/-- The derivative of the convolution `f * g` is given by `f * Dg`, when `f` is locally integrable
and `g` is `C^1` and compactly supported. Version where `g` depends on an additional parameter in an
open subset `s` of a parameter space `P` (and the compact support `k` is independent of the
parameter in `s`). -/
theorem hasFDerivAt_convolution_right_with_param {g : P → G → E'} {s : Set P} {k : Set G}
    (hs : IsOpen s) (hk : IsCompact k) (hgs : ∀ p, ∀ x, p ∈ s → x ∉ k → g p x = 0)
    (hf : LocallyIntegrable f μ) (hg : ContDiffOn 𝕜 1 (↿g) (s ×ˢ univ)) (q₀ : P × G)
    (hq₀ : q₀.1 ∈ s) :
    HasFDerivAt (fun q : P × G => (f ⋆[L, μ] g q.1) q.2)
      ((f ⋆[L.precompR (P × G), μ] fun x : G => fderiv 𝕜 (↿g) (q₀.1, x)) q₀.2) q₀ := by
  let g' := fderiv 𝕜 ↿g
  have A : ∀ p ∈ s, Continuous (g p) := fun p hp ↦ by
    refine hg.continuousOn.comp_continuous (continuous_const.prod_mk continuous_id') fun x => ?_
    simpa only [prod_mk_mem_set_prod_eq, mem_univ, and_true_iff] using hp
  have A' : ∀ q : P × G, q.1 ∈ s → s ×ˢ univ ∈ 𝓝 q := fun q hq ↦ by
    apply (hs.prod isOpen_univ).mem_nhds
    simpa only [mem_prod, mem_univ, and_true_iff] using hq
  -- The derivative of `g` vanishes away from `k`.
  have g'_zero : ∀ p x, p ∈ s → x ∉ k → g' (p, x) = 0 := by
    intro p x hp hx
    refine' (hasFDerivAt_zero_of_eventually_const 0 _).fderiv
    have M2 : kᶜ ∈ 𝓝 x := hk.isClosed.isOpen_compl.mem_nhds hx
    have M1 : s ∈ 𝓝 p := hs.mem_nhds hp
    rw [nhds_prod_eq]
    filter_upwards [prod_mem_prod M1 M2]
    rintro ⟨p, y⟩ ⟨hp, hy⟩
    exact hgs p y hp hy
  /- We find a small neighborhood of `{q₀.1} × k` on which the derivative is uniformly bounded. This
    follows from the continuity at all points of the compact set `k`. -/
  obtain ⟨ε, C, εpos, h₀ε, hε⟩ :
      ∃ ε C, 0 < ε ∧ ball q₀.1 ε ⊆ s ∧ ∀ p x, ‖p - q₀.1‖ < ε → ‖g' (p, x)‖ ≤ C := by
    have A : IsCompact ({q₀.1} ×ˢ k) := isCompact_singleton.prod hk
    obtain ⟨t, kt, t_open, ht⟩ : ∃ t, {q₀.1} ×ˢ k ⊆ t ∧ IsOpen t ∧ IsBounded (g' '' t) := by
      have B : ContinuousOn g' (s ×ˢ univ) :=
        hg.continuousOn_fderiv_of_isOpen (hs.prod isOpen_univ) le_rfl
      apply exists_isOpen_isBounded_image_of_isCompact_of_continuousOn A (hs.prod isOpen_univ) _ B
      simp only [prod_subset_prod_iff, hq₀, singleton_subset_iff, subset_univ, and_self_iff,
        true_or_iff]
    obtain ⟨ε, εpos, hε, h'ε⟩ :
      ∃ ε : ℝ, 0 < ε ∧ thickening ε ({q₀.fst} ×ˢ k) ⊆ t ∧ ball q₀.1 ε ⊆ s := by
      obtain ⟨ε, εpos, hε⟩ : ∃ ε : ℝ, 0 < ε ∧ thickening ε (({q₀.fst} : Set P) ×ˢ k) ⊆ t
      · exact A.exists_thickening_subset_open t_open kt
      obtain ⟨δ, δpos, hδ⟩ : ∃ δ : ℝ, 0 < δ ∧ ball q₀.1 δ ⊆ s
      · exact Metric.isOpen_iff.1 hs _ hq₀
      refine' ⟨min ε δ, lt_min εpos δpos, _, _⟩
      · exact Subset.trans (thickening_mono (min_le_left _ _) _) hε
      · exact Subset.trans (ball_subset_ball (min_le_right _ _)) hδ
    obtain ⟨C, Cpos, hC⟩ : ∃ C, 0 < C ∧ g' '' t ⊆ closedBall 0 C; exact ht.subset_closedBall_lt 0 0
    refine' ⟨ε, C, εpos, h'ε, fun p x hp => _⟩
    have hps : p ∈ s := h'ε (mem_ball_iff_norm.2 hp)
    by_cases hx : x ∈ k
    · have H : (p, x) ∈ t := by
        apply hε
        refine' mem_thickening_iff.2 ⟨(q₀.1, x), _, _⟩
        · simp only [hx, singleton_prod, mem_image, Prod.mk.inj_iff, eq_self_iff_true, true_and_iff,
            exists_eq_right]
        · rw [← dist_eq_norm] at hp
          simpa only [Prod.dist_eq, εpos, dist_self, max_lt_iff, and_true_iff] using hp
      have : g' (p, x) ∈ closedBall (0 : P × G →L[𝕜] E') C := hC (mem_image_of_mem _ H)
      rwa [mem_closedBall_zero_iff] at this
    · have : g' (p, x) = 0 := g'_zero _ _ hps hx
      rw [this]
      simpa only [norm_zero] using Cpos.le
  /- Now, we wish to apply a theorem on differentiation of integrals. For this, we need to check
    trivial measurability or integrability assumptions (in `I1`, `I2`, `I3`), as well as a uniform
    integrability assumption over the derivative (in `I4` and `I5`) and pointwise differentiability
    in `I6`. -/
  have I1 :
    ∀ᶠ x : P × G in 𝓝 q₀, AEStronglyMeasurable (fun a : G => L (f a) (g x.1 (x.2 - a))) μ := by
    filter_upwards [A' q₀ hq₀]
    rintro ⟨p, x⟩ ⟨hp, -⟩
    refine' (HasCompactSupport.convolutionExists_right L _ hf (A _ hp) _).1
    apply hk.of_isClosed_subset (isClosed_tsupport _)
    

Proof state:
case h.mk.intro
𝕜 : Type u𝕜
G : Type uG
E : Type uE
E' : Type uE'
E'' : Type uE''
F : Type uF
F' : Type uF'
F'' : Type uF''
P : Type uP
inst✝¹⁶ : NormedAddCommGroup E
inst✝¹⁵ : NormedAddCommGroup E'
inst✝¹⁴ : NormedAddCommGroup E''
inst✝¹³ : NormedAddCommGroup F
f f' : G → E
g✝ g'✝ : G → E'
x✝ x' : G
y y' : E
inst✝¹² : IsROrC 𝕜
inst✝¹¹ : NormedSpace 𝕜 E
inst✝¹⁰ : NormedSpace 𝕜 E'
inst✝⁹ : NormedSpace 𝕜 E''
inst✝⁸ : NormedSpace ℝ F
inst✝⁷ : NormedSpace 𝕜 F
inst✝⁶ : CompleteSpace F
inst✝⁵ : MeasurableSpace G
inst✝⁴ : NormedAddCommGroup G
inst✝³ : BorelSpace G
inst✝² : NormedSpace 𝕜 G
inst✝¹ : NormedAddCommGroup P
inst✝ : NormedSpace 𝕜 P
μ : Measure G
L : E →L[𝕜] E' →L[𝕜] F
g : P → G → E'
s : Set P
k : Set G
hs : IsOpen s
hk : IsCompact k
hgs : ∀ (p : P) (x : G), p ∈ s → x ∉ k → g p x = 0
hf : LocallyIntegrable f
hg : ContDiffOn 𝕜 1 (↿g) (s ×ˢ univ)
q₀ : P × G
hq₀ : q₀.1 ∈ s
g' : P × G → P × G →L[𝕜] E' := fderiv 𝕜 ↿g
A : ∀ p ∈ s, Continuous (g p)
A' : ∀ (q : P × G), q.1 ∈ s → s ×ˢ univ ∈ 𝓝 q
g'_zero : ∀ (p : P) (x : G), p ∈ s → x ∉ k → g' (p, x) = 0
ε C : ℝ
εpos : 0 < ε
h₀ε : ball q₀.1 ε ⊆ s
hε : ∀ (p : P) (x : G), ‖p - q₀.1‖ < ε → ‖g' (p, x)‖ ≤ C
p : P
x : G
hp : (p, x).1 ∈ s
⊢ tsupport (g (p, x).1) ⊆ k

Next tactic:
exact closure_minimal (support_subset_iff'.2 fun z hz => hgs _ _ hp hz) hk.isClosed