq40NyPjZWGDze7jjPDBOdy6pQ0apT07rtKOXpU0ZKSe/ZU1LhxUkJCkV7CsswSKRs35rbffze3nt1yChIXZ8Jd06amNWliGiEPcCYCIBzv6FHps8/MbiPff597vkIFqVs3qX9/qW/NpYrs29VsPXC6O%2B6Q3nvPewXD/2RnS1dfLc2bJ0lKkUwAlBRVtar0ww9Ss2Y5Dz9xwvTm/f57bvMEvhMnCv42Vaual/GEPM9x48Zmb1gA8CAAAnls3y598olpa9fmnv/e1VEdrGUFP3HdOqlFi/IvEP5p9myzZc1f8gVASZva3qI3Lp2aE/Z27iz4pYKDzTV4zZubgJe31ajBsikAioYACBTg99%2BlTz%2BVlkzbpW83Nyj8wf/%2Bt/Tcc94pDH4lPV062e8WRX/1Uc650wOgW6GqpOPKUu41pdWq5Ya8vLfnncfsdQClxxXsQAGaNzeXbOnGVOkcnXtL56Yqs5vUsSP/OTvV0aP5h2s9x1u3StOzUtWnkOeGKV2P3u9WfIsQNW9uPns1a3qtdAAORAAEzuW886QqVXIXEzyLd1a10ZQrpKgo6aqrpJ49TWMrusCSkWEuE8gb8DzX5v3xR8HPWxfaRn3Svyz4Ac2ba/Srlcq%2BYAAoAEPAQFE89pj0wgtn/dLJ6Noa1muHvpwfrsOH83%2BtSROpa1fTunQx%2B5bCt3l2wPAsqeJpmzaZHTAyMwt%2Bblxc7lBt3harfXI1bZIzg%2BP0IWC9%2BabZsxAAvIQACBRFerqZDjxzZv7ztWpJc%2BZIbdooO1v6%2BWfpm29M%2B/FHKSsr/8NbtTJB8LLLpM6dzdNhj6NHTcjLuzCy5zg1teDnVayYO8M270SMpk2lypUL%2BYZz50p/%2B5uUlpY/AN53n9m7EAC8iAAIFMf335s1Y9LSpHbtzFYilc4%2BdJecbJYM/PZbsy/x%2BvVnPqZJE6lDB9Pat5cuuMDM8kTpWZZZ7HvbNnMdXt4tzjZvPvfiyA0a5Aa7vDNt69YtxUzbo0elDz5QyqpVip46Vcnff6%2BoDh1K%2BGIAUHIEQMBLDh6UFi0ybfFi6ddfz3xMxYpSmzZSYqJ08cVS69YmdBAKzy4tTdqxw1yXt327OfYEvm3bCl8zTzJreeddFNmzUHKjRlJYWPnV7St7AQNwLgIgYJNjx8z6v8uWmdvly02gOV1EhNSypVlmsEUL00t4/vlmJ7pAXvMtO1s6dEjavdvscpG37dxpWmETLyTz/sTF5e5lm7c1alRg5225IwACsBsBEPARWVlmssFPP0krV0qrV0tr1hTci1WxYu7erZ4tvRo0MDOP69WTIiO9W39Rpaeb7ZQPHTK9ogcOSPv3m7Zvn7R3r2n79xc%2B4cKjShUzUbthQ/MexMebcNeokXkvyrMnr6QIgADsRgAEfFhWlrlube1aM2T866/mWsItW84djqKizBBnnTpmTbmaNc0s5KpVTWiKjjYhsXJlEyYjIsyesKGhUkiIuQ7Os8VxdrapJTPTBLj0dLO17cmTJqAeP24mTqSmSikp5vrH5GTTy3n0qHTkiGmHD5vzRRUUZOqPizNhztMaNDCtYUPzc/gbAiAAuxEAAT/kWY/OM3t161Zzf%2BdOM2RanJBlh%2BBgE0hr1zatTh3TYmNNq1fPTLaIiTFhNNAQAAHYLQB/tQKBr0IFM1mhadOzfz011Qyj7ttnhlgPHjTXyx09anrlPD10qanmusOTJ01zu03vXkF/FgYFme8dFiaFh5tew4oVzbV0kZGmRUWZVqWK6W2sWtX0PFavntsTWbVqbu8iAMD76AEEcAbLMkO%2Bnt8OLpfptQvkSSfeRA8gALvxNziAM7hcZui1QgXTQkIIf2UhKSlJCQkJSkxMtLsUAA5HDyAAeBk9gADsRg8gAACAwxAAAQAAHIYACAAA4DAEQAAAAIdhHUAvsCxmUAaMn3%2BWPv3UbH3Rrp3Uv79ZEA8AAD/CLGAveOUVaeRIs2Cup1WqlHsbEZF737Mll%2Bd83ufkbXm/5jkODydolpuMDOnvf5c%2B%2Byz/%2BdhYac4cqVUre%2BqCX2IWMAC70QPoBcePmx0W3G6zC0N5iojIDYSe48KaZzeHvMfh4fmPPbs%2B5D0OC8t/HBxcvj%2BX7Z566szwJ5mtNq65Rtq2zbwRAAD4AXoAvSAtTTpyRDpxIrcdP57/ft7zJ0%2Be/WsnTpivnf6YzEy7f0ITAENDc4NhWJi57znnOfY0zwLDnpb3XN4FiIODzX1P89wPDs5tQUFnHgcFmeZy5b/1HJ/epPzHnvuSFJR%2BSp0G1FVo6tECf/5fH52iA90GnfX5eW89NeRtntrz/pye98TTwsNZjDmQ0AMIwG70AJaCZVlKTU0t0mM9e6KWh8xMEwRPncrd0zVv85w//dbtzr1fUHO7zW16eu59z7m8srJyv1%2BgaarftUJHdaqQx8x54Xs99sJ15VqHy5XbE5v3UgJP8%2BzFGx1tWt59eGvUyN2PN4R/9V7ndrvldrtz7nt%2Bb6SkpNhVEgBJkZGRcjn0L2t6AEvB81c8AADwP07uhScAlkJxegAlKTExUStWrCj29ynp80r63JSUFMXFxWn37t3F/ofh7Vq99j2vvFJaubLgry9dKrVsWbbfU6Zn1dPreuWVPTV9%2BlydOpX/koC0tNyWnCylpEh//mna0aPm8oOtW48pO7voXdDR0VLDhtLWrd/qzju76rzzpPPOkxo3lurUKXwoujSfH8nHPwclfN7pPYD79%2B9X27ZttX79etWtW9crtZbmud5%2BHr%2BDCudv/8Z8%2Bf1xcg8gg0Gl4HK5ivWPLzg4uET/WEv6vNI%2BNyoqqtjPtaNWr3zPl16SevQwY%2BGnGzRI6tix7L/nacLC/lSrViX7ORMS2mvduvU6ckQ6eFA6cEDav1/au1favVvas8e0XbtMYExOln75RZL66dVX879W5cpSkyZSs2bS%2BedLF1xgbps0MdcuepTk8yP5%2BOegDL%2BnZP7zCeR/Y6V9f/gdVDh/%2BTfmb%2B%2BPUxAAvWjo0KFefV5pn%2Bvt7%2BfT78/ll0vz5kn//re0bJkkKbtmTQUNGyY9/nj5fM8yep7nucHBUq1aphXWWZmWJu3YYSY2T5myRLGxl2nLFmnLFnMuLU1avdq0vEJCTChs3jxC0kh9/XWI2reX6tcv3uQVn/4clOH3LCl/%2Bjl5f8r3ud7%2Bnk55f5yCIWCcgRmKhUv9/Xddcv75WnH4sKJq1LC7HK9KTzchcNMm6fffpfXrTduwwQTDs6la1SyTeNFFpl18sekxdPJklD179uQMUdWrV8/ucnwOv4MKx/tTON6fonHwr2AUJCwsTKNGjVIY69qdVWh8vAaOGqWwyEi7S/G60FCpeXPT%2BvTJPW9ZZij511%2BlVasy9dln62VZLbRxY5COHZMWLjTNIyxMuvBCqU0bKTHR3CYk5B9CDmSef1v8Gzs7fgcVjvencLw/RUMPIIByk54u/fabuZ5wzZrcoeOzzZ0KDze9g4mJprVrJzVqFJhrH9JDAcBuBEAAXpWdbYaRV640WyuvXGna2ZbEq1bNBMH27U1r21aqUsX7NZc1AiAAuxEAAdguO1vavFlavtyEwuXLpVWrzpx07XKZ6wc7dpQuvVTq0EFq2tT/egkJgADsRgAE4JPcbjN0/NNP0o8/Sj/8IG3ffubjatY0YbBTJ9PatDHXKvoyAiAAuxEAAfiNQ4dMEFy2zNyuWHHmtoTh4WbYuHNn6bLLTDisXNmeegtCAARgtyC7C4A9pk%2Bfrh49eqhGjRpyuVxas2bNOZ8zefJkuVyuM9qp0/8HDnAlee8CkWVZeuqppxQbG6uIiAhdfvnl%2Bu233wp9zlNPPXXG5ycmJqbI37NWLem666Tnn5cWLza7nSxbJr34ojlfvboJhIsWSc88I3Xvbpahad9eeuwxac6cs19r6C1JSUlKSEhQYmKifUX4iDfeeEPx8fEKDw9XmzZttGTJkgIfy%2B8eY/Hixerdu7diY2Plcrk0c%2BZMu0uyRXHfh4ULF5718/P77797qWLfRAB0qOPHj6tjx44aO3ZssZ4XFRWl/fv352vh4eHlVKVvKul7F2heeOEFvfzyy3r99de1YsUKxcTEqFu3bufcHvGCCy7I9/lZt25diWsICzM9fA8/LM2cKR0%2BbNYkfPtt6ZZbzCLUmZlmGPmFF6RevczEkrZtTSCcO7fg9QvLw9ChQ7V%2B/foSb4sVKD799FONGDFCjz/%2BuFavXq3LLrtMV199tXbt2lXgc/jdY373tGrVSq%2B//rrdpdiqpO/Dxo0b831%2BmjRpUk4V%2BgkLjrZ9%2B3ZLkrV69epzPnbSpElWdHS0F6ryD8V57wJNdna2FRMTY40dOzbn3KlTp6zo6GjrrbfeKvB5o0aNslq1auWNEnNs325Zkydb1h13WNZ551mWWbUwt4WEWFaHDpb1xBOWtWCBZZ06Vf41JScnW5Ks5OTk8v9mPqht27bWvffem%2B9c8%2BbNrX/9619nfTy/e84kyZoxY4bdZdiuKO/DggULLEnWsWPHvFSVf6AHEMWSlpamBg0aqF69err22mu1%2BvT9wOAI27dv14EDB9S9e/ecc2FhYerSpYuW/bVVXkE2b96s2NhYxcfHa8CAAdq2bVu51tqwoTR4sPTee9LWrWa/4ylTpNtuM1/LzDTDyM88I11xhekh7NnTbP%2B8Zo2ZoYyyk56erpUrV%2Bb77EhS9%2B7dC/3s8LsHpdW6dWvVqVNHV111lRYsWGB3ObYjAKLImjdvrsmTJ2vWrFn6%2BOOPFR4ero4dO2rz5s12lwYvO3DggCSpdu3a%2Bc7Xrl0752tn065dO02ZMkXffPON3nnnHR04cEAdOnTQkSNHyrXevOLipEGDpEmTzKzibdukd96RBg6UateWTpyQvvlGeuQRqXVrqU4dM5z8wQfSvn1eKzNg/fHHH8rKyirWZ4ffPSiNOnXqaOLEifriiy80ffp0NWvWTFdddZUWL15sd2n2srsLEuVv6tSpVqVKlXLa4sWLc75WmmHMrKwsq1WrVtbw4cPLslyfUl7vnb85/X1YuHChJcnat29fvsfdddddVo8ePYr8umlpaVbt2rWtcePGlXXJJZKdbVlr11rWK69YVq9ellWp0plDxi1bWtbDD1vW/PklHy528hDw3r17LUnWsmXL8p1/9tlnrWbNmhXpNZzwu%2BdcxBCwZVklfx%2BuvfZaq3fv3uVQkf9gL2AH6NOnj9q1a5dzv27dumXyukFBQUpMTAzov8LL673zN6e/D263W5LpCaxTp07O%2BUOHDp3Rs1OYSpUqqWXLlj7zGXK5pJYtTRsxwixE/cMP0vz50rx5ZpHqdetMe%2BklqVIlM2x8zTXS1VebIeVzsqyz74XnEDVq1FBwcPAZvX3F%2Bew44XcPylf79u01depUu8uwFUPADhAZGanGjRvntIiIiDJ5XcuytGbNmnwBINCU13vnb05/HxISEhTW0Ck6AAAfWklEQVQTE6P58%2BfnPCY9PV2LFi1Shw4divy6brdbGzZs8NnPUGio1KWL9OyzZneSQ4ekjz821xTGxEjHj0uzZ0v33SfFx0sJCWboeOFCKSPjtBfLyjLr1cTHS/XqmXP33WcuSnSQ0NBQtWnTJt9nR5Lmz59f5M%2BOE373oHytXr3a8Z8fegAd6ujRo9q1a5f2/XVR08aNGyVJMTExOeuy3Xrrrapbt67GjBkjSXr66afVvn17NWnSRCkpKZowYYLWrFmjpKQke34ImxTlvQt0LpdLI0aM0OjRo9WkSRM1adJEo0ePVsWKFXXzzTfnPO6qq65S3759NWzYMEnSww8/rN69e6t%2B/fo6dOiQnn32WaWkpGjw4MF2/SjFUqOGNGCAadnZ0tq10tdfm7ZsmVmCZsMG0ztYpYqZTHLttdLVPS1V%2B8dA6fPP87/gtGnSd9%2BZrU7q17fnh7LBP//5Tw0aNEiXXHKJLr30Uk2cOFG7du3SvffeK4nfPQVJS0vTli1bcu5v375da9asUbVq1VTfQZ%2Bfc70PI0eO1N69ezVlyhRJ0vjx49WwYUNdcMEFSk9P19SpU/XFF1/oiy%2B%2BsOtH8A12j0HDHpMmTbIkndFGjRqV85guXbpYgwcPzrk/YsQIq379%2BlZoaKhVs2ZNq3v37mdcx%2BMERXnvnCA7O9saNWqUFRMTY4WFhVmdO3e21q1bl%2B8xDRo0yPe%2B9O/f36pTp45VoUIFKzY21urXr5/122%2B/ebny8nHsmGV98ollDRpkWTVq5L9usHvQ/Hwnkv/6zCR7zt15p93le11SUpLVoEEDKzQ01Lr44outRYsW5XyN3z1n51nO5PSW971ygnO9D4MHD7a6dOmS8/jnn3/eatSokRUeHm5VrVrV6tSpk/XVV1/ZU7wPYSs4AChjWVlmyHj2bGnWLOmhX2/Tbfog5%2BspkqIlJUuKkqSKFc0WJcHB9hQMwHEIgABQzk5c1VsVv5udc/%2BMAChpyddp6tCtEhkQgFcwCQQAylnFti0K/fo2xavz1ZUUGysNGWJmHWdmeqk4AI5EAASA8jZkiFShQoFfXn7JUFWtamYZT5wode9uZhnffTdhEED5YAgYALzh00/NFiQZGfmHgPv3l6ZNU0Z2sBYskL74QpoxQzp8OPepNWpIN9wg9e8vde7MpYIASo8ACADesnOnNHGiUlavVvTXXyt51ixF9e59xsMyM6XFi6XPPjOB8I8/cr9Wp44JggMHSomJZvFqACguAiAAeFlKSoqio6OVnJysqKioQh%2BbmSktWCB98ok0fbr055%2B5X2vcWLr5ZrNXcdOm5Vw0gIBCAAQALytOAMwrPV365huzG8l//yudOJH7tbZtpb//3SxSXbNmORQNIKAQAAHAy0oaAPNKSzNrDE6davYpzsoy50NCzN7EgwebXUhCQ8uwcAABgwAIAF5WFgEwr4MHzRDxhx9KK1fmnq9e3QwR33671Lp1qb8NgABCAAQALyvrAJjX%2BvXSBx%2BYnsG/tquWJF10kXTHHeZ6wWrVyvRbAvBDBEAA8LLyDIAeWVlmDcH33zfXC6anm/NhYWZJmbvvlrp0YRYx4FQEQADwMm8EwLyOHJE%2B%2Bkh6911p7drc802aSPfcI912m1lrEIBzEAABwEuSkpKUlJSkrKwsbdq0yWsB0MOyzDWC77xjAmFamjkfGirdeKP0j39IHTvSKwg4AQEQALzM2z2AZ5OWZpaTefvt/BNHWraU7rvPLClTubItpQHwAgIgAHiZLwTAvH7%2BWXrrLdMrePKkORcVZWYPDx1qhooBBBYCIAB4ma8FQI9jx8wM4jfekDZvzj1/9dXSAw9I3bpJQUH21Qeg7BAAAcDLfDUAemRnmxnEr70mzZljrh2UpPPPN0Fw0CCpYkV7awRQOgRAAPAyXw%2BAeW3daoLg%2B%2B9LqanmXPXq0r33SsOGSTEx9tYHoGTozAeAIsrIyNBjjz2mli1bqlKlSoqNjdWtt96qfXlXXA4wjRpJ48dLe/ZIr7wixcebZWWee05q0EC66y5pwwa7qwRQXARAACiiEydOaNWqVXriiSe0atUqTZ8%2BXZs2bVKfPn3sLq3cRUVJI0aYawP/3/%2BTLr3ULC793ntSQoJ03XXSsmV2VwmgqBgCBoBSWLFihdq2baudO3eqfv36RXqOPw0BF2bZMunFF81OI57/STp1kkaONBNHWE8Q8F30AAJAKSQnJ8vlcqlKlSp2l%2BJ1HTpIM2aYIeC77pIqVJCWLpV69ZJat5Y%2B/9xsSQfA99ADCAAldOrUKXXq1EnNmzfX1KlTC3yc2%2B2W2%2B3OuZ%2BSkqK4uDi/7wE83b590ssvmzUFjx8355o1kx5/XBo4UAoJsbc%2BALnoAQSAAkybNk2VK1fOaUuWLMn5WkZGhgYMGKDs7Gy98cYbhb7OmDFjFB0dndPi4uLKu3RbxMZKL70k7doljRolVakibdwo3Xqr1Ly5NGmSlJFhd5UAJHoAAaBAqampOnjwYM79unXrKiIiQhkZGbrpppu0bds2fffdd6pevXqhr%2BOUHsDTpaSYRaXHjZP%2B%2BMOcO%2B886YknzFZz9AgC9iEAAkAxeMLf5s2btWDBAtWsWbPYrxEok0CKKi1NevNNM2Hk8GFzrkkT6cknzdBwcLC99QFORAAEgCLKzMzUDTfcoFWrVmn27NmqXbt2zteqVaum0NDQIr2O0wKgx/HjJgg%2B/3xuj2BCgvTMM1LfvswaBryJAAgARbRjxw7Fx8ef9WsLFizQ5ZdfXqTXcWoA9EhLkyZMMNcLHjtmzl1yiTR6tNS1K0EQ8AYCIAB4mdMDoMeff5rrA8ePN6FQkq680vQQXnKJvbUBgY5ZwAAAW1SpYoZ/t26VHnhACg2VvvtOSkyU%2Bvc35wGUDwIgAMBWtWqZXsBNm8ySMS6X9Nln0vnnm%2B3njhyxu0Ig8BAAAQA%2BoUED6YMPpNWrpR49zJqBr74qNWpkhorzrKQDoJQIgAAAn9KqlTR3rjRvnjlOTpYefli64AKz9RxXrgOlRwAEAPikbt2klSul996TYmLMNYH9%2BpmJImvX2l0d4N8IgADgJUlJSUpISFBiYqLdpfiN4GDpjjukzZvNnsLh4dLChVLr1tLQoVwfCJQUy8AAgJexDEzJ7dghPfqo9Pnn5n61atJzz0l3382OIkBx0AMIAPAbDRuaGcILFkgtW0pHj0r/%2BIfUrp20fLnd1QH%2BgwAIAPA7l18urVplZglHR5trBdu3l4YMMaEQQOEIgAAAvxQSIt1/v7RxozR4sJkdPHGi1KyZNGUKs4WBwhAAAQB%2BrXZtafJkadEis1TMH3%2BYQHjllSYcAjgTARAAEBA6dzaLSI8dK0VEmNnCF15otptLT7e7OsC3EAABAAGjQgXpscek336TevY0we/JJ6WLL5Z%2B/NHu6gDfQQAEAASc%2BHhpzhzpo4%2BkmjVNIOzQQXrwQen4cburA%2BxHAAQABCSXSxo4UFq/Xho0yEwKGT/eDAsvXGh3dYC9CIAAgIBWo4aZFfz111JcnLRtm3TFFdKwYVJamt3VAfYgAAKAl7AVnL169pR%2B/dWsFShJSUmmN3DxYnvrAuzAVnAA4GVsBWe/b7%2BV7rxT2rXLDBWPGGG2lIuIsLsywDvoAQQAOE7XrtK6ddJdd5lrA195RWrTxuwuAjgBARAA4EhRUdI770izZ0sxMdKGDWZP4dGjpawsu6sDyhcBEADgaL16mWsDb7xRysyUHn/c7DW8Y4fdlQHlhwAIAHC86tWlzz4zW8pFRkpLl0qtWkkff2x3ZUD5IAACACAzGWTwYGnNGunSS6WUFOnmm8251FS7qwPKFgEQAIA8zjvPLA0zapQUFGTWEGSCCAINARAAgNOEhEhPPWV2DImLkzZvNr2Cr71mZg0D/o4ACABAAS67zAwJX3edlJ4u3X%2B/dMMN0p9/2l0ZUDoEQAAAClGtmjRjhvTqq1KFCub44oullSvtrgwoOQIgAADn4HKZ3r9ly6T4eGn7dqlDB%2BnNNxkShn8iAAKAl7AXsP%2B75BIzGcQzJHzffdKtt0rHj9tdGVA87AUMAF7GXsD%2Bz7KkceOkf/3L7BrSsqU0fbrUuLHdlQFFQw8gAADF5HJJDz8sffed2UZu3TrTOzh7tt2VAUVDAAQAoIQ6dzaTQTp2lJKTpd69pf/8R8rOtrsyoHAEQAAASiE21vQEDh1q7o8aZZaKYfcQ%2BDICIAAApRQaKr3%2BuvT%2B%2B1JYmDRzptS%2BvbRli92VAWdHAASAEhoyZIhcLpfGjx9vdynwEbffbraRi42V1q%2BX2raVvv3W7qqAMxEAAaAEZs6cqZ9%2B%2BkmxsbF2lwIf07at9PPPUrt20rFjUs%2BeUlKS3VUB%2BREAAaCY9u7dq2HDhmnatGmqUKGC3eXAB9WpY/YRHjTILBMzbJi5RjAz0%2B7KAIMACADFkJ2drUGDBumRRx7RBRdcYHc58GHh4dIHH0hjx5plY954Q%2BrVy8wWBuxGAASAYnj%2B%2BecVEhKi%2B%2B%2B/v8jPcbvdSklJydfgDC6X9NhjZpHoihWlefPMFnI7dthdGZyOAAgABZg2bZoqV66c0xYtWqRXX31VkydPlsvlKvLrjBkzRtHR0TktLi6uHKuGL7r%2Bemnp0tzJIe3aScuX210VnIyt4ACgAKmpqTp48GDO/c8//1yPP/64goJy/3bOyspSUFCQ4uLitKOAbh232y23251zPyUlRXFxcWwF50B79pjFoteskSIipI8%2BMuEQ8DYCIAAU0ZEjR7R///5853r06KFBgwbp9ttvV7NmzYr0OuwF7GypqdKAAdKcOWaIeMIEM0kE8KYQuwsAAH9RvXp1Va9ePd%2B5ChUqKCYmpsjhD4iMlP77XxP63n5bGj5c2rXLTBYJ4sIseAkfNQAAvCwkRHrzTWn0aHP/xRfNkjHp6fbWBedgCBgAvIwhYOQ1ZYp0551mjcBu3aQvvjC9hEB5ogcQAAAb3XqrNHu2VKmSNH%2B%2BdMUV0qFDdleFQEcABADAZj16mJ1DatSQVq6UOnVirUCULwIgAAA%2B4JJLpO%2B/l%2BrXlzZvNiFw/Xq7q0KgIgACAOAjmjaVli2TEhKkvXulzp2lFSvsrgqBiAAIAIAPqVtXWrxYSkyUjhyRrrxSWrTI7qoQaAiAAOAlSUlJSkhIUGJiot2lwMdVry79739mQkhamtSzpzR3rt1VIZCwDAwAeBnLwKCoTp2S/vY3M0u4QgXp00%2Blvn3trgqBgB5AAAB8VHi4WRfwppukjAwTBj/%2B2O6qEAgIgAAA%2BLDQUGnaNGnwYCkrS/r736UPPrC7Kvg7AiAAAD4uJER6/33pnnuk7Gzp9tul996zuyr4MwIgAAB%2BIChIeustaehQybKku%2B6SJk60uyr4KwIgAAB%2BwuWSXntNeuABc3/IEOntt%2B2tCf6JAAgAgB9xuaRXXpH%2B%2BU9z/9576QlE8REAAQDwMy6X9NJL0oMPmvtDhnBNIIqHAAgAgB9yuaRx43KHg%2B%2B%2Bm9nBKDoCIAAAfsozHDxsmJkYcscdrBOIoiEAAoCXsBUcyoPLJU2YkLtEzKBB0vTpdlcFX8dWcADgZWwFh/KQnS3deac0ebLZNm7mTOmaa%2ByuCr6KHkAAAAJAUJD07rtS//5m27gbbpAWLrS7KvgqAiAAAAEiOFj68EOpd2/p1Clzu3y53VXBFxEAAQAIIBUqSJ99Jl11lZSWJl19tfTrr3ZXBV9DAAQAIMCEh5trANu3l44elbp3l7Zts7sq%2BBICIAAAAahyZemrr6QWLaT9%2B6Vu3aQDB%2ByuCr6CAAgAQICqVk2aN0%2BKjzc9gD17SsnJdlcFX0AABAAggNWpI82fL9WuLf3yi3TddWaCCJyNAAgAQIBr1EiaO1eKipIWLZJuvlnKyrK7KtiJAAgAgANcdJH03/9KoaHSjBnS8OFm%2Bzg4EwEQAACHuPxyado0s33cm29Ko0fbXRHsQgAEAC9hL2D4ghtvNHsHS9L//Z/0wQf21gN7sBcwAHgZewHDFzz2mPTCC1JIiDRnjlkmBs5BDyAAAA40Zow0cKCUmWn2DV671u6K4E0EQAAAHCgoSJo0SerSRUpNla65Rtq71%2B6q4C0EQAAAHCoszMwIbt7chL9rrzX7ByPwEQABAHCwqlXNNYC1aklr1kgDBrBGoBMQAAEAcLj4eGnWLCk83Owf/NBDdleE8kYABIBi2rBhg/r06aPo6GhFRkaqffv22rVrl91lAaXSrp304Yfm%2BNVXzTqBCFwEQAAohq1bt6pTp05q3ry5Fi5cqF9%2B%2BUVPPPGEwsPD7S4NKLUbb5See84cDx9u9hBGYGIdQAAohgEDBqhChQr60NNVUgKsAwhfZlnS4MGmNzA6WvrxRzNJBIGFHkAAKKLs7Gx99dVXatq0qXr06KFatWqpXbt2mjlzZqHPc7vdSklJydcAX%2BVySe%2B8I3XoICUnS336SMeO2V0VyhoBEACK6NChQ0pLS9PYsWPVs2dPzZs3T3379lW/fv20aNGiAp83ZswYRUdH57S4uDgvVg0Un2d5mPr1pc2bpZtuMgtGI3AwBAwABZg2bZqGDBmSc/%2Brr77S5ZdfroEDB%2Bqjjz7KOd%2BnTx9VqlRJH3/88Vlfx%2B12y%2B1259xPSUlRXFwcQ8DweWvXSpdeKp04IT3wgDR%2BvN0VoayE2F0AAPiqPn36qF27djn3a9asqZCQECUkJOR73Pnnn6%2BlS5cW%2BDphYWEKCwsrtzqB8nLhheZawBtuMDODW7WSbr/d7qpQFhgCBoACREZGqnHjxjktOjpaiYmJ2rhxY77Hbdq0SQ0aNLCpSqB89esnjRplju%2B910wKgf%2BjBxAAiuGRRx5R//791blzZ11xxRWaO3euvvzySy1cuNDu0oBy8%2BSTZjh4xgwTCFeulOrUsbsqlAbXAAJAMb3//vsaM2aM9uzZo2bNmunpp5/WddddV%2BTnswwM/FFqqrke8LffzAzhBQuk0FC7q0JJEQABwMsIgPBXW7ZIiYnSn3%2Ba4WB2C/FfXAMIAACKpHFjado0s1bgW29J779vd0UoKQIgAAAosmuukZ5%2B2hzfd5%2B5HhD%2BhwAIAACK5fHHpd69JbfbLBFz5IjdFaG4CIAAAKBYgoKkKVPMkPDOndItt0hZWXZXheIgAAKAlyQlJSkhIUGJiYl2lwKUWpUq0hdfSBER0jffSM88Y3dFKA5mAQOAlzELGIHkww%2BlW281E0O%2B/lrq0cPuilAU9AACAIASGzRIGjJEsiwzFLx7t90VoSgIgAAAoFTGj5cuvthMBunfX8rIsLsinAsBEAAAlEp4uPT551J0tPTDD9LIkXZXhHMhAAIAgFI77zxp0iRzPG6cNGuWvfWgcARAAABQJvr2lUaMMMe33Sbt2mVrOSgEARAAAJSZ5583%2BwUfOyYNGMD1gL6KAAgAAMpMaKj06ae51wM%2B%2BaTdFeFsCIAAAKBMxcdL775rjseOlebPt7cenIkACAAAytyNN5r1ASWzVuDBg/bWg/wIgAAAoFy88orUooUJf4MHS9nZdlcEDwIgAHgJewHDaSIipE8%2BMesEfvONWTAavoG9gAHAy9gLGE7z1lvSP/4hVagg/fST1Lq13RWBHkAAAFCuhgyRrr/eLAkzcKB0/LjdFYEACAAAypXLZWYFx8ZKGzdKDz9sd0UgAAIAgHJXvbr0wQfm%2BK232CrObgRAAADgFV27Sg89ZI7vuks6cMDeepyMAAgAALzmueekCy%2BUDh%2BW7rxTYiqqPQiAAADAa8LCpGnTzO2cOdLbb9tdkTMRAAEAgFe1aCGNGWOOH3pI2rzZ3nqciAAIAAC87oEHpCuvlE6ckG69VcrMtLsiZyEAAgAArwsKkiZNkqKipB9/lF54we6KnIUACABewlZwQH7160uvvWaOn3pKWrPG1nIcha3gAMDL2AoOyGVZ0g03SDNmSC1bSitWmAkiKF/0AAIAANu4XGZh6Jo1pXXrpP/8x%2B6KnIEACAAAbFWrlgmBkjR2rLR8ub31OAEBEAAA2K5fP%2Bnmm6XsbOm226RTp%2ByuKLARAAEAgE%2BYMEGKiZE2bJBGjbK7msBGAAQAAD6hevXcoeCXXmIouDwRAAGgGNLS0jRs2DDVq1dPEREROv/88/Xmm2/aXRYQMK67TrrlFoaCyxsBEACK4cEHH9TcuXM1depUbdiwQQ8%2B%2BKCGDx%2Bu//73v3aXBgSMV1%2BVatc2Q8HPPGN3NYGJAAgAxfDDDz9o8ODBuvzyy9WwYUPdc889atWqlX7%2B%2BWe7SwMCRvXq0htvmOPnn5dWrbK3nkBEAASAYujUqZNmzZqlvXv3yrIsLViwQJs2bVKPHj0KfI7b7VZKSkq%2BBqBw/fpJf/ublJUl3XGHlJFhd0WBhQAIAMUwYcIEJSQkqF69egoNDVXPnj31xhtvqFOnTgU%2BZ8yYMYqOjs5pcXFxXqwY8F%2BvvSZVqyb98ov04ot2VxNYCIAAUIBp06apcuXKOW3JkiWaMGGCfvzxR82aNUsrV67UuHHjdN999%2Bnbb78t8HVGjhyp5OTknLZ7924v/hSA/6pdWxo/3hw//bT0%2B%2B/21hNI2AsYAAqQmpqqgwcP5tyvW7euoqOjNWPGDPXq1Svn/F133aU9e/Zo7ty5RXpd9gIGis6ypGuukebOlTp1khYtkoLovio13kIAKEBkZKQaN26c0zIyMpSRkaGg0/73CQ4OVnZ2tk1VAoHNs1dwpUrS0qXSxIl2VxQYCIAAUERRUVHq0qWLHnnkES1cuFDbt2/X5MmTNWXKFPXt29fu8oCA1aCBNHq0OX70UWnvXnvrCQQMAQNAMRw4cEAjR47UvHnzdPToUTVo0ED33HOPHnzwQblcriK9BkPAQPFlZUkdO0o//ST17StNn253Rf6NAAgAXkYABEpm3Trp4oulzExpxgzp%2Buvtrsh/MQQMAAD8QsuW0iOPmONhwySW1Cw5AiAAAPAbTzwhNWpkrgP8v/%2Bzuxr/RQAEAAB%2BIyJCevNNc/z669KKFfbW468IgAAAwK906ybdfLNZI3DIEHNNIIqHAAgAXpKUlKSEhAQlJibaXQrg915%2BWapSRVq9WkpKsrsa/8MsYADwMmYBA2Xj7bele%2B%2BVIiOlDRukunXtrsh/0AMIAAD80t13S%2B3aSamp0j//aXc1/oUACAAA/FJQkNkmLihI%2Buwzad48uyvyHwRAAADgty66SBo%2B3BwPHSq53fbW4y8IgAAAwK/95z9SnTrSli3SCy/YXY1/IAACAAC/FhUljRtnjkePlnbssLUcvxBidwEAAAClNWCA9Mkn0mWXMRu4KAiAAADA77lc0syZ5hbnxhAwAAAICIS/oiMAAgAAOAwBEAAAwGEIgADgJewFDMBXsBcwAHgZewEDsBs9gAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAL2ErOAC%2Bgq3gAMDL2AoOgN3oAQQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAsBfpk%2Bfrh49eqhGjRpyuVxas2bNGY9xu90aPny4atSooUqVKqlPnz7as2ePDdUCQMkRAAHgL8ePH1fHjh01duzYAh8zYsQIzZgxQ5988omWLl2qtLQ0XXvttcrKyvJipQBQOqwDCACn2bFjh%2BLj47V69WpddNFFOeeTk5NVs2ZNffjhh%2Brfv78kad%2B%2BfYqLi9OcOXPUo0ePIr0%2B6wACsBs9gABQRCtXrlRGRoa6d%2B%2Becy42NlYtWrTQsmXLCnye2%2B1WSkpKvgYAdiIAAkARHThwQKGhoapatWq%2B87Vr19aBAwcKfN6YMWMUHR2d0%2BLi4sq7VAAoFAEQgCNNmzZNlStXzmlLliwp8WtZliWXy1Xg10eOHKnk5OSctnv37hJ/LwAoCyF2FwAAdujTp4/atWuXc79u3brnfE5MTIzS09N17NixfL2Ahw4dUocOHQp8XlhYmMLCwkpXMACUIXoAAThSZGSkGjdunNMiIiLO%2BZw2bdqoQoUKmj9/fs65/fv369dffy00AAKAr6EHEAD%2BcvToUe3atUv79u2TJG3cuFGS6fmLiYlRdHS07rzzTj300EOqXr26qlWrpocfflgtW7ZU165d7SwdAIqFHkAA%2BMusWbPUunVr9erVS5I0YMAAtW7dWm%2B99VbOY1555RVdf/31uummm9SxY0dVrFhRX375pYKDg%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%3D%3D
label_cols:
- label
- label
- label
- lmfdb_label
- label
- label
labels:
- 676.a.5408.1
- 676.a.5408.1
- 676.a.5408.1
- 676.a.5408.1
- 676.a.5408.1
- 676.a.5408.1
tables:
- g2c_curves
- g2c_endomorphisms
- g2c_ratpts
- g2c_galrep
- g2c_tamagawa
- g2c_plots
timestamp: '2024-04-20T15:41:20.658814'